Study on improving the diagnostic performance of transrectal ultrasound for prostate cancer diagnosis based on deep learning
10.3760/cma.j.cn131148-20210620-00421
- VernacularTitle:基于深度学习提高经直肠超声诊断前列腺癌效能的研究
- Author:
Lingyan ZHANG
1
;
Chuan YANG
;
Yumin ZHUO
;
Yinying LIANG
;
Jun HUANG
Author Information
1. 暨南大学附属第一医院超声科，广州 510630
- Keywords:
Ultrasonography, transrectal;
Prostatic neoplasms;
Deep learning;
Image classification
- From:
Chinese Journal of Ultrasonography
2022;31(1):43-49
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of transrectal ultrasound images classification network model of prostate cancer based on deep learning in the classification of benign and malignant prostate tissue in transrectal ultrasound images.Methods:A total of 1 462 two-dimensional images of transrectal prostate biopsy with clear pathologic results(including 658 images of malignant tumor, 804 images of benign tumor) from 203 patients with suspicious prostate cancer(including 89 cases of malignant tumor, 114 cases of benign tumor) were collected from May 2018 to May 2021 in the First Affiliated Hospital of Jinan University. They were divided into the training database, validation database, and test database. And the training and validation database were used to train and obtain the intelligence-assisted diagnosis network model, and then the test database was used to test the network model and two ultrasound doctors of different ages. With pathologic diagnosis as the gold standard, the diagnostic performance among them was evaluated.Results:①The sensitivity of network model was 66.7% the specificity was 91.9%, the accuracy was 80.5%, the precision(positive predictive value) was 87.1%. The area under the ROC curve was 0.922. ②The accuracy of the junior and senior ultrasound doctors was 57.5%, 62.0%; the specificity was 62.0%, 66.3%; the sensitivity was 51.5%, 56.8%; the precision was 53.1%, 58.1%, respectively. ③The accuracy, sensitivity, specificity, precision of classification: the network model > the ultrasound doctors, the differences were significant( P<0.05); the senior ultrasound doctor>the junior ultrasound doctor, the differences were not significant( P>0.05). Conclusions:The intelligence-assisted diagnosis network model based on deep learning can classify benign and malignant prostate tissue in transrectal ultrasound images, improve the accuracy of ultrasound doctors in diagnosing prostate cancer. It is of great significance to improve the efficiency of screening for patients with high clinical suspicion of prostate cancer.
