Pancreatic neuroendocrine tumor: prediction of tumor grades by radiomics models based on ultrasound images

Author:

Dong Yi12,Yang Dao-Hui3,Tian Xiao-Fan12,Lou Wen-Hui4,Wang Han-Zhang5,Chen Sheng12,Qiu Yi-Jie12,Wang Wenping2,Dietrich Christoph F.6

Affiliation:

1. Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China

2. Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China

3. Department of ultrasound, Xiamen Branch, Zhongshan Hospital, Fudan University, Xiamen, China

4. Department of Pancreatic Surgery, Zhongshan Hospital, Fudan University, Shanghai, China

5. Precision Health Institute, GE Healthcare China, Shanghai, China

6. Department General Internal Medicine, Hirslanden Clinics Beau-Site, Salem and Permancence, Bern, Switzerland

Abstract

Objective We aimed to investigate whether the radiomics analysis based on B-mode ultrasound (BMUS) images could predict histopathological tumor grades in pancreatic neuroendocrine tumors (pNETs). Methods A total of 64 patients with surgery and histopathologically confirmed pNETs were retrospectively included (34 male and 30 female, mean age 52.4 ± 12.2 years). Patients were divided into training cohort (n = 44) and validation cohort (n = 20). All pNETs were classified into Grade 1 (G1), Grade 2 (G2), and Grade 3 (G3) tumors based on the Ki-67 proliferation index and the mitotic activity according to WHO 2017 criteria. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator were used for feature selection. Receiver operating characteristic curve analysis was used to evaluate the model performance. Results Finally, 18 G1 pNETs, 35 G2 pNETs, and 11 G3 pNETs patients were included. The radiomic score derived from BMUS images to predict G2/G3 from G1 displayed a good performance with an area under the receiver operating characteristic curve of 0.844 in the training cohort, and 0.833 in the testing cohort. The radiomic score achieved an accuracy of 81.8% in the training cohort and 80.0% in the testing cohort, a sensitivity of 0.750 and 0.786, a specificity of 0.833 and 0.833 in the training/testing cohorts. Clinical benefit of the score also exhibited superior usefulness of the radiomic score, as shown by the decision curve analysis. Conclusions Radiomic data constructed from BMUS images have the potential for predicting histopathological tumor grades in patients with pNETs. Advances in knowledge The radiomic model constructed from BMUS images has the potential for predicting histopathological tumor grades and Ki-67 proliferation indexes in patients with pNETs.

Publisher

Oxford University Press (OUP)

Subject

Radiology, Nuclear Medicine and imaging,General Medicine

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