Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation

Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts from pathology images, we performed the entire process of predicting patient survival automatically. In addition, we established a method to correctly predict survival even in cases where the amount of data is small. After segmenting nuclei from pathological images extracted from patients who underwent liver transplantation, we trained a deep learning model to distinguish survival/death by overlapping the segmented mask image and the original volume image. The cohort was collected from the liver transplantation group (n=67). Approximately two pathological images were collected from each patient, and one of the large pathological images was split into an average of 30 small-sized images to train the classification model. The VIT (Vision Transformer) model provided by the python timm library was used to classify whether the pathological images had recurred cancer. The methods used for survival analysis were CoxPH and Kaplan-Meier models, and survival results obtained from deep learning models were compared with other patient variables to determine how well they predicted patient survival. The indicators measured for comparison were C-index and AUC, NRI and HR were calculated. As the number of patients being diagnosed and the number of images resulting from them become more complex and larger, experts may make misjudgments. Artificial intelligence technology quickly and accurately judges this complex and large amount of data.