ArticleLa Radiologia medica2022
Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.
Article in La Radiologia medica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Preoperative radiomics models for predicting risks of distinct recurrence patterns in pancreatic ductal adenocarcinoma based on contrast enhanced CT.Scientific reports · 2026Article
- Review
- Radiomics in differential diagnosis of pancreatic tumors.European journal of radiology open · 2025Article
- Radiomics analysis of dual-layer detector spectral CT-derived iodine maps for predicting Ki-67 PI in pancreatic ductal adenocarcinoma.BMC medical imaging · 2025Article
- Computer tomography-based radiomics combined with machine learning for predicting the time since onset of epidural hematoma.International journal of legal medicine · 2025Article
- All You Need to Know About TACE: A Comprehensive Review of Indications, Techniques, Efficacy, Limits, and Technical Advancement.Journal of clinical medicine · 2025Review
- Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics.Journal of clinical medicine · 2024Review
- An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies.Current oncology (Toronto, Ont.) · 2024Review
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer.Gland surgery · 2023Review
- Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment.La Radiologia medica · 2023Article
- Artificial Intelligence to Early Predict Liver Metastases in Patients with Colorectal Cancer: Current Status and Future Prospectives.Life (Basel, Switzerland) · 2023Review
- Radiomics in gastrointestinal stromal tumours: an up-to-date review.Japanese journal of radiology · 2023Review
- Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Peritoneal Carcinosis: What the Radiologist Needs to Know.Diagnostics (Basel, Switzerland) · 2023Review
- Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma.Diagnostics (Basel, Switzerland) · 2023Review
- Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics.Infectious agents and cancer · 2023Review
- Dose Reduction Strategies for Pregnant Women in Emergency Settings.Journal of clinical medicine · 2023Review
- Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications.Journal of clinical medicine · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a predictive model for liver metastases in patients with pancreatic ductal adenocarcinoma (PDAC) based on textural features of the primary tumor extracted by computed tomography (CT) images. MATERIALS AND
methodsPatients with a pathologically proved PDAC who underwent CT between December 2020 and January 2022 were retrospectively identified. Treatment-naïve patients were included. Sex, age, tumor size, vascular infiltration and 39 arterial and portal phase textural features were analyzed. The variables significantly correlated to tumor size according to the Pearson's product-moment correlation test were excluded from analysis; the remaining variables were compared between metastatic (M +) and non-metastatic (M-) patients using Fisher's or Mann-Whitney test. The features with a significant difference between groups were entered into a binomial logistic regression test to develop a predictive model for liver metastases.
resultsThis study included 220 patients. Eight variables (tumor size, arterial HU_MAX, arterial GLRLM_LRLGE, arterial GLZLM_SZHGE, arterial GLZLM_LZLGE, portal GLCM_CORRELATION, portal GLRLM_LRLGE, and portal GLZLM_SZHGE) were significantly different between groups. The logistic regression model was statistically significant (χ
conclusionsCT texture analysis of PDAC can identify features that may predict the likelihood of liver metastases.
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Registered trials
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