ArticleCancer immunology, immunotherapy : CII2024
The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy.
Article in Cancer immunology, immunotherapy : CII, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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7 citing papers in PubMed, 11 citations in OpenAlex.
- Article
- An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.BMC cancer · 2025Article
- CREB3L1 facilitates pancreatic tumor progression and reprograms intratumoral tumor-associated macrophages to shape an immunotherapy-resistance microenvironment.Journal for immunotherapy of cancer · 2025Article
- Machine Learning-Based Radiomics for Differentiating Pancreatic Lesions: A Potential Tool to Enhance Clinical Decision-Making and Nursing Management.Journal of nursing management · 2025Article
- Predictive Radiomics-Based Model for Recurrence-Free Survival After Curative Resection in Patients with Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2025Article
- A nomogram-based radiomics for predicting survival to concurrent chemoradiotherapy in inoperable pancreatic cancer: a dual-center cohort study.Frontiers in immunology · 2025Article
- Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.International journal of molecular sciences · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
Abstract
objectiveTo construct a prognostic model based on MR features and clinical data to evaluate the progression free survival (PFS), overall survival (OS) and objective response rate (ORR) of pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy.
methods105 pancreatic cancer patients with hepatic metastases who received chemoimmunotherapy were assigned to the training set (n = 52), validation set (n = 22), and testing set (n = 31). Multi-lesion volume of interest were delineated, multi-sequence radiomics features were extracted, and the radiomics models for predicting PFS, OS and ORR were constructed, respectively. Clinical variables were extracted, and the clinical models for predicting PFS, OS and ORR were constructed, respectively. The nomogram was jointly constructed by radiomics model and clinical model.
resultThe ORR exhibits no significant correlation with either PFS or OS. The area under the curve (AUC) of nomogram for predicting 6-month PFS reached 0.847 (0.737-0.957), 0.786 (0.566-1.000) and 0.864 (0.735-0.994) in the training set, validation set and testing set, respectively. The AUC of nomogram for predicting 1-year OS reached 0.770 (0.635-0.906), 0.743 (0.479-1.000) and 0.818 (0.630-1.000), respectively. The AUC of nomogram for predicting ORR reached 0.914 (0.828-1.00), 0.938 (0.840-1.00) and 0.846 (0.689-1.00), respectively.
conclusionThe prognostic models based on MR imaging features and clinical data are effective in predicting the PFS, OS and ORR of chemoimmunotherapy in pancreatic cancer patients with hepatic metastasis, and can be used to evaluate the prognosis of patients.
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