Abstract
Background
Consensus guidelines for malignancy screening in idiopathic inflammatory myopathy (IIM) were recently published by the International Myositis Assessment and Clinical Studies (IMACS) working group. This international multicentre study audited historical cancer screening practices against IMACS recommendations and examined the distribution of observed malignancies across IMACS-defined risk categories in a real-world cohort.
Methods
This retrospective study included patients with IIM from eight centres across seven countries/regions. Patients were stratified using IMACS-defined cancer risk categories. Historical screening was compared with the guideline recommendations. Multivariable logistic regression identified predictors of cancer and underscreening.
Results
Of the 795 patients (72.3% female), 51.7%, 37.9% and 10.4% were classified as high risk, moderate risk and standard risk, respectively. Across all sites, few high-risk (6.2%), moderate-risk (2.7%) and standard-risk (11.1%) patients underwent a full panel of IMACS-recommended tests. Within 3 years of diagnosis, 86 patients (10.8%) developed malignancy. Among observed cancers, 77.4% occurred in high-risk and 22.6% in moderate-risk patients, with none detected in the standard-risk group. Anti-transcription intermediary factor 1-gamma (TIF1γ) positivity (OR 4.64, 95% CI 2.35 to 9.16, p<0.001), smoking (OR 3.00, 95% CI 1.64 to 5.50, p<0.001), night sweats (OR 12.76, 95% CI 2.72 to 59.87, p=0.001) and increasing age (OR 1.05 95% CI 1.03 to 1.07 per year, p<0.001) were independently associated with cancer, whereas anti-Mi2 antibodies and non-dermatomyositis subtypes were protective. Cancer risk increased with each additional high-risk feature (OR 1.70) and decreased with each low-risk feature (OR 0.76). Underscreened individuals had lower rates of immune-mediated necrotising myopathy (5.6% vs 14.3%, p=0.005) and anti-TIF1γ positivity (6.8% vs 31.4%, p<0.001).
Conclusion
Observed cancer distribution across IMACS risk categories was consistent with the framework’s stratification logic, but differential screening intensity precludes formal assessment of predictive validity. Variation between historical practice and current recommendations underscores the need for continued education and prospective longitudinal studies to assess cumulative risk, age thresholds, screening uptake and outcomes in real-world clinical settings.
Keywords: Dermatomyositis, Polymyositis, Risk Factors
WHAT IS ALREADY KNOWN ON THIS TOPIC
Idiopathic inflammatory myopathy (IIM) is associated with an increased risk of malignancy, especially in certain subgroups.
Screening practices have historically been variable and non-standardised.
The International Myositis Assessment and Clinical Studies (IMACS) group recently proposed consensus guidelines, but their performance in real-world cohorts was unknown.
WHAT THIS STUDY ADDS
Most detected cancers occurred in patients retrospectively classified as high risk under the IMACS framework, consistent with its stratification logic.
Anti-TIF1γ, smoking, night sweats and older age emerged as strong independent predictors.
Historical screening often failed to align with guideline recommendations, leaving some high-risk patients underscreened.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
This study provides a real-world audit of cancer screening practices in IIM and highlights additional clinical features that may inform future refinement of the IMACS framework.
It supports implementing structured, risk-based screening in routine care.
Bridging gaps between guidelines and practice will require tailored, context-specific strategies.
Introduction
Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of multisystem autoimmune diseases characterised primarily by a predilection for skeletal muscle inflammation, often accompanied by a range of extramuscular manifestations. The major clinical subtypes include dermatomyositis (DM), polymyositis (PM), antisynthetase syndrome (ASyS), immune-mediated necrotising myopathy (IMNM), overlap myositis (OM), inclusion body myositis (IBM) and juvenile-onset IIM (JIIM).
An increased risk of malignancy in adult-onset IIM, particularly in DM, is well established. This risk is highest within 3 years before or after an IIM diagnosis and is particularly pronounced in the presence of specific clinical features such as anti-TIF1γ antibodies.1 Cancer remains the leading cause of death among patients with IIM2,5 and malignancies are often diagnosed at an advanced stage.6 Accordingly, early identification of cancer is likely to improve survival and cancer-related outcomes in patients with IIM.
While the association of IIM with malignancy has been long-recognised, cancer screening practices in this population remain highly variable.7 In the absence of an international consensus, clinicians have relied on local practice patterns or individual discretion, resulting in considerable heterogeneity. Growing evidence on clinical and serological predictors of malignancy in IIM1 has underscored the need for standardised approaches. In response, the International Myositis Assessment and Clinical Studies (IMACS) group published evidence-based and consensus-based recommendations in 2023.8 Under this framework, patients with IIM (excluding JIIM and IBM) are stratified into high-risk, moderate-risk and standard-risk categories based on demographic, clinical and serological features. All eligible patients are recommended to undergo a basic cancer screening workup consisting of comprehensive history, examination, routine blood tests, urinalysis and chest X-ray at IIM diagnosis. Moderate-risk or high-risk patients should additionally undergo enhanced screening, including dedicated imaging and tumour marker analysis.
This research addresses critical gaps in understanding how cancer incidence patterns and screening practices differ across socioeconomic contexts, where disparities in healthcare access and costs may influence both the feasibility of implementing comprehensive screening protocols and the underlying cancer risk profiles in patients with IIM.9,11 Here, we audit historical cancer screening practices against IMACS recommendations and describe the distribution of malignancies detected within 3 years of IIM diagnosis across IMACS-defined risk strata.
Materials and methods
Population and study design
We conducted a retrospective analysis of malignancy screening practices in patients with IIM. Data were collected from eight tertiary rheumatology centres across seven countries/regions (Japan, Australia, India, Hong Kong, the UK, Brazil and Italy). Patients (aged ≥18 years) diagnosed with IIM between 2014 and 2023 were included in this study. The study cohort was identified from institutional research registries and/or a search of electronic medical records using the International Classification of Diseases codes related to myositis. All patients fulfilled the 2017 European Alliance of Associations for Rheumatology (EULAR)/American College of Rheumatology classification criteria for IIM.12 Overlap myositis was defined as IIM with a concurrent systemic autoimmune rheumatic disease, such as systemic sclerosis, mixed connective tissue disease or Sjögren’s disease.13 Patients with juvenile onset myositis and those without sufficient data to perform cancer risk stratification were excluded. Patient demographics, clinical features, malignancy risk factors and details regarding malignancy screening tests were collected using a predesigned data extraction template. To ensure data standardisation, the Indian cohort used a structured glossary with predefined criteria for all clinical variables that was shared with other centres.12 Night sweats were documented as present if recorded at baseline assessment. Follow-up was retrospective, based on review of medical records to ascertain outcomes from cohort entry until last documented follow-up, cancer diagnosis or death. Myositis-specific antibodies (MSA) and myositis-associated antibodies (MAA) were tested by commercially available line-blot immunoassays (Autoimmune Inflammatory Myopathy profile 16 antigens; EUROIMMUN, Germany, RNA immunoprecipitation (IP) and IP/immunoblotting assay) or enzyme-linked assays (MESACUP, MBL, Tokyo, Japan), with thresholds determined by local laboratories.
The occurrence, timing and type of cancer were recorded relative to IIM diagnosis. Each patient was retrospectively assigned a cancer risk category (standard, moderate or high) based on the 2023 IMACS guidelines.8 This risk stratification system incorporates demographic, clinical and serological risk factors to guide screening intensity. High-risk patients were defined as those with two or more high-risk features. Moderate-risk classification requires at least one high-risk feature or two intermediate-risk features. All other patients were considered to have a standard risk. In keeping with the IMACS framework, patients with IBM were not assigned to any cancer risk category, as they are considered not to be at an increased risk of developing cancer. Nonetheless, they were included in the analysis to allow evaluation of screening practices in populations for whom extensive screening is not guideline-supported. All patients with multiple MSAs or MAAs were considered in the risk stratification process and the factor with the highest risk was considered for risk stratification.
Outcomes
The primary outcome was the degree of concordance between existing malignancy screening practices and the IMACS guideline recommendations across participating centres. Secondary outcomes included the distribution of patients across IMACS-defined risk categories, the observed cancer detection rate within each risk category (acknowledging non-uniform ascertainment), clinical and serological associations with detected cancer and predictors of cancer underscreening relative to guideline-recommended investigations. We did not aim to formally evaluate the diagnostic accuracy or predictive validity of the IMACS framework, as this would require uniform application of a reference standard across risk groups.
Statistical analysis
Descriptive statistics were reported as frequencies and percentages for categorical variables and as means with SD or median with IQR for continuous variables. Between-group comparisons were performed using χ2 tests for categorical data and the Mann-Whitney U test for continuous data.
Clinically relevant variables associated with cancer were considered for inclusion in the multivariable logistic regression to identify independent predictors. These included sex, age, IIM subtype, selected MSAs and MAAs, smoking history and clinical features. Multivariable logistic regression was also used to identify predictors of malignancy underscreening, defined as fewer investigations than those recommended by the IMACS guidelines for the assigned risk category. Specifically, high-risk patients would be considered underscreened if they did not undergo both basic and enhanced screening tests at IIM diagnosis and annual basic screening during follow-up where applicable. Moderate-risk patients were classified as such if they did not receive both basic and enhanced screening at diagnosis, while standard-risk patients were classified as such if they did not undergo the full suite of basic screening investigations at diagnosis. For this analysis, covariates included age, sex, cancer risk category, fever, weight loss, night sweats, smoking, IIM subtype and recruitment site. ORs and 95% CIs were reported.
To quantify the risk of malignancy in patients post a diagnosis of IIM, compared with the general population, we calculated the standardised incidence ratio (SIR). Patients with a history of malignancy preceding the IIM diagnosis or diagnosed simultaneously (prevalent cases) were excluded from the analysis to ensure the assessment of incident risk. Person-years at risk were calculated for each patient starting from the date of IIM diagnosis. For patients who developed cancer, time at risk was censored at the date of malignancy diagnosis. For patients who remained cancer-free, time at risk was censored at the date of the last follow-up or death. This strict censoring method was employed to prevent survival bias (immortal time bias), ensuring that the follow-up duration after a cancer diagnosis did not artificially inflate the person-years. The SIR was calculated as the ratio of the observed number of incident malignancies (O) to the expected number of malignancies (E) in the general population. The expected number (E) was derived by multiplying the total person-years accumulated by the cohort by the reference incidence rate. Global incidence rates were derived from the GLOBOCAN 2022 database. A reference incidence rate of 300 per 100 000 person-years was applied, representing the conservative global average for the 50–59 years age group, consistent with the median age of the study cohort.13 Further sensitivity analyses were performed in cohorts who developed cancer after IIM diagnosis as well as in those who completed a minimum follow-up period of 3 years and developed an incident malignancy within 3 years of IIM diagnosis. Analyses were performed using R (Version 4.5.1), with p<0.05 considered statistically significant.
Results
Cohort characteristics
A total of 795 patients with IIM were identified from eight institutions across seven countries/regions (online supplemental figure 1). The mean age at diagnosis was 50.2±16.3 years, with 72.2% being female (table 1). The median duration of follow-up was 70.7 (40.0–106.7) months. The mean age at diagnosis was lowest in India and highest in Australia. The most common IIM subtype was DM (388/795, 48.8%), followed by ASyS (181/795, 22.8%), OM (86/795, 10.8%), PM (76/795, 9.6%), IMNM (50/795, 6.3%) and IBM (14/795, 1.8%). There were some regional variations in subtype distribution: IBM was more common in the Australian cohort, IMNM was more common in both the UK and Australia, while ASyS was most common in the Japan and Hong Kong groups.
Table 1. Participant characteristics.
| Centre location | Australia | Brazil | Hong Kong | India | Italy | Japan | UK | Total |
|---|---|---|---|---|---|---|---|---|
| Patients with IIM (n) | 69 | 156 | 58 | 224 | 97 | 145 | 46 | 795 |
| Female, n (%) | 45 (65.2) | 118 (75.6) | 39 (67.2) | 185 (82.6) | 70 (72.2) | 91 (62.8) | 26 (56.5) | 574 (72.2) |
| Age at IIM diagnosis (mean±SD) years | 59.8±13.6 | 47.0±15.5 | 56.8±12.5 | 39.1±13.0 | 56.8±15.2 | 58.3±15.3 | 52.7±13.0 | 50.2±16.3 |
| IIM subtypes | ||||||||
| DM/CADM, n (%) | 26 (37.7) | 105 (67.3) | 33 (56.9) | 93 (41.5) | 48 (49.5) | 72 (49.7) | 11 (23.9) | 388 (48.8) |
| PM, n (%) | 11 (15.9) | 11 (7.1) | 8 (13.8) | 19 (8.5) | 18 (18.6) | 1 (0.7) | 8 (17.4) | 76 (9.6) |
| ASyS, n (%) | 10 (14.5) | 27 (17.3) | 12 (20.7) | 52 (23.2) | 15 (15.5) | 58 (40.0) | 7 (15.2) | 181 (22.8) |
| IMNM, n (%) | 10 (14.5) | 12 (7.7) | 4 (6.9) | 0 | 4 (4.1) | 7 (4.8) | 13 (28.3) | 50 (6.3) |
| OM, n (%) | 2 (2.9) | 1 (0.6) | 1 (1.7) | 60 (26.8) | 10 (10.3) | 5 (3.4) | 7 (15.2) | 86 (10.8) |
| IBM, n (%) | 10 (14.5) | 0 | 0 | 0 | 2 (2.1) | 2 (1.4) | 0 | 14 (1.8) |
| Risk category as per IMACS guidelines | ||||||||
| High risk, n (%) | 31 (44.9) | 113 (72.4) | 34 (58.6) | 70 (31.2) | 58 (59.8) | 73 (50.3) | 25 (54.3) | 404 (50.8) |
| Moderate risk, n (%) | 28 (40.6) | 35 (22.4) | 22 (37.9) | 93 (41.5) | 32 (33.0) | 66 (45.5) | 20 (43.5) | 296 (37.2) |
| Standard risk, n (%) | 0 | 8 (5.1) | 2 (3.4) | 61 (27.2) | 5 (5.2) | 4 (2.8) | 1 (2.2) | 81 (10.2) |
| Screening not routinely required,* n (%) | 10 (14.5) | 0 | 0 | 0 | 2 (2.1) | 2 (1.4) | 0 | 14 (1.8) |
| Cancers detected (within 3 years), n (%) | 11 (15.9) | 11 (7.1) | 7 (12.1) | 8 (3.6) | 22 (22.7) | 23 (15.9) | 4 (8.7) | 86 (10.8) |
IBM or JIIM.
ASyS, antisynthetase syndrome; CADM, clinically amyopathic dermatomyositis; DM, dermatomyositis; IBM, inclusion body myositis; IIM, idiopathic inflammatory myopathy; IMACS, International Myositis and Clinical Studies Group; IMNM, immune-mediated necrotising myopathy; JIIM, juvenile-onset IIM; OM, overlap myositis; PM, polymyositis.
Malignancy risk classification
Across all patients, 50.8% were classified as high risk for malignancy, 37.2% as moderate risk and 10.2% as standard risk. The remainder (1.8%) had IBM and were not considered at risk under the IMACS guidelines. The most prevalent high-risk feature overall was age >40 years at diagnosis (70.9%), followed by DM diagnosis (39.2%) (table 2). This pattern was consistent across all countries/regions, except for Brazil, where the most common high-risk factor was persistent disease activity despite immunosuppression (67.9%). Among the intermediate-risk and low-risk factors, male sex (27.8%) and interstitial lung disease (ILD) (42.3%) were the most frequently observed overall.
Table 2. Prevalence of IMACS malignancy risk factors.
| Centre location | Australia | Brazil | Hong Kong | India | Italy | Japan | UK | Total |
|---|---|---|---|---|---|---|---|---|
| High-risk factors | ||||||||
| DM, n (%) | 24 (34.8) | 80 (51.3) | 28 (48.3) | 93 (41.5) | 37 (38.1) | 41 (28.3) | 9 (19.6) | 312 (39.2) |
| Anti-TIF1γ(+), n (%) | 5 (7.2) | 17 (10.9) | 10 (17.2) | 8 (3.6) | 7 (7.2) | 22 (15.2) | 1 (2.2) | 70 (8.8) |
| Anti-NXP-2(+), n (%) | 3 (4.3) | 4 (2.6) | 3 (5.2) | 6 (2.7) | 4 (4.1) | 7 (4.8) | 1 (2.2) | 28 (3.5) |
| Age >40 years at time of onset, n (%) | 61 (88.4) | 101 (64.7) | 54 (93.1) | 102 (45.5) | 85 (87.6) | 124 (85.5) | 37 (80.4) | 564 (70.9) |
| Persistent high disease activity despite immunosuppression, n (%) | 6 (8.7) | 106 (67.9) | 19 (32.8) | 6 (2.7) | 18 (18.6) | 34 (23.4) | 17 (37.0) | 206 (25.9) |
| Dysphagia, n (%) | 23 (33.3) | 77 (49.4) | 14 (24.1) | 37 (16.5) | 32 (33.0) | 8 (5.5) | 13 (28.3) | 204 (25.7) |
| Cutaneous necrosis, n (%) | 2 (2.9) | 55 (35.3) | 6 (10.3) | 9 (4.0) | 14 (14.4) | 13 (9.0) | 1 (2.2) | 100 (12.6) |
| Intermediate risk factors | ||||||||
| CADM, n (%) | 2 (2.9) | 25 (16.0) | 5 (8.6) | 0 (0.0) | 11 (11.3) | 31 (21.4) | 2 (4.3) | 76 (9.6) |
| PM, n (%) | 11 (15.9) | 11 (7.1) | 8 (13.8) | 19 (8.5) | 18 (18.6) | 1 (0.7) | 8 (17.4) | 76 (9.6) |
| IMNM, n (%) | 10 (14.5) | 12 (7.7) | 4 (6.9) | 0 (0.0) | 4 (4.1) | 7 (4.8) | 13 (28.3) | 50 (6.3) |
| Anti-SAE1(+), n (%) | 5 (7.2) | 7 (4.5) | 5 (8.6) | 5 (2.2) | 2 (2.1) | 1 (0.7) | 1 (2.2) | 26 (3.3) |
| Anti-HMGCR(+), n (%) | 7 (10.1) | 1 (0.6) | 3 (5.2) | 1 (0.4) | 3 (3.1) | 1 (0.7) | 9 (19.6) | 25 (3.1) |
| Anti-Mi2(+), n (%) | 4 (5.8) | 15 (9.6) | 3 (5.2) | 18 (8.0) | 7 (7.2) | 6 (4.1) | 7 (15.2) | 60 (7.5) |
| Anti-MDA5(+), n (%) | 3 (4.3) | 29 (18.6) | 7 (12.1) | 10 (4.5) | 3 (3.1) | 28 (19.3) | 1 (2.2) | 81 (10.2) |
| Male sex, n (%) | 24 (34.8) | 38 (24.4) | 19 (32.8) | 39 (17.4) | 27 (27.8) | 54 (37.2) | 20 (43.5) | 221 (27.8) |
| Low-risk factors | ||||||||
| ASyS, n (%) | 10 (14.5) | 27 (17.3) | 12 (20.7) | 52 (23.2) | 15 (15.5) | 58 (40.0) | 7 (15.2) | 181 (22.8) |
| OM, n (%) | 2 (2.9) | 1 (0.6) | 1 (1.7) | 60 (26.8) | 10 (10.3) | 5 (3.4) | 7 (15.2) | 86 (10.8) |
| Anti-SRP(+), n (%) | 1 (1.4) | 4 (2.6) | 6 (10.3) | 6 (2.7) | 1 (1.0) | 8 (5.5) | 3 (6.5) | 29 (3.6) |
| Anti-Jo1(+), n (%) | 3 (4.3) | 12 (7.7) | 9 (15.5) | 25 (11.2) | 11 (11.3) | 24 (16.6) | 4 (8.7) | 88 (11.1) |
| Non-anti-Jo1 ASyS antibody(+), n (%) | 8 (11.6) | 15 (9.6) | 15 (25.9) | 14 (6.2) | 5 (5.2) | 36 (24.8) | 5 (10.9) | 98 (12.3) |
| MAA, n (%) | 14 (20.3) | 46 (29.5) | 22 (37.9) | 45 (20.1) | 48 (49.5) | 29 (20.0) | 17 (37.0) | 221 (27.8) |
| Raynaud’s phenomenon, n (%) | 10 (14.5) | 89 (57.1) | 6 (10.3) | 69 (30.8) | 33 (34.0) | 21 (14.5) | 12 (26.1) | 240 (30.2) |
| Inflammatory arthropathy, n (%) | 18 (26.1) | 73 (46.8) | 17 (29.3) | 106 (47.3) | 33 (34.0) | 37 (25.5) | 16 (34.8) | 300 (37.7) |
| ILD, n (%) | 14 (20.3) | 71 (45.5) | 26 (44.8) | 75 (33.5) | 44 (45.4) | 92 (63.4) | 14 (30.4) | 336 (42.3) |
| No IIM-related malignancy screening required | ||||||||
| IBM, n (%) | 10 (14.5) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 2 (2.1) | 2 (1.4) | 0 (0.0) | 14 (1.8) |
IBM or JIIM.
ASyS, antisynthetase syndrome; CADM, clinically amyopathic dermatomyositis; DM, dermatomyositis; HMGCR, 3-hydroxy-3-methylglutaryl-coenzyme A reductase ; IBM, inclusion body myositis; IIM, idiopathic inflammatory myopathy; ILD, interstitial lung disease; IMACS, International Myositis and Clinical Studies Group; IMNM, immune-mediated necrotising myopathy; JIIM, juvenile-onset IIM; MAA, myositis-associated antibody; MDA5, Melanoma differentiation-associated gene 5; NXP2, Nuclear matrix protein 2; OM, overlap myositis; PM, polymyositis; SAE, Small ubiquitin-like modifier activating enzyme subunit 1; SRP, Signal recognition particle; TIF1γ, Transcription intermediary factor 1-gamma.
Among the high-risk patients (n=404), 47.9% had two high-risk factors, 26.8% had three, 14.5% had four, 8.8% had five and 2.0% had six. Notably, although ASyS and OM are considered ‘low-risk’ diagnoses under IMACS criteria, the majority of patients with ASyS/OM (75.7%, 202/267) were classified as moderate or high risk. This was largely driven by the age criterion, with 81.2% (164/202) of these patients aged >40 years at presentation. Strikingly, among moderate-risk to high-risk patients with ASyS/OM, 27.7% (56/202) were aged only 41–50 years, indicating that risk classification may be influenced by relatively modest increases in age.
Malignancy occurrence and risk factors
Across the entire cohort, a total of 91 cancers were detected at any time point, representing an overall malignancy prevalence of 11.4% (91/795). 10.8% of patients (86/795) were diagnosed with malignancy between 3 years prior to and 3 years following their IIM diagnosis, during a total follow-up of 5459.5 patient-years. This represented an incidence rate of 1.58 per 100 patient-years, although not all patients completed the full follow-up window. The most common cancer types were breast (19/86, 22.1%), lung (12/86, 14.0%), unknown primary (6/86, 7.0%) and melanoma (5/86, 5.8%), with a total of 25 different primary sites identified overall (online supplemental table 1). Based on the expected general population rates (16.4 expected cases), patients with IIM demonstrated a significantly elevated SIR of 5.25 (95% CI 4.20 to 6.48).
A sensitivity analysis was conducted restricting the cohort strictly to new cases detected after IIM onset (0–3 years postdiagnosis). Excluding patients who had cancer prior to IIM diagnosis, 61 malignancies were observed across 768 patients contributing 5328.1 person-years of follow-up (an incidence rate of 1.14 per 100 person-years). Compared with the expected background incidence (16.0 expected cases), this maintained a highly significant SIR of 3.82 (95% CI 2.92 to 4.90). This indicates that even when excluding pre-existing malignancies, patients with IIM face a nearly fourfold increased risk of developing cancer compared with the age-matched general population.
Another sensitivity analysis conducted in a cohort patients, strictly restricted to patients who completed a minimum follow-up of 3 years and developed an incident malignancy within 3 years of IIM onset. In 608 patients contributing 1822.8 person-years of follow-up, 58 incident malignancies were observed. Compared with the expected background incidence (5.47 expected cases), the SIR was remarkably elevated at 10.60 (95% CI 8.05 to 13.71). This demonstrates that when strictly evaluating patients with robust long-term follow-up and preventing potential survival or drop-out bias, the risk of incident malignancy in the early years following an IIM diagnosis is nearly 11-fold higher than the age-matched general population.
Clinical characteristics of patients with cancer
Clinical characteristics were compared between patients with cancer diagnosed within 3 years of IIM onset (n=86) and those without cancer during follow-up (n=709) (table 3). Patients who developed cancer were significantly older at IIM onset (62.3±12.8 vs 48.7±16.1 years, p<0.001), more likely to be male (41.9% vs 26.1%, p=0.003) and more likely to have night sweats (5.1% vs 0.9%, p=0.014), dysphagia (39.5% vs 24.0%, p=0.003) and a history of smoking (38.5% vs 16.0%, p<0.001).
Table 3. Clinical and serological differences between patients with and without cancer within 3 years of IIM diagnosis.
| Variable | Cancer within 3 years (n=86) | Without cancer (n=709) | P value |
|---|---|---|---|
| Male sex | 36 (41.9) | 185 (26.1) | 0.003 |
| Age | 62.3±12.8 | 48.7±16.1 | <0.001 |
| DM | 58 (67.4) | 254 (35.8) | <0.001 |
| CADM | 7 (8.1) | 69 (9.7) | 0.846 |
| PM | 4 (4.7) | 72 (10.2) | 0.120 |
| IMNM | 6 (7.0) | 44 (6.2) | 0.813 |
| IBM | 2 (2.3) | 12 (1.7) | 0.657 |
| OM | 0 (0.0) | 86 (12.1) | <0.001 |
| ASyS | 9 (10.5) | 172 (24.3) | 0.003 |
| Family history cancer | 13 (16.7) | 55 (8.2) | 0.020 |
| Smoker (ever) | 30 (38.5) | 108 (16.0) | <0.001 |
| Weight loss | 34 (43.0) | 266 (39.3) | 0.545 |
| Night sweats | 4 (5.1) | 6 (0.9) | 0.014 |
| Fever | 21 (24.4) | 240 (33.9) | 0.089 |
| High disease activity | 27 (31.4) | 179 (25.2) | 0.241 |
| Dysphagia | 34 (39.5) | 170 (24.0) | 0.003 |
| Cutaneous necrosis | 12 (14.0) | 88 (12.4) | 0.730 |
| Raynaud’s phenomenon | 17 (19.8) | 223 (31.5) | 0.025 |
| Inflammatory arthritis | 17 (19.8) | 283 (39.9) | <0.001 |
| ILD | 22 (25.6) | 314 (44.3) | <0.001 |
| Anti-Mi2 | 2 (2.3) | 58 (8.2) | 0.052 |
| Anti-MDA5 | 3 (3.5) | 78 (11.0) | 0.035 |
| Anti-SAE1 | 3 (3.5) | 23 (3.2) | 0.754 |
| Anti-HMGCR | 4 (4.7) | 21 (3.0) | 0.336 |
| Anti-NXP-2 | 4 (4.7) | 24 (3.4) | 0.532 |
| Anti-TIF1γ | 32 (37.2) | 38 (5.4) | <0.001 |
| Anti-Jo1 | 7 (8.1) | 81 (11.4) | 0.467 |
| Non-Jo1 antisynthetase antibody | 9 (10.5) | 89 (12.6) | 0.728 |
| Anti-SRP | 2 (2.3) | 27 (3.8) | 0.760 |
| MAA | 18 (20.9) | 203 (28.6) | 0.161 |
| IMACS risk group: standard | 0 (0.0) | 81 (11.4) | <0.001 |
| IMACS risk group: moderate | 19 (22.1) | 277 (39.1) | 0.002 |
| IMACS risk group: high | 65 (75.6) | 339 (47.8) | <0.001 |
ASyS, antisynthetase syndrome; CADM, clinically amyopathic dermatomyositis; DM, dermatomyositis; HMGCR, 3-hydroxy-3-methylglutaryl-coenzyme A reductase; IBM, inclusion body myositis; IIM, idiopathic inflammatory myopathy; ILD, interstitial lung disease; IMACS, International Myositis Assessment and Clinical Studies Group; IMNM, immune-mediated necrotising myopathy; MAA, myositis-associated antibody; MDA5, Melanoma differentiation-associated gene 5 ; NXP2, Nuclear matrix protein 2; OM, overlap myositis; PM, polymyositis; SAE1, Small ubiquitin-like modifier activating enzyme subunit 1; SRP, Signal recognition particle; TIF1γ, Transcription intermediary factor 1-gamma.
DM was over-represented among cancer cases (67.4% vs 35.8%, p<0.001), while ASyS, OM, inflammatory arthritis, ILD and Raynaud’s phenomenon were significantly less common (all p<0.05). Notably, nine patients wih ASyS were diagnosed with IIM-related malignancy; all presented with IIM at >55 years of age. None of the patients with OM developed malignancy. Anti-TIF1γ positivity had the strongest serological association with cancer (37.2% vs 5.4%, p<0.001), whereas anti-MDA-5 was less common in patients with cancer (3.5% vs 11.0%, p=0.035). The other antibody profiles showed no significant differences.
In multivariable logistic regression (online supplemental table 2), increasing age at IIM onset (OR 1.05 per year, 95% CI 1.03 to 1.07, p<0.001), smoking history (OR 3.00, 95% CI 1.64 to 5.50, p<0.001) and night sweats (OR 12.76, 95% CI 2.72 to 59.87, p=0.001) were independent predictors of cancer. Compared with DM, those with ASyS (OR 0.22, 95% CI 0.09 to 0.54, p=0.001), IMNM (OR 0.31, 95% CI 0.10 to 0.97, p=0.045) and PM (OR 0.31, 95% CI 0.10 to 0.97, p=0.044) had significantly reduced risk. Anti-TIF1γ antibody positivity remained a strong independent predictor of cancer (OR 4.64, 95% CI 2.35 to 9.16, p<0.001), whereas anti-MDA-5 antibodies trended towards being protective (OR 0.30, 95% CI 0.08 to 1.07, p=0.064).
Distribution of detected cancers across IMACS risk categories
After retrospective application of the IMACS criteria, 75.6% (65/86) of patients who developed cancer within 3 years of IIM diagnosis were classified as high risk and 22.1% (19/86) as moderate risk. None of the patients classified as standard-risk developed cancer. Two patients with IBM, excluded from the IMACS risk categories, were nonetheless diagnosed with cancer (splenic marginal zone lymphoma and non-small cell carcinoma of the lung). Patients with detected cancer were more frequently classified as high risk (77.4% vs 48.6%, p<0.001) and less frequently as moderate risk (22.6% vs 39.7%, p=0.002) or standard risk (0.0% vs 11.6%, p<0.001), which were consistent with the stratification logic of the IMACS framework.
Cancer risk burden and malignancy
The cumulative burden and balance of IMACS risk features appeared to influence malignancy risk. Patients who develop cancer typically have multiple high-risk features (commonly 2–4) and few or no low-risk features (figure 1A). In contrast, patients without cancer often had few high-risk features, but frequently also had low-risk features (figure 1B). The distinction between these groups became more pronounced when smoking and night sweats were included in the analysis (figure 1C,D).
Figure 1. Heatmaps depicting the distribution of patients with and without cancer within 3 years of IIM diagnosis, stratified by the number of IMACS-defined high-risk and low-risk features (panels A and B). Panels C and D additionally incorporate the key features: smoking and night sweats.
A logistic regression model incorporating risk factor counts confirmed that each additional high-risk IMACS feature increased cancer odds by 70% (OR 1.70, 95% CI 1.43 to 2.04, p<0.001), while each additional low-risk feature conferred a 24% reduction in odds (OR 0.76, 95% CI 0.63 to 0.91, p=0.003). Smoking (OR 3.17, 95% CI 1.84 to 5.44, p<0.001) and night sweats (OR 9.60, 95% CI 2.31 to 39.84, p=0.002) continued to be strong independent predictors in this model. These findings suggest that both the burden and relative balance of risk features modulate cancer risk in patients with IIM.
Alliance between established screening practices and IMACS recommendations
A marked discrepancy between the historical screening practices and the newly introduced IMACS guidelines was observed. Across all sites, few high-risk (6.2%), moderate-risk (2.7%) and standard-risk (11.1%) patients underwent a full panel of recommended tests (table 4). Frequently underused tests (performed in <50% of indicated cases) included cervical screening, mammography, cancer antigen (CA)-125, prostate-specific antigen, fluorodeoxyglucose positron emission tomography (FDG-PET)/CT, lower gastrointestinal tract endoscopy and nasoendoscopy (figure 2). Among high-risk patients followed for >1 year, few (5.8%, 22/382) underwent annual screening, as currently recommended.
Table 4. Alignment between historical screening practices and 2023 IMACS cancer screening guidelines.
| Centre location | Australia | Brazil | Hong Kong | India | Italy | Japan | UK | Total |
|---|---|---|---|---|---|---|---|---|
| High risk | ||||||||
| N | 31 | 113 | 34 | 70 | 58 | 73 | 25 | 404 |
| Concordance | 6 (19.4) | 3 (2.7) | 6 (17.6) | 2 (2.9) | 3 (5.2) | 5 (6.8) | 0 (0.0) | 25 (6.2) |
| Underscreened | 25 (80.6) | 110 (97.3) | 28 (82.4) | 68 (97.1) | 55 (94.8) | 68 (93.2) | 25 (100.0) | 379 (93.8) |
| Moderate risk | ||||||||
| N | 28 | 35 | 22 | 93 | 32 | 66 | 20 | 296 |
| Concordance | 3 (10.7) | 3 (8.6) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 2 (3.0) | 0 (0.0) | 8 (2.7) |
| Underscreened | 23 (82.1) | 29 (82.9) | 14 (63.6) | 92 (98.9) | 28 (87.5) | 63 (95.5) | 20 (100.0) | 269 (90.9) |
| Overscreened* | 2 (7.1) | 3 (8.6) | 8 (36.4) | 1 (1.1) | 4 (12.5) | 1 (1.5) | 0 (0.0) | 19 (6.4) |
| Standard risk | ||||||||
| N | 0 | 8 | 2 | 61 | 5 | 4 | 1 | 81 |
| Concordance | NA | 4 (50.0) | 0 (0.0) | 2 (3.3) | 2 (40.0) | 1 (25.0) | 0 (0.0) | 9 (11.1) |
| Underscreened | NA | 0 (0.0) | 1 (50.0) | 58 (95.1) | 0 (0.0) | 3 (75.0) | 1 (100.0) | 63 (77.8) |
| Overscreened† | NA | 4 (50.0) | 1 (50.0) | 1 (1.6) | 3 (60.0) | 0 (0.0) | 0 (0.0) | 9 (11.1) |
Overscreening definition: performance of PET/CT and/or endoscopy in addition to the full suite of ‘basic’ and ‘enhanced’ malignancy screening tests, despite a lack of red flag symptoms.
Overscreening definition: performance of ‘enhanced’ screening tests to evaluate for IIM-related malignancy despite a lack of red flag symptoms. Performance of malignancy screening tests as part of national screening programmes and performance of CT thorax to investigate for ILD were not considered overscreening.
IIM, idiopathic inflammatory myopathy; ILD, interstitial lung disease ; IMACS, International Myositis Assessment and Clinical Studies Group; NA, not available; PET, positron emission tomography.
Figure 2. Proportion of patients who underwent specific screening tests when clinically indicated. Data reflect only patients with a clear indication for each test, based on IMACS risk stratification or the presence of risk factors such as smoking and night sweat symptoms. For nasoendoscopy, data are shown only for patients from Hong Kong and Japan, where this test is regionally relevant. CA-125, cancer antigen 125; CXR, chest X-ray; ESR, erythrocyte sedimentation rate; FBE, full blood examination; FOBT, faecal occult blood test; GI, gastrointestinal; IMACS, International Myositis Assessment and Clinical Studies Group; LFT, liver function test; MMG, mammography; PET, positron emission tomography; PSA, prostate-specific antigen; SPEP, serum protein electrophoresis; UEC, urea, electrolytes and creatinine; US, ultrasound.
Screening practices varied regionally. Of the total cohort, 73 patients underwent FDG-PET/CT without prior CT of the chest, abdomen or pelvis; this approach was most common in Hong Kong (48/73, 65.8%). Malignancy was detected in 12 (16.4%) patients screened with an upfront PET/CT. Overall, PET/CT usage was frequent in Hong Kong (51/58, 87.9%) but rare in Brazil (1/156, 0.6%), the UK (2/46, 4.3%) and India (19/224, 8.5%). Nasoendoscopy was also commonly used in Hong Kong (52/58, 89.7%) and Japan (29/145, 20.0%), but infrequently elsewhere, reflecting regional clinical guidelines and local epidemiological factors.
There was some evidence of overscreening among the moderate-risk (6.4%), standard-risk (11.1%) and IBM (14.3%) cohorts. Commonly overperformed tests in standard-risk cohorts included chest CT and abdomen/pelvis CT.
Predictors of underscreening relative to IMACS recommendations
Clinical and demographic characteristics associated with underscreening, defined relative to the IMACS guidelines, are summarised in table 5. Underscreened patients were more likely to be female (74.7% vs 51.4%, p < 0.001) and to reside in India (30.7% vs 8.6%, p<0.001) or the UK (6.5% vs 0%, p=0.028) than those who underwent recommended screening tests. Conversely, those in Australia (6.8% vs 15.7%, p=0.007) or Hong Kong (6.1% vs 21.4%, p<0.001) were less likely to be underscreened. Underscreened individuals had lower rates of IMNM (5.6% vs 14.3%, p=0.005) and anti-TIF1γ positivity (6.8% vs 31.5%, p<0.001) but were more likely to have weight loss (41.1% vs 28.1%, p=0.043) and ILD (44.2% vs 31.4%, p=0.040). A high-risk IMACS classification status was associated with underscreening (53.2% vs 37.1%, p = 0.010), likely reflecting the increased screening demands in this group.
Table 5. Clinical and demographic characteristics associated with underscreening relative to 2023 IMACS recommendations*.
| Variable | Underscreened N=711 |
Not underscreened N=70 |
P value |
|---|---|---|---|
| Female sex | 531 (74.7) | 36 (51.4) | <0.001 |
| Age | 49.7±16.2 | 51.6±17.6 | 0.261 |
| Place: Australia | 48 (6.8) | 11 (15.7) | 0.007 |
| Place: Brazil | 139 (19.6) | 17 (24.3) | 0.344 |
| Place: HK | 43 (6.1) | 15 (21.4) | <0.001 |
| Place: India | 218 (30.7) | 6 (8.6) | <0.001 |
| Place: Italy | 83 (11.7) | 12 (17.1) | 0.182 |
| Place: Japan | 134 (18.9) | 9 (12.9) | 0.216 |
| Place: UK | 46 (6.5) | 0 (0.0) | 0.028 |
| DM | 283 (39.8) | 25 (35.7) | 0.504 |
| CADM | 70 (9.9) | 10 (14.3) | 0.242 |
| PM | 68 (9.6) | 8 (11.4) | 0.616 |
| IMNM | 40 (5.6) | 10 (14.3) | 0.005 |
| OM | 81 (11.4) | 5 (7.1) | 0.278 |
| ASyS | 169 (23.8) | 12 (17.1) | 0.210 |
| Family history cancer | 59 (8.7) | 8 (12.7) | 0.283 |
| Smoker (ever) | 124 (18.1) | 12 (19.1) | 0.856 |
| Weight loss | 281 (41.1) | 18 (28.1) | 0.043 |
| Night sweats | 9 (1.3) | 1 (1.6) | 0.869 |
| Fever | 244 (34.3) | 17 (24.3) | 0.09 |
| High disease activity | 188 (26.4) | 17 (24.3) | 0.696 |
| Dysphagia | 179 (25.2) | 20 (28.6) | 0.534 |
| Cutaneous necrosis | 95 (13.4) | 5 (7.1) | 0.137 |
| Clinically amyopathic | 73 (10.7) | 12 (18.8) | 0.052 |
| Raynaud’s phenomenon | 220 (30.9) | 20 (28.6) | 0.682 |
| Inflammatory arthritis | 277 (39.0) | 21 (30.0) | 0.141 |
| ILD | 314 (44.2) | 22 (31.4) | 0.040 |
| Anti-Mi2 | 59 (8.3) | 1 (1.4) | 0.039 |
| Anti-MDA5 | 77 (10.8) | 4 (5.7) | 0.180 |
| Anti-SAE1 | 24 (3.4) | 2 (2.9) | 0.818 |
| Anti-HMGCR | 21 (3.0) | 4 (5.7) | 0.211 |
| Anti-NXP-2 | 26 (3.7) | 2 (2.9) | 0.731 |
| Anti-TIF1γ | 48 (6.8) | 22 (31.4) | <0.001 |
| Anti-Jo1 | 84 (11.8) | 4 (5.7) | 0.124 |
| Non-Jo1 antisynthetase antibody | 90 (12.7) | 8 (11.4) | 0.767 |
| Anti-SRP | 25 (3.5) | 4 (5.71) | 0.353 |
| MAA | 199 (28.0) | 19 (27.1) | 0.880 |
| IMACS risk: standard | 63 (8.9) | 18 (25.7) | <0.001 |
| IMACS risk: moderate | 270 (38.0) | 26 (37.1) | 0.891 |
| IMACS risk: high | 378 (53.2) | 26 (37.1) | 0.010 |
| Cancer detected | 62 (8.7) | 17 (24.3) | <0.001 |
IBM excluded.
ASyS, antisynthetase syndrome; CADM, clinically amyopathic dermatomyositis; DM, dermatomyositis; HK, Hong Kong; HMGCR, 3-hydroxy-3-methylglutaryl-coenzyme A reductase; IBM, inclusion body myositis; ILD, interstitial lung disease; IMACS, International Myositis Assessment and Clinical Studies Group; IMNM, immune-mediated necrotising myopathy; MAA, myositis-associated antibody; MDA5, Melanoma differentiation-associated gene 5; NXP2, Nuclear matrix protein 2; OM, overlap myositis; PM, polymyositis; SAE, Small ubiquitin-like modifier activating enzyme subunit 1 ; SRP, Signal recognition particle; TIF1γ, Transcription intermediary factor 1-gamma.
In multivariable analysis (online supplemental table 3), female sex remained independently associated with increased odds of underscreening (OR 6.34, 95% CI 2.96 to 13.54, p<0.001). Compared with Hong Kong, residence in India (OR 74.18, 95% CI 15.25 to 360.74, p<0.001) and Japan (OR 12.33, 95% CI 4.01 to 37.93, p<0.001) were strongly independently associated with higher odds of underscreening. Relative to DM, ASyS (OR 5.48, 95% CI 1.25 to 23.95, p=0.024) and PM (OR 5.58, 95% CI 1.14 to 27.30, p=0.034) diagnoses were more likely to be underscreened. Weight loss remained independently associated with underscreening (OR 3.03, 95% CI 1.15 to 7.98, p=0.024), whereas anti-TIF1γ positivity was strongly protective (OR 0.06, 95% CI 0.02 to 0.18, p<0.001) consistent with higher screening compliance in this high-risk serological subgroup.
Discussion
This is the first multicentre, international audit of historical cancer screening practices in IIM benchmarked against the 2023 IMACS recommendations, and to describe the distribution of detected malignancies across IMACS-defined risk strata in a real-world cohort. Based on the retrospective application of the IMACS criteria, 51.7%, 37.9% and 10.4% of our cohort was classified as high risk, moderate risk and standard risk. Accordingly, under the current recommendations, 89.6% of our patients would have qualified for enhanced screening at the time of IIM diagnosis. The overall cancer detection rate within 3 years of IIM diagnosis in our cohort was 10.8%.
The observed distribution of detected cancers was consistent with the IMACS stratification logic, with the majority of malignancies occurring in patients retrospectively classified as high risk. However, this observation must be interpreted with caution: because screening intensity was strongly correlated with assigned risk category (high-risk patients underwent more investigations than standard-risk patients), occult malignancies may have been disproportionately missed in lower-risk groups. Patients who developed cancer were significantly more likely to have been classified as high-risk (77.4% vs 48.6%, p<0.001) and significantly less likely to be in the moderate-risk (22.6% vs 39.7%, p=0.002) or standard-risk categories (0% vs 11.6%, p<0.001). No malignancies were observed in the standard-risk group, although two patients with IBM, excluded from the IMACS framework, were diagnosed with cancer. This underscores the need for appropriate clinical vigilance in the excluded subtypes. Notably, emerging evidence suggests an association between IBM and T cell large granular lymphocytic leukaemia.14 Further prospective studies are required to clarify malignancy risk in this population.
Several individual components of the IMACS model demonstrated discriminatory capacity in this cohort. Increasing age, DM diagnosis and anti-TIF1γ positivity were all strongly associated with cancer. In addition, we evaluated clinical ‘red flags’ features referenced in the guideline but not formally integrated into the risk stratification algorithm. Smoking and night sweats were independently associated with cancer. Notably, anti- nuclear matrix protein 2(NXP-2) antibodies were not significantly associated with cancer in our cohort, aligning with recent meta-analyses1 15 suggesting that previously reported positive associations may reflect comparisons with general population rather than with anti-NXP-2-negative IIM controls.16 17 Similarly, persistent disease activity despite immunosuppression was not significantly predictive in our analysis, although this may reflect heterogeneity in how the criterion was interpreted across sites and investigators.
Features designated as ‘low risk’ in the IMACS guidelines, such as ASyS, OM, ILD, inflammatory arthritis and Raynaud’s phenomenon, were confirmed to be significantly less common among patients with malignancy in our cohort. We also noted that the anti-Mi2 antibody was independently protective against cancer in our adjusted model, while anti-MDA-5 demonstrated a strong trend towards protection that narrowly missed statistical significance. Importantly, our data indicate that cancer risk is influenced by individual high-risk features and by the cumulative balance of high-risk and low-risk factors, a nuance not captured in the current IMACS algorithm. Each additional high-risk feature increased the odds of malignancy by 70%, whereas each additional low-risk feature conferred a 24% risk reduction. This highlights the potential value of incorporating the net risk burden rather than relying solely on binary thresholds, although these observations are exploratory and must be interpreted cautiously given the retrospective design.
Our study revealed a marked discrepancy between the IMACS guidelines and historical real-world clinical practice. Most patients, irrespective of the risk category, did not receive the full suite of recommended screening tests. Indeed, only 6.2% of high-risk patients completed all tests and annual screening efforts were low. Screening practices also varied by region. For example, FDG-PET/CT was used routinely in Hong Kong, but rarely elsewhere. Interpretation of these variations should consider contextual factors including availability of investigations, prevailing clinical norms and healthcare system structure (universal healthcare models, mixed public-private systems, predominantly private systems and hybrid models). Countries/Regions with universal healthcare systems (Australia and Hong Kong) demonstrated higher screening completion, whereas those with predominantly fee-for-service models (India) showed lower screening intensity (OR 74.18). This aligns with the global literature demonstrating that healthcare financing mechanisms substantially impact preventive care access.18 We observed that sex disparities in screening were particularly pronounced in resource-constrained settings, echoing findings from the 2023 Lancet Commission on Women, Power and Cancer.1819 Together, these findings reflect that historical practice may be shaped by system-level factors and also underscore the importance of increasing understanding of malignancy screening principles in IIM. As dissemination and awareness of the IMACS recommendations increase, alignment with these guidelines is likely to improve over time in routine rheumatology practice.
Clinical practice guidelines are inherently iterative and refined as new evidence emerges. Our results highlight aspects of the IMACS risk stratification model that warrant further evaluation in prospective studies. For example, the age criterion classified some patients with otherwise low-risk phenotypes, such as ASyS and OM, into higher-risk categories. Among intermediate-risk to high-risk patients with ASyS/OM in our study, 27.7% were aged only 41–50 years, and none in this age group developed malignancy. Interactions between age and other clinical risk features therefore warrant further prospective assessment. Similarly, the observed influence of cumulative high-risk and low-risk features and the strong associations observed for red flag features support further rigorous longitudinal evaluation of how these factors may best inform risk assessment.
Screening feasibility should also be considered. Our results indicate that the full screening protocol appears far removed from current practice, even in major tertiary centres. The challenge is likely to be even greater in regional or resource-constrained settings. Rationalising the screening algorithm to prioritise high-yield features may enhance feasibility and adherence. In some settings, FDG-PET/CT has been adopted as a streamlined first line investigation. Although cost and availability limit global applicability, FDG-PET/CT offers several advantages including cancer detection along with evaluation of assessment of IIM-related inflammatory activity.15 PET-CT may also offer diagnostic and economic advantages over multimodal testing, particularly when indirect costs (such as travel, missed work) are considered.16 Survey data also show that patients prefer simplified screening pathways and many express willingness to bear higher out-of-pocket costs for FDG-PET/CT over traditional panels.17 Future research should evaluate the comparative effectiveness and patient acceptability of single FDG-PET/CT-based strategies versus multimodal screening approaches.
A fundamental limitation of this study is the non-uniform ascertainment of cancer outcomes across IMACS risk categories. As demonstrated in our audit, screening intensity correlated strongly with assigned risk, meaning that patients in lower-risk categories underwent fewer investigations and may have had undetected malignancies. This differential verification precludes formal assessment of the sensitivity, specificity or predictive validity of the IMACS framework, and the observed cancer distribution across risk strata should be interpreted descriptively rather than as evidence of diagnostic performance. A prospective study applying a uniform reference standard across all risk categories would be required to formally evaluate the predictive accuracy of the IMACS stratification system. As a retrospective analysis, standardisation of certain variables, particularly evaluation of persistent disease activity and patient-reported symptoms such as night sweats, was challenging. Furthermore, screening performed in primary care settings may not have been consistently captured. Detailed data on immunosuppressant use, including agent type, timing and duration, were not available, precluding adjustment for treatment-related effects. Some myositis-specific antibodies (eg, anti-Ha, anti-KS) were not routinely assessed, and patients with IBM were under-represented, likely reflecting the rheumatology-focused nature of case ascertainment. As an audit of real-world practice, lung CT was not routinely performed in all patients, which may have resulted in under-ascertainment of ILD. However, as ILD is classified as a low-risk feature within the IMACS framework, incomplete detection would not have altered risk categorisation or the primary findings of this study. Follow-up duration varied across patients. Cancer outcomes were identified through retrospective review of medical records rather than uniform systematic screening and occult malignancy or malignancy diagnosed outside the study centres cannot be excluded. In addition, deaths prior to cancer diagnosis could not be systematically ascertained across all centres and may have contributed to underascertainment.
Additional limitations include potential ascertainment bias due to the tertiary care setting, variation in healthcare system access across countries/regions and heterogeneity in data capture due to retrospective abstractions from medical records. The retrospective design also limited our ability to capture patient preferences and shared decision-making processes that may have influenced screening decisions. The potential inaccuracy in some variables such as night sweats, which were not systematically collected and verification is necessary in a prospective cohort study using standardised questionnaires. Nonetheless, the international, multicentre design and large sample size strengthen the generalisability of our findings.
Conclusions and future directions
Our findings provide a real-world audit of cancer screening practices in IIM benchmarked against the IMACS recommendations and describe the distribution of detected malignancies across risk categories. Prospective longitudinal studies are needed to evaluate the clinical relevance of cumulative risk burden, age-based thresholds and the contribution of red-flag symptoms. Further implementation and outcomes studies will be important to assess screening uptake, patient experience and real-world impact across diverse healthcare settings, including resource-limited contexts.
Supplementary material
The views and opinions expressed are solely those of the authors and do not represent or reflect those of any affiliated institution. Portions of the data from the Hong Kong cohort (Tang IYK, Chan SCW, Li PH et al. Validation of the International Myositis Assessment and Clinical Studies Group guideline on cancer risk stratification. Rheumatology (Oxford). 2025;64(4):2106–2114. doi: 10.1093/rheumatology/keae504) and the Australian cohort (Teh I, Huang V, Oon S et al. Real-world implications of IMACS malignancy screening guidelines for idiopathic inflammatory myopathies: an evaluation of compliance and economic impact at a tertiary referral centre. Int J Rheum Dis. 2024;27. doi: 10.1111/1756-185X.15198) have been published previously.
Footnotes
Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study was approved by the Institutional Ethics Committee of SGPGIMS, Lucknow, India (IEC 2024-291-IP-EXP-62; approval dated 8 January 2025). All participants provided informed consent before taking part in the study.
Data availability free text: Data are available on reasonable request to the corresponding author.
Data availability statement
Data are available on reasonable request.
References
- 1.Oldroyd AGS, Allard AB, Callen JP, et al. A systematic review and meta-analysis to inform cancer screening guidelines in idiopathic inflammatory myopathies. Rheumatology (Oxford) 2021;60:2615–28. doi: 10.1093/rheumatology/keab166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Dobloug GC, Garen T, Brunborg C, et al. Survival and cancer risk in an unselected and complete Norwegian idiopathic inflammatory myopathy cohort. Semin Arthritis Rheum. 2015;45:301–8. doi: 10.1016/j.semarthrit.2015.06.005. [DOI] [PubMed] [Google Scholar]
- 3.Dobloug GC, Svensson J, Lundberg IE, et al. Mortality in idiopathic inflammatory myopathy: results from a Swedish nationwide population-based cohort study. Ann Rheum Dis. 2018;77:40–7. doi: 10.1136/annrheumdis-2017-211402. [DOI] [PubMed] [Google Scholar]
- 4.Hočevar A, Viršček A, Krošel M, et al. Survival of patients with idiopathic inflammatory myopathies in Slovenia. Front Med. 2021;8:801078. doi: 10.3389/fmed.2021.801078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Nuño-Nuño L, Joven BE, Carreira PE, et al. Mortality and prognostic factors in idiopathic inflammatory myositis: a retrospective analysis of a large multicenter cohort of Spain. Rheumatol Int. 2017;37:1853–61. doi: 10.1007/s00296-017-3799-x. [DOI] [PubMed] [Google Scholar]
- 6.Kang EH, Lee SJ, Ascherman DP, et al. Temporal relationship between cancer and myositis identifies two distinctive subgroups of cancers: impact on cancer risk and survival in patients with myositis. Rheumatology (Oxford) 2016;55:1631–41. doi: 10.1093/rheumatology/kew215. [DOI] [PubMed] [Google Scholar]
- 7.Dutton K, Soden M. Malignancy screening in autoimmune myositis among Australian rheumatologists. Intern Med J. 2017;47:1367–75. doi: 10.1111/imj.13556. [DOI] [PubMed] [Google Scholar]
- 8.Oldroyd AGS, Callen JP, Chinoy H, et al. International guideline for idiopathic inflammatory myopathy-associated cancer screening: an International Myositis Assessment and Clinical Studies Group (IMACS) initiative. Nat Rev Rheumatol. 2023;19:805–17. doi: 10.1038/s41584-023-01045-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Teh I, Huang V, Oon S, et al. Real-world implications of IMACS malignancy screening guidelines for idiopathic inflammatory myopathies: An evaluation of compliance and economic impact at a tertiary referral center. Int J Rheum Dis. 2024;27:e15198. doi: 10.1111/1756-185X.15198. [DOI] [PubMed] [Google Scholar]
- 10.Tang IYK, Chan SCW, Li PH, et al. Validation of the International Myositis Assessment and Clinical Studies Group guideline on cancer risk stratification. Rheumatology (Oxford) 2025;64:2106–14. doi: 10.1093/rheumatology/keae504. [DOI] [PubMed] [Google Scholar]
- 11.Hočevar A, Tomšič M, Perdan Pirkmajer K. Application of a cancer risk stratification tool to a cohort of inflammatory myopathy patients in daily practice-a single-centre experience. Rheumatology (Oxford) 2023;62:e309–10. doi: 10.1093/rheumatology/kead197. [DOI] [PubMed] [Google Scholar]
- 12.Mehta P, Gupta L. Combined case record forms for collaborative datasets of patients and controls of idiopathic inflammatory myopathies. Indian J Rheumatol. 2020;15 doi: 10.4103/injr.injr_56_20. [DOI] [Google Scholar]
- 13.Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. doi: 10.3322/caac.21834. [DOI] [PubMed] [Google Scholar]
- 14.Greenberg SA, Pinkus JL, Amato AA, et al. Association of inclusion body myositis with T cell large granular lymphocytic leukaemia. Brain. 2016;139:1348–60. doi: 10.1093/brain/aww024. [DOI] [PubMed] [Google Scholar]
- 15.Bentick G, Fairley J, Nadesapillai S, et al. Defining the clinical utility of PET or PET-CT in idiopathic inflammatory myopathies: a systematic literature review. Semin Arthritis Rheum. 2022;57:152107. doi: 10.1016/j.semarthrit.2022.152107. [DOI] [PubMed] [Google Scholar]
- 16.Kundrick A, Kirby J, Ba D, et al. Positron emission tomography costs less to patients than conventional screening for malignancy in dermatomyositis. Semin Arthritis Rheum. 2019;49:140–4. doi: 10.1016/j.semarthrit.2018.10.021. [DOI] [PubMed] [Google Scholar]
- 17.Jicha KI, Bazewicz CG, Helm MF, et al. Cancer screening for dermatomyositis: a survey of indirect costs, burden, and patient willingness to pay. Cutis. 2023;112:89–95. doi: 10.12788/cutis.0833. [DOI] [PubMed] [Google Scholar]
- 18.Yabroff KR, Reeder-Hayes K, Zhao J, et al. Health Insurance Coverage Disruptions and Cancer Care and Outcomes: Systematic Review of Published Research. JNCI. 2020;112:671–87. doi: 10.1093/jnci/djaa048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ginsburg O, Vanderpuye V. Beddoe A et al.Women, power, and cancer: a Lancet CommissionThe Lancet. 2023;402:2113–66. doi: 10.1016/S0140-6736(23)01701-4. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data are available on reasonable request.