Evaluation of radiomics and radiologic signatures for diagnosis and clinical outcomes in pancreatic adenocarcinoma...

BackgroundBiopsy is the only definitive method for diagnosing pancreatic ductal adenocarcinoma(PDAC) in the preoperative setting. With the increasing use of neoadjuvant therapy, alternative diagnostic modalities would aid in cases where fine-needle aspiration(FNA) is inconclusive or challenging. We hypothesized that radiomic signatures generated from computed tomography(CT) scans can help diagnose PDAC with high fidelity and radiologic/radiomics features have utility in predicting clinical outcomes.MethodsPatients with PDAC who underwent oncologic pancreatectomy from 2017-2019 were selected. Preoperative CT scans were reviewed by two body radiologists and two pancreatic surgeons. Tumors were segmented using two radiomics software platforms(Platform A&B).ResultsTwenty-five patients were included (n = 20 PDAC,n = 5 pancreatic neuroendocrine tumors(pNET)). Neoadjuvant chemotherapy was administered in 80% of PDAC patients, and 60% underwent pancreaticoduodenectomy. Platform A extracted 455 independent radiomics features and Platform B extracted 2078 radiomics features corresponding to shape, texture, filter, and intensity. One principal component from the shape feature group showed significant discrimination between PDAC and pNET, within both Platform A and B(area under the curve(AUC) = 0.81,p = 0.038 and AUC=0.89,p = 0.017, respectively). Additionally, a gray-level co-occurrence matrix sum entropy feature identified with Platform B differentiated malignant pancreatic tissue from normal parenchyma and PDAC from normal gland(p < 0.05).ConclusionRadiologic/radiomic features are reliable tools for PDAC diagnosis and associate with clinical outcomes. An improved understanding of PDAC radiomics may have implications for prognosis, margin status, and treatment response assessments.