ArticleNPJ precision oncology2024
Development and interpretation of a multimodal predictive model for prognosis of gastrointestinal stromal tumor.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of Survival Prediction Tools in Patients With Gastrointestinal Stromal Tumors: A Systematic Review and Meta-Analysis.Journal of surgical oncology · 2026Pooled it
- Machine learning based on preoperative CT for noninvasive prediction of recurrence-free survival in gastrointestinal stromal tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- Comparison of nomograms and predictive tools in gastrointestinal stromal tumor.Journal of gastrointestinal oncology · 2026Article
- [Current status and challenges of artificial intelligence and organoid technologies in precision diagnosis and treatment of gastrointestinal stromal tumors].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Review
- Comparison of laparoscopic versus open approach surgery for gastric gastrointestinal stromal tumors in unfavorable surgical locations.BMC cancer · 2026Article
- Radiomics-based model for predicting neoadjuvant therapy response in esophageal cancer: Limitations and suggestions.World journal of gastrointestinal oncology · 2026Article
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- Computational pathology approach for assessment of prognosis and immunotherapy response in pan-gastrointestinal cancer.Journal of translational medicine · 2025Article
- A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification.Bioengineering (Basel, Switzerland) · 2025Article
- Prediction of lymph node metastasis in lung adenocarcinoma using a PET/CT radiomics-based ensemble learning model and its pathological basis.Frontiers in oncology · 2025Article
- Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer.Scientific reports · 2024Article
- Integrated multicenter deep learning system for prognostic prediction in bladder cancer.NPJ precision oncology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal stromal tumor (GIST) is the most common mesenchymal original tumor in gastrointestinal (GI) tract and is considered to have varying malignant potential. With the advancement of computer science, radiomics technology and deep learning had been applied in medical researches. It's vital to construct a more accurate and reliable multimodal predictive model for recurrence-free survival (RFS) aiding for clinical decision-making. A total of 254 patients underwent surgery and pathologically diagnosed with GIST in The First Hospital of China Medical University from 2019 to 2022 were included in the study. Preoperative contrast enhanced computerized tomography (CE-CT) and hematoxylin/eosin (H&E) stained whole slide images (WSI) were acquired for analysis. In the present study, we constructed a sum of 11 models while the multimodal model (average C-index of 0.917 on validation set in 10-fold cross validation) performed the best on external validation cohort with an average C-index of 0.864. The multimodal model also reached statistical significance when validated in the external validation cohort (n = 42) with a p-value of 0.0088 which pertained to the recurrence-free survival (RFS) comparison between the high and low groups using the optimal threshold on the predictive score. We also explored the biological significance of radiomics and pathomics features by visualization and quantitative analysis. In the present study, we constructed a multimodal model predicting RFS of GIST which was prior over unimodal models. We also proposed hypothesis on the correlation between morphology of tumor cell and prognosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.