White light endoscopy is one of the primary diagnostic assessment methods for luminal organs such as the gastrointestinal tract and the respiratory tract. Traditional fiber-optic endoscopes use incoherent fiber bundles for diffuse white light illumination and coherent fiber bundles (CFB) to carry detection light from the distal end to the proximal end of the endoscope.^{1 – 3}

Background/Objectives: Pembrolizumab monotherapy is approved in Canada for first-line treatment of advanced NSCLC with PD-L1 ≥ 50% and no EGFR/ALK aberrations. However, approximately 55% of these patients do not respond to pembrolizumab, underscoring the need for the early intervention of non-responders to optimize treatment strategies. Distinguishing the 55% sub-cohort prior to treatment is a real-world dilemma. Methods: In this retrospective study, we analyzed two patient cohorts treated with pembrolizumab monotherapy (training set: n = 97; test set: n = 17). The treatment response was assessed using baseline and follow-up CT scans via RECIST 1.1 criteria. Results: A logistic regression model, incorporating pre-treatment CT radiomic features of lung tumors and clinical variables, achieved high predictive accuracy (AUC: 0.85 in training; 0.81 in testing, 95% CI: 0.63–0.99). Notably, radiomic features from the peritumoral region were found to be independent predictors, complementing the standard CT evaluations and other clinical characteristics. Conclusions: This pragmatic model offers a valuable tool to guide first-line treatment decisions in NSCLC patients with high PD-L1 expression and has the potential to advance personalized oncology and improve timely disease management.

Health Canada approved pembrolizumab in the first-line setting for advanced non-small-cell lung cancer with PD-L1 ≥ 50% and no EGFR/ALK aberration. The keynote 024 trial showed 55% of such patients progress with pembrolizumab monotherapy. We propose that the combination of baseline CT and clinical factors can help identify those patients who may progress. In 138 eligible patients from our institution, we retrospectively collected their baseline variables, including baseline CT findings (primary lung tumor size and metastatic site), smoking pack years, performance status, tumor pathology, and demographics. The treatment response was assessed via RECIST 1.1 using the baseline and first follow-up CT. Associations between the baseline variables and progressive disease (PD) were tested by logistic regression analyses. The results showed 46/138 patients had PD. The baseline CT “number of involved organs” by metastasis and smoking pack years were independently associated with PD (p < 0.05), and the ROC analysis showed a good performance of the model that integrated these variables in predicting PD (AUC: 0.79). This pilot study suggests that the combination of baseline CT disease and smoking PY can identify who may progress on pembrolizumab monotherapy and can potentially facilitate decision-making for the optimal first-line treatment in the high PD-L1 cohort.

Objective: Pembrolizumab monotherapy is an anti-PD-1 immunotherapy that is approved as a first-line treatment for non-small cell lung cancer (NSCLC) patients with high PD-L1 expression (≥50%). However, approximately 55% of these patients do not respond. Early identification of likely non-responders is critical to enable timely transition to alternative treatments. Materials: This study analyzed a retrospective cohort of NSCLC patients treated with first-line PD-L1 monotherapy, divided into a discovery training set (n: 97; 27 non-responders) and a preliminary test set (n: 17; 9 non-responders). Treatment response was assessed using baseline and follow-up CT scans in accordance with the response evaluation criteria in solid tumors (RECIST v1.1). Methods: Our objective was to extract deep learning (DL) features from the two groups of patients and apply transfer learning techniques to identify patients at risk of progression on pembrolizumab monotherapy. A nonparametric statistical test (Mann–Whitney U) was employed to rank the discriminative power of the 128 features from these training groups. Two types of support vector machine (SVM-RBF and SVM-Polynomial) classifiers were employed to investigate the discriminating power of the highest-ranked features as measured by F1 score and AUC values over ROC curves at the three levels of the data (slice, lesion, and patient) with and without clinical descriptors. Results: SVM-RBF performed best when trained on the 10 highest-ranked DL features and five clinical descriptors, achieving AUC of 0.742 (CI 95% 0.47–1.00), SN of 88.9%, SP of 75% and F1 score of 84.2% on preliminary test set patients, whereas an AUC of 0.902 ± 0.031, SN of 81.5%, SP of 81.4% and F1 score of 71% were observed for the discovery training set. Conclusions: Integrating CT-based DL features with clinical descriptors demonstrated balanced performance, offering a promising tool to identify patients at risk of progression on pembrolizumab monotherapy to support first-line treatment decisions in PD-L1-high NSCLC.