Quantitative computed tomography image analysis to predict pancreatic neuroendocrine tumor grade Journal Article


Authors: Pulvirenti, A.; Yamashita, R.; Chakraborty, J.; Horvat, N.; Seier, K.; McIntyre, C. A.; Lawrence, S. A.; Midya, A.; Koszalka, M. A.; Gonen, M.; Klimstra, D. S.; Reidy, D. L.; Allen, P. J.; Do, R. K. G.; Simpson, A. L.
Article Title: Quantitative computed tomography image analysis to predict pancreatic neuroendocrine tumor grade
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.
Journal Title: JCO Clinical Cancer Informatics
Volume: 5
ISSN: 2473-4276
Publisher: American Society of Clinical Oncology  
Date Published: 2021-01-01
Start Page: 679
End Page: 694
Language: English
DOI: 10.1200/cci.20.00121
PUBMED: 34138636
PROVIDER: scopus
PMCID: PMC8462651
DOI/URL:
Notes: Article -- Export Date: 2 August 2021 -- Source: Scopus
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MSK Authors
  1. Mithat Gonen
    885 Gonen
  2. Diane Lauren Reidy
    260 Reidy
  3. David S Klimstra
    965 Klimstra
  4. Kinh Gian Do
    197 Do
  5. Kenneth Seier
    63 Seier
  6. Natally Horvat
    43 Horvat
  7. Abhishek Midya
    15 Midya