Large scale digital prostate pathology image analysis combining feature extraction and deep neural network
This work addresses the tedious and variable manual interpretation of prostate cancer pathology images, potentially easing clinical translation, but it is incremental as it combines existing feature extraction and deep learning methods.
The authors tackled the problem of automating prostate cancer histopathology analysis by developing a pipeline that localizes cancer regions, performs Gleason grading, and extracts features, achieving 75% accuracy in differentiating Gleason 3+4 from 4+3 slides on a dataset of 368 whole slide images.
Histopathological assessments, including surgical resection and core needle biopsy, are the standard procedures in the diagnosis of the prostate cancer. Current interpretation of the histopathology images includes the determination of the tumor area, Gleason grading, and identification of certain prognosis-critical features. Such a process is not only tedious, but also prune to intra/inter-observe variabilities. Recently, FDA cleared the marketing of the first whole slide imaging system for digital pathology. This opens a new era for the computer aided prostate image analysis and feature extraction based on the digital histopathology images. In this work, we present an analysis pipeline that includes localization of the cancer region, grading, area ratio of different Gleason grades, and cytological/architectural feature extraction. The proposed algorithm combines the human engineered feature extraction as well as those learned by the deep neural network. Moreover, the entire pipeline is implemented to directly operate on the whole slide images produced by the digital scanners and is therefore potentially easy to translate into clinical practices. The algorithm is tested on 368 whole slide images from the TCGA data set and achieves an overall accuracy of 75% in differentiating Gleason 3+4 with 4+3 slides.