ArticleFrontiers in oncology2020
Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Contrast-enhanced CT radiomics for prediction of recurrence-free survival in gallbladder carcinoma after surgical resection.European radiology · 2022Trial
- Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.Scientific reports · 2022Trial
- Multimodal fusion-based prediction of postoperative survival in gallbladder adenocarcinoma: Model development and validation.Scientific reports · 2026Article
- Psoas muscle CT radiomics-based machine learning models to predict response to infliximab in patients with Crohn's disease.Annals of medicine · 2025Article
- Deep learning-based segmentation of gallbladder cancer on abdominal computed tomography scans: a multicenter study.Abdominal radiology (New York) · 2025Article
- Update on the Role of Imaging in the Diagnosis, Staging, and Prognostication of Gallbladder Cancer.The Indian journal of radiology & imaging · 2025Review
- Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.BMC cancer · 2025Article
- Molecular biomarkers involved in the progression of gallbladder inflammatory lesions to invasive cancer: A proteomic approach.Biomolecules & biomedicine · 2024Article
- Applications of artificial intelligence in biliary tract cancers.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2024Review
- The Many Hidden Faces of Gallbladder Carcinoma on CT and MRI Imaging-From A to Z.Diagnostics (Basel, Switzerland) · 2024Article
- Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.Abdominal radiology (New York) · 2024Article
- Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics.European radiology · 2023Article
- Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study.BMC medical imaging · 2023Article
- CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy.Frontiers in oncology · 2023Article
- Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Radiomics of Contrast-Enhanced Computed Tomography: A Potential Biomarker for Pretreatment Prediction of the Response toFrontiers in cell and developmental biology · 2022Article
- Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy.Frontiers in oncology · 2022Article
- Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients.Journal of personalized medicine · 2021Article
- Effectivity of Palliative Care Bundle on Advanced Gallbladder Cancer: A Randomised Controlled Trial.Indian journal of palliative careArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.
methodsThis retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.
resultsFifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014-2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (
conclusionGBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest.
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