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Volume 13 Issue 4
Jul.  2022
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Article Navigation >  Medical Journal of Peking Union Medical College Hospital  > 2022  >  13(4): 597-604
WANG Jixian, GUI Kun, CHEN Bingxian, RU Guoqing, ZHAO Di, CHEN Wanyuan, ZHANG Zhiyong. Gastric Cancer Diagnostic Model Based on Convolutional Neural Network[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 597-604. doi: 10.12290/xhyxzz.2022-0021
Citation: WANG Jixian, GUI Kun, CHEN Bingxian, RU Guoqing, ZHAO Di, CHEN Wanyuan, ZHANG Zhiyong. Gastric Cancer Diagnostic Model Based on Convolutional Neural Network[J]. Medical Journal of Peking Union Medical College Hospital, 2022, 13(4): 597-604. doi: 10.12290/xhyxzz.2022-0021
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Gastric Cancer Diagnostic Model Based on Convolutional Neural Network

doi: 10.12290/xhyxzz.2022-0021
  • 1.

    Department of Pathology, Peking University People's Hospital, Beijing 100044, China

  • 2.

    Ningbo Konfoong Biotech International Co., Ltd, Hangzhou 310014, China

  • 3.

    Departments of Pathology, Zhejiang Provincial People's Hospital & People's Hospital of Hangzhou Medical College, Hangzhou 310014, China

  • 4.

    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

  • 5.

    Department of Pathology, Tangshan Gongren Hospital, Tangshan, Hebei 063000, China

Funds:

Zhejiang Provincial Public Welfare Technology Application Research Project GF20F020087

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    Abstract
  •   Objective  To build a diagnostic model of gastric cancer based on deep learning and evaluate the performance of the model.  Methods  The pathological sections of patients diagnosed with normal gastric mucosa, chronic gastritis, high-grade intraepithelial neoplasia or gastric adenocarcinoma by endoscopic examination in Zhejiang Provincial People's Hospital from January 2015 to January 2020 were retrospectively selected. The pathology slides were scanned at ×20 magnification to generate whole slide images (WSIs). These WSIs were randomly divided into patch classification data set, slide classification training set and slide classification test set at a ratio of 2:2:1. After the lesion regions of the patch classification data set were annotated and the patches were selected, they were randomly divided into training set, test set and validation set at a ratio of 20:1:1. The deep learning model Efficientnet and ResNet were used to train and the convolutional neural network (CNN) model for cancer and non-cancer classification was constructed. Based on the patch classification test set and validation set, the performance of the model was evaluated. The results were evaluated by the patch classification accuracy and the area under the curve (AUC). This model was used for image stitching to generate the cancerous heat map of WSIs and extract the slide-level cancer and non-cancer classification features of the heat map. LightGBM slide-level classification algorithm were trained and evaluated, and the gastric cancer of WSIs were diagnosed and recognized. The results were evaluated by AUC, accuracy, sensitivity and specificity.  Results  A total of 500 pathological sections of benign gastric diseases (normal gastric mucosa, chronic gastritis) and 500 pathological sections of gastric cancer (high-grade intraepithelial neoplasia and gastric adenocarcinoma) that met the inclusion and exclusion criteria were selected. The patch classification data set, slide classification training set and slide classification test set were 400, 400 and 200, respectively. The patch classification training set, test set, validation set were 402 000, 20 000, 20 000, respectively. CNN model based on Efficientnet-b1 network structure for patch classification in test set and validation set achieved the highest accuracy[test set: 91.3% (95% CI: 88.2%-95.4%); validation set: 92.5%(95% CI: 89.0%-95.3%)]and the highest AUC[test set: 0.95(95% CI: 0.93-0.98); validation set: 0.96(95% CI: 0.92-0.98)]. The AUC of the model based on LightGBM algorithm was 0.98(95% CI: 0.89-0.98), with accuracy of 88.0%(95% CI: 81.6%-94.3%), sensitivity 100%(95% CI: 88.0%-100%), and specificity 67.0%(95% CI: 57.0%-85.0%).  Conclusion  The CNN diagnostic model based on the pathology slides of gastric biopsy can locate the cancerous tissues, classify patch-level and slide-level lesion natures accurately, identify gastric cancer accurately, which has the potential to improve the diagnosis efficiency.
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  • [1] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019[J]. Cancer J Clin, 2019, 69: 7-34. doi:  10.3322/caac.21551
    [2] Yoshikawa K, Maruyama K. Characteristics of gastric cancer invading to the proper muscle layer-with special reference to mortality and cause of death[J]. JPN J Clin Oncol, 1985, 15: 499-503.
    [3] Everett SM, Axon AT. Early gastric cancer in Europe[J]. Gut, 1997, 41: 142-150. doi:  10.1136/gut.41.2.142
    [4] Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66: 115-132. doi:  10.3322/caac.21338
    [5] Oh CM, Won YJ, Jung KW, et al. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2013[J]. Cancer Res Treat, 2016, 48: 436-450. doi:  10.4143/crt.2016.089
    [6] Matsuda T, Ajiki W, Marugame T, et al. Population-based survival of cancer patients diagnosed between 1993 and 1999 in Japan: a chronological and international comparative study[J]. JPN J Clin Oncol, 2011, 41: 40-51. doi:  10.1093/jjco/hyq167
    [7] Jin L, Shi F, Chun Q, et al. Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers[J]. Neuro Oncol, 2021, 23: 44-52. doi:  10.1093/neuonc/noaa163
    [8] Naik N, Madani A, Esteva A, et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains[J]. Nat Commun, 2020, 11: 5727. doi:  10.1038/s41467-020-19334-3
    [9] Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J]. Nat Med, 2018, 24: 1559-1567. doi:  10.1038/s41591-018-0177-5
    [10] Sharma H, Zerbe N, Klempert I, et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology[J]. Comput Med Imaging Graph, 2017, 61: 2-13. doi:  10.1016/j.compmedimag.2017.06.001
    [11] Sharma H, Zerbe N, Heim D, et al. A multi-resolution approach for combining visual information usingnuclei segmentation and classification in histopathological images[C]. Proceedings of the 10th International Conference on Com-puter Vision Theoryand Applications (VISAPP 2015), 2015, 3: 37-46.
    [12] Arends MJ, Fukayama M, Klimstra DS, et al. WHO Classification of tumours of the digestive system[M]. 5thed. Lyon: IARC Press, 2019: 1-635.
    [13] Garcia E, Hermoza R, Castanon C B, et al. Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning[C]. IEEE International Symposium on Computer-based Medical Systems, 2017. doi: 10.1109/CBMS.2017.94.
    [14] Tomita N, Abdollahi B, Wei J, et al. Attention-Based Deep Neural Networks for Detection of Cancerous and Precancer-ous Esophagus Tissue on Histopathological Slides[J]. JAMA Netw Open, 2019, 2: e1914645. doi:  10.1001/jamanetworkopen.2019.14645
    [15] Iizuka O, Kanavati F, Kato K, et al. Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours[J]. Sci Rep, 2020, 10: 1504. doi:  10.1038/s41598-020-58467-9
    [16] Wang S, Zhu Y, Yu L, et al. RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification[J]. Med Image Anal, 2019, 58: 101549. doi:  10.1016/j.media.2019.101549
    [17] Song Z, Zou S, Zhou W, et al. Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning[J]. Nat Commun, 2020, 11: 4294. doi:  10.1038/s41467-020-18147-8
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