Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors Conference Paper


Authors: Chakraborty, J.; Pulvirenti, A.; Yamashita, R.; Midya, A.; Gönen, M.; Klimstra, D. S.; Reidy, D. L.; Allen, P. J.; Do, R. K. G.; Simpson, A. L.
Editors: Petrick, N.; Mori, K.
Title: Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors
Conference Title: Medical Imaging 2018: Computer-Aided Diagnosis
Abstract: Pancreatic neuroendocrine tumors (PanNETs) account for approximately 5% of all pancreatic tumors, affecting one individual per million each year.1 PanNETs are difficult to treat due to biological variability from benign to highly malignant, indolent to very aggressive. The World Health Organization classifies PanNETs into three categories based on cell proliferative rate, usually detected using the Ki67 index and cell morphology: Low-grade (G1), intermediate-grade (G2) and high-grade (G3) tumors. Knowledge of grade prior to treatment would select patients for optimal therapy: G1/G2 tumors respond well to somatostatin analogs and targeted or cytotoxic drugs whereas G3 tumors would be targeted with platinum or alkylating agents.2, 3 Grade assessment is based on the pathologic examination of the surgical specimen, biopsy or ne-needle aspiration; however, heterogeneity in the proliferative index can lead to sampling errors.4 Based on studies relating qualitatively assessed shape and enhancement characteristics on CT imaging to tumor grade in PanNET,5 we propose objective classification of PanNET grade with quantitative analysis of CT images. Fifty-five patients were included in our retrospective analysis. A pathologist graded the tumors. Texture and shape-based features were extracted from CT. Random forest and naive Bayes classifiers were compared for the classification of G1/G2 and G3 PanNETs. The best area under the receiver operating characteristic curve (AUC) of 0:74 and accuracy of 71:64% was achieved with texture features. The shape-based features achieved an AUC of 0:70 and accuracy of 78:73%. © 2018 SPIE.
Keywords: image analysis; pathology; image enhancement; computerized tomography; medical imaging; tumors; patient treatment; decision trees; texture features; pancreatic neuroendocrine tumors; quantitative image analysis; computer aided diagnosis; grade prediction; shape features; grade predictions
Journal Title Proceedings of SPIE
Volume: 10575
Conference Dates: 2018 Feb 12-15
Conference Location: Houston, TX
ISBN: 0277-786X
Publisher: SPIE  
Date Published: 2018-02-27
Start Page: 10575 1N
Language: English
DOI: 10.1117/12.2293577
PROVIDER: scopus
DOI/URL:
Notes: Conference Paper -- Export Date: 1 June 2018 -- Source: Scopus
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Citation Impact
MSK Authors
  1. Mithat Gonen
    912 Gonen
  2. Diane Lauren Reidy
    267 Reidy
  3. David S Klimstra
    967 Klimstra
  4. Peter Allen
    500 Allen
  5. Kinh Gian Do
    202 Do
  6. Amber L Simpson
    62 Simpson
  7. Abhishek Midya
    15 Midya