Abstract
Background/Aim
Cancer pharmacotherapy has advanced substantially with the introduction of novel agents such as immune checkpoint inhibitors (ICIs). However, managing drug-induced interstitial lung disease (DIILD) associated with these agents remains a major clinical challenge in maintaining treatment. Detailed investigations into recent changes in DIILD reporting and specific drug categories contributing to these changes remain limited. Therefore, we aimed to identify factors contributing to recent changes in DIILD reports by analyzing temporal trends in antineoplastic agent-associated DIILD using the Japanese Adverse Drug Event Report database.
Materials and Methods
We analyzed the database reports from 2004 to 2024. The reporting odds ratio (ROR) and Weibull model were used to evaluate safety signals and the time to onset of ICI-associated DIILD, respectively.
Results
The mean rate of antineoplastic agent-associated DIILD among total adverse event reports was significantly higher in the most recent period (2016-2024) than in the earlier period (2004-2015) (25.9% vs. 15.3%, p<0.05). An analysis by drug category suggested that an increase in ICI-associated reports from 2016 onward largely contributed to this upward trend. Significant signals were detected for ICI-associated DIILD (ROR for total reports: 4.13; ROR for reports with fatal outcomes: 2.73). Furthermore, there has been a shift towards combination therapy with ICIs and cytotoxic chemotherapy, accounting for the largest proportion of reports with fatal outcomes of ICI-associated DIILD in 2024. The Weibull analysis indicated an early failure pattern for ICI-associated DIILD.
Conclusion
The recent increase in antineoplastic agent-associated DIILD may primarily be driven by the expanding use of ICIs, particularly ICI in combination with chemotherapy. Because DIILD tends to occur in the early phase of treatment, strict monitoring is warranted during the initial therapy period.
Keywords: Drug-induced interstitial lung disease, immune checkpoint inhibitor, Japanese Adverse Drug Event Report database, pharmacovigilance
Introduction
Interstitial lung disease comprises a heterogeneous group of disorders characterized by inflammation and fibrosis of the alveolar septa and is caused by several factors, including infections, autoimmune diseases, and environmental exposure (1). Drug-induced interstitial lung disease (DIILD) is associated with a wide range of medications, including antineoplastic agents, antirheumatic drugs, and Kampo medicines (2, 3), and can lead to fatal outcomes in severe cases. Therefore, early detection and appropriate management of DIILD are necessary to improve the safety of pharmacotherapy (2). Because Japanese patients have been reported to be highly susceptible to DIILD (4, 5), vigilant monitoring and rapid intervention are imperative.
The recent approval and expanded indications for the use of immune checkpoint inhibitors (ICIs) and molecular-targeted agents have broadened therapeutic options in cancer pharmacotherapy and substantially improved patient prognosis (6, 7). However, many of these novel antineoplastic agents are associated with an increased risk of DIILD, and DIILD related to agents including ICIs, cyclin-dependent kinase (CDK) 4/6 inhibitors, and trastuzumab-deruxtecan has been reported (8-11). Among antineoplastic agents, ICIs are associated with a relatively high incidence of DIILD (8, 12, 13). The clinical manifestations of ICI-associated DIILD are often nonspecific, making diagnosis difficult. Severe cases may require intensive care and can result in fatal outcomes (14, 15).
Previous studies have demonstrated that antineoplastic agent-induced DIILD during cancer pharmacotherapy is often severe and associated with poor prognosis (3, 16). However, detailed investigations examining how recent changes in cancer pharmacotherapy, particularly the introduction of novel high-risk agents such as ICIs, have influenced the reporting trends and outcomes of DIILD in patients with cancer remain limited. Clarifying how the number of DIILD reports and the distribution of suspected drugs have changed with the widespread use of novel agents is necessary to understand the evolving clinical role and relative risk of DIILD among treatment-related adverse events over time.
The Japanese Adverse Drug Event Report (JADER) database contains large-scale, long-term adverse event data (3, 17, 18), enabling the evaluation of reporting trends, time-to-onset, and outcomes for individual drugs. Therefore, we analyzed the temporal trends and outcomes of antineoplastic agent-associated DIILD using JADER reports from 2004 to 2024.
Materials and Methods
Study design and data source. This retrospective study was conducted using the JADER database, a publicly available spontaneous reporting database on the website of the Pharmaceuticals and Medical Devices Agency of Japan (19). The JADER database has been continuously published since 2004 and is coded according to the Medical Dictionary for Regulatory Activities (MedDRA). Its data quality has been previously evaluated, demonstrating its validity as a research platform (17, 18).
Data extraction and definition. Reports recorded between January 2004 and December 2024 were analyzed. The Anatomical Therapeutic Chemical classification System (20) was used to extract antineoplastic agents. The JADER database comprises four datasets: patient demographics (DEMO), drug information (DRUG), adverse events (REAC), and medical history (HIST). In the REAC dataset, adverse events are coded using Preferred Terms. In this study, DIILD was defined based on the Preferred Term “interstitial lung disease” (code: 10022611) in MedDRA version 28.1 J (21).
Duplicate records within the DRUG and REAC tables were removed, and the datasets were merged using case identification numbers. For causality assessment, only drugs classified as “suspected drugs” were included. During data aggregation, an exceptionally high number of reports related to COVID-19 vaccines were found to have been published from 2021 onward. As this massive volume may lead to bias in comparisons with earlier years, reports related to COVID-19 vaccines were excluded.
Signal detection. The reporting odds ratio (ROR) was used to detect adverse event signals. ROR is widely used to evaluate the association between a target drug and a specific adverse event (22) and is calculated using the following formula:
ROR = (a/b)/(c/d)
95%CI = exp { ln(ROR) ± 1.96√[(1/a) + (1/b) + (1/c) + (1/d)] }
where
a was the number of DIILD cases after receiving the target drug;
b was the number of cases experiencing other adverse events after receiving the target drug;
c was the number of DIILD cases after receiving non-target drugs; and
d was the number of cases experiencing other adverse events after receiving non-target drugs.
The 95% confidence interval (CI) was calculated using the standard error. A signal was defined as present when the lower limit of the 95% CI was greater than 1. The interpretation of the ROR and the theoretical background regarding its biases were based on the methodology described by Faillie (22).
Time-to-onset analysis. For the time-to-onset analysis, the time to onset of DIILD was calculated using the treatment initiation date and the date of adverse event onset. A value of 0.5 days was added to the difference between the initiation and onset dates. The Weibull model (shape parameter, β) was used to evaluate the onset timing (23).
Statistical analysis. Welch’s t-test was used to compare the proportion of DIILD reports between two periods (2015 and earlier vs. 2016 and later). This cut-off year was selected because the use of ICIs in clinical practice expanded rapidly from 2016 onward, following the approval of nivolumab for non-small-cell lung cancer in Japan in late 2015 (24). A chi-squared test was performed to compare the distribution of ICI usage categories (ICI monotherapy, ICI combination therapy, and ICI with cytotoxic chemotherapy). All statistical analyses were performed using JMP® Pro 17.0.0 (SAS Institute Inc., Cary, NC, USA) and R software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). A value of p<0.05 was considered significant.
Results
Annual trends in DIILD reporting. DIILD reports were categorized into three groups by drug, namely, antineoplastic agents other than ICIs, ICIs, and other drugs, in order to analyze annual trends (Figure 1). Both the number of DIILD reports associated with all antineoplastic agents, including ICIs, and their proportion among all adverse event reports demonstrated a gradual increase, with a particularly pronounced upward trend beginning in 2016. Furthermore, the number of ICI-associated DIILD reports increased rapidly after 2016, peaking around 2020, and subsequently showed a declining trend in recent years. Despite this recent decline in absolute numbers, ICIs continue to account for a substantial proportion of DIILD reports among antineoplastic agents. These patterns were consistent for both total reports and reports with fatal outcomes.
Figure 1.
Annual trends in the number and proportion of reports of drug-induced interstitial lung disease (DIILD) by drug category. Number (left panel) and proportion (right panel) of DIILD reports by drug category among total reports (A) and reports with fatal outcomes (B). ICIs: Immune checkpoint inhibitors.
Comparison of DIILD reporting between two periods (2004-2015 vs. 2016-2024). To evaluate temporal changes in DIILD reporting, the mean annual rate of antineoplastic agent-associated DIILD among all adverse event reports was compared between two periods: the earlier period (≤2015) and the later period (≥2016). The cut-off for these periods was defined based on the time at which ICIs became widely used. For total reports, the mean rate was significantly higher for the later period than for the earlier period (25.9±3.1% vs. 15.3±2.2%, p<0.05) (Figure 2A). No significant difference between the two periods was observed for the rate of reports with fatal outcomes (3.9±0.4% vs. 3.6±0.9%, p=0.43) (Figure 2B).
Figure 2.
A comparison of the drug-induced interstitial lung disease (DIILD) reporting rate for antineoplastic agents between 2004-2015 and 2016-2024. (A) Total reports: A comparison of the mean annual DIILD reporting rate for antineoplastic agents (antineoplastic agent-associated DIILD reports/total adverse event reports). (B) Reports with fatal outcomes: A comparison of the mean annual DIILD rate for reports with fatal outcomes associated with antineoplastic agents. Diamonds indicate the mean values. The boxes indicate the interquartile range (25th to 75th percentiles), and the horizontal lines within the boxes indicate the median; whiskers indicate the range excluding outliers.
Total reports and reports with fatal outcomes associated with antineoplastic agents. Antineoplastic agent-associated DIILD reports during the study period were aggregated for both total reports and reports with fatal outcomes, and the top 10 drugs suspected as being responsible for DIILD are listed in Table I. For total reports, the most frequently reported suspected drugs were nivolumab, pembrolizumab, methotrexate, gemcitabine, ipilimumab, everolimus, docetaxel, gefitinib, osimertinib, and fluorouracil. Regarding the reports of fatal outcomes, the leading suspected drugs were nivolumab, gefitinib, pembrolizumab, docetaxel, methotrexate, oxaliplatin, fluorouracil, gemcitabine, erlotinib, and irinotecan.
Table I. Top 10 antineoplastic agents ranked by the number of reports overall and in reports with fatal outcomes of antineoplastic agentassociated drug-induced interstitial lung disease.
RORs for total reports and reports with fatal outcomes of ICI-associated DIILD. In the analysis of all drugs, the ROR for ICI-associated DIILD was 4.13 (95% CI=4.02-4.24) for total reports and 2.73 (95% CI=2.55-2.93) for reports with fatal outcomes (Table II). When the analysis was restricted to antineoplastic agents, the RORs for ICI-associated DIILD were 1.77 (95% CI=1.72-1.82) and 1.41 (95% CI=1.31-1.52), respectively (Table II). The analysis was then restricted to cases of reported DIILD to minimize the potential dilution of fatal outcomes in DIILD reports due to fatal adverse events frequently reported with antineoplastic agents other than ICIs. With other antineoplastic agents as the reference, the ROR for fatal outcomes for ICI-associated DIILD was 0.71 (95% CI=0.66-0.76) (Table III).
Table II. Reporting odds ratios (ROR) for immune checkpoint inhibitor (ICI)-associated drug-induced interstitial lung disease (DIILD): all reports.
CI: Confidence interval.
Table III. Reporting odds ratios (ROR) for fatal outcomes of interstitial lung disease induced by immune checkpoint inhibitors (ICIs) compared with that induced by other antineoplastic agents.
CI: Confidence interval; DIILD: drug-induced interstitial lung disease.
Distribution and annual trends of ICI usage categories in DIILD cases. ICI usage was categorized as ICI monotherapy, ICI combination therapy (i.e., concurrent use of multiple ICIs), or ICI plus chemotherapy, and the distribution of total reports and reports with fatal outcomes was analyzed. Patients receiving concurrent multiple ICIs and chemotherapy were classified as ICI plus chemotherapy. ICI monotherapy accounted for the largest proportion of cases (4,185; 52.1%), followed by ICI combination therapy (2,292; 28.6%) and ICI plus chemotherapy (1,549; 19.3%). Similar trends were observed for reports with fatal outcomes: ICI monotherapy accounted for 528 cases (44.9%), ICI combination therapy for 387 cases (32.9%), and ICI plus chemotherapy for 262 cases (22.3%).
The annual trends of ICI-associated DIILD according to ICI usage category were analyzed (Figure 3). To evaluate the most recent reporting status, a cross-sectional analysis for the year 2024 was performed. In this single-year analysis, a chi-squared test revealed a significant difference in the distribution among the three groups in 2024 for both total reports and reports with fatal outcomes (p<0.05). In both analyses, the ICI plus chemotherapy group accounted for the largest proportion among the three usage categories in 2024.
Figure 3.
Annual trends in the number and proportion of drug-induced interstitial lung disease (DIILD) reports by immune checkpoint inhibitor (ICI) usage category. Number (left panel) and proportion (right panel) of DIILD reports by usage category among total reports (A) and reports with fatal outcomes (B).
Weibull analysis of ICI-associated DIILD. The Weibull model was used to evaluate the time-to-onset of ICI-associated DIILD. The shape parameter (β) for all ICIs (excluding cytotoxic T-lymphocyte-associated protein 4 inhibitors) was 0.91 (95% CI=0.90-0.91). For individual drugs, the β values were as follows: avelumab (0.82, 95% CI=0.76-0.89), atezolizumab (0.79, 95% CI=0.78-0.82), durvalumab (0.90, 95% CI=0.87-0.93), pembrolizumab (0.93, 95% CI=0.91-0.94), and nivolumab (0.95, 95% CI=0.94-0.96). For all evaluated drugs, the shape parameter β was <1, indicating an early failure pattern.
Discussion
An analysis of the JADER database revealed that the mean annual rate of antineoplastic agent-associated DIILD among total adverse event reports was significantly higher in the later period (≥2016) than in the earlier period (≤2015) of study (Figure 2). This finding suggests that an increase in ICI-associated DIILD reports primarily contributed to this trend. In addition, reports have shifted from those of ICI monotherapy to ICI plus chemotherapy in recent years, with the ICI plus chemotherapy group accounting for the largest proportion of total reports and of reports with fatal outcomes in 2024.
These reporting trends are consistent with the results reported by Jiang et al. (2), who used the US Food and Drug Administration Adverse Event Reporting System database. These results reflect the recent expansion of ICI indications for different cancer types and a shift in treatment trends toward their combination with cytotoxic chemotherapy.
The top 10 drugs with the highest number of DIILD reports among all reports were nivolumab, pembrolizumab, methotrexate, gemcitabine, ipilimumab, everolimus, docetaxel, gefitinib, osimertinib, and fluorouracil. Many of these drugs are associated with a high risk of lung injury (25-31), and our results are generally consistent with these previous findings. ICIs have become widely used in clinical practice in recent years, and their presence among these top-ranked drugs possibly reflects the increasing number of ICI-associated DIILD reports. In all reports, DIILD signals were detected in the analyses of all drugs and antineoplastic agents, suggesting that ICIs represent a major drug category associated with DIILD. Regarding reports with fatal outcomes, ICIs such as nivolumab (421 cases) and pembrolizumab (333 cases) were also ranked among the most frequently reported drugs. In addition, signals of DIILD with fatal outcomes were detected for ICIs in the analyses of all drugs and antineoplastic agents. However, the ROR analysis of fatal outcomes restricted to antineoplastic agents may have been influenced by a reporting bias related to the high frequency of fatal adverse events other than DIILD. Therefore, to evaluate fatal outcomes following DIILD onset more appropriately, a subgroup analysis was performed by restricting the population to cases with reported DIILD (Table III). In this subgroup analysis, with antineoplastic agents other than ICI as the reference group, the ROR for fatal outcomes in the ICI group was 0.71 (95% CI=0.66-0.76), and no signal was observed. This suggests that although ICI-associated DIILD accounts for numerous cases, the risk of fatality may be lower than that of conventional antineoplastic agents. As suggested in a previous report (14), this may be attributable to early diagnosis and appropriate therapeutic interventions, which help maintain a relatively favorable prognosis for ICI-associated DIILD.
Over the entire study period (2004-2024), ICI monotherapy accounted for approximately half of all ICI-associated DIILD reports, followed by ICI combination therapy and ICI plus chemotherapy. However, this pattern was possibly influenced by the longer period of accumulation of reports, as ICIs were initially approved as monotherapies. Recent reports indicate that chemoimmunotherapy is commonly used in clinical practice to treat advanced non-small-cell lung cancer (32-35). The changes in distribution observed in this study may reflect the impact of similar treatment trends. Indeed, the trends of these three groups (Figure 3) have demonstrated a substantial shift in their distribution in recent years. In particular, in the single-year analysis for 2024, the number of reports for the ICI plus chemotherapy group was significantly higher than that for the ICI monotherapy group, both for total reports and reports with fatal outcomes. This suggests that the focus of risk management for ICI-associated DIILD may have shifted from monotherapy to combination therapy. Furthermore, a potential synergistic increase in risk owing to the overlap between chemotherapy-induced pulmonary toxicity and the immunological mechanisms of ICIs cannot be excluded. Considering the report by Tsukita et al. (36), which demonstrated a significantly higher frequency of pneumonitis in patients receiving ICI plus chemotherapy than in those receiving ICI as monotherapy, strict monitoring is warranted during treatment with ICI plus chemotherapy, given its significantly higher frequency overall and in reports with fatal outcomes.
In the Weibull analysis, the shape parameter (β) was less than 1 for several ICIs, indicating a pattern in which DIILD tends to occur in the early phase of treatment. This finding is consistent with the pattern of onset associated with epidermal growth factor receptor inhibitors (such as gefitinib) reported by Matsumoto et al. (3) in a previous JADER analysis. These findings suggest that ICI-associated DIILD may occur relatively early after treatment initiation and may not simply reflect dose-dependent cumulative toxicity. Furthermore, previous studies by Delaunay et al. (15) and Nishino et al. (37) reported a median time to onset of approximately 2-3 months after treatment initiation, which the results of our study are in agreement with. Therefore, strict monitoring, including shorter intervals between imaging examinations, is warranted for at least 3-6 months after initiating ICI-containing regimens, particularly high-risk ICI plus chemotherapy combination therapy.
This study has several limitations inherent to the use of spontaneous reporting databases, including the potential for underreporting, reporting bias, and lack of detailed clinical information. Consequently, it is difficult to establish causal relationships or estimate the true incidence rates. Despite these limitations, this analysis provides important insights into the characteristics of DIILD reporting, reflecting the real-world clinical practice in Japan.
Conclusion
The recent increase in the number of reports on antineoplastic agent-associated DIILD is partly driven by the widespread use of ICIs. In particular, ICI plus chemotherapy combination therapy, which is becoming increasingly common in clinical practice, requires careful clinical attention given the rapid increase in DIILD reports and the substantial number of reports with fatal outcomes. As demonstrated by the Weibull analysis, DIILD tended to occur in the early phase of treatment. Therefore, strict monitoring is warranted during the early phase of ICI treatment, particularly with ICI plus chemotherapy, to improve the safety of cancer pharmacotherapy.
Conflicts of Interest
The Authors declare no conflicts of interest related to this study.
Authors’ Contributions
Yuki Ibayashi contributed to the conception and design of the study, data extraction, statistical analysis, and manuscript drafting. Hiroaki Watanabe, Yusuke Shimura, Yukiko Nishiyama, and Yoshimichi Sai contributed to data interpretation and critical revision of the manuscript. Yukio Suga supervised the study and approved the final version of the manuscript.
Artificial Intelligence (ΑΙ) Disclosure
During the preparation of this manuscript, large language models (DeepL and ChatGPT) were used solely for language editing and stylistic improvements. No sections involving the generation, analysis, or interpretation of research data were produced using generative AI. All scientific content was created and verified by the authors. Furthermore, no figures or visual data were generated or modified using generative AI or machine learning-based image enhancement tools.
References
- 1.Buschulte K, Kabitz HJ, Hagmeyer L, Hammerl P, Esselmann A, Wiederhold C, Skowasch D, Stolpe C, Joest M, Veitshans S, Höffgen M, Maqhuzu P, Schwarzkopf L, Hellmann A, Pfeifer M, Behr J, Karpavicius R, Guenther A, Polke M, Höger P, Somogyi V, Lederer C, Markart P, Kreuter M. Baseline characteristics from the EXCITING-ILD registry. BMJ Open Respir Res. 2025;12(1):e002902. doi: 10.1136/bmjresp-2024-002902. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jiang T, Su H, Xu J, Li C, Zhang N, Li Y, Wu Y, Ni R, Ming Y, Li Z, Li L, Liu Y. Drug-induced interstitial lung disease: a real-world pharmacovigilance study of the FDA Adverse Event Reporting System from 2004 to 2021. Ther Adv Drug Saf. 2024;15:20420986231224227. doi: 10.1177/20420986231224227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Matsumoto K, Nakao S, Hasegawa S, Matsui T, Shimada K, Mukai R, Tanaka M, Uranishi H, Nakamura M. Analysis of drug-induced interstitial lung disease using the Japanese Adverse Drug Event Report database. SAGE Open Med. 2020;8:2050312120918264. doi: 10.1177/2050312120918264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Azuma A, Kudoh S. From the Japanese Association of Medical Sciences: High prevalence of drug-induced pneumonia in Japan. JMAJ. 2007;50(5):405–411. [Google Scholar]
- 5.Imatoh T, Ushiki A, Ota M, Ito M, Sekine A, Yamashita T, Mashimo Y, Nakamura R, Saito K, Saito Y, Hanaoka M. Association of HLA-DRB1*04:05 allele with drug-induced interstitial lung disease in Japanese population. Pharmacogenomics J. 2020;20(6):823–830. doi: 10.1038/s41397-020-0172-3. [DOI] [PubMed] [Google Scholar]
- 6.Haslam A, Prasad V. Estimation of the percentage of US patients with cancer who are eligible for and respond to checkpoint inhibitor immunotherapy drugs. JAMA Netw Open. 2019;2(5):e192535. doi: 10.1001/jamanetworkopen.2019.2535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Robert C. A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun. 2020;11(1):3801. doi: 10.1038/s41467-020-17670-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Li C, Faiz SA, Boysen-Osborn M, Sheshadri A, Wattana MK. Immune checkpoint inhibitor-associated pneumonitis: a narrative review. West J Emerg Med. 2025;26(2):210–218. doi: 10.5811/westjem.20305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Nawa H, Niimura T, Yagi K, Goda M, Zamami Y, Ishizawa K. Evaluation of potential complication of interstitial lung disease with abemaciclib and palbociclib treatments. Cancer Rep (Hoboken) 2022;5(1):e1402. doi: 10.1002/cnr2.1402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Raschi E, Fusaroli M, Ardizzoni A, Poluzzi E, De Ponti F. Cyclin-dependent kinase 4/6 inhibitors and interstitial lung disease in the FDA adverse event reporting system: a pharmacovigilance assessment. Breast Cancer Res Treat. 2021;186(1):219–227. doi: 10.1007/s10549-020-06001-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Liao D, Zhang J, Yan T, Chen Y, Fu Y, Xie N, Long M. A systematic review of mechanisms, incidence, and management of trastuzumab deruxtecan induced ILD/pneumonitis in solid tumors. Drug Des Devel Ther. 2025;19:1655–1668. doi: 10.2147/DDDT.S508773. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Spagnolo P, Chaudhuri N, Bernardinello N, Karampitsakos T, Sampsonas F, Tzouvelekis A. Pulmonary adverse events following immune checkpoint inhibitors. Curr Opin Pulm Med. 2022;28(5):391–398. doi: 10.1097/MCP.0000000000000895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kim S, Lim JU. Immune checkpoint inhibitor-related interstitial lung disease in patients with advanced non-small cell lung cancer: systematic review of characteristics, incidence, risk factors, and management. J Thorac Dis. 2022;14(5):1684–1695. doi: 10.21037/jtd-22-93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Naidoo J, Wang X, Woo KM, Iyriboz T, Halpenny D, Cunningham J, Chaft JE, Segal NH, Callahan MK, Lesokhin AM, Rosenberg J, Voss MH, Rudin CM, Rizvi H, Hou X, Rodriguez K, Albano M, Gordon RA, Leduc C, Rekhtman N, Harris B, Menzies AM, Guminski AD, Carlino MS, Kong BY, Wolchok JD, Postow MA, Long GV, Hellmann MD. Pneumonitis in patients treated with anti-programmed death-1/programmed death ligand 1 therapy. J Clin Oncol. 2017;35(7):709–717. doi: 10.1200/JCO.2016.68.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Delaunay M, Cadranel J, Lusque A, Meyer N, Gounant V, Moro-Sibilot D, Michot JM, Raimbourg J, Girard N, Guisier F, Planchard D, Metivier AC, Tomasini P, Dansin E, Pérol M, Campana M, Gautschi O, Früh M, Fumet JD, Audigier-Valette C, Couraud S, Dalle S, Leccia MT, Jaffro M, Collot S, Prévot G, Milia J, Mazieres J. Immune-checkpoint inhibitors associated with interstitial lung disease in cancer patients. Eur Respir J. 2017;50(2):1700050. doi: 10.1183/13993003.00050-2017. [DOI] [PubMed] [Google Scholar]
- 16.Kaku S, Horinouchi H, Watanabe H, Yonemori K, Okusaka T, Boku N, Yamazaki N, Kawai A, Ohe Y, Kusumoto M. Incidence and prognostic factors in severe drug-induced interstitial lung disease caused by antineoplastic drug therapy in the real world. J Cancer Res Clin Oncol. 2022;148(7):1737–1746. doi: 10.1007/s00432-022-03932-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Nomura K, Takahashi K, Hinomura Y, Kawaguchi G, Matsushita Y, Marui H, Anzai T, Hashiguchi M, Mochizuki M. Effect of database profile variation on drug safety assessment: an analysis of spontaneous adverse event reports of Japanese cases. Drug Des Devel Ther. 2015;9:3031–3041. doi: 10.2147/DDDT.S81998. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tsuchiya M, Obara T, Sakai T, Nomura K, Takamura C, Mano N. Quality evaluation of the Japanese Adverse Drug Event Report database (JADER) Pharmacoepidemiol Drug Saf. 2020;29(2):173–181. doi: 10.1002/pds.4944. [DOI] [PubMed] [Google Scholar]
- 19.Pharmaceuticals and Medical Devices Agency (PMDA) Japanese Adverse Drug Event Report (JADER) database. Available at: https://www.pmda.go.jp/safety/info-services/drugs/adr-info/suspected-adr/0005.html. [Last accessed on April 20, 2026]
- 20.WHO Collaborating Centre for Drug Statistics Methodology Anatomical Therapeutic Chemical (ATC) classification index. Available at: https://www.whocc.no/atc_ddd_index/ [Last accessed on April 20, 2026]
- 21.Maintenance and Support Services Organization (MSSO) Medical Dictionary for Regulatory Activities (MedDRA) Available at: https://www.meddra.org/ [Last accessed on April 20, 2026]
- 22.Faillie JL. Case-non-case studies: Principle, methods, bias and interpretation. Therapies. 2019;74(2):225–232. doi: 10.1016/j.therap.2019.01.006. [DOI] [PubMed] [Google Scholar]
- 23.Sauzet O, Cornelius V. Generalised weibull model-based approaches to detect non-constant hazard to signal adverse drug reactions in longitudinal data. Front Pharmacol. 2022;13:889088. doi: 10.3389/fphar.2022.889088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Pharmaceuticals and Medical Devices Agency (PMDA) Report on the Deliberation Results: Opdivo Intravenous Infusion. December 7, 2015. Available at: https://www.pmda.go.jp/files/000216478.pdf. [Last accessed on April 20, 2026]
- 25.Imokawa S, Colby TV, Leslie KO, Helmers RA. Methotrexate pneumonitis: review of the literature and histopathological findings in nine patients. Eur Respir J. 2000;15(2):373. doi: 10.1034/j.1399-3003.2000.15b25.x. [DOI] [PubMed] [Google Scholar]
- 26.Umemura S, Yamane H, Suwaki T, Katoh T, Yano T, Shiote Y, Takigawa N, Kiura K, Kamei H. Interstitial lung disease associated with gemcitabine treatment in patients with non-small-cell lung cancer and pancreatic cancer. J Cancer Res Clin Oncol. 2011;137(10):1469–1475. doi: 10.1007/s00432-011-1013-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.White DA, Camus P, Endo M, Escudier B, Calvo E, Akaza H, Uemura H, Kpamegan E, Kay A, Robson M, Ravaud A, Motzer RJ. Noninfectious pneumonitis after everolimus therapy for advanced renal cell carcinoma. Am J Respir Crit Care Med. 2010;182(3):396–403. doi: 10.1164/rccm.200911-1720OC. [DOI] [PubMed] [Google Scholar]
- 28.Tamiya A, Naito T, Miura S, Morii S, Tsuya A, Nakamura Y, Kaira K, Murakami H, Takahashi T, Yamamoto N, Endo M. Interstitial lung disease associated with docetaxel in patients with advanced non-small-cell lung cancer. Anticancer Res. 2012;32(3):1103–1106. [PubMed] [Google Scholar]
- 29.Kudoh S, Kato H, Nishiwaki Y, Fukuoka M, Nakata K, Ichinose Y, Tsuboi M, Yokota S, Nakagawa K, Suga M, Jiang H, Itoh Y, Armour A, Watkins C, Higenbottam T, Nyberg F. Interstitial lung disease in Japanese patients with lung cancer. Am J Respir Crit Care Med. 2008;177(12):1348–1357. doi: 10.1164/rccm.200710-1501OC. [DOI] [PubMed] [Google Scholar]
- 30.Ohe Y, Kato T, Sakai F, Kusumoto M, Endo M, Saito Y, Baba T, Sata M, Yamaguchi O, Sakamoto K, Sugeno M, Tamura R, Tokimoto T, Shimizu W, Gemma A. Real-world use of osimertinib for epidermal growth factor receptor T790M-positive non-small cell lung cancer in Japan. Jpn J Clin Oncol. 2020;50(8):909–919. doi: 10.1093/jjco/hyaa067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Shimura T, Fuse N, Yoshino T, Minashi K, Tahara M, Doi T, Joh T, Ohtsu A. Clinical features of interstitial lung disease induced by standard chemotherapy (FOLFOX or FOLFIRI) for colorectal cancer. Ann Oncol. 2010;21(10):2005–2010. doi: 10.1093/annonc/mdq061. [DOI] [PubMed] [Google Scholar]
- 32.Ahluwalia VS, Parikh RB. Chemoimmunotherapy vs immunotherapy monotherapy receipt in advanced non-small cell lung cancer. JAMA Netw Open. 2025;8(2):e2459380. doi: 10.1001/jamanetworkopen.2024.59380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Divan HA, Bittoni MA, Krishna A, Carbone DP. Real-world patient characteristics and treatment patterns in US patients with advanced non-small cell lung cancer. BMC Cancer. 2024;24(1):424. doi: 10.1186/s12885-024-12126-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Gandhi L, Rodríguez-Abreu D, Gadgeel S, Esteban E, Felip E, De Angelis F, Domine M, Clingan P, Hochmair MJ, Powell SF, Cheng SY, Bischoff HG, Peled N, Grossi F, Jennens RR, Reck M, Hui R, Garon EB, Boyer M, Rubio-Viqueira B, Novello S, Kurata T, Gray JE, Vida J, Wei Z, Yang J, Raftopoulos H, Pietanza MC, Garassino MC, KEYNOTE-189 Investigators Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N Engl J Med. 2018;378(22):2078–2092. doi: 10.1056/NEJMoa1801005. [DOI] [PubMed] [Google Scholar]
- 35.Paz-Ares L, Luft A, Vicente D, Tafreshi A, Gümüş M, Mazières J, Hermes B, Çay Şenler F, Csőszi T, Fülöp A, Rodríguez-Cid J, Wilson J, Sugawara S, Kato T, Lee KH, Cheng Y, Novello S, Halmos B, Li X, Lubiniecki GM, Piperdi B, Kowalski DM, KEYNOTE-407 Investigators Pembrolizumab plus chemotherapy for squamous non-small-cell lung cancer. N Engl J Med. 2018;379(21):2040–2051. doi: 10.1056/NEJMoa1810865. [DOI] [PubMed] [Google Scholar]
- 36.Tsukita Y, Tozuka T, Kushiro K, Hosokawa S, Sumi T, Uematsu M, Honjo O, Yamaguchi O, Asao T, Sugisaka J, Saito G, Shiihara J, Morita R, Katakura S, Yasuda T, Hisakane K, Miyauchi E, Morita S, Kobayashi K, Asahina H. Immunotherapy or chemoimmunotherapy in older adults with advanced non-small cell lung cancer. JAMA Oncol. 2024;10(4):439–447. doi: 10.1001/jamaoncol.2023.6277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Nishino M, Ramaiya NH, Awad MM, Sholl LM, Maattala JA, Taibi M, Hatabu H, Ott PA, Armand PF, Hodi FS. PD-1 inhibitor-related pneumonitis in advanced cancer patients: radiographic patterns and clinical course. Clin Cancer Res. 2016;22(24):6051–6060. doi: 10.1158/1078-0432.CCR-16-1320. [DOI] [PMC free article] [PubMed] [Google Scholar]