深度学习图片分类模型ResNet-18用于判定口腔鳞状细胞癌浸润方式的初步研究

doi:10.13701/j.cnki.kqyxyj.2023.10.013

口腔医学研究 ›› 2023, Vol. 39 ›› Issue (10): 917-922.DOI: 10.13701/j.cnki.kqyxyj.2023.10.013

深度学习图片分类模型ResNet-18用于判定口腔鳞状细胞癌浸润方式的初步研究

吴天赐¹, 郁佳鑫¹, 黄晓峰², 陈盛², 王育新¹, 蒲玉梅^1*

1.南京大学医学院附属口腔医院南京市口腔医院口腔颌面外科江苏南京 210008;
2.南京大学医学院附属口腔医院南京市口腔医院病理科江苏南京 210008

收稿日期:2023-05-15 出版日期:2023-10-28 发布日期:2023-10-25
通讯作者: *蒲玉梅,E-mail:puyumei1981@126.com
作者简介:吴天赐(1997~ ),男,安徽人,本科,住院医师,研究方向:口腔颌面外科学。
基金资助:
南京大学医学院附属口腔医院3456骨干人才资助项目(编号:0222C101),江苏省重点研发计划项目(社会发展)BE2021609

Preliminary Study on Deep Learning Picture Classification Model for Identification and Classification of Invasion Pattern of Oral Squamous Cell Carcinoma

WU Tianci¹, YU Jiaxin¹, HUANG Xiaofeng², CHEN Sheng², WANG Yuxin¹, PU Yumei^1*

1. Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China;
2. Department of Oral Pathology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China

Received:2023-05-15 Online:2023-10-28 Published:2023-10-25

摘要/Abstract

摘要： 目的: 探究深度学习网络模型(ResNet-18)用于判定口腔鳞状细胞癌(oral squamous cell carcinoma,OSCC)最差浸润方式(worst pattern of invasion,WPOI)的可行性及效果。方法: 应用 ResNet-18模型对收集的491张OSCC患者数字化病理切片进行研究,训练其识别并区分非肿瘤区域、WPOI 1~3级、WPOI 4~5级,利用分类准确率对模型进行评估。结果: ResNet-18神经网络可以有效区分 OSCC非肿瘤区域、WPOI 1~3级、WPOI 4~5级,其准确率可达99.5%。结论: 深度学习网络模型ResNet-18可以有效区分 OSCC非肿瘤区域、WPOI 1~3级、WPOI 4~5级,辅助医师提高诊断速度。

关键词: 口腔鳞状细胞癌, 病理, 深度学习, 残差神经网络, 最差浸润方式

Abstract: Objective: To explore the feasibility and effect of the deep learning network model (ResNet-18) to determine the worst infiltration mode (worst pattern of invasion, WPOI) of oral squamous cell carcinoma (oral squamous cell carcinoma, OSCC). Methods: The 491 digital pathological sections collected by ResNet-18 model were trained to identify and distinguish non-tumor areas, WPOI 1-3 and WPOI 4-5, and the model was evaluated using the classification accuracy. Results: ResNet-18 neural network can effectively distinguish non-tumor areas of OSCC, WPOI 1-3 and WPOI 4-5, with an accuracy of 99.5%. Conclusion: The deep learning network model ResNet-18 can effectively distinguish the non-tumor areas of OSCC, WPOI 1-3, and WPOI 4-5, and assist physicians to improve the diagnosis speed.

Key words: oral squamous cell carcinoma, pathology, deep learning, ResNet, worst pattern of invasion

吴天赐, 郁佳鑫, 黄晓峰, 陈盛, 王育新, 蒲玉梅. 深度学习图片分类模型ResNet-18用于判定口腔鳞状细胞癌浸润方式的初步研究[J]. 口腔医学研究, 2023, 39(10): 917-922.

WU Tianci, YU Jiaxin, HUANG Xiaofeng, CHEN Sheng, WANG Yuxin, PU Yumei. Preliminary Study on Deep Learning Picture Classification Model for Identification and Classification of Invasion Pattern of Oral Squamous Cell Carcinoma[J]. Journal of Oral Science Research, 2023, 39(10): 917-922.

参考文献

[1] Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA Cancer J Clin, 2021, 71(3):209-249.
[2] 陈新,徐文华,周健,等.口腔鳞状细胞癌现状[J].口腔医学,2017,37(5):462-465.
[3] Vats R, Rai R, Kumar M. Detecting oral cancer: The potential of artificial intelligence [J]. Curr Med Imaging, 2022, 18(9):919-923.
[4] Malkani N, Kazmi S, Rashid MU. Epidemiological assessment of oral cancer burden in Pakistan [J]. Cancer Invest, 2021, 39(10):842-853.
[5] Lakhera KK, Nama Y, Maan P, et al. Worst pattern of invasion as a predictor of nodal metastasis in early-stage oral squamous cell carcinoma [J]. Indian J Surg Oncol, 2023, 14(1):160-168.
[6] Mishra A, Das A, Dhal I, et al. Worst pattern of invasion in oral squamous cell carcinoma is an independent prognostic factor [J]. J Oral Biol Craniofac Res, 2022, 12(6):771-776.
[7] Pu Y, Ding L, Wang Y, et al. Biopsy pattern of invasion type to determine the surgical approach in early-stage oral squamous cell carcinoma [J]. Virchows Arch, 2021, 479(1):109-119.
[8] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition [C]. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016:770-778.
[9] Warin K, Limprasert W, Suebnukarn S, et al. Performance of deep convolutional neural network for classification and detection of oral potentially malignant disorders in photographic images [J]. Int J Oral Maxillofac Surg, 2022, 51(5):699-704.
[10] Panigrahi S, Bhuyan R, Kumar K, et al. Multistage classification of oral histopathological images using improved residual network [J]. Math Biosci Eng, 2022, 19(2):1909-1925.
[11] Yang SY, Li SH, Liu JL, et al. Histopathology-based diagnosis of oral squamous cell carcinoma using deep learning [J]. J Dent Res, 2022, 101(11):1321-1327.
[12] Mishra A, Das A, Dhal I, et al. Worst pattern of invasion in oral squamous cell carcinoma is an independent prognostic factor [J]. J Oral Biol Craniofac Res, 2022, 12(6):771-776.
[13] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553):436-444.
[14] Ahn Y, Hwang JJ, Jung YH, et al. Automated mesiodens classification system using deep learning on panoramic radiographs of children [J]. Diagnostics (Basel), 2021, 11(8):1477.
[15] Kamat M, Rai BD, Puranik RS, et al. A comprehensive review of surgical margin in oral squamous cell carcinoma highlighting the significance of tumor-free surgical margins [J]. J Cancer Res Ther, 2019, 15(3):449-454.

深度学习图片分类模型ResNet-18用于判定口腔鳞状细胞癌浸润方式的初步研究

Preliminary Study on Deep Learning Picture Classification Model for Identification and Classification of Invasion Pattern of Oral Squamous Cell Carcinoma

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李易玮, 孙雅楠, 潘军臣, 胡雅英, 张钰莹, 马纪源, 张佳莉. Barx2驱动口腔鳞状细胞癌肿瘤特异性MHC-Ⅱ类信号诱导CD4⁺T/CD8⁺T细胞活化[J]. 口腔医学研究, 2023, 39(8): 684-689.
[2]	冯婧, 闫弘静, 鞠昊, 郑凯娟, 金玲, 蔡研. 繁茂性牙骨质-骨结构不良伴发单纯性骨囊肿1例[J]. 口腔医学研究, 2023, 39(7): 649-651.
[3]	陈卓晅, 陈林林. 早期口腔鳞状细胞癌浸润深度检查方法的研究进展[J]. 口腔医学研究, 2023, 39(6): 487-490.
[4]	温佳欢, 傅裕杰, 任根强, 陈宇飞, 张旗. 基于深度学习的点云补全网络修复上颌磨牙钙化根管的研究[J]. 口腔医学研究, 2023, 39(5): 455-459.
[5]	杨可, 孙雅楠, 胡雅英, 吕宜楠, 郑小风, 李易玮, 张佳莉. cyclin D1表达模式深度学习识别和预测模型对HPV阴性口腔鳞状细胞癌和口咽鳞状细胞癌患者预后的预测作用[J]. 口腔医学研究, 2023, 39(4): 308-315.
[6]	蒋志敏, 马国武, 孙波. Nomogram预测根治性切除局部晚期口腔鳞状细胞癌患者的预后[J]. 口腔医学研究, 2023, 39(1): 56-61.
[7]	吕宜楠, 胡雅英, 郑小风, 杨可, 张佳莉. IL-12A和EBI3在OSCC中的表达及其与免疫微环境的关系[J]. 口腔医学研究, 2022, 38(6): 533-539.
[8]	彭艳霜, 刘英. Sox基因在口腔鳞状细胞癌中作用及机制的研究进展[J]. 口腔医学研究, 2022, 38(4): 312-315.
[9]	胡勤刚, 泥艳红, 王雨晗. 代谢组学在口腔鳞状细胞癌早期诊断及精准诊疗中的研究进展[J]. 口腔医学研究, 2022, 38(3): 207-211.
[10]	朱红标, 刘强冬, 曾子强, 娄伟明, 戴芳, 吴婧婷, 邓恬, 邓立彬, 宋莉. 基于口内数码照图像深度学习的牙周病早期筛查研究[J]. 口腔医学研究, 2022, 38(11): 1092-1095.
[11]	齐元莉, 闫红娟, 祖木热提古丽·阿不来提, 张超杰, 赵举红, 徐江. 口腔鳞状细胞癌中CASZ1的表达及临床意义[J]. 口腔医学研究, 2022, 38(1): 30-33.
[12]	李杨, 李雯, 陈茁, 李帅泽, 乔彬. 口腔鳞状细胞癌患者循环肿瘤细胞上皮-间充质转化状态的研究[J]. 口腔医学研究, 2022, 38(1): 34-38.
[13]	林晶, 金成日, 玄云泽. β-Lapachone通过诱导ROS生成调控口腔鳞状细胞癌增殖与凋亡的机制研究[J]. 口腔医学研究, 2022, 38(1): 39-44.
[14]	帕克扎提·色依提, 王铁梅, 徐子能, 白海龙, 丁鹏, 刘澍, 滕跃辉, 冯英连, 王蓉, 单珊, 钟双泽. 基于深度学习在曲面体层图像中人工智能辅助诊断系统初步研究[J]. 口腔医学研究, 2021, 37(9): 845-849.
[15]	王译雪, 徐颖婕, 丛蓓蓓, 魏昕. 淫羊藿素调控Ki-67及Notch-1基因表达对人口腔鳞状细胞癌细胞的促凋亡效应研究[J]. 口腔医学研究, 2021, 37(6): 505-508.