Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

• Published in: Journal of clinical medicine 2023-11, Vol.12 (23), p.7380
• Main Authors: Pacella, Giulia, Brunese, Maria Chiara, D'Imperio, Eleonora, Rotondo, Marco, Scacchi, Andrea, Carbone, Mattia, Guerra, Germano
• Format: Article
• Language: English
• Subjects: Adjuvant treatment; Analysis; Artificial intelligence; Cancer; Cancer therapies; Care and treatment; Clinical medicine; CT imaging; Mortality; Pancreatectomy; Pancreatic cancer; Patient outcomes; Prognosis; Veins & arteries
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm12237380

Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. The PubMed database was searched for papers published in the English language no earlier than January 2018. We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.