ArticleCell reports. Medicine2024
Interpretable multi-modal artificial intelligence model for predicting gastric cancer response to neoadjuvant chemotherapy.
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Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
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28citing papers in PubMed, 4 pooled it
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Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Pooled it
- Artificial intelligence models in predicting lymph node metastasis in early gastric cancer: a systematic review and meta -analysis.Wideochirurgia i inne techniki maloinwazyjne = Videosurgery and other miniinvasive techniques · 2026Pooled it
- Neoadjuvant Therapy for Locally Advanced Gastric/Gastroesophageal Junction Adenocarcinoma: Current Status, Challenges, and Future Perspectives.Cancer medicine · 2026Pooled it
- Proximal vs. total gastrectomy for proximal advanced gastric cancer after neoadjuvant chemotherapy: a systematic review and meta-analysis of propensity score-matched studies.Frontiers in oncology · 2026Pooled it
- Multimodal features and prognostic risk assessment in locally advanced gastric cancer patients following neoadjuvant therapy based on machine learning algorithms: a multicenter study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026Article
- Microbiome-guided cancer immunotherapy: immune mechanisms, resistance pathways, and translational opportunities for precision oncology.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- Global trends in artificial intelligence applications for gastric cancer prediction, treatment, and management: a topic modeling and bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- [Bottlenecks and breakthroughs in gastric cancer diagnosis and treatment: Towards a new era of precision and intelligent integration].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Review
- Discordance in tumour response assessment for gastric cancer after neoadjuvant chemotherapy using different methods.Abdominal radiology (New York) · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Temporal radiomics for non-invasive preoperative prediction of pathologic complete response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.Cancer biology & medicine · 2026Article
- Artificial intelligence-driven personalized dietary recommendations for gastric cancer high-risk populations: a narrative review.Frontiers in nutrition · 2026Review
- Artificial intelligence for biomarker prediction in gastric cancer: from histopathology to multimodal integration.Frontiers in oncology · 2026Review
- Innovative pathological and therapeutic approaches for poorly cohesive gastric cancer.Frontiers in oncology · 2026Review
- Multiview deep-learning-enabled histopathology for prognostic and therapeutic stratification in stage II colorectal cancer: A retrospective multicenter study.PLoS medicine · 2026Observational
- Distant metastasis risk and prognosis in elderly gastric cancer patients after neoadjuvant chemotherapy and surgery.Frontiers in oncology · 2026Article
- AI-engineered multifunctional nanoplatforms: synergistically bridging precision diagnosis and intelligent therapy in next-generation oncology.Journal of nanobiotechnology · 2025Review
- A multimodal synergistic model for personalized neoadjuvant immunochemotherapy in esophageal cancer.Cell reports. Medicine · 2025Article
- A multimodal AI model for precision prognosis in clear cell renal cell carcinoma: A multicenter study.NPJ digital medicine · 2025Article
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Authors and funding
36 authors.
Peng GaoDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Qiong XiaoDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Hui TanDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Jiangdian SongSchool of Health Management, China Medical University, Shenyang 110122, China.
Yu FuDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Jingao XuNeusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang 110169, China.
Junhua ZhaoDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Yuan MiaoDepartment of Pathology, The First Hospital of China Medical University, Shenyang 110001, China.
Xiaoyan LiDepartment of Pathology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, China.
Yi JingNeusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang 110169, China.
Yingying FengThe School of Computer Science and Engineering, Northeastern University, Shenyang 110167, China.
Zitong WangDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Yingjie ZhangDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Enbo YaoDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Tongjia XuDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Jipeng MeiDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Hanyu ChenDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China.
Xue JiangDepartment of Radiology, The First Hospital of China Medical University, Shenyang 110001, China.
Yuchong YangDepartment of GI Medical Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300202, China.
Zhengyang WangDepartment of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China.
Xianchun GaoState Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, National Clinical Research Center for Digestive Diseases, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Minwen ZhengDepartment of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Liying ZhangDepartment of Pathology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Min JiangDepartment of Oncology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
Yuying LongDepartment of Pathology, Shenyang Fifth People Hospital, Shenyang 110001, China.
Lijie HeDepartment of Oncology, People's Hospital of Liaoning Province, People's Hospital of China Medical University, Shenyang 110000, China.
Jinghua SunDepartment of Medical Oncology, The Second Hospital of Dalian Medical University, Dalian 116021, China.
Yanhong DengThe Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China.
Bin WangDepartment of Gastroenterology & Chongqing Key Laboratory of Digestive Malignancies, Daping Hospital, Army Medical University (Third Military Medical University), Chongqing 400042, China; School of Medicine, Chongqing University, Chongqing 400000, China; Institute of Pathology and Southwest Cancer Center, and Key Laboratory of Tumor Immunopathology of Ministry of Education of China, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing 400038, China.
Yan ZhaoDepartment of Stomach Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, The Liaoning Provincial Key Laboratory of Interdisciplinary Research on Gastrointestinal Tumor Combining Medicine with Engineering, Shenyang 110042, China.
Yi BaCancer Medical Center & Department of Medical Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing 100730, China.
Guan WangDepartment of Radiology, The First Hospital of China Medical University, Shenyang 110001, China. Electronic address: cmuwangguan@sina.com.
Yong ZhangDepartment of Pathology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology, Shenyang 110042, China. Electronic address: zhycmu@163.com.
Ting DengDepartment of GI Medical Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300202, China. Electronic address: xymcdengting@126.com.
Dinggang ShenSchool of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai 201807, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China. Electronic address: dinggang.shen@gmail.com.
Zhenning WangDepartment of Surgical Oncology and General Surgery, The First Hospital of China Medical University, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors (China Medical University), Ministry of Education, Shenyang 110001, China. Electronic address: znwang@cmu.edu.cn.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neoadjuvant chemotherapy assessment is imperative for prognostication and clinical management of locally advanced gastric cancer. We propose an incremental supervised contrastive learning model (iSCLM), an interpretable artificial intelligence framework integrating pretreatment CT scans and H&E-stained biopsy images, for improved decision-making regarding neoadjuvant chemotherapy. We have constructed and tested iSCLM using retrospective data from 2,387 patients across 10 medical centers and evaluated its discriminative ability in a prospective cohort (132 patients; ChiCTR2300068917). iSCLM achieves areas under receiver operating characteristic curves of 0.846-0.876 across different test cohorts. Computed tomography (CT) and pathological attention heatmaps from Shapley additive explanations and global sort pooling illustrate additional benefits for capturing morphological features through supervised contrastive learning. Specifically, pathological top-ranked tiles exhibit decreased distances to tumor-invasive borders and increased inflammatory cell infiltration in responders compared with non-responders. Moreover, CD11c expression is elevated in responders. The developed interpretable model at the molecular pathology level accurately predicts chemotherapy efficacy.
Indexed as
Artificial IntelligenceNeoadjuvant TherapyStomach NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesROC CurveTomography, X-Ray ComputedTreatment Outcomeartificial intelligencecomputed tomographygastric cancerneoadjuvant chemotherapywhole-slide image
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