Trial reportLa Radiologia medica2022
Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features.
Trial report in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 30 citations in OpenAlex.
- Prediction of the mitotic index and preoperative risk stratification of gastrointestinal stromal tumors with CT radiomic features.La Radiologia medica · 2023Trial
- 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
- Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.Journal of imaging informatics in medicine · 2026Article
- Radiomics models with baseline MRI and clinical data to predict target therapy response and high-risk mortality in metastatic GIST.Scientific reports · 2025Article
- Review
- Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors.World journal of gastrointestinal oncology · 2024Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases.European radiology · 2024Article
- Quantitative ultrasound (QUS) in the evaluation of liver steatosis: data reliability in different respiratory phases and body positions.La Radiologia medica · 2024Article
- Development and validation of a computed tomography-based radiomics signature to predict "highest-risk" from patients with high-risk gastrointestinal stromal tumor.Journal of gastrointestinal oncology · 2024Article
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- An overview on multimodal imaging for the diagnostic workup of pleural mesothelioma.Japanese journal of radiology · 2024Review
- Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery.Clinical Medicine Insights. Oncology · 2024Article
- Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer.Gland surgery · 2023Review
- Radiomics in gastrointestinal stromal tumours: an up-to-date review.Japanese journal of radiology · 2023Review
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
objectiveDevelopment and validation of a radiomics nomogram for predicting recurrence and adjuvant therapy benefit populations in high/intermediate-risk gastrointestinal stromal tumors (GISTs) based on computed tomography (CT) radiomic features.
methodsRetrospectively collected from 2009.07 to 2015.09, 220 patients with pathological diagnosis of intermediate- and high-risk stratified gastrointestinal stromal tumors and received imatinib treatment were randomly divided into (6:4) training cohort and validation cohort. The 2D-tumor region of interest (ROI) was delineated from the portal-phase images on contrast-enhanced (CE) CT, and radiological features were extracted. The most valuable radiological features were obtained using a Lasso-Cox regression model. Integrated construction was conducted of nomograms of radiomics characteristics to predict recurrence-free survival (RFS) in patients receiving adjuvant therapy.
resultsEight radiomic signatures were finally selected. The area under the curve (AUC) of the radiomics signature model for predicting 3-, 5-, and 7-year RFS in the training and validation cohorts (training cohort AUC = 0.80, 0.84, 0.76; validation cohort AUC = 0.78, 0.80, 0.76). The constructed radiomics nomogram was more accurate than the clinicopathological nomogram for predicting RFS in GIST (C-index: 0.864 95%CI, 0.817-0.911 vs. 0.733 95%CI, 0.675-0.791). Kaplan-Meier survival curve analysis showed a greater benefit from adjuvant therapy in patients with high radiomics scores (training cohort: p < 0.0001; validation cohort: p = 0.017), while there was no significant difference in the low-score group (p > 0.05).
conclusionIn this study, a nomogram constructed based on preoperative CT radiomics features could be used for RFS prediction in high/intermediate-risk GISTs and assist the clinical decision-making for GIST patients.
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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.