Development and validation of a computed tomography-based radiomics signature to predict "highest-risk" from patients with high-risk gastrointestinal stromal tumor

Jiabin Zheng; Qianchao Liao; Xiaobo Chen; Minping Hong; Alessandro Mazzocca; Milena Urbini; Zaiyi Liu; Yong Li

doi:10.21037/jgo-23-963

Development and validation of a computed tomography-based radiomics signature to predict "highest-risk" from patients with high-risk gastrointestinal stromal tumor

J Gastrointest Oncol. 2024 Feb 29;15(1):125-133. doi: 10.21037/jgo-23-963. Epub 2024 Feb 20.

Authors

Jiabin Zheng^#¹, Qianchao Liao^#², Xiaobo Chen^{3

4}, Minping Hong⁵, Alessandro Mazzocca⁶, Milena Urbini⁷, Zaiyi Liu^{3

4}, Yong Li¹

Affiliations

¹ Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
² Department of Gastrointestinal Surgery, Huizhou First Hospital, Huizhou, China.
³ Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
⁴ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
⁵ Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
⁶ Medical Oncology, Università Campus Bio-Medico, Rome, Italy.
⁷ Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.

^# Contributed equally.

Abstract

Background: Some patients with high-risk gastrointestinal stromal tumor (GIST) experience disease progression after complete resection and adjuvant therapy. It is of great significance to distinguish these patients among those with high-risk GIST. Radiomics has been demonstrated as a promising tool to predict various tumors prognosis.

Methods: From January 2006 to December 2018, a total of 100 high-risk GIST patients (training cohort: 60; validation cohort: 40) from Guangdong Provincial People's Hospital with preoperative enhanced computed tomography (CT) images were enrolled. The radiomics features were extracted and a risk score was built using least absolute shrinkage and selection operator-Cox model. The clinicopathological factors were analyzed and a nomogram was established with and without radiomics risk score. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomograms.

Results: We selected 11 radiomics features associated with recurrence or metastasis. The risk score was calculated and significantly associated with disease-free survival (DFS) in both the training and validation group. Cox regression analysis showed that Ki67 was an independent risk factor for DFS [P=0.004, hazard ratio 4.615, 95% confidence interval (CI): 1.624-13.114]. The combined radiomics nomogram, which integrated the radiomics risk score and significant clinicopathological factors, showed good performance in predicting DFS, with a C-index of 0.832 (95% CI: 0.761-0.903), which was better than the clinical nomogram (C-index 0.769, 95% CI: 0.679-0.859) in training cohort. The calibration curves and the DCA plot suggested satisfying accuracy and clinical utility of the model.

Conclusions: The CT-based radiomics nomogram, combined with the clinicopathological factors and risk score, has good potential to assess the recurrence or metastasis of patients with high-risk GIST.

Keywords: Gastrointestinal stromal tumor (GIST); computed tomography (CT); high-risk; highest-risk; radiomics.