ArticleJournal of digital imaging2023
Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 6 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- An umbrella review of systematic evidence on the Low Dose Computed Tomography (LDCT) for lung cancer screening.Future oncology (London, England) · 2025Pooled it
- Quality and accuracy of radiomics models in predicting KRAS status in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Predictive value ofFrontiers in oncology · 2024Pooled it
- Prognostic value of consolidation-to-tumor ratio on computed tomography in NSCLC: a meta-analysis.World journal of surgical oncology · 2023Pooled it
- Predicting lung cancer survival with attention-based CT slices combination.Health information science and systems · 2026Article
- Limited incremental value and cross-cohort transferability of CT radiomics for survival prediction after TACE in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Development of a Risk Prediction Model for Bone Metastasis in Lung Adenocarcinoma With a T1 Primary Tumor Based on Intratumoral and Peritumoral Radiomics.Cancer medicine · 2026Article
- Development and multicenter validation of a predictive model for malignant pleural effusion recurrence.iScience · 2026Article
- Differentiation of pulmonary tuberculosis from non-tuberculous solid lung lesions using radiomics and clinical-semantic features on contrast-enhanced CT.Frontiers in medicine · 2026Article
- Radiomics-based explainable artificial intelligence to predict treatment response following lung stereotactic body radiation therapy.The British journal of radiology · 2025Article
- Predictive value of temporal muscle thickness for prognosis in newly diagnosed IDH wild-type glioblastoma patients: evaluated for a Chinese population.European radiology · 2025Article
- Prognostic Value of Pre-Treatment Diffusion Kurtosis Imaging for Progression-Free Survival Prediction in Advanced Nasopharyngeal Carcinoma.Cancer medicine · 2025Article
- CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score.Discover oncology · 2025Article
- The correlation of tertiary lymphoid structures with tumor spread through air spaces and prognosis in lung adenocarcinoma: focusing on pathological spatial features.World journal of surgical oncology · 2025Article
- Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy.Frontiers in oncology · 2025Article
- Predicting 90-day postoperative outcomes in spontaneous supratentorial intracerebral hemorrhage: a double-center study of clinical-radiomics integration using non-contrast CT.Frontiers in neurology · 2025Article
- Development and Validation of a Nomogram Incorporating Nutritional and Lipid Metabolism Indices to Predict Survival in Non-Small Cell Lung Cancer Patients with Malignant Pleural Effusion.Cancer management and research · 2025Article
- The Clinical Value of Systemic Immune Inflammatory Index in Predicting the Prognosis of Patients with Bloodstream Infection.Journal of inflammation research · 2025Article
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6 authors.
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Abstract
The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan-Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan-Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.
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