CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Yinghao Meng; Hao Zhang; Qi Li; Fang Liu; Xu Fang; Jing Li; Jieyu Yu; Xiaochen Feng; Mengmeng Zhu; Na Li; Guodong Jing; Li Wang; Chao Ma; Jianping Lu; Yun Bian; Chengwei Shao

doi:10.3389/fonc.2021.707288

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Front Oncol. 2021 Nov 8:11:707288. doi: 10.3389/fonc.2021.707288. eCollection 2021.

Authors

Yinghao Meng^{1

2}, Hao Zhang¹, Qi Li¹, Fang Liu¹, Xu Fang¹, Jing Li¹, Jieyu Yu¹, Xiaochen Feng¹, Mengmeng Zhu¹, Na Li¹, Guodong Jing¹, Li Wang¹, Chao Ma¹, Jianping Lu¹, Yun Bian¹, Chengwei Shao¹

Affiliations

¹ Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
² Department of Radiology, No.971 Hospital of Navy, Qingdao, Shandong, China.

Abstract

Purpose: To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).

Materials and methods: In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.

Results: We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.

Conclusions: The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

Keywords: carcinoma; multidetector computed tomography; pancreatic neoplasm; prognosis; radiomics; tumor-stroma ratio.