Histopathology Image-Based Deep Learning Prediction of Prognosis in Small Cell Lung and Colon Cancer

Abstract

Histopathology image analysis plays a crucial role in cancer diagnosis and prognosis. In this study, we propose a deep learning-based approach for predicting prognosis in small cell lung cancer (SCLC) and colon cancer using histopathology images. We collected a large dataset of histopathology images from patients diagnosed with SCLC and colon cancer, along with their clinical outcomes. We employed convolutional neural network architecture to train our deep learning models and utilized transfer learning techniques to leverage the knowledge learned from pre-trained models. Our models were trained to identify important histopathological features associated with prognoses, such as tumour cell morphology, tumour infiltration, and immune cell presence. We evaluated the performance of our models using cross-validation and achieved high accuracy, sensitivity, and specificity in predicting patient prognosis. Additionally, we conducted extensive comparative analyses with traditional histopathological prognostic markers and demonstrated that our deep-learning models outperformed these markers in terms of predictive accuracy. Our proposed achieved 99.80 and 99.42 for training and testing accuracy, respectively, with 8.32 seconds. Our results highlight the potential of deep learning-based histopathology image analysis as a valuable tool for prognostic prediction in SCLC and colon cancer. The integration of such models into clinical practice has the potential to enhance patient stratification, treatment decision-making, and overall patient outcomes.