ArticleFrontiers in artificial intelligence2026
Brief report: Artificial intelligence meets small cell lung cancer-integrating clinicopathological and wholeslide image data for prognostic prediction in SCLC.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Small-cell lung cancer (SCLC) represents a unique clinical challenge characterized by its aggressive nature, poor prognosis, and limited therapeutic options. Upfront prediction of survival outcomes in this disease could impact patient care by refining risk stratification and thus, personalizing treatment strategies. Here, we investigate the utility of a deep learning (DL) model using digital pathology to predict outcomes of patients diagnosed with SCLC. Methods: We built a random forest (RF) model using clinical data and a DL based model using whole-slide image (WSI) as inputs from a total of 307 patients diagnosed with SCLC, including a training set of 263 patients, and a validation set comprising 44 patients who participated in the CANTABRICO phase IIIB clinical trial. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) with 5-fold crossvalidation to minimize bias and variance of the performance. We report the mean and 95% confidence interval of the AUC values across the folds. Results: In the training set, the RF model achieved an AUC of 0.728 (95% CI: 0.662-0.792) for long-term overall survival (LT_OS) prediction, while the combined RF and DL model achieved an AUC of 0.744 (95% CI: 0.680-0.807). For long-term progression-free survival (LT_PFS) prediction, the RF model achieved an AUC of 0.689 (95% CI: 0.625-0.753), whereas the combined model achieved an AUC of 0.704 (95% CI: 0.640-0.767). Application of the combined RF and DL model to the validation cohort yielded an AUC for LT_OS of 0.604 (95% CI: 0.582-0.626) and an AUC for LT_PFS 0.690 (95% CI: 0.643-0.738), indicating potential clinical applicability. Conclusion: Our results showcase the feasibility of integrating clinicopathological data with WSI through a deep learning model to predict outcomes in patients with SCLC. This approach holds promise in helping physicians to personalize treatment strategies that better suit individual patient needs.
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