ArticleCancer biomarkers : section A of Disease markers2021
Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer with liver metastases or other metastatic patterns.
Article in Cancer biomarkers : section A of Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Establishment and validation of a diagnostic model for evaluating synchronous liver metastasis in pancreatic cancer using comprehensive blood biochemical indicators.Oncology letters · 2026Article
- Nomogram based on preoperative contrast-enhanced CT predicts early liver metastasis in pancreatic ductal adenocarcinoma.Quantitative imaging in medicine and surgery · 2025Article
- Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram.Bioengineering (Basel, Switzerland) · 2023Article
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
- Prognostic Predicting Model of Pancreatic Body Tail Carcinoma Using Clinical and CT Radiomic Data.Technology in cancer research & treatmentArticle
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with advanced pancreatic cancer (APC) and liver metastases have much poorer prognoses than patients with other metastatic patterns.
objectiveThis study aimed to develop and validate a radiomics model to discriminate patients with pancreatic cancer and liver metastases from those with other metastatic patterns.
methodsWe evaluated 77 patients who had APC and performed texture analysis on the region of interest. 58 patients and 19 patients were allocated randomly into the training and validation cohorts with almost the same proportion of liver metastases. An independentsamples t-test was used for feature selection in the training cohort. Random forest classifier was used to construct models based on these features and a radiomics signature (RS) was derived. A nomogram was constructed based on RS and CA19-9, and was validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods.
resultsThe constructed nomogram demonstrated good discrimination in the training (AUC = 0.93) and validation (AUC = 0.81) cohorts. In both cohorts, patients with RS > 0.61 had much poorer overall survival than patients with RS < 0.61.
conclusionsThis study presents a radiomics nomogram incorporating RS and CA19-9 to discriminate patients who have APC with liver metastases from patients with other metastatic patterns.
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