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Artificial intelligence recognition of pathological T stage and tumor invasion in rectal cancer based on large panoramic pathological sections

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Intelligent Medicine 2022年第3期2卷 141-151页

作者： Yiheng Ju Longbo Zheng Peng Zhao Fangjie Xin Fengjiao Wang Yuan Gao Xianxiang Zhang Dongsheng Wang Yun Lu Department of General Surgery Medical CenterAffiliated Hospital of Qingdao UniversityQingdaoShandong 266000China Department of Pathology Affiliated Hospital of Qingdao UniversityQingdaoShandong 266000China Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery QingdaoShandong 266071China Hainan Branch of Affiliated Hospital of Qingdao University Lingshui Li Autonomous CountyHainan 572400China

Background The incidence of colorectal cancer is increasing worldwide,and it currently ranks third among all cancers.Moreover,pathological diagnosis is becoming increasingly arduous.Artificial intelligence has demon-strated the ability to fully excavate image features and assist doctors in making decisions.large panoramic patho-logical sections contain considerable amounts of pathological information.In this study,we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer,as well as for T staging and prognostic analysis.Methods We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District(internal dataset)and 42 cases from Shinan and Laoshan Hospital District(external dataset)that had tissue surgically removed from January to September 2019.After sectioning,staining,and scanning,a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained.The patients in the internal dataset were randomly divided into a training cohort(n=88)and a test cohort(n=38)at a ratio of 7:3.We chose DeepLabV3+and ResNet50 as target models for our experiment.We used the Dice similarity coefficient,accuracy,sensitivity,specificity,receiver operating characteristic(ROC)curve,and area under the curve(AUC)to evaluate the performance of the artificial intelligence platform in the test set and validation set.Finally,we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations.Results In the test set,the accuracy of image segmentation was 95.8%,the Dice coefficient was 0.92,the accuracy of automatic T-staging recognition was 86%,and the ROC AUC value was 0.93.In the validation set,the accuracy of image segmentation was 95.3%,the Dice coefficient was 0.90,the accuracy of automatic classification was 85%,the ROC AUC value was 0.92,and the image analysis time was 0.2 s.There was a difference

关键词： large panoramic pathological sections Rectal cancer Whole-slide imaging Deep learning Survival analysis

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