Prognosis Prediction of Patients with Soft Tissue Malignant Tumors Using Pathology Images of Biopsy Specimens

Abstract

Malignant soft-tissue tumors are rare cancers, occurring in about 40 per million people per year. Rare cancers should be diagnosed by a pathologist who specializes in that organ, but there is a chronic shortage of pathologists in Japan. In order to reduce the burden on pathologists and improve the reliability of diagnosis, there is a need for pathological diagnosis of rare cancers using machine learning techniques. Although there are studies using machine learning to predict patient prognosis from pathology images, most of them use large data sets and networks such as CNNs (Convolutional Neural Network). However, soft-tissue malignant tumors are rare cancers, making it difficult to collect large amounts of training data, and existing methods are not expected to be used. Therefore, this paper proposes a method to predict the survival period using unsupervised machine learning. In the proposed method, the autoencoder extracts pathological features from small image patches extracted from pathological images, and the K-means clustering and K-nearest neighbor regression predict the survival period from extracted features. The experiment on pathology images of 27 patients showed a mean prediction error of 17.89 months.