Naoko Matsumoto

Introduction: Pneumonitis represents one of the clinically significant adverse events observed in patients with non-small-cell lung cancer (NSCLC) who receive durvalumab as consolidation therapy after chemoradiotherapy (CRT). Although clinical factors such as radiation dose (e.g., V20) and interstitial lung abnormalities (ILAs) have been reported as risk predictors, accurate and objective prognostication remains difficult. This study aimed to develop a radiomics-based machine learning model to predict grade ≥ 2 pneumonitis. Methods: This retrospective study included patients with unresectable NSCLC who received CRT followed by durvalumab. Radiomic features, including first-order and texture and shape-based features with wavelet transformation were extracted from whole-lung regions on pre-durvalumab computed tomography (CT) images. Machine learning models, support vector machines, k-nearest neighbor, neural networks, and naïve Bayes classifiers were developed and evaluated using a testing cohort. Model performance was assessed using five-fold cross-validation. Conventional predictors, including V20 and ILAs, were also assessed using logistic regression and receiver operating characteristic analysis. Results: Among 123 patients, 44 (35.8%) developed grade ≥ 2 pneumonitis. The best-performing model, a support vector machine, achieved an AUC of 0.88 and accuracy of 0.81, the conventional model showed lower performance with an AUC of 0.71 and accuracy of 0.64. Conclusions: Radiomics-based machine learning demonstrated superior performance over clinical parameters in predicting pneumonitis. This approach may enable individualized risk stratification and support early intervention in patients with NSCLC.

Introduction/Background: Chemoradiotherapy (CRT) followed by durvalumab, an immune checkpoint inhibitor, is the standard treatment for locally advanced non-small-cell lung cancer (NSCLC). Interstitial lung disease (ILD) is a life-threatening toxicity caused by these treatments; however, risk factors for the ILD have not yet been established. Interstitial lung abnormalities (ILAs) are computed tomography (CT) findings which manifest as minor interstitial shadows. We aimed to investigate whether ILAs could be risk factors for grade-two or higher ILD during durvalumab therapy. Patients and Methods: Patients with NSCLC who received durvalumab after CRT from July 2018 to June 2021 were retrospectively enrolled. We obtained patient characteristics, laboratory data, radiotherapeutic parameters, and chest CT findings before durvalumab therapy. Results: A total of 148 patients were enrolled. The prevalence of ILAs before durvalumab treatment was 37.8%. Among 148 patients, 63.5% developed ILD during durvalumab therapy. The proportion of patients with grade-two or higher ILD was 33.8%. The univariate logistic regression analysis revealed that older age, high dose-volume histogram parameters, and the presence of ILAs were significant risk factors for grade-two or higher ILD. The multivariate analysis showed that ILAs were independent risk factors for grade-two or higher ILD (odds ratio, 3.70; 95% confidence interval, 1.69–7.72; p < 0.001). Conclusions: We showed that pre-existing ILAs are risk factors for ILD during durvalumab treatment after CRT. We should pay attention to the development of grade-two or higher ILD during durvalumab treatment in patients with ILAs.

Rationale: Pembrolizumab, an immune-checkpoint inhibitor (ICI), has been shown to be effective for treatment-naive patients with non–small cell lung cancer (NSCLC) and high expression of programmed death-ligand 1 (PD-L1). Therefore, treatment regimens containing pembrolizumab have become a standard therapy for these patients. However, the use of pembrolizumab is limited owing to the side effects of ICIs. Patient concerns and diagnoses: The patient was a 65-year-old man with a left lung mass surrounded by interstitial shadow. The tumor was diagnosed as adenocarcinoma, cT4N3M0, stage IIIC, and the tumor cells showed high PD-L1 expression. It was unclear whether the interstitial shadow was interstitial lung disease (ILD) or lymphangitis carcinomatosa. Interventions and outcomes: The patient received carboplatin and nab-paclitaxel, a less risky regimen for ILD, as the first-line therapy. Administration of 2 cycles of this regimen markedly improved both the tumor diameter and interstitial shadow. The interstitial shadow was clinically diagnosed as lymphangitis carcinomatosa and not ILD. Subsequently, the patient was treated with pembrolizumab, and the tumor showed much further shrinkage with no deterioration of the interstitial shadow. To date, the patient is alive with no complaints and no disease progression, and has continued pembrolizumab treatment for a total of 12 months. Lessons: In patients at a high risk of ICI-related side effects, platinum-doublet chemotherapy may be permitted as the first-line therapy for NSCLC with high PD-L1 expression. However, if the risk associated with ICIs is resolved, early switching from chemotherapy to pembrolizumab might be desirable, even if the chemotherapy is effective.