Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.

Abstract

BACKGROUD:

Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited.

METHODS:

The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram.

RESULTS:

A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883).

CONCLUSIONS:

Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

• Authors: Xing Wang, Jia-Jun Qiu, Chun-Lu Tan, Yong-Hua Chen, Qing-Quan Tan, Shu-Jie Ren, Fan Yang, Wen-Qing Yao, Dan Cao, Neng-Wen Ke, Xu-Bao Liu
• Journal: Frontiers in oncology (Front Oncol) Vol. 12 Pg. 843376 ( 2022) ISSN: 2234-943X [Print] Switzerland
• PMID: 35433485 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Wang, Qiu, Tan, Chen, Tan, Ren, Yang, Yao, Cao, Ke and Liu.