Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes

Dong Tian; Hao-Ji Yan; Haruhiko Shiiya; Masaaki Sato; Aya Shinozaki-Ushiku; Jun Nakajima

doi:10.1016/j.jtcvs.2022.05.046

Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes

J Thorac Cardiovasc Surg. 2023 Feb;165(2):502-516.e9. doi: 10.1016/j.jtcvs.2022.05.046. Epub 2022 Jul 20.

Authors

Dong Tian¹, Hao-Ji Yan², Haruhiko Shiiya³, Masaaki Sato⁴, Aya Shinozaki-Ushiku⁵, Jun Nakajima⁶

Affiliations

¹ Department of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; Heart and Lung Transplant Research Laboratory, Affiliated Hospital of North Sichuan Medical College, Nanchong, China; Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
² School of Medical Imaging, North Sichuan Medical College, Nanchong, China.
³ Department of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, Tokyo, Japan; Department of Cardiovascular and Thoracic Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
⁴ Department of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
⁵ Department of Pathology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
⁶ Department of Thoracic Surgery, The University of Tokyo Graduate School of Medicine, Tokyo, Japan. Electronic address: nakajima-tho@h.u-tokyo.ac.jp.

PMID: 36038386
DOI: 10.1016/j.jtcvs.2022.05.046

Abstract

Objective: For patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors.

Methods: This retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022. The radiomic features were extracted from preoperative unenhanced computed tomography images. After strict feature selection, random forest and random survival forest models were fitted to predict pathological and survival outcomes, respectively. The model performance was assessed by the area under the curve (AUC) and validated internally by the bootstrap method.

Results: In total, 124 patients with a median age of 61 years were included. The radiomics random forest models of WHO type and TNM stage showed satisfactory performance with an AUC_WHO of 0.898 (95% CI, 0.753-1.000) and an AUC_TNM of 0.766 (95% CI, 0.642-0.886). For overall survival and progression-free survival prediction, the radiomics random survival forest models showed good performance (integrated AUCs, 0.923; 95% CI, 0.691-1.000 and 0.702; 95% CI, 0.513-0.875, respectively), and the integrated AUCs increased to 0.935 (95% CI, 0.705-1.000) and 0.811 (95% CI, 0.647-0.942), respectively, when combined with clinicopathological features.

Conclusions: Machine learning-based radiomic computed tomography phenotyping might allow for the satisfactory prediction of pathological and survival outcomes and further improve prognostic performance when integrated with clinicopathological features in patients with thymic epithelial tumors.

Keywords: clinicopathological outcomes; computed tomography; machine learning; radiomics; thymic epithelial tumors.

MeSH terms

Humans
Machine Learning
Middle Aged
Neoplasms, Glandular and Epithelial*
Retrospective Studies
Tomography, X-Ray Computed* / methods

Supplementary concepts

Thymic epithelial tumor