ArticlePloS one2017
Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma.AJR. American journal of roentgenology · 2020Trial
- Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation.Abdominal radiology (New York) · 2019Trial
- Automated Radiomics Model for Preoperative Pancreatic Neuroendocrine Tumor Grade Prediction.Annals of surgical oncology · 2026Article
- Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma.Bioengineering (Basel, Switzerland) · 2025Review
- Preoperative Prognostic Factors in Resectable Pancreatic Cancer: State of the Art and Prospects.Annals of surgical oncology · 2025Review
- Enhancing diagnostic frameworks in pancreatic cancer imaging: A critical appraisal.World journal of radiology · 2025Article
- Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study.Frontiers in oncology · 2025Article
- Computed Tomography-Based Radiomics with Machine Learning Outperforms Radiologist Assessment in Estimating Colorectal Liver Metastases Pathologic Response After Chemotherapy.Annals of surgical oncology · 2024Article
- Understanding the Conundrum of Pancreatic Cancer in the Omics Sciences Era.International journal of molecular sciences · 2024Review
- radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer.Scientific reports · 2024Article
- Preoperative prediction of early recurrence in resectable pancreatic cancer integrating clinical, radiologic, and CT radiomics features.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- An insight to PDAC tumor heterogeneity across pancreatic subregions using computed tomography images.Frontiers in oncology · 2024Article
- Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application.Abdominal radiology (New York) · 2024Review
- Article
- Pancreatic Cancer Biomarkers: Oncogenic Mutations, Tissue and Liquid Biopsies, and Radiomics-A Review.Digestive diseases and sciences · 2023Review
- Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach.IEEE journal of biomedical and health informatics · 2023Article
- Differentiation between Pancreatic Ductal Adenocarcinoma and Normal Pancreatic Tissue for Treatment Response Assessment using Multi-Scale Texture Analysis of CT Images.Journal of biomedical physics & engineering · 2022Article
- Comment on "Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer".World journal of gastroenterology · 2022Article
- Multimodal survival prediction in advanced pancreatic cancer using machine learning.ESMO open · 2022Article
- Update on quantitative radiomics of pancreatic tumors.Abdominal radiology (New York) · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.
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