Abstract
Purpose
Recent studies have shown that intermuscular adipose tissue (IMAT) is a significant prognostic factor for breast cancer. To date, no clinical studies have investigated whether IMAT can be used to predict chemotherapy toxicity in older adult patients with early-stage breast cancer.
Methods
We included 304 patients diagnosed with stage I–III breast cancer between January 2020 and December 2022 in Harbin Medical University Cancer Hospital. All patients were aged ≥ 65 years and treated with neoadjuvant or adjuvant chemotherapy. IMAT within the pectoralis muscle was measured using computed tomography imaging. Logistic regression analysis was used to identify independent predictors of chemotherapy toxicity. A nomogram was built, and the model performance was assessed using accuracy, discrimination, and clinical benefits. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to evaluate changes in model performance after the addition of adipose tissue.
Results
Of the 304 patients (184 in the training cohort and 120 in the validation cohort), 30.3% developed grade 3–5 chemotherapy toxicities. Three independent predictors were identified in the multivariate analysis: hemoglobin level, IMAT area, and primary prophylaxis with granulocyte colony-stimulating factor. The nomogram demonstrated area under the receiver operating characteristic curve values of 0.708 (95% confidence interval [CI], 0.616–0.801) and 0.751 (95% CI, 0.655–0.846) in the training and validation cohorts, respectively. The nomogram showed good calibration (Hosmer-Lemeshow test, p > 0.05), and incorporating IMAT improved nomogram performance in both cohorts (all NRI and IDI > 0, p < 0.05). Decision curve analysis revealed that the nomogram was clinically useful.
Conclusion
A nomogram including IMAT may be useful for predicting the individual probability of chemotherapy toxicity and guiding therapy in older adults with early-stage breast cancer.
Keywords: Adipose Tissue, Aged, Breast Neoplasms, Drug Therapy, Nomograms
INTRODUCTION
Breast cancer is the most common malignant tumor and a leading cause of cancer-related deaths among women [1]. Its incidence rates increase with age, with nearly 50% of breast cancer cases diagnosed in individuals aged ≥ 65 years [2]. Although adjuvant chemotherapy improves survival in early-stage breast cancer, it is significantly underutilized in older adult patients [3,4]. One reason for his underutilization is the increased risk of toxicity and hospitalization burden in older women [5,6]. Therefore, identifying predictors of chemotherapy toxicity has been the focus of breast cancer research [7,8].
Recent studies have highlighted the role of myosteatosis in cancer [9,10,11]. Myosteatosis is characterized by ectopic fat infiltration into skeletal muscles, which negatively affects muscle mass, strength, and mobility [12,13,14]. Intermuscular adipose tissue (IMAT) is the extracellular fat beneath the fascia and between muscle groups, a key component of myosteatosis [15,16]. IMAT predicts the onset of mobility limitations and is directly linked to insulin sensitivity, as well as the loss of muscle mass, strength, and mobility in older people compared with younger ones [17,18]. IMAT is a distinct adipose tissue depot with an inflammatory and immunogenic secretome that affects the metabolic activity of nearby muscle tissues [19]. In addition, the endocrine action of IMAT can affect distant tissues via systemic circulation [20].
In the context of cachexia, reduced muscle quality has been observed in patients with breast cancer undergoing chemotherapy. Following taxane-based treatment, patients with metastatic breast cancer show altered muscle attenuation, indicating reduced muscle quality [21]. Compared with controls, breast cancer survivors who received anthracyclines showed a significant increase (~30%) in thigh IMAT content, which was notably correlated with impaired cardiorespiratory fitness [22]. Previous studies have demonstrated that cancer patients have more IMAT than non-cancer individuals and are more susceptible to exercise intolerance [23]. Breast cancer is particularly affected by adipose tissue owing to the unique histological structure of the breast [24]. A longitudinal clinical study has confirmed the development of IMAT and skeletal muscle atrophy in patients with breast cancer treated with chemotherapy [11]. Patients with greater visceral or intramuscular adiposity were more likely to experience toxic effects and subsequent dose delays or reductions [25].
To date, existing toxicity prediction models have not considered the impact of IMAT in predicting chemotherapy toxicity risk in older adults with early-stage breast cancer [26]. Nomograms are widely acknowledged as reliable tools for quantifying cancer risk [27,28]. Therefore, this study aimed to develop and validate a nomogram incorporating IMAT to predict chemotherapeutic toxicity in older adult patients with early-stage breast cancer.
METHODS
Patient eligibility
From January 2020 to December 2022, this retrospective study collected data from 304 female patients aged ≥ 65 years. Eligible patients had stage I–III breast cancer and received adjuvant or neoadjuvant chemotherapy at Harbin Medical University Cancer Hospital. The inclusion criteria for this study were as follows: (1) age at diagnosis ≥ 65 years, (2) pathologically confirmed breast cancer, (3) complete clinical data, and (4) computed tomography (CT) scans performed less than 2 weeks before treatment. The exclusion criteria included a history of malignancy, loss to follow-up, and artifacts on CT images that affected the evaluation.
This study was approved by the Institutional Ethics Review Board of Harbin Medical University Cancer Hospital (KY2022-10). Owing to the retrospective nature of this study, the requirement for informed consent was waived.
Data collection
Patient characteristics were extracted from the clinical data recorded before the initiation of adjuvant or neoadjuvant chemotherapy, including age, diabetes mellitus, hypertension, cardiovascular disease, body mass index (BMI), Eastern Cooperative Oncology Group (ECOG) performance status, Charlson Comorbidity Index (CCI), pathological characteristics (tumor stage and estrogen, progesterone, or human epidermal growth factor receptor 2 [HER2] status, and proliferation index), and laboratory data (hemoglobin, white blood cell count, platelet count, albumin, creatinine, and liver function). The following treatment characteristics were recorded: chemotherapy regimen, relative dose intensity (RDI), and the use of granulocyte colony-stimulating factor (G-CSF) as primary prophylaxis. The RDI was dichotomized using 85% as the cut-off point [29,30]. The chemotherapy regimens included polychemotherapy and monochemotherapy. Monochemotherapy included taxanes (paclitaxel and docetaxel), anthracyclines (epirubicin), and capecitabine. Polychemotherapy regimens included combinations, such as anthracyclines (doxorubicin or epirubicin) and cyclophosphamide (AC regimen), AC regimen followed by a taxane (AC-T regimen), taxanes (paclitaxel and docetaxel) and cyclophosphamide (TC regimen), and taxanes (paclitaxel and docetaxel) and carboplatin (PC regimen). Additionally, for HER2-positive patients, targeted therapies, such as trastuzumab, were combined with chemotherapy. Pegylated recombinant human G-CSF (xinruibai; Qilu Pharmaceutical Co., Ltd., Jinan, China) was administered subcutaneously at a dose of 100 μg/kg 48 h after chemotherapy.
CT assessment
Non-contrast chest CT examinations were performed using a Siemens Medical Solutions and Discovery or GE Healthcare 64-section multi-detector row CT scanner. IMAT within the pectoralis muscle was quantitatively assessed using fat tissue thresholds (ranging from −190 to −30 Hounsfield units [HU]) to identify visible fat within the muscle fascia above the aortic arch [31,32]. Briefly, chest CT images were loaded into ImageJ software (version 1.53; National Institutes of Health, Bethesda, USA) and converted to an 8-bit format [15]. The left and right pectoralis muscles (major and minor) were manually outlined, and the IMAT area was measured [31,33].
Study endpoint
The primary outcome was grade 3–5 chemotherapy toxicity or adverse events (AEs), assessed from the start to the end of the chemotherapy course. Toxicity was monitored after each chemotherapy cycle. The first and last routine blood tests were performed before chemotherapy and approximately 5–7 days after chemotherapy, respectively. A comprehensive examination, including blood tests, liver function tests, and electrocardiography, was conducted within 3 months of chemotherapy completion. Toxicity during treatment was identified by reviewing electronic medical records. If clinicians did not grade the toxicity but provided sufficient descriptions, grading was assigned retrospectively using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE version 4.0). Laboratory results during chemotherapy were reviewed to identify hematological toxicity and were graded using NCI-CTCAE version 4.0. Subsequently, AEs were independently reviewed by two physicians to confirm grade 3 (severe), 4 (life-threatening or disabling), and 5 (death) chemotherapy-related AEs. For grade 3–5 toxicity, the highest grade observed during treatment was used to classify patients. Patients could experience different types of toxicities at the same highest grade, and all types were reported.
Statistical analysis
Descriptive statistics were reported as means with standard deviations or medians (interquartile range) for continuous variables and percentages for categorical variables. Student’s t-test or Mann-Whitney U test was used to determine differences between the two groups. The χ2 test was used to compare categorical variables. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for chemotherapy toxicity. Variables with p < 0.10 in univariate analysis were further examined in multivariate analysis. A two-tailed p-value < 0.05 was considered statistically significant. All statistical analyses were conducted using R software (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria) and SPSS version 26.0 (SPSS Inc., Chicago, USA).
RESULTS
According to a 6:4 ratio, patients were randomly divided into training and validation cohorts. The training and validation cohorts comprised 184 and 120 patients, respectively. The two cohorts had a high degree of similarity across most variables, except for hemoglobin levels (p = 0.032). Among the 304 patients, 30.3% developed grade 3–5 chemotherapy toxicities. Detailed information on AEs is provided in Supplementary Table 1.
Sample characteristics of the training cohort
The median age of the patients was 67 years (range: 65–77 years). Moreover, 35 (19.0%), 122 (66.3%), and 27 (14.7%) patients had stage I, II, and III cancer, respectively. The most common histological type was invasive ductal carcinoma (n = 207, 68.1%), followed by mixed carcinoma (n = 71, 23.4%), invasive lobular carcinoma (n = 11, 3.6%), in situ ductal carcinoma (n = 6, 2.0%), and special types (n = 9, 2.9%). Thirty-one (16.9%) patients had triple-negative disease, and 30.1% had HER2-positive disease. Approximately one-third of the patients received neoadjuvant chemotherapy, and 90.8% received primary prophylaxis with G-CSF (Table 1). The IMAT area correlated with ECOG performance status, BMI, and CCI, whereas the IMAT HU did not correlate with these parameters (Supplementary Table 2).
Table 1. Baseline characteristics of patients.
| Variables | Training cohort | Validation cohort | |||||
|---|---|---|---|---|---|---|---|
| Without grade 3–5 toxicity (n = 134) | With grade 3–5 toxicity (n = 50) | p-value | Without grade 3–5 toxicity (n = 78) | With grade 3–5 toxicity (n = 42) | p-value | ||
| Age (yr) | 67.6 ± 2.7 | 68.1 ± 3.0 | 0.274 | 67.7 ± 3.2 | 67.7 ± 3.6 | 0.971 | |
| Diabetes mellitus | 0.854 | 0.142 | |||||
| Yes | 13 (9.7) | 6 (12.0) | 5 (6.4) | 7 (16.7) | |||
| No | 121 (90.3) | 44 (88.0) | 73 (93.6) | 35 (83.3) | |||
| Hypertension | 0.396 | 1.000 | |||||
| Yes | 28 (20.9) | 7 (14.0) | 14 (17.9) | 7 (16.7) | |||
| No | 106 (79.1) | 43 (86.0) | 64 (82.1) | 35 (83.3) | |||
| Cardiovascular disease | 0.567 | 1.000 | |||||
| Yes | 6 (4.5) | 4 (8.0) | 6 (7.7) | 3 (7.1) | |||
| No | 128 (95.5) | 46 (92.0) | 72 (92.3) | 39 (92.9) | |||
| ECOG performance status score | 1.01 ± 0.12 | 1.12 ± 0.33 | 0.002 | 1.00 ± 0.00 | 1.17 ± 0.38 | < 0.001 | |
| Charlson Comorbidity Index | 2.39 ± 0.77 | 2.70 ± 1.34 | 0.052 | 2.31 ± 0.61 | 2.81 ± 1.45 | 0.009 | |
| BMI (kg/m2) | 25.2 ± 3.2 | 25.0 ± 3.4 | 0.726 | 24.8 ± 3.3 | 24.1 ± 3.4 | 0.252 | |
| ALT (U/L) | 15 (12–21) | 19 (13–26) | 0.078 | 16 (12–23) | 15 (13–21) | 0.722 | |
| AST (U/L) | 21 (18–24) | 21 (18–27) | 0.773 | 22 (18–25) | 20 (18–25) | 0.720 | |
| γ-GGT (U/L) | 21 (17–31) | 24 (16–38) | 0.373 | 21 (16–29) | 21 (15–34) | 0.849 | |
| Total bilirubin (μmol/L) | 13.1 (10.8–17.1) | 12.4 (10.6–16.0) | 0.330 | 13.5 (10.9–16.0) | 13.4 (10.4–17.8) | 0.696 | |
| Albumin (g/L) | 41.9 ± 2.6 | 42.5 ± 3.4 | 0.193 | 41.9 ± 2.4 | 42.2 ± 3.3 | 0.514 | |
| Serum creatinine (μmol/L) | 71.2 ± 10.7 | 72.5 ± 16.6 | 0.543 | 70.7 ± 12.2 | 73.3 ± 12.3 | 0.277 | |
| Platelet count (× 109/L) | 244.2 ± 55.0 | 242.9 ± 54.8 | 0.886 | 244.6 ± 60.9 | 227.4 ± 64.5 | 0.151 | |
| Hemoglobin (g/L) | 141.8 ± 10.0 | 137.9 ± 11.6 | 0.027 | 140.3 ± 11.7 | 133.3 ± 11.7 | 0.002 | |
| WBC (× 109/L) | 6.43 ± 1.74 | 6.25 ± 1.56 | 0.535 | 6.38 ± 1.60 | 5.94 ± 1.27 | 0.127 | |
| CEA (ng/mL) | 2.42 (1.52–3.55) | 2.30 (1.80–3.15) | 0.917 | 2.42 (1.61–3.56) | 1.96 (1.36–3.35) | 0.268 | |
| CA153 (U/mL) | 11.2 (8.1–16.1) | 11.1 (8.4–18.2) | 0.371 | 12.8 (8.5–17.8) | 10.4 (6.1–16.4) | 0.225 | |
| IMAT area (cm2) | 4.91 ± 2.67 | 6.42 ± 3.64 | 0.003 | 4.26 ± 2.29 | 6.26 ± 3.64 | < 0.001 | |
| IMAT mean (HU) | −56.00 ± 5.81 | −57.97 ± 19.66 | 0.298 | −56.14 ± 11.23 | −61.04 ± 14.02 | 0.040 | |
| Clinical stage | 0.941 | 0.614 | |||||
| I | 26 (19.4) | 9 (18.0) | 19 (24.4) | 7 (16.7) | |||
| II | 89 (66.4) | 33 (66.0) | 43 (55.1) | 25 (59.5) | |||
| III | 19 (14.2) | 8 (16.0) | 16 (20.5) | 10 (23.8) | |||
| Histologic type | 0.256 | 0.736 | |||||
| Invasive ductal carcinoma | 88 (65.7) | 35 (70.0) | 54 (69.2) | 30 (71.4) | |||
| Invasive lobular carcinoma | 7 (5.2) | 0 (0.0) | 2 (2.6) | 2 (4.8) | |||
| Other | 39 (29.1) | 15 (30.0) | 22 (28.2) | 10 (23.8) | |||
| ER | 0.868 | 0.858 | |||||
| Positive | 92 (68.7) | 33 (66.0) | 51 (65.4) | 26 (61.9) | |||
| Negative | 42 (31.3) | 17 (34.0) | 27 (34.6) | 16 (38.1) | |||
| PR | 0.986 | 0.180 | |||||
| Positive | 73 (54.5) | 28 (56.0) | 43 (55.1) | 17 (40.5) | |||
| Negative | 61 (45.5) | 22 (44.0) | 35 (44.9) | 25 (59.5) | |||
| HER2 | 0.183 | 0.951 | |||||
| Positive | 43 (32.1) | 22 (44.0) | 26 (33.3) | 13 (31.0) | |||
| Negative | 91 (67.9) | 28 (56.0) | 52 (66.7) | 29 (69.0) | |||
| Ki-67 (%) | 0.215 | 0.112 | |||||
| ≥ 30 | 49 (36.6) | 24 (48.0) | 26 (33.3) | 21 (50.0) | |||
| < 30 | 85 (63.4) | 26 (52.0) | 52 (66.7) | 21 (50.0) | |||
| Subtype | 0.848 | 0.262 | |||||
| TNBC | 22 (16.5) | 9 (18.0) | 13 (16.7) | 11 (26.2) | |||
| Luminal-A | 51 (38.3) | 16 (32.0) | 29 (37.2) | 10 (23.8) | |||
| Luminal-B | 22 (16.5) | 8 (16.0) | 12 (15.4) | 10 (23.8) | |||
| HER2-enriched | 38 (28.6) | 17 (34.0) | 24 (30.8) | 11 (26.2) | |||
| Treatment setting | 0.845 | 1.000 | |||||
| Neoadjuvant | 47 (35.1) | 19 (38.0) | 26 (33.3) | 14 (33.3) | |||
| Adjuvant | 87 (64.9) | 31 (62.0) | 52 (66.7) | 28 (66.7) | |||
| Chemotherapy | 0.047 | 0.050 | |||||
| Polychemotherapy | 132 (98.5) | 46 (92.0) | 77 (98.7) | 38 (90.5) | |||
| Monochemotherapy | 2 (1.5) | 4 (8.0) | 1 (1.3) | 4 (9.5) | |||
| Chemotherapy regiment | 0.083 | 0.807 | |||||
| Anthracycline | 95 (70.9) | 28 (56.0) | 49 (63.6) | 25 (59.5) | |||
| Nonanthracycline | 39 (29.1) | 22 (44.0) | 28 (36.4) | 17 (40.5) | |||
| Baseline dose reduction | 0.024 | 0.260 | |||||
| Yes | 25 (18.7) | 2 (4.0) | 15 (19.2) | 4 (9.5) | |||
| No | 109 (81.3) | 48 (96.0) | 63 (80.8) | 38 (90.5) | |||
| Primary prophylaxis with G-CSF | < 0.001 | 0.009 | |||||
| Yes | 129 (96.3) | 38 (76.0) | 76 (97.4) | 35 (83.3) | |||
| No | 5 (3.7) | 12 (24.0) | 2 (2.6) | 7 (16.7) | |||
ECOG = Eastern Cooperative Oncology Group; BMI = body mass index; ALT = alanine aminotransferase; AST = aspartate aminotransferase; γ-GGT = γ-glutamyl transferase; WBC = white blood cell; CEA = carcinoembryonic antigen; CA153 = cancer antigen 153; IMAT = intermuscular adipose tissue; HU = Hounsfield units; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor 2; Ki-67 = proliferation index; TNBC = triple-negative breast cancer; G-CSF = granulocyte colony-stimulating factor.
Construction of the nomogram
In the training cohort, univariate analysis showed that γ-glutamyl transferase, hemoglobin, IMAT area, baseline dose reduction, chemotherapy, and primary prophylaxis with G-CSF were significantly associated with the development of grade 3–5 toxicities (p < 0.05). Multivariate analysis identified hemoglobin levels, IMAT area, and primary prophylaxis with G-CSF as significant independent predictors of grade 3–5 chemotherapy toxicity. Similar results were obtained in the validation cohort (Table 2). Therefore, these three factors were used in the nomograms (Figure 1A).
Table 2. Univariate and multivariate logistic analysis in the training and validation cohorts.
| Variables | Univariate analysis | Multivariate analysis | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | p-value | OR (95% CI) | p-value | |||
| Training cohort | ||||||
| Age (yr) | 1.065 (0.951–1.192) | 0.275 | ||||
| Diabetes mellitus | ||||||
| Yes vs. No | 1.269 (0.454–3.545) | 0.649 | ||||
| Hypertension | ||||||
| Yes vs. No | 1.623 (0.659–3.994) | 0.292 | ||||
| Cardiovascular disease | ||||||
| Yes vs. No | 1.855 (0.501–6.870) | 0.355 | ||||
| BMI (kg/m2) | 0.982 (0.889–1.085) | 0.724 | ||||
| ALT (U/L) | 1.030 (0.999–1.063) | 0.061 | 1.000 (0.953–1.050) | 0.984 | ||
| AST (U/L) | 1.030 (0.999–1.062) | 0.059 | 1.010 (0.967–1.054) | 0.658 | ||
| γ-GGT (U/L) | 1.019 (1.004–1.034) | 0.013 | 1.008 (0.988–1.028) | 0.427 | ||
| Total bilirubin (μmol/L) | 0.957 (0.889–1.031) | 0.245 | ||||
| Albumin (g/L) | 1.081 (0.961–1.215) | 0.193 | ||||
| Serum creatinine (μmol/L) | 1.008 (0.983–1.033) | 0.543 | ||||
| Platelet count (× 109/L) | 1.000 (0.994–1.006) | 0.885 | ||||
| Hemoglobin (g/L) | 0.965 (0.935–0.997) | 0.030 | 0.962 (0.927–0.998) | 0.039 | ||
| WBC (× 109/L) | 0.939 (0.771–1.144) | 0.533 | ||||
| CEA (ng/mL) | 1.048 (0.975–1.126) | 0.205 | ||||
| CA153 (U/mL) | 1.048 (0.975–1.126) | 0.267 | ||||
| IMAT area (cm2) | 1.008 (0.994–1.022) | 0.004 | 1.152 (1.021–1.300) | 0.022 | ||
| IMAT mean (HU) | 0.979 (0.940–1.019) | 0.291 | ||||
| Clinical stage | ||||||
| I | Ref. | |||||
| II | 0.822 (0.268–2.523) | 0.732 | ||||
| III | 0.881 (0.352–2.204) | 0.786 | ||||
| Histologic type | ||||||
| Invasive ductal carcinoma | Ref. | |||||
| Invasive lobular carcinoma | 1.034 (0.507–2.109) | 0.927 | ||||
| Other | 0.000 (0.000–0.000) | 0.999 | ||||
| ER | ||||||
| Positive vs. Negative | 0.886 (0.445–1.766) | 0.731 | ||||
| PR | ||||||
| Positive vs. Negative | 1.064 (0.553–2.045) | 0.854 | ||||
| HER2 | ||||||
| Positive vs. Negative | 1.663 (0.854–3.236) | 0.134 | ||||
| Ki-67 (%) | ||||||
| ≥ 30 vs. < 30 | 1.601 (0.830–3.088) | 0.160 | ||||
| Subtype | ||||||
| TNBC | Ref. | |||||
| Luminal-A | 0.914 (0.349–2.397) | 0.856 | ||||
| Luminal-B | 0.701 (0.315–1.563) | 0.386 | ||||
| HER2-enriched | 0.813 (0.302–2.189) | 0.682 | ||||
| Treatment setting | ||||||
| Neoadjuvant vs. Adjuvant | 1.135 (0.579–2.222) | 0.713 | ||||
| Chemotherapy | 0.136 | |||||
| Poly vs. Mono | 5.739 (1.017–32.380) | 0.048 | 4.624 (0.617–34.631) | |||
| Chemotherapy regiment | 0.162 | |||||
| Anthracycline vs. Nonanthracycline | 0.522 (0.267–1.022) | 0.058 | 0.568 (0.258–1.254) | |||
| Baseline dose reduction | 0.074 | |||||
| Yes vs. No | 0.182 (0.041–0.798) | 0.024 | 0.237 (0.049–1.148) | |||
| Primary prophylaxis with G-CSF | 0.003 | |||||
| Yes vs. No | 0.123 (0.041–0.370) | < 0.001 | 0.149 (0.043–0.514) | |||
| Validation cohort | ||||||
| Age (yr) | 1.002 (0.896–1.121) | 0.970 | ||||
| Diabetes mellitus | 0.147 | |||||
| Yes vs. No | 2.930 (0.865–9.854) | 0.087 | 2.866 (0.692–11.870) | |||
| Hypertension | ||||||
| Yes vs. No | 0.914 (0.338–2.477) | 0.860 | ||||
| Cardiovascular disease | ||||||
| Yes vs. No | 0.923 (0.219–3.895) | 0.913 | ||||
| BMI (kg/m2) | 0.934 (0.831–1.050) | 0.251 | ||||
| ALT (U/L) | 0.994 (0.959–1.031) | 0.748 | ||||
| AST (U/L) | 1.000 (0.953–1.049) | 0.985 | ||||
| γ-GGT (U/L) | 1.013 (0.994–1.033) | 0.176 | ||||
| Total bilirubin (μmol/L) | 1.030 (0.957–1.109) | 0.426 | ||||
| Albumin (g/L) | 1.047 (0.914–1.199) | 0.510 | ||||
| Serum creatinine (μmol/L) | 1.017 (0.986–1.048) | 0.283 | ||||
| Platelet count (× 109/L) | 0.995 (0.989–1.002) | 0.152 | ||||
| Hemoglobin (g/L) | 0.950 (0.918–0.984) | 0.004 | 0.949 (0.911–0.989) | 0.013 | ||
| WBC (× 109/L) | 0.815 (0.626–1.061) | 0.129 | ||||
| CEA (ng/mL) | 1.011 (0.989–1.035) | 0.329 | ||||
| CA153 (U/mL) | 1.001 (0.978–1.025) | 0.925 | ||||
| IMAT area (cm2) | 1.274 (1.101–1.475) | 0.001 | 1.282 (1.085–1.516) | 0.004 | ||
| IMAT mean (HU) | 0.964 (0.925–1.004) | 0.076 | 0.969 (0.934–1.005) | 0.091 | ||
| Clinical stage | ||||||
| I | Ref. | |||||
| II | 0.589 (0.182–1.904) | 0.377 | ||||
| III | 0.930 (0.367–2.361) | 0.879 | ||||
| Histologic type | ||||||
| Invasive ductal carcinoma | Ref. | |||||
| Invasive lobular carcinoma | 1.222 (0.512–2.919) | 0.651 | ||||
| Other | 2.200 (0.270–17.924) | 0.461 | ||||
| ER | ||||||
| Positive vs. Negative | 0.860 (0.395–1.873) | 0.705 | ||||
| PR | ||||||
| Positive vs. Negative | 0.553 (0.259–1.184) | 0.128 | ||||
| HER2 | ||||||
| Positive vs. Negative | 0.897 (0.400–2.007) | 0.791 | ||||
| Ki-67 (%) | 0.068 | |||||
| ≥ 30 vs. < 30 | 2.000 (0.929–4.304) | 0.076 | 2.477 (0.935–6.563) | |||
| Subtype | ||||||
| TNBC | Ref. | |||||
| Luminal-A | 1.846 (0.631–5.405) | 0.263 | ||||
| Luminal-B | 0.752 (0.273–2.072) | 0.582 | ||||
| HER2-enriched | 1.818 (0.604–5.471) | 0.287 | ||||
| Treatment setting | ||||||
| Neoadjuvant vs. Adjuvant | 0.840 (0.389–1.818) | 0.659 | ||||
| Chemotherapy | 0.433 | |||||
| Poly vs. Mono | 8.105 (0.875–75.039) | 0.065 | 2.855 (0.208–39.220) | |||
| Chemotherapy regiment | ||||||
| Anthracycline vs. Nonanthracycline | 1.190 (0.550–2.574) | 0.659 | ||||
| Baseline dose reduction | ||||||
| Yes vs. No | 0.442 (0.137–1.430) | 0.173 | ||||
| Primary prophylaxis with G-CSF | 0.085 | |||||
| Yes vs. No | 0.132 (0.026–0.666) | 0.014 | 0.191 (0.029–1.258) | |||
Variables with p < 0.10 in univariate analysis were further investigated in multivariate analysis. Bold values indicate statistical significance with p < 0.05.
OR = odds ratio; CI = confidence interval; BMI = body mass index; ALT = alanine aminotransferase; AST = aspartate aminotransferase; γ-GGT = γ-glutamyl transferase; WBC = white blood cell; CEA = carcinoembryonic antigen; CA153 = cancer antigen 153; IMAT = intermuscular adipose tissue; HU = Hounsfield units; Ref. = reference; ER = estrogen receptor; PR = progesterone receptor; HER2 = human epidermal growth factor receptor 2; Ki-67 = proliferation index; TNBC = triple-negative breast cancer; G-CSF = granulocyte colony-stimulating factor; Poly = polychemotherapy; Mono = monochemotherapy.
Figure 1. The nomogram and its performance in the training and validation cohorts. (A) Nomogram incorporating hemoglobin, primary prophylaxis with G-CSF, and the IMAT area. Calibration curves of the nomogram in the training and validation cohorts (B). Decision curve analysis of the nomogram and independent clinical risk factors in the training (C) and validation (D) cohorts.
G-CSF = granulocyte colony-stimulating factor; IMAT = intermuscular adipose tissue.
Performance and validation of the nomogram
In the training cohort, the nomogram demonstrated good discriminatory power for predicting grade 3–5 chemotherapy toxicity (area under the receiver operating characteristic curve [AUC] = 0.708, 95% confidence interval [CI], 0.616–0.801). Using data from the validation cohort, the nomogram outperformed single variables in predicting grade 3–5 chemotherapy toxicity (AUC = 0.751, 95% CI, 0.655–0.846) (Figure 2). To assess possible overfitting, the DeLong test was used to compare AUCs between the training and validation cohorts. The calibration curves revealed good agreement between the predicted and observed probabilities for predicting grade 3–5 chemotherapy toxicity in the training and validation cohorts (Figure 1B). The Hosmer-Lemeshow test showed a good model fit (p > 0.05).
Figure 2. Discriminative performance of the models in training and validation cohorts. Receiver operating characteristic curves of the nomogram in the training cohort (A) and validation cohort (B).
AUC = area under the receiver operating characteristic curve; G-CSF = granulocyte colony-stimulating factor; IMAT = intermuscular adipose tissue.
Incremental predictive value of IMAT area
Because the IMAT area showed a certain level of discriminative efficiency, we evaluated whether its inclusion provided additional predictive value to the nomogram (Table 3). After adding the IMAT area to the model, the nomogram dramatically improved discriminative efficacy compared to Model 1 (hemoglobin and primary prophylaxis with G-CSF) in the training (net reclassification index [NRI] = 0.443, integrated discrimination improvement [IDI] = 0.537, p < 0.05) and validation cohorts (NRI = 0.045, IDI = 0.097, p < 0.05).
Table 3. Evaluating the improved performance of models by adding intermuscular adipose tissue area.
| Variables | NRI (95% CI) | p-value | IDI (95% CI) | p-value | Improved AUC | |
|---|---|---|---|---|---|---|
| Training cohort | ||||||
| Model 1 | Ref. | Ref. | Ref. | |||
| Model 2 | 0.443 (0.125–0.762) | 0.006 | 0.045 (0.011–0.078) | 0.009 | 0.028 | |
| Validation cohort | ||||||
| Model 1 | Ref. | Ref. | Ref. | |||
| Model 2 | 0.537 (0.174–0.900) | 0.004 | 0.097 (0.039–0.156) | 0.001 | 0.067 | |
Model 1: Hemoglobin + primary prophylaxis with G-CSF. Model 2: Hemoglobin + primary prophylaxis with G-CSF + IMAT area.
NRI = net reclassification index; CI = confidence interval; IDI = integrated discrimination improvement; AUC = area under the receiver operating characteristic curve; Ref. = reference; G-CSF = granulocyte colony-stimulating factor; IMAT = intermuscular adipose tissue.
Clinical use
The decision curve analysis (DCA) for the discriminative model derived from the nomogram in the training cohort is presented in Figure 1C. DCA showed that when the threshold probability was 23%, using the nomogram to predict the probability of grade 3–5 chemotherapy toxicity provided additional clinical benefit compared with the model without the IMAT area. The DCA of the nomogram for the validation cohort is shown in Figure 1D.
DISCUSSION
This study found that the IMAT area was a significant predictor of chemotherapy toxicity in older adult patients with early-stage breast cancer. A nomogram was developed by combining the IMAT area with clinical variables to accurately predict grade 3–5 chemotherapy toxicity. Notably, incorporating the IMAT area significantly improved the accuracy and discriminatory power of the nomogram.
Our findings indicate that IMAT accumulation plays a role in chemotherapeutic toxicity during breast cancer treatment, consistent with the findings of previous studies. Adipose mass is the only independent predictor for the reduction in the RDI of docetaxel in obese breast cancer patients [34]. Researchers have also found that high visceral or intramuscular adiposity is associated with chemotherapy tolerance and adherence [25]. A longitudinal clinical study reported IMAT development and skeletal muscle atrophy at the cellular level in patients with breast cancer treated with chemotherapy [11]. Previous studies have shown that patients with depleted muscle mass experience higher chemotherapy-related toxicity [35]. Our results confirmed that higher IMAT areas were associated with the occurrence of chemotherapy-related toxicity, whereas depletion of muscle mass resulted in higher chemotherapy-related toxicity. Experimental data also suggest that muscle pro-anabolic strategies could serve as powerful tools to preserve lean tissue in the setting of cancer or chemotherapy [36]. Moreover, our study found that IMAT was significantly correlated with the ECOG performance status score and CCI. Previous studies have demonstrated that a higher CCI is associated with reduced treatment effectiveness and tolerance in older women with breast cancer [37]. Moreover, poor performance status is associated with a low therapeutic response and increased mortality rate [38]. However, additional studies are necessary to confirm our results and develop therapeutic interventions, with the ultimate goal of mitigating the risk of chemotherapy toxicity.
Notably, BMI was not associated with toxicity in this study. The absence of an association between BMI and toxicity could be due to the limitations of BMI in distinguishing between fat and muscle mass and in capturing variations in adipose tissue characteristics, such as density or distribution [39]. Individuals with similar BMIs may have different body compositions [40]. Changes in blood pressure regulation, adipose tissue inflammation, and dysglycemia are direct metabolic consequences of abdominal obesity [41]. Furthermore, the sarcopenic phenotype of obesity is associated with an increased risk of metabolic disorders, cardiovascular diseases, and reduced physical function in older adults [42].
The higher-than-expected incidence of hepatic toxicity observed in this study (5.9%) may be explained by the following interrelated mechanisms: (1) The cohort’s median age of 67 years reflects age-associated declines in hepatic function. Reduced hepatic blood flow [43] and CYP450 activity [44] impaired the detoxification of anthracyclines in older adult patients [45]. A higher prevalence of hepatic steatosis in older adults exacerbates oxidative stress caused by reactive drug metabolites [46]. (2) Hyperinsulinemia upregulates hepatic SREBP-1c, promoting lipid accumulation and sensitizing hepatocytes to drug-induced injury [47]. Moreover, low vitamin D levels enhance transforming growth factor-β1 signaling, promoting hepatic fibrosis during chemotherapy [48]. (3) Proton pump inhibitors alter the gut microbiota and increase hepatic bile acid deconjugation and cholestasis risk [49,50]. Thus, elevated hepatic toxicity reflects the interplay among age-related vulnerabilities, metabolic comorbidities, and concomitant medication effects. Therefore, the hepatic toxicity of breast cancer chemotherapy should be further evaluated in future studies.
The nomogram estimates the probability of grade 3–5 chemotherapy toxicity in individuals, aiding in personalized treatment strategies and shared decision-making. High-risk patients may benefit from dose adjustments or enhanced supportive care, whereas low-risk patients can follow standard regimens. Although not a standalone decision tool, it complements clinical judgment and patient preferences in guiding chemotherapy decisions. To facilitate the application of the nomogram in real-world practice, we developed a dynamic nomogram (https://nomogram-wenjuan.shinyapps.io/IMAT_BC_Toxicity-1).
Our data suggest that IMAT plays a powerful role in predicting chemotherapy toxicity in older adults with early-stage breast cancer. The nomogram appears to be a valuable tool for predicting chemotherapy toxicity and contributing to the individualized management of older patients with breast cancer. Nonetheless, this study had certain limitations. First, given the retrospective nature of the study, confirming the generalizability of the findings requires prospective clinical trials. Second, we used a single CT image to estimate adipose tissue; however, further studies are needed to investigate other metrics of adipose tissue quality. Third, the mechanism underlying the predictive value of the adipose signature remains unclear; thus, additional studies are necessary. Finally, the high rate of grade 3 hepatic failure (5.9%) may be due to liver conditions and intensive treatment, whereas the lower non-hematologic toxicity rates could reflect our focus on severe events, potentially leading to underreporting of milder cases. In addition, lower non-hematologic toxicity rates likely reflect improved supportive care, tailored dosing, underreporting, and patient selection. These factors should be considered in future studies. The decision to pursue adjuvant or neoadjuvant chemotherapy for early-stage breast cancer in older adult patients is often complex. The development of grade 3–5 chemotherapy toxicity can compromise the ability of older adults to complete the course of chemotherapy, possibly reducing the potential benefits of treatment [4].
We developed and validated a nomogram that combines IMAT area and clinical variables to predict grade 3–5 chemotherapy toxicity in older patients with early-stage breast cancer. Moreover, the inclusion of the IMAT area in the nomogram substantially enhanced both accuracy and discrimination ability. These findings may help clinicians predict the individual probability of chemotherapy toxicity and guide therapy to mitigate the risk of chemotherapy toxicity.
Footnotes
Funding: This work was supported by the Climbing Program of Harbin Medical University Cancer Hospital (PDTS2024B-01).
Conflict of Interest: The authors declare that they have no competing interests.
Data Availability: Requests for data can be made by contacting the corresponding author.
- Conceptualization: Huang WJ, Zhang X, Duan Z, Wang RT.
- Data curation: Huang WJ, Xie HB, Zhao L, Zhou RH, Wang S, Zhang X.
- Formal analysis: Huang WJ.
- Writing - original draft: Huang WJ.
- Writing - review & editing: Huang WJ, Duan Z, Wang RT.
SUPPLEMENTARY MATERIALS
Chemotherapy-related grade 3–5 toxicities in training and validation cohorts
Correlation of intermuscular adipose tissue with systemic conditions
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Associated Data
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Supplementary Materials
Chemotherapy-related grade 3–5 toxicities in training and validation cohorts
Correlation of intermuscular adipose tissue with systemic conditions