ArticleJournal of magnetic resonance imaging : JMRI2020
Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Pancreas image mining: a systematic review of radiomics.European radiology · 2021Pooled it
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- A multimodal MRI radiomics model for distinguishing borderline from malignant ovarian epithelial tumors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Predicting overall survival in pancreatic ductal adenocarcinoma usingEJNMMI research · 2026Article
- Intra- and peritumoral radiomics for predicting equivocal HER2 status of breast cancer on contrast-enhanced mammography.Frontiers in oncology · 2026Article
- Early postoperative recurrence prediction in pancreatic ductal adenocarcinoma: a nomogram based on dual-energy computed tomography andQuantitative imaging in medicine and surgery · 2026Article
- Integrated pretreatment stratification system for pancreatic cancer: combining anatomical resectability and tumor biological parameters.Cancer biology & medicine · 2025Article
- MRI Radiomics Signatures of 21-Gene Recurrence Score for Predicting Survival in ER+/HER2- Breast Cancer.Cancer medicine · 2025Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Development and validation of a CT-measured body composition radiomics model for prognostic assessment in resected pancreatic adenocarcinoma.Scientific reports · 2025Article
- Preoperative prediction for early recurrence in patients with pancreatic ductal adenocarcinoma: combining radiomics and abdominal fat analysis.BMC medical imaging · 2025Article
- A machine learning model based on preoperative multiparametric quantitative DWI can effectively predict the survival and recurrence risk of pancreatic ductal adenocarcinoma.Insights into imaging · 2025Article
- Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection.European radiology · 2024Article
- Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans.Scientific reports · 2024Article
- Preoperative CA19‑9 level and dual time point FDG‑PET/CT as strong biological indicators of borderline resectability in pancreatic cancer: A retrospective study.Oncology letters · 2024Article
- Uncovering the clinicopathological features of early recurrence after surgical resection of pancreatic cancer.Scientific reports · 2024Article
- Preoperative evaluating early recurrence in resectable pancreatic ductal adenocarcinoma by using CT radiomics.Abdominal radiology (New York) · 2024Article
- Nomogram for predicting post-progression-free survival in patients with recurrent pancreatic ductal adenocarcinoma after radical surgery: a retrospective analysis.Frontiers in medicine · 2024Article
- MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma.Abdominal radiology (New York) · 2024Article
- Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer.World journal of radiology · 2023Article
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Authors and funding
18 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required. PURPOSE: To develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice. STUDY TYPE: Retrospective. POPULATION: We enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126). FIELD STRENGTH/SEQUENCE: 3.0T axial T ASSESSMENT: ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated. STATISTICAL TESTS: The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit.
resultsThe AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort). DATA
conclusionThe radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.
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