| 1 |
Bray F，Laversanne M，Sung H，et al.Global cancer statistics 2022：GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］.CA Cancer J Clin，2024，74（3）：229-263.
|
| 2 |
郑荣寿，陈茹，韩冰峰，等.2022年中国恶性肿瘤流行情况分析［J］.中华肿瘤杂志，2024，46（3）：221-231.
|
| 3 |
Siegel RL，Giaquinto AN，Jemal A.Cancer statistics，2024［J］.CA Cancer J Clin，2024，74（1）：12-49.
|
| 4 |
Tang H，Wang H，Fang Y，et al.Neoadjuvant chemoradiotherapy versus neoadjuvant chemotherapy followed by minimally invasive esophagectomy for locally advanced esophageal squamous cell carcinoma：a prospective multicenter randomized clinical trial［J］.Ann Oncol，2023，34（2）：163-172.
|
| 5 |
Klevebro F，Alexandersson von Döbeln G，Wang N，et al.A randomized clinical trial of neoadjuvant chemotherapy versus neoadjuvant chemoradiotherapy for cancer of the oesophagus or gastro-oesophageal junction［J］.Ann Oncol，2016，27（4）：660-667.
|
| 6 |
Deo RC.Machine Learning in Medicine［J］.Circulation，2015，132（20）：1920-1930.
|
| 7 |
Misra I，Maaten LVD.Self-Supervised Learning of Pretext-Invariant Representations［C］//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）.Seattle，WA，USA：IEEE，2019：6706-6716.
|
| 8 |
Chauhan NK，Singh K.A Review on Conventional Machine Learning vs Deep Learning［C］//2018 International Conference on Computing， Power and Communication Technologies（GUCON）.Greater Noida：IEEE；2018.
|
| 9 |
Klionsky DJ，Abdelmohsen K，Abe A，et al.Guidelines for the use and interpretation of assays for monitoring autophagy（3rd edition）［J］.Autophagy，2016，12（1）：1-222.
|
| 10 |
Nohara Y，Matsumoto K，Soejima H，et al.Explanation of machine learning models using shapley additive explanation and application for real data in hospital［J］.Comput Methods Programs Biomed，2022，214：106584.
|
| 11 |
Okamura A，Watanabe M，Okui J，et al.Neoadjuvant Chemotherapy or Neoadjuvant Chemoradiotherapy for Patients with Esophageal Squamous Cell Carcinoma：Real-World Data Comparison from A Japanese Nationwide Study［J］.Ann Surg Oncol，2023，30（9）：5885-5894.
|
| 12 |
Reynolds JV，Preston SR，O’Neill B，et al.Trimodality therapy versus perioperative chemotherapy in the management of locally advanced adenocarcinoma of the oesophagus and oesophagogastric junction（Neo-AEGIS）：an open-label，randomised，phase 3 trial［J］.Lancet Gastroenterol Hepatol，2023，8（11）：1015-1027.
|
| 13 |
Kato K，Machida R，Ito Y，et al.Doublet chemotherapy，triplet chemotherapy，or doublet chemotherapy combined with radiotherapy as neoadjuvant treatment for locally advanced oesophageal cancer（JCOG1109 NExT）：a randomised，controlled，open-label，phase 3 trial［J］.Lancet，2024，404（10447）：55-66.
|
| 14 |
Wu YY，Dai L，Yang YB，et al.Long-Term Survival and Recurrence Patterns in Locally Advanced Esophageal Squamous Cell Carcinoma Patients with Pathologic Complete Response After Neoadjuvant Chemotherapy Followed by Surgery［J］.Ann Surg Oncol，2024，31（8）：5047-5054.
|
| 15 |
Ou J，Zhou HY，Qin HL，et al.Baseline CT radiomics features to predict pathological complete response of advanced esophageal squamous cell carcinoma treated with neoadjuvant chemotherapy using paclitaxel and cisplatin［J］.Eur J Radiol，2024，181：111763.
|
| 16 |
Liu Y，Wang Y，Wang X，et al.MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy：a multicenter study［J］.Cancer Imaging，2024，24（1）：16.
|
| 17 |
Fan L，Yang Z，Chang M，et al.CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients［J］.J Transl Med，2024，22（1）：579.
|
| 18 |
Lu S，Wang C，Liu Y，et al.The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma［J］.Eur Radiol，2024，34（1）：485-494.
|
| 19 |
Wang J，Zhu X，Zeng J，et al.Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation［J］.Eur Radiol，2023，33（12）：8554-8563.
|
| 20 |
Li Z，Wang F，Zhang H，et al.A radiomics strategy based on CT intra-tumoral and peritumoral regions for preoperative prediction of neoadjuvant chemoradiotherapy for esophageal cancer［J］.Eur J Surg Oncol，2024，50（4）：108052.
|
| 21 |
Noordman BJ，Verdam MGE，Lagarde SM，et al.Effect of Neoadjuvant Chemoradiotherapy on Health-Related Quality of Life in Esophageal or Junctional Cancer：Results From the Randomized CROSS Trial［J］.J Clin Oncol，2018，36（3）：268-275.
|
| 22 |
Buck A，Prade VM，Kunzke T，et al.Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients［J］.J Pathol，2022，256（2）：202-213.
|
| 23 |
Xie Y，Liu Q，Ji C，et al.An artificial neural network-based radiomics model for predicting the radiotherapy response of advanced esophageal squamous cell carcinoma patients：a multicenter study［J］.Sci Rep，2023，13（1）：8673.
|
| 24 |
Li X，Gao H，Zhu J，et al.3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma：A Prospective Study （ChiCTR2000039279）［J］.Int J Radiat Oncol Biol Phys，2021，111（4）：926-935.
|
| 25 |
Jin X，Zheng X，Chen D，et al.Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics［J］.Eur Radiol，2019，29（11）：6080-6088.
|
| 26 |
Tolkach Y，Wolgast LM，Damanakis A，et al.Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas：a retrospective algorithm development and validation study［J］.Lancet Digit Health，2023，5（5）：e265-e275.
|
| 27 |
Kotter E，Pinto Dos Santos D.Ethics and artificial intelligence［J］.Radiologie（Heidelb），2024，64（6）：498-502.
|
| 28 |
Morgan E，Soerjomataram I，Rumgay H，et al.The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040：New Estimates From GLOBOCAN 2020［J］.Gastroenterology，2022，163（3）：649-658.e2.
|
| 29 |
Cui Y，Li Z，Xiang M，et al.Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures［J］.Radiat Oncol，2022，17（1）：212.
|
| 30 |
Xiong J，Yu W，Ma J，et al.The Role of PET-Based Radiomic Features in Predicting Local Control of Esophageal Cancer Treated with Concurrent Chemoradiotherapy［J］.Sci Rep，2018，8（1）：9902.
|
| 31 |
Gong J，Lu J，Zhang W，et al.A CT-based subregional radiomics nomogram for predicting local recurrence-free survival in esophageal squamous cell cancer patients treated by definitive chemoradiotherapy：a multicenter study［J］.J Transl Med，2024，22（1）：1108.
|
| 32 |
Kawahara D，Nishioka R，Murakami Y，et al.A nomogram based on pretreatment radiomics and dosiomics features for predicting overall survival associated with esophageal squamous cell cancer［J］.Eur J Surg Oncol，2024，50（7）：108450.
|
| 33 |
Kouzu K，Tsujimoto H，Imamura Y，et al.Development and Validation Study of the Prognostic Impact of Deep Learning-Determined Myxoid Stroma After Neoadjuvant Chemotherapy in Patients with Esophageal Squamous Cell Carcinoma［J］.Ann Surg Oncol，2024，31（9）：6300-6308.
|
| 34 |
Draguet C，Barragán-Montero AM，Vera MC，et al.Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer［J］.Radiother Oncol，2022，176：101-107.
|
| 35 |
Chu Y，Zhu C，Hobbs BP，et al.Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer［J］.Int J Radiat Oncol Biol Phys，2024，120（4）：1172-1180.
|
| 36 |
Li C，Zhang J，Ning B，et al.Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy［J］.Radiat Oncol，2024，19（1）：72.
|
| 37 |
Skrede OJ，De Raedt S，Kleppe A，et al.Deep learning for prediction of colorectal cancer outcome：a discovery and validation study［J］.Lancet，2020，395（10221）：350-360.
|
| 38 |
Wang R，Dai W，Gong J，et al.Development of a novel combined nomogram model integrating deep learningpathomics，radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients［J］.J Hematol Oncol，2022，15（1）：11.
|
| 39 |
Sun JM，Shen L，Shah MA，et al.Pembrolizumab plus chemotherapy versus chemotherapy alone for first-line treatment of advanced oesophageal cancer （KEYNOTE-590）：a randomised，placebo-controlled，phase 3 study［J］.Lancet，2021，398（10302）：759-771.
|
| 40 |
Janjigian YY，Shitara K，Moehler M，et al.First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric，gastro-oesophageal junction，and oesophageal adenocarcinoma （CheckMate 649）：a randomised，open-label，phase 3 trial［J］.Lancet，2021，398（10294）：27-40.
|
| 41 |
Shen L，Kato K，Kim SB，et al.Tislelizumab Versus Chemotherapy as Second-Line Treatment for Advanced or Metastatic Esophageal Squamous Cell Carcinoma （RATIONALE-302）：A Randomized Phase III Study［J］.J Clin Oncol，2022，40（26）：3065-3076.
|
| 42 |
Li C，Zhao S，Zheng Y，et al.Preoperative pembrolizumab combined with chemoradiotherapy for oesophageal squamous cell carcinoma （PALACE-1）［J］.Eur J Cancer，2021，144：232-241.
|
| 43 |
Wang JL，Tang LS，Zhong X，et al.A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma［J］.Front Immunol，2024，15：1405146.
|
| 44 |
Zhu Y，Yao W，Xu BC，et al.Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers［J］.BMC Cancer，2021，21（1）：1167.
|
| 45 |
Shi L，Li C，Bai Y，et al.CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma［J］.Eur Radiol，2025，35（3）：1594-1604.
|
| 46 |
Qi WX，Li S，Xiao J，et al.A machine learning approach using （18）F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors［J］.Front Immunol，2024，15：1351750.
|
| 47 |
Lin F，Zhu LX，Ye ZM，et al.Computed Tomography-Based Intratumor Heterogeneity Predicts Response to Immunotherapy Plus Chemotherapy in Esophageal Squamous Cell Carcinoma［J］.Acad Radiol，2024，31（12）：4886-4899.
|
| 48 |
Li B，Qin W，Yang L，et al.From pixels to patient care：deep learning-enabled pathomics signature offers precise outcome predictions for immunotherapy in esophageal squamous cell cancer［J］.J Transl Med，2024，22（1）：195.
|
| 49 |
Yang Y，Yi Y，Wang Z，et al.A combined nomogram based on radiomics and hematology to predict the pathological complete response of neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma［J］.BMC Cancer，2024，24（1）：460.
|
| 50 |
Qi Y，Hu Y，Lin C，et al.A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients［J］.Front Immunol，2025，16：1530279.
|
| 51 |
Fick CN，Dunne EG，Sihag S，et al.Immunotherapy for Resectable Locally Advanced Esophageal Carcinoma［J］.Ann Thorac Surg，2024，118（1）：130-140.
|
| 52 |
Goetz L，Seedat N，Vandersluis R，et al.Generalization-a key challenge for responsible AI in patient-facing clinical applications［J］.NPJ Digit Med，2024，7（1）：126.
|