Research graph
References from Deep learning-based radiomics and pathomics for decoding tumor microenvironment and predicting immunotherapy outcomes in gastric cancer. Local targets link to admitted publications; unresolved targets remain external evidence.
Gastric cancer
10.1016/s0140-6736(25)00052-2 · 2025 · External reference
Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning
10.1016/j.radonc.2023.109793 · 2023 · External reference
Gastric cancer: a review
10.1001/jama.2025.20034 · 2026 · External reference
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
10.1016/s0140-6736(21)00797-2 · 2021 · External reference
Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for HER2-negative advanced gastric cancer (KEYNOTE-859): a multicentre, randomised, double-blind, phase 3 trial
10.1016/s1470-2045(23)00515-6 · 2023 · External reference
Zolbetuximab plus mFOLFOX6 in patients with CLDN18.2-positive, HER2-negative, untreated, locally advanced unresectable or metastatic gastric or gastro-oesophageal junction adenocarcinoma (SPOTLIGHT): a multicentre, randomised, double-blind, phase 3 trial
10.1016/s0140-6736(23)00620-7 · 2023 · External reference
PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives
10.1186/s12943-024-02059-y · 2024 · External reference
Immune checkpoint inhibitors for treatment of advanced gastric or gastroesophageal junction cancer: current evidence and future perspectives
10.21147/j.issn.1000-9604.2020.03.02 · 2020 · External reference
Innate immune defense mechanisms by myeloid cells that hamper cancer immunotherapy
10.3389/fimmu.2020.01395 · 2020 · External reference
Comprehensive molecular characterization of gastric adenocarcinoma
10.1038/nature13480 · 2014 · External reference
Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study
10.1016/s1470-2045(20)30445-9 · 2020 · External reference
Epstein-Barr virus-associated gastric cancer: a distinct subtype
10.1016/j.canlet.2020.09.019 · 2020 · External reference
IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network
10.1016/j.jare.2024.07.036 · 2025 · External reference
PD-L1(P146R) is prognostic and a negative predictor of response to immunotherapy in gastric cancer
10.1016/j.ymthe.2021.09.013 · 2022 · External reference
Inconsistencies in the predictive value of PD-L1 in metastatic gastroesophageal cancer
10.1016/s2468-1253(24)00043-8 · 2024 · External reference
Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning
10.1093/bfgp/elad032 · 2024 · External reference
Mutational analysis of microsatellite-stable gastrointestinal cancer with high tumour mutational burden: a retrospective cohort study
10.1016/s1470-2045(22)00783-5 · 2023 · External reference
Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice
10.1002/path.5310 · 2019 · External reference
Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response
10.1126/science.aau0447 · 2019 · External reference
The blockade of immune checkpoints in cancer immunotherapy
10.1038/nrc3239 · 2012 · External reference
High levels of tumor cell-intrinsic STING signaling are associated with increased infiltration of CD8(+) T cells in dMMR/MSI-H gastric cancer
10.1038/s41598-024-71974-3 · 2024 · External reference
Computational measurement of tumor immune microenvironment in gastric adenocarcinomas
10.1038/s41598-018-32299-0 · 2018 · External reference
Lymphocyte-rich gastric cancer: associations with Epstein-Barr virus, microsatellite instability, histology, and survival
10.1097/01.mp.0000076980.73826.c0 · 2003 · External reference
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy
10.1016/j.ccell.2021.04.014 · 2021 · External reference
Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy
10.1093/annonc/mdw168 · 2016 · External reference
Insights into the mechanisms of immune-checkpoint inhibitors gained from spatiotemporal dynamics of the tumor microenvironment
10.1002/advs.202508692 · 2025 · External reference
The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth
10.1016/j.ccell.2023.02.016 · 2023 · External reference
Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology
10.1038/s41571-019-0252-y · 2019 · External reference
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
10.1038/s41571-021-00560-7 · 2022 · External reference
Prognostic and predictive value of a pathomics signature in gastric cancer
10.1038/s41467-022-34703-w · 2022 · External reference
Deep learning in cancer diagnosis, prognosis and treatment selection
10.1186/s13073-021-00968-x · 2021 · External reference
Deep learning
10.1109/hotchips.2015.7477328 · 2015 · External reference
A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study
10.1016/s1470-2045(18)30413-3 · 2018 · External reference
CD8(+) T cells in the cancer-immunity cycle
10.1093/english/efy035 · 2023 · External reference
Roles of cancer-associated fibroblasts (CAFs) in anti- PD-1/PD-L1 immunotherapy for solid cancers
10.1186/s12943-023-01731-z · 2023 · External reference
Gadolinium(III)-based polymeric magnetic resonance imaging agents for tumor imaging
10.2174/0929867324666170314121946 · 2018 · External reference
Radiomics: images are more than pictures, they are data
10.1148/radiol.2015151169 · 2016 · External reference
Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study
10.1016/s2589-7500(21)00065-0 · 2021 · External reference
The biological meaning of radiomic features
10.1148/radiol.2021202553 · 2021 · External reference
Top 10 challenges in cancer immunotherapy
10.1016/j.immuni.2019.12.011 · 2020 · External reference
Understanding the tumor immune microenvironment (TIME) for effective therapy
10.1038/s41591-018-0014-x · 2018 · External reference
Patient-reported outcomes from the phase III IMpassion130 trial of atezolizumab plus nab-paclitaxel in metastatic triple-negative breast cancer
10.1016/j.annonc.2020.02.003 · 2020 · External reference
Tumor battlefield within inflamed, excluded or desert immune phenotypes: the mechanisms and strategies
10.1186/s40164-024-00543-1 · 2024 · External reference
Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer
10.1038/s41746-025-02268-9 · 2026 · External reference
CT-based radiomics for predicting the treatment response to PD-1/PD-L1 inhibitors combined with chemotherapy in unresectable gastric cancer
10.1186/s13244-026-02214-7 · 2026 · External reference
Multi-task deep learning for medical image computing and analysis: a review
10.1016/j.compbiomed.2022.106496 · 2023 · External reference
GAST-NET: a multi-modal and multi-task deep learning framework for preoperative prediction of perineural invasion and prognostic risk in gastric cancer
10.1016/j.ijmedinf.2026.106348 · 2026 · External reference
End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer
10.1016/j.ejrad.2025.112408 · 2025 · External reference
Type, density, and location of immune cells within human colorectal tumors predict clinical outcome
10.1126/science.1129139 · 2006 · External reference
Regulation of tumor metastasis by myeloid-derived suppressor cells
10.1146/annurev-med-051013-052304 · 2015 · External reference
Interactions between cancer stem cells and their niche govern metastatic colonization
10.1038/nature10694 · 2011 · External reference
International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study
10.1016/s0140-6736(18)30789-x · 2018 · External reference
From the immune contexture to the Immunoscore: the role of prognostic and predictive immune markers in cancer
10.1016/j.coi.2013.03.004 · 2013 · External reference
Biology-guided deep learning predicts prognosis and cancer immunotherapy response
10.1038/s41467-023-40890-x · 2023 · External reference
TME-guided deep learning predicts chemotherapy and immunotherapy response in gastric cancer with attention-enhanced residual Swin Transformer
10.1016/j.xcrm.2025.102242 · 2025 · External reference
Patterns of immune infiltration in breast cancer and their clinical implications: a gene-expression-based retrospective study
10.1371/journal.pmed.1002194 · 2016 · External reference
Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy
10.1016/j.ejro.2024.100626 · 2025 · External reference
Deep learning techniques for medical image segmentation: achievements and challenges
10.1007/s10278-019-00227-x · 2019 · External reference
Artificial intelligence in radiology
10.1038/s41568-018-0016-5 · 2018 · External reference
A survey on vision transformer
10.1109/tpami.2022.3152247 · 2023 · External reference
ScribFormer: Transformer makes CNN work better for scribble-based medical image segmentation
10.1109/tmi.2024.3363190 · 2024 · External reference
Vision-transformer-based transfer learning for mammogram classification
10.3390/diagnostics13020178 · 2023 · External reference
BUViTNet: Breast ultrasound detection via vision transformers
10.3390/diagnostics12112654 · 2022 · External reference
HCA-DAN: hierarchical class-aware domain adaptive network for gastric tumor segmentation in 3D CT images
10.1186/s40644-024-00711-w · 2024 · External reference
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
10.1016/j.media.2024.103280 · 2024 · External reference
Medical image segmentation of gastric adenocarcinoma based on dense connection of residuals
10.1002/acm2.14233 · 2024 · External reference
UnetTransCNN: integrating transformers with convolutional neural networks for enhanced medical image segmentation
10.3389/fonc.2025.1467672 · 2025 · External reference
High interobserver variability among pathologists using combined positive score to evaluate PD-L1 expression in gastric, gastroesophageal junction, and esophageal adenocarcinoma
10.1016/j.modpat.2023.100154 · 2023 · External reference
Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade
10.1126/science.aan6733 · 2017 · External reference
Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair-deficient cancer: results from the phase II KEYNOTE-158 study
10.1200/jco.19.02105 · 2020 · External reference
Determinants of response and intrinsic resistance to PD-1 blockade in microsatellite instability-high gastric cancer
10.1158/2159-8290.cd-21-0219 · 2021 · External reference
A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics
10.1186/s13244-023-01438-1 · 2023 · External reference
Characterizing diversity in the tumor-immune microenvironment of distinct subclasses of gastroesophageal adenocarcinomas
10.1016/j.annonc.2020.04.011 · 2020 · External reference
PD-L1 overexpression in EBV-positive gastric cancer is caused by unique genomic or epigenomic mechanisms
10.1038/s41598-021-81667-w · 2021 · External reference
Current status of immune checkpoint inhibitors for gastric cancer
10.1007/s10120-020-01090-4 · 2020 · External reference
Digital pathology and artificial intelligence
10.1016/s1470-2045(19)30154-8 · 2019 · External reference
The immune subtypes and landscape of gastric cancer and to predict based on the whole-slide images using deep learning
10.3389/fimmu.2021.685992 · 2021 · External reference
Deep learning-based stratification of gastric cancer patients from hematoxylin and eosin-stained whole slide images by predicting molecular features for immunotherapy response
10.1016/j.ajpath.2023.06.004 · 2023 · External reference
Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers
10.1111/imr.12405 · 2016 · External reference
Association between tertiary lymphoid structures and clinical outcomes in cancer patients treated with immune checkpoint inhibitors: an updated meta-analysis
10.3389/fimmu.2024.1385802 · 2024 · External reference
Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression
10.1038/s43018-021-00232-6 · 2021 · External reference
Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response
10.1038/s41698-024-00579-w · 2024 · External reference
Predicting mutational status of driver and suppressor genes directly from histopathology with deep learning: a systematic study across 23 solid tumor types
10.3389/fgene.2021.806386 · 2021 · External reference
Immunotherapy for esophageal and gastric cancer
10.1200/edbk_175231 · 2017 · External reference
Thirty years of Epstein-Barr virus-associated gastric carcinoma
10.1007/s00428-019-02724-4 · 2020 · External reference
Frequent microsatellite instability in papillary and solid-type, poorly differentiated adenocarcinomas of the stomach
10.1007/s10120-012-0226-6 · 2013 · External reference
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
10.1038/s41591-019-0462-y · 2019 · External reference
Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning
10.1038/s41598-021-02168-4 · 2021 · External reference
Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study
10.1016/s2589-7500(21)00133-3 · 2021 · External reference
A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology
10.1038/s41467-022-30459-5 · 2022 · External reference
Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer
10.1136/jitc-2024-008927 · 2024 · External reference
Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: a systematic review and meta-analysis
10.1016/j.ijmedinf.2024.105685 · 2025 · External reference
Measuring the performance of markers for guiding treatment decisions
10.7326/0003-4819-154-4-201102150-00006 · 2011 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond
10.1002/sim.2929 · 2008 · External reference
Decision curve analysis: a novel method for evaluating prediction models
10.1177/0272989x06295361 · 2006 · External reference
A guide to systematic review and meta-analysis of prediction model performance
10.1136/bmj.i6460 · 2017 · External reference
Meta-analysis of prediction model performance across multiple studies: which scale helps ensure between-study normality for the C-statistic and calibration measures
10.1177/0962280217705678 · 2018 · External reference
CT-based radiomics nomograms for preoperative prediction of diffuse-type and signet ring cell gastric cancer: a multicenter development and validation cohort
10.1186/s12967-022-03232-x · 2022 · External reference
Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer
10.1016/j.annonc.2020.03.295 · 2020 · External reference
Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer
10.1172/jci175834 · 2024 · External reference
Multimodal radiopathomics approach for predictions of prognosis and immunotherapy response in patients with gastric cancer: a multicohort retrospective study
10.1097/js9.0000000000002939 · 2025 · External reference
Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning
10.1016/j.canlet.2025.217930 · 2025 · External reference
Tackling the small data problem in medical image classification with artificial intelligence: a systematic review
10.1088/2516-1091/ad525b · 2024 · External reference
A guide to cross-validation for artificial intelligence in medical imaging
10.1148/ryai.220232 · 2023 · External reference
Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data
10.1038/s41392-024-01932-y · 2024 · External reference
Revisiting the trustworthiness of saliency methods in radiology AI
10.1148/ryai.220221 · 2024 · External reference
Redefining radiology: a review of artificial intelligence integration in medical imaging
10.20944/preprints202306.1124.v1 · 2023 · External reference
SurvivalCNN: a deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables
10.1016/j.artmed.2022.102424 · 2022 · External reference
Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data
10.1016/j.clnu.2024.02.005 · 2024 · External reference
Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics
10.1016/j.xcrm.2023.101146 · 2023 · External reference
Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study
10.1136/jitc-2023-007807 · 2023 · External reference
Emerging immunotherapy targets in early drug development
10.3390/ijms26115394 · 2025 · External reference
Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings
10.1148/radiol.2019190928 · 2019 · External reference
Making radiomics more reproducible across scanner and imaging protocol variations: a review of harmonization methods
10.3390/jpm11090842 · 2021 · External reference
The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping
10.1148/radiol.2020191145 · 2020 · External reference
Impact of CT acquisition settings on the stability of radiomic features and the performance of pulmonary nodule classification models
10.1186/s13244-025-02179-z · 2026 · External reference
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
10.1016/j.media.2025.103638 · 2025 · External reference
Real-world implementation of digital pathology: results from an intercontinental survey
10.1016/j.labinv.2023.100261 · 2023 · External reference
Integrated digital pathology at scale: a solution for clinical diagnostics and cancer research at a large academic medical center
10.1093/jamia/ocab085 · 2021 · External reference
FDA perspective on the regulation of artificial intelligence in health care and biomedicine
10.1001/jama.2024.21451 · 2025 · External reference
Ethical responsibility in medical AI: a semi-systematic thematic review and multilevel governance model
10.3390/healthcare14030287 · 2026 · External reference
Unintended consequences of machine learning in medicine
10.1001/jama.2017.7797 · 2017 · External reference
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
10.1038/s41591-021-01595-0 · 2021 · External reference
Addressing fairness issues in deep learning-based medical image analysis: a systematic review
10.1038/s41746-024-01276-5 · 2024 · External reference
Tislelizumab plus chemotherapy versus placebo plus chemotherapy as first line treatment for advanced gastric or gastro-oesophageal junction adenocarcinoma: RATIONALE-305 randomised, double blind, phase 3 trial
10.1136/bmj-2023-078876 · 2024 · External reference
Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study
10.1007/s10120-023-01398-x · 2023 · External reference
Understanding sources of variation to improve the reproducibility of radiomics
10.3389/fonc.2021.633176 · 2021 · External reference
Built to last? Reproducibility and reusability of deep learning algorithms in computational pathology
10.1016/j.modpat.2023.100350 · 2024 · External reference
Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review
10.1186/s12874-022-01801-8 · 2022 · External reference
Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update
10.1148/ryai.240300 · 2024 · External reference
CT-based delta radiomics in predicting the prognosis of stage IV gastric cancer to immune checkpoint inhibitors
10.3389/fonc.2022.1059874 · 2022 · External reference
Interpretable combined models for predicting treatment response and hematologic toxicity in locally advanced gastric cancer treated with PD-1 blockade and neoadjuvant chemotherapy
10.1016/j.ejrad.2025.112256 · 2025 · External reference
Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction
10.1136/jitc-2024-009140 · 2025 · External reference
Delta-radiomics in cancer immunotherapy response prediction: a systematic review
10.1016/j.ejro.2023.100511 · 2023 · External reference
Exosomal PD-L1 retains immunosuppressive activity and is associated with gastric cancer prognosis
10.1245/s10434-019-07431-7 · 2019 · External reference
Prognostic value of soluble PD-L1 and exosomal PD-L1 in advanced gastric cancer patients receiving systemic chemotherapy
10.1038/s41598-023-33128-9 · 2023 · External reference
Helicobacter pylori CagA promotes immune evasion of gastric cancer by upregulating PD-L1 level in exosomes
10.1016/j.isci.2023.108414 · 2023 · External reference
Spatial omics: navigating to the golden era of cancer research
10.1002/ctm2.696 · 2022 · External reference
Multiplex tissue imaging: spatial revelations in the tumor microenvironment
10.3390/cancers14133170 · 2022 · External reference
Beyond predictive accuracy: statistical validation of feature importance in biomedical machine learning
10.1016/j.cmpb.2025.109085 · 2025 · External reference
MRI-derived lymph nodes morphological and topological structure (LNs-MTS) model for evaluating immune status and prognosis in rectal cancer
10.1002/advs.202506523 · 2025 · External reference
Cancer mRNA vaccines: clinical application progress and challenges
10.1016/j.canlet.2025.217752 · 2025 · External reference
The research progress of gastric cancer vaccines: a narrative review
10.21037/tcr-2025-aw-2299 · 2026 · External reference
mRNA vaccines: the dawn of a new era of cancer immunotherapy
10.3389/fimmu.2022.887125 · 2022 · External reference
Personalized neoantigen cancer vaccines: current progression, challenges and a bright future
10.1007/s10238-024-01436-7 · 2024 · External reference
Neoantigen mRNA vaccines induce progenitor-exhausted T cells that support anti-PD-1 therapy in gastric cancer with peritoneal metastasis
10.1007/s10120-025-01640-8 · 2025 · External reference
Unintended consequences of machine learning in medicine
10.1001/jama.2017.7797 · ExternalCitation · doi-reference
FDA perspective on the regulation of artificial intelligence in health care and biomedicine
10.1001/jama.2024.21451 · ExternalCitation · doi-reference
Gastric cancer: a review
10.1001/jama.2025.20034 · ExternalCitation · doi-reference
Medical image segmentation of gastric adenocarcinoma based on dense connection of residuals
10.1002/acm2.14233 · ExternalCitation · doi-reference
MRI-derived lymph nodes morphological and topological structure (LNs-MTS) model for evaluating immune status and prognosis in rectal cancer
10.1002/advs.202506523 · ExternalCitation · doi-reference
Insights into the mechanisms of immune-checkpoint inhibitors gained from spatiotemporal dynamics of the tumor microenvironment
10.1002/advs.202508692 · ExternalCitation · doi-reference
Spatial omics: navigating to the golden era of cancer research
10.1002/ctm2.696 · ExternalCitation · doi-reference
Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice
10.1002/path.5310 · ExternalCitation · doi-reference
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond
10.1002/sim.2929 · ExternalCitation · doi-reference
Thirty years of Epstein-Barr virus-associated gastric carcinoma
10.1007/s00428-019-02724-4 · ExternalCitation · doi-reference
Frequent microsatellite instability in papillary and solid-type, poorly differentiated adenocarcinomas of the stomach
10.1007/s10120-012-0226-6 · ExternalCitation · doi-reference
Current status of immune checkpoint inhibitors for gastric cancer
10.1007/s10120-020-01090-4 · ExternalCitation · doi-reference
Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study
10.1007/s10120-023-01398-x · ExternalCitation · doi-reference
Neoantigen mRNA vaccines induce progenitor-exhausted T cells that support anti-PD-1 therapy in gastric cancer with peritoneal metastasis
10.1007/s10120-025-01640-8 · ExternalCitation · doi-reference
Personalized neoantigen cancer vaccines: current progression, challenges and a bright future
10.1007/s10238-024-01436-7 · ExternalCitation · doi-reference
Deep learning techniques for medical image segmentation: achievements and challenges
10.1007/s10278-019-00227-x · ExternalCitation · doi-reference
Deep learning-based stratification of gastric cancer patients from hematoxylin and eosin-stained whole slide images by predicting molecular features for immunotherapy response
10.1016/j.ajpath.2023.06.004 · ExternalCitation · doi-reference
Patient-reported outcomes from the phase III IMpassion130 trial of atezolizumab plus nab-paclitaxel in metastatic triple-negative breast cancer
10.1016/j.annonc.2020.02.003 · ExternalCitation · doi-reference
Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer
10.1016/j.annonc.2020.03.295 · ExternalCitation · doi-reference
Characterizing diversity in the tumor-immune microenvironment of distinct subclasses of gastroesophageal adenocarcinomas
10.1016/j.annonc.2020.04.011 · ExternalCitation · doi-reference
SurvivalCNN: a deep learning-based method for gastric cancer survival prediction using radiological imaging data and clinicopathological variables
10.1016/j.artmed.2022.102424 · ExternalCitation · doi-reference
Epstein-Barr virus-associated gastric cancer: a distinct subtype
10.1016/j.canlet.2020.09.019 · ExternalCitation · doi-reference
Cancer mRNA vaccines: clinical application progress and challenges
10.1016/j.canlet.2025.217752 · ExternalCitation · doi-reference
Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning
10.1016/j.canlet.2025.217930 · ExternalCitation · doi-reference
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy
10.1016/j.ccell.2021.04.014 · ExternalCitation · doi-reference
The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth
10.1016/j.ccell.2023.02.016 · ExternalCitation · doi-reference
Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data
10.1016/j.clnu.2024.02.005 · ExternalCitation · doi-reference
Beyond predictive accuracy: statistical validation of feature importance in biomedical machine learning
10.1016/j.cmpb.2025.109085 · ExternalCitation · doi-reference
From the immune contexture to the Immunoscore: the role of prognostic and predictive immune markers in cancer
10.1016/j.coi.2013.03.004 · ExternalCitation · doi-reference
Multi-task deep learning for medical image computing and analysis: a review
10.1016/j.compbiomed.2022.106496 · ExternalCitation · doi-reference
Interpretable combined models for predicting treatment response and hematologic toxicity in locally advanced gastric cancer treated with PD-1 blockade and neoadjuvant chemotherapy
10.1016/j.ejrad.2025.112256 · ExternalCitation · doi-reference
End-to-end deep learning model with multi-channel and attention mechanisms for multi-class diagnosis in CT-T staging of advanced gastric cancer
10.1016/j.ejrad.2025.112408 · ExternalCitation · doi-reference
Delta-radiomics in cancer immunotherapy response prediction: a systematic review
10.1016/j.ejro.2023.100511 · ExternalCitation · doi-reference
Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy
10.1016/j.ejro.2024.100626 · ExternalCitation · doi-reference
Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: a systematic review and meta-analysis
10.1016/j.ijmedinf.2024.105685 · ExternalCitation · doi-reference
GAST-NET: a multi-modal and multi-task deep learning framework for preoperative prediction of perineural invasion and prognostic risk in gastric cancer
10.1016/j.ijmedinf.2026.106348 · ExternalCitation · doi-reference
Top 10 challenges in cancer immunotherapy
10.1016/j.immuni.2019.12.011 · ExternalCitation · doi-reference
Helicobacter pylori CagA promotes immune evasion of gastric cancer by upregulating PD-L1 level in exosomes
10.1016/j.isci.2023.108414 · ExternalCitation · doi-reference
IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network
10.1016/j.jare.2024.07.036 · ExternalCitation · doi-reference
Real-world implementation of digital pathology: results from an intercontinental survey
10.1016/j.labinv.2023.100261 · ExternalCitation · doi-reference
TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers
10.1016/j.media.2024.103280 · ExternalCitation · doi-reference
SegQC: a segmentation network-based framework for multi-metric segmentation quality control and segmentation error detection in volumetric medical images
10.1016/j.media.2025.103638 · ExternalCitation · doi-reference
High interobserver variability among pathologists using combined positive score to evaluate PD-L1 expression in gastric, gastroesophageal junction, and esophageal adenocarcinoma
10.1016/j.modpat.2023.100154 · ExternalCitation · doi-reference
Built to last? Reproducibility and reusability of deep learning algorithms in computational pathology
10.1016/j.modpat.2023.100350 · ExternalCitation · doi-reference
Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning
10.1016/j.radonc.2023.109793 · ExternalCitation · doi-reference
Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics
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