Dr. Rikiya Yamashita Profile

Dr. Rikiya Yamashita

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Proceedings Article | 27 February 2018 Presentation + Paper

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

Jayasree Chakraborty, Alessandra Pulvirenti, Rikiya Yamashita, Abhishek Midya, Mithat Gönen, David Klimstra, Diane Reidy, Peter Allen, Richard K. Do, Amber Simpson

Proceedings Volume 10575, 105751N (2018) https://doi.org/10.1117/12.2293577

KEYWORDS: Tumors, Feature extraction, Computed tomography, Shape analysis, Pancreatic cancer, Image analysis, Cancer

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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%.

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