ArticleAsian Pacific journal of cancer prevention : APJCP2025
Survival Prediction in Stomach Cancer with Deep Learning: Unveiling Model Decisions with LIME and SHAP.
Article in Asian Pacific journal of cancer prevention : APJCP, 2025. 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
objectiveStomach cancer is anticipated to remain a significant global health concern, underscoring the urgent need for sophisticated prognostic models. The aim of the study is to build an intuitive deep learning model for predicting survival probabilities in stomach cancer patients, validating it with external data and merging SHAP and LIME to improve the therapeutic relevance and reliability.
methodsA deep learning survival model was developed with multilayer perceptron, on 1,350 documented stomach cancer cases from the AIIMS, Bhubaneswar Cancer Registry (2018-2022). The model was refined utilizing the Adam optimizer (learning rate = 0.002) with dropout regularization. External validation was performed on an independent cohort of 388 patients from Hi-Tech Medical College and Hospital. Performance was assessed using accuracy, precision, sensitivity, specificity, F1-score, balanced accuracy, Matthews correlation coefficient, concordance index, and AUROC score. LIME and SHAP were utilized to improve interpretability by evaluating both local and global feature contributions.
resultComplex interactions between important prognostic factors such as age, stage, treatment approaches, and socioeconomic level were well explained by LIME and SHAP, thus exposing important elements impacting survival results. Performance measures of the model measured through various metrics showed good generalizability over several datasets.
conclusionThis article focused on interpretable artificial intelligence models in the prognosis for stomach cancer with patient-specific survival projections. Artificial intelligence techniques such as LIME and SHAP improves clinician trust, hence promoting patient specific treatment recommendations.
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