机器学习在食管癌诊疗中的研究进展

doi:10.11896/jsjkx.250100065

Abstract

Abstract: EC is a highly lethal malignancy worldwide,particularly in China.Its low early diagnosis rates and poor prognosis present significant challenges to clinical management.In recent years,ML technology,based on multi-modal data,has shown great potential in early EC diagnosis and treatment.Traditional ML methods integrate radiomic features from EC imaging and clinical textual data,thereby effectively improving the sensitivity of early lesion diagnosis and providing scientific support for the stratified management of high-risk patients.CNNs with their efficient parameter-sharing mechanisms and excellent local feature extraction capabilities,further enhance the accuracy of early EC diagnosis and screening.Moreover,combining CNNs with Transformer models based on self-attention mechanisms significantly strengthens global feature modeling,which demonstrating broad application potential in lesion segmentation,early diagnosis,treatment outcome prediction,and survival analysis of EC.However,the high heterogeneity of EC lesions and the class imbalance of imaging data continue to pose significant challenges to the clinical application of ML technologies.To advance intelligent diagnostic and therapeutic technologies for EC,this paper focuses on three critical areas:early screening and diagnosis,treatment outcome prediction and survival analysis,and image segmentation.It systematically reviews the current research status and challenges of traditional ML,CNNs,and emerging Transformer technologies in EC diagnosis and treatment,aiming to provide valuable insights and references for future research on intelligent EC diagnosis and treatment.

Key words: Esophageal cancer, Machine learning, Differential diagnosis, Survival analysis, Semantic segmentation

CLC Number:

TP391

WANG Yongquan, SU Mengqi, SHI Qinglei, MA Yining, SUN Yangfan, WANG Changmiao, WANG Guoyou, XI Xiaoming, YIN Yilong, WAN Xiang. Research Progress of Machine Learning in Diagnosis and Treatment of Esophageal Cancer[J].Computer Science, 2025, 52(9): 4-15.

References

[1]SUNG H,FERLAY J,SIEGEL R L,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]QIU H,CAO S,XU R.Analys is of the time trend of China’s cancer incidence rate,mortality and burden based on the global epidemiol ogical data in 2020 and comparison with data from t he United States and the United Kingdom[J].Cancer,2022,41(4):165-177.
[3]DENG W,LIN S H.Advances in radiotherapy for esophagealcancer[J].Annals of Translational Medicine,2018,6(4):79.
[4]KATO H,NAKAJIMA M.Treatments for esophageal cancer:a review[J].General Thoracic and Cardiovascular Surgery,2013,61:330-335.
[5]SHAW P,SANKARANARAYANAN S,LORENZ P.EarlyEsophageal Malignancy Detection Using Deep Transfer Lear-ning and Explainable AI[C]//Proceedings of the 6th Internatio-nal Conference on Communication and Information Systems.IEEE,2022.
[6]WU C,ZHANG Y,WANG Y,et al.Multi-Modal IntermediateFeature Interaction Autoencoder for Overall Survival Prediction of Esophageal Squamous Cell Cancer[C]//Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging.IEEE,2024.
[7]LI Z,LIU F,YANG W,et al.A survey of convolutional neural networks:analysis,applications,and prospects[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(12):6999-7019.
[8]YU Z,JIANG S,WANG L.Esophageal Image Segmentationwith Dual Attention Based on TransUNet[C]//Proceedings of the 2024 4th International Conference on Consumer Electronics and Computer Engineering.IEEE,2024.
[9]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems.2017:6000-6010.
[10]WANG S,LI B Z,KHABSA M,et al.Linformer:Self-Attention with Linear Complexity[J].arXiv:2006.04768,2020.
[11]AZAD R,KAZEROUNI A,HEIDARI M,et al.Advances in medical image analysis with vision transformers:a comprehensive review[J].Medical Image Analysis,2024,91:103000.
[12]ERICKSON B J,KORFIATIS P,AKKUS Z,et al.Machinelearning for medical imaging[J].Radiographics:A Review Publication of the Radiological Society of North America,2017,37(2):505-515.
[13]YAMAGUCHI J,YONEYAMA A,MINAMOTO T.Automatic detection of early esophageal cancer from endoscope image using fractal dimension and discrete wavelet transform[C]//Procee-dings of the 12th International Conference on Information Technology-New Generations.IEEE,2015.
[14]XUE Y,LI N,WEI X,et al.Deep learning-based earlier detection of esophageal cancer using improved empirical wavelet transform from endoscopic image[J].IEEE Access,2020,8:123765-123772.
[15]PADHA A,SAHOO A.Quantum deep neural networks for time series analysis[J].Quantum Information Processing,2024,23(6):205.
[16]LYU H,KAPCIO K,PURRMAN K,et al.Applying Machine Learning to Predict Esophageal Cancer Recurrence after Esophagectomy[C]//Proceedings of the 2023 IEEE International Conference on Digital Health.IEEE,2023.
[17]SU Y,HUANG C,YANG C,et al.Prediction of Survival in Patients With Esophageal Cancer After Immunotherapy Based on Small-Size Follow-Up Data[J].IEEE Open Journal of Engineering in Medicine and Biology,2024(5):769-782.
[18]SUN J,YANG Y,WANG Y,et al.Survival risk prediction ofesophageal cancer based on self-organizing maps clustering and support vector machine ensembles[J].IEEE Access,2020,8:131449-131460.
[19]PRINCE T,WONDMANEHGETAHUN B,AMBACHEW-GOSHU K,et al.Multi-Classification and Segmentation of Esophageal Lesions Using an Improved Deep Learning Model from Endoscopic Images[C]//Proceedings of the 2023 Eighth International Conference on Science Technology Engineering and Mathematics.IEEE,2023.
[20]NGIAM J,KHOSLA A,KIM M,et al.Multimodal Deep Lear-ning[C]//Proceedings of the International Conference on Machine Learning.IEEE,2011.
[21]GUAN Y,CUI H,XU Y,et al.Predicting esophageal fistularisks using a multimodal self-attention network[C]//Procee-dings of the Medical Image Computing and Computer Assisted Intervention.2021.
[22]LIU D,JIANG H,RAO N,et al.Depth information-based automatic annotation of early esophageal cancers in gastroscopic images using deep learning techniques[J].IEEE Access,2020,8:97907-90919.
[23]FU Y,ZHOU Y,YUAN X,et al.Efficient Esophageal LesionDetection using Polarization Regularized Network Slimming[C]//Proceedings of the 2022 IEEE 8th International Confe-rence on Cloud Computing and Intelligent Systems.IEEE,2022.
[24]YAN X,ZHOU Y,YI Z.Self-supervised Feature Representation Distillation for Esophageal Cancer Screening[C]//Proceedings of the 2024 International Joint Conference on Neural Networks.IEEE,2024.
[25]VAN RIEL S,VAN DER SOMMEN F,ZINGER S,et al.Automatic detection of early esophageal cancer with CNNS using transfer learning[C]//Proceedings of the 2018 25th IEEE International Conference on Image Processing.IEEE,2018.
[26]ABURASAIN R Y.Esophageal Cancer Classification in Initial Stages Using Deep and Transfer Learning[C]//Proceedings of the 2024 IEEE International Conference on Advanced Systems and Emergent Technologies.IEEE,2024.
[27]TSAO Y M,MUKUNDAN A,KARMAKAR R,et al.Hyperspectral Imaging Applied to Identify Early Esophageal Cancer[C]//Proceedings of the 2024 Conference on Lasers and Electro-Optics Pacific Rim.IEEE,2024.
[28]DING S,HUANG H,LI Z,et al.SCNET:A novel UGI cancer screening framework based on semantic-level multimodal data fusion[J].IEEE Journal of Biomedical and Health Informatics,2020,25(1):143-151.
[29]GHATWARY N,ZOLGHARNI M,JANAN F,et al.Learningspatiotemporal features for esophageal abnormality detection from endoscopic videos[J].IEEE Journal of Biomedical and Health Informatics,2020,25(1):131-142.
[30]LIN Z,CAI W,HOU W,et al.CT-guided survival prediction of esophageal cancer[J].IEEE Journal of Biomedical and Health Informatics,2021,26(6):2660-2669.
[31]WU C,WANG S,WANG Y,et al.A Novel Multi-modal Population-graph based Framework for Patients of Esophageal Squamous Cell Cancer Prognostic Risk Prediction[J].IEEE Journal of Biomedical and Health Informatics,2024,29(5):3206-3219.
[32]HUANG G,ZHU J,LI J,et al.Channel-attention U-Net:Channel attention mechanism for semantic segmentation of esophagus and esophageal cancer[J].IEEE Access,2020,8:122798-122810.
[33]ZHOU D,HUANG G,LI J,et al.Eso-net:A novel 2.5D seg-mentation network with the multi-structure response filter for the cancerous esophagus[J].IEEE Access,2020,8:155548-155562.
[34]SUN Y,KHOR H G,HUANG S,et al.Second-Course Esophageal Gross Tumor Volume Segmentation in CT with Prior Anatomical and Radiotherapy Information[C]//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.IEEE,2023.
[35]YOUSEFI S,SOKOOTI H,ELMAHDY M S,et al.Esophageal tumor segmentation in CT images using a dilated dense attention Unet(DDAUnet)[J].IEEE Access,2021,9:99235-99248.
[36]GUO D,JIN D,ZHU Z,et al.Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020.
[37]LI H,LIU D,ZENG Y,et al.Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions[J].ar-Xiv:2306,05912,2023.
[38]LIN Q,TAN W,CAI S,et al.Lesion-decoupling-based segmentation with large-scale colon and esophageal datasets for early cancer diagnosis[J].IEEE Transactions on Neural Networks and Learning Systems,2023,35(8):11142-11156.
[39]JIN Q,CUI H,SUN C,et al.Shape-aware contrastive deep su-pervision for esophageal tumor segmentation from CT scans[C]//Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine.IEEE,2023.
[40]LOU X,ZHU Y,PUNITHAKUMAR K,et al.Esophagus segmentation in computed tomography images using a U-Net neural network with a semiautomatic labeling method[J].IEEE Access,2020,8:202459-202468.
[41]ZHOU Y,YUAN X,ZHANG X,et al.Evolutionary neural architecture search for automatic esophageal lesion identification and segmentation[J].IEEE Transactions on Artificial Intelligence,2021,3(3):436-450.
[42]WANG S,LI B Z,KHABSA M,et al.Linformer:Self-attention with linear complexity[J].arXiv:2006,04768,2020.
[43]CHEN T,KORNBLITH S,NOROUZI M,et al.A SimpleFramework for Contrastive Learning of Visual Representations[J].arXiv:2002.05709,2020.
[44]RAJKOMAR A,DEAN J,KOHANE I S.Machine Learning in Medicine[J].The New England Journal of Medicine,2019,380:1347-1358.
[45]TJOA E,GUAN C.A Survey on Explainable Artificial Intelligence(XAI):Toward Medical XAI[J].IEEE Transactions on Neural Networks and Learning Systems,2019,32:4793-4813.
[46]SAMEK W,MONTAVON G,VEDALDI A,et al.Explainable AI:Interpreting,Explaining and Visualizing Deep Learning[M].Cham:Springer,2019.
[47]KANG Z,ZHANG H,CHEN M,et al.EsccNet:A Hybrid CNN and Transformers Model for the Classification of Whole Slide Images of Esophageal Squamous Cell Carcinoma[C]//Procee-dings of the 2024 5th International Conference on Computer Engineering and Application.IEEE,2024.
[48]YUE H,LIU J,KUANG H,et al.A Fully Automated CT-Guided Learning for Survival Prediction of Esophageal Cancer[C]//2023 IEEE International Conference on Bioinformatics and Biomedicine(BIBM).2023:1670-1675.
[49]YAO J,YE X,XIA Y,et al.Effective opportunistic esophageal cancer screening using noncontrast CT imaging[C]//Procee-dings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.Springer,2022.
[50]ZHANG Y,HE N,YANG J,et al.mmFormer:MultimodalMedical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation[C]//Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention.2022.
[51]LI T,SAHU A K,TALWALKAR A,et al.Federated Learning:Challenges,Methods,and Future Directions[J].IEEE Signal
Processing Magazine,2019,37:50-60.
[52]FRAPPIER M.The Book of Why:The New Science of Cause and Effect[M].Basic Books,2018.
[53]MILLETARI F,NAVAB N,AHMADI S A.V-Net:Fully Con-volutional Neural Networks for Volumetric Medical Image Segmentation[J].arXiv:1606.04797,2016.
[54]ZHAO Y,WANG X,CHE T,et al.Multi-task deep learning for medical image computing and analysis:A review[J].Computers in Biology and Medicine,2022,153:106496.
[55]GU A,DAO T.Mamba:Linear-time sequence modeling with selective state spaces[J].arXiv:2312,00752,2023.
[56]SHI Q,DUAN W,CHEN W,et al.PGP:Prior-Guided Pretraining for Small-sample Esophageal Cancer Segmentation[C]//2024 IEEE International Conference on Bioinformatics and Biomedicine(BIBM).2024:3701-3704.
[57]ZHOU J,CUI G,ZHANG Z,et al.Graph Neural Networks:A Review of Methods and Applications[J].arXiv:1812.08434,2018.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Research Progress of Machine Learning in Diagnosis and Treatment of Esophageal Cancer

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LIU Leyuan, CHEN Gege, WU Wei, WANG Yong, ZHOU Fan. Survey of Data Classification and Grading Studies [J]. Computer Science, 2025, 52(9): 195-211.
[2]	JIANG Rui, FAN Shuwen, WANG Xiaoming, XU Youyun. Clustering Algorithm Based on Improved SOM Model [J]. Computer Science, 2025, 52(8): 162-170.
[3]	ZENG Xinran, LI Tianrui, LI Chongshou. Active Learning for Point Cloud Semantic Segmentation Based on Dynamic Balance and DistanceSuppression [J]. Computer Science, 2025, 52(8): 180-187.
[4]	WANG Yicheng, NING Tai, LIU Xinyu, LUO Ye. Position-aware Based Multi-modality Lung Cancer Survival Prediction Method [J]. Computer Science, 2025, 52(6A): 240500089-8.
[5]	YANG Jixiang, JIANG Huiping, WANG Sen, MA Xuan. Research Progress and Challenges in Forest Fire Risk Prediction [J]. Computer Science, 2025, 52(6A): 240400177-8.
[6]	WANG Jiamin, WU Wenhong, NIU Hengmao, SHI Bao, WU Nier, HAO Xu, ZHANG Chao, FU Rongsheng. Review of Concrete Defect Detection Methods Based on Deep Learning [J]. Computer Science, 2025, 52(6A): 240900137-12.
[7]	WU Xingli, ZHANG Haoyue, LIAO Huchang. Review of Doctor Recommendation Methods and Applications for Consultation Platforms [J]. Computer Science, 2025, 52(5): 109-121.
[8]	JIANG Wenwen, XIA Ying. Improved U-Net Multi-scale Feature Fusion Semantic Segmentation Network for RemoteSensing Images [J]. Computer Science, 2025, 52(5): 212-219.
[9]	ZHOU Yi, MAO Kuanmin. Research on Individual Identification of Cattle Based on YOLO-Unet Combined Network [J]. Computer Science, 2025, 52(4): 194-201.
[10]	JIAO Jian, CHEN Ruixiang, HE Qiang, QU Kaiyang, ZHANG Ziyi. Study on Smart Contract Vulnerability Repair Based on T5 Model [J]. Computer Science, 2025, 52(4): 362-368.
[11]	HAN Lin, WANG Yifan, LI Jianan, GAO Wei. Automatic Scheduling Search Optimization Method Based on TVM [J]. Computer Science, 2025, 52(3): 268-276.
[12]	XIONG Qibing, MIAO Qiguang, YANG Tian, YUAN Benzheng, FEI Yangyang. Malicious Code Detection Method Based on Hybrid Quantum Convolutional Neural Network [J]. Computer Science, 2025, 52(3): 385-390.
[13]	ZUO Xuhong, WANG Yongquan, QIU Geping. Study on Integrated Model of Securities Illegal Margin Trading Accounts Identification Based on Trading Behavior Characteristics [J]. Computer Science, 2025, 52(2): 125-133.
[14]	SHANG Qiuyan, LI Yicong, WEN Ruilin, MA Yinping, OUYANG Rongbin, FAN Chun. Two-stage Multi-factor Algorithm for Job Runtime Prediction Based on Usage Characteristics [J]. Computer Science, 2025, 52(2): 261-267.
[15]	WANG Qian, HE Lang, WANG Zhanqing, HUANG Kun. Road Extraction Algorithm for Remote Sensing Images Based on Improved DeepLabv3+ [J]. Computer Science, 2024, 51(8): 168-175.