Predicting Tumor-related Indicators Based on Deep Learning and HE Stained Pathological Images:A Survey

Abstract

Accurate diagnosis of tumor is very important for customizing treatment plans and predicting prognosis.Pathological diagnosis is considered the "gold standard" for tumor diagnosis,but the development of pathology still faces great challenges,such as the lack of pathologists,especially in underdeveloped areas and small hospitals,has led to long-term overload of pathologists.At the same time,pathological diagnosis relies heavily on the professional knowledge and diagnostic experience of pathologists,and this subjectivity of pathological diagnosis has led to a surge in diagnostic inconsistencies.The breakthrough of whole slide images (WSI) technology and deep learning methods provides new development opportunities for computer-aided diagnosis and prognosis prediction.Histopathological sections stained with hematoxylin-eosin (HE) can show cell morphology and tissue structure very well,and are simple to make,inexpensive,and widely used.What can be predicted from pathological images alone? After the deep learning method was applied to the field of pathological images,this question got a new answer.In this paper,we first summarize the overall research framework of tumor-related indicators prediction based on deep learning and pathological images.According to the development sequence of the overall research framework,it can be summarized into three progressive stages:WSI predictions based on manually selected single patch,WSI predictions based on majority voting,and WSI predictions with general applicability;Secondly,four supervised or weakly supervised learning methods commonly used in WSI prediction are briefly introduced:convolutional neural network (CNN),recurrent neural network (RNN),graph neural network (GNN),multiple instance learning (MIL).Then,we reviewed the related deep learning methods used in this field,what are the tumor-related indicators that can be predicted through pathological images,and the latest research progress.We mainly reviewed the literature from two aspects:predicting tumor-related indicators (tumor classification,tumor grading,tumor area recognition) that pathologists can read and recognize,and predicting tumor-related indicators (genetic variation prediction,molecular subtype prediction,treatment effect evaluation,survival time prediction) that pathologists cannot read and recognize.Finally,the general problems in this field are summarized,and the possible development direction in the future is suggested.

Keywords

• pathological images|deep learning|tumor-related indicators|computer-aided diagnosis|prediction model|medical image analysis