1. Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade
- Author
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Peter J. Allen, Abhishek Midya, Diane Lauren Reidy, Rikiya Yamashita, Alessandra Pulvirenti, Natally Horvat, Amber L. Simpson, Jayasree Chakraborty, Richard K. G. Do, Sharon A. Lawrence, David S. Klimstra, Maura A Koszalka, Mithat Gonen, Caitlin A. McIntyre, and Kenneth Seier
- Subjects
Noninvasive imaging ,medicine.medical_specialty ,Pancreatic neuroendocrine tumor ,medicine.diagnostic_test ,business.industry ,General Medicine ,Neuroendocrine tumors ,medicine.disease ,030218 nuclear medicine & medical imaging ,Pancreatic Neoplasms ,03 medical and health sciences ,Tumor grade ,0302 clinical medicine ,Text mining ,Predictive Value of Tests ,030220 oncology & carcinogenesis ,Original Reports ,Image Processing, Computer-Assisted ,medicine ,Humans ,Radiology ,Quantitative computed tomography ,Tomography, X-Ray Computed ,business ,Pathological - Abstract
PURPOSE The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade. METHODS Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade. RESULTS Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance. CONCLUSION CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.
- Published
- 2021