Unmasking pancreatic cancer: advanced biomedical imaging for its detection in native versus arterial dual-energy computed tomography (DECT) scans - Gotta, Jennifer; Grünewald, Leon David; Martin, Simon; Booz, Christian; Eichler, Katrin; Mahmoudi, Scherwin; Özdemir-Rezazadeh, Canan; Reschke, Philipp; Biciusca, Teodora; Juergens, Lisa-Joy; Mader, Christoph; Hammerstingl, Renate; et al. (International journal of imaging systems and technology, 2024)
This study investigates the potential of a machine learning classifier using dual- energy computed tomography (DECT) radiomics to differentiate between malignant pancreatic lesions and normal pancreas tissue. A total of 100 patients who underwent third-generation DECT between November 2018 and October 2022 were included, with 60 patients having pancreatic cancer and 40 normal pancreatic tissue. Radiomics features were extracted from non-contrast and arterial-enhanced DECT scans with stepwise feature reduction used to identify relevant features. Thetrained machine learning classifiers achieved a diagnostic accuracy of 0.97 in the arterial-enhanced model and 0.88 in non-contrast scans with sensitivities of 0.97 and 0.96, respectively. Areas under the curve were 0.97 (95% CI, 0.92-1.0, p < 0.001) and 0.96 (95% CI, 0.90-1.0, p < 0.001), respectively with no significant differences between both models (p= 0.52). This approach shows promise in enhancing pancreatic cancer detection and improving patient diagnoses, particulary in specific patient groups.