J Cancer 2021; 12(23):7079-7087. doi:10.7150/jca.63370 This issue

Research Paper

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Zhi-Min Zou1,6, Tian-Zhi An4, Jun-Xiang Li5, Zi-Shu Zhang1, Yu-Dong Xiao1,2,3, Jun Liu1,2,3✉

1. Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, 410011, China.
2. Clinical Research Center for Medical Imaging in Hunan Province, Changsha, 410011, China.
3. Department of Radiology Quality Control Center, Changsha, 410011, China.
4. Department of Interventional Radiology, the Affiliated Hospital of Guizhou Medical University, Guiyang, 550002, China.
5. Department of Interventional Radiology, Guizhou Medical University Affiliated Cancer Hospital, Guiyang, 550004, China.
6. Department of Radiology, Hunan Children's Hospital, Changsha, 410007, China.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Zou ZM, An TZ, Li JX, Zhang ZS, Xiao YD, Liu J. Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study. J Cancer 2021; 12(23):7079-7087. doi:10.7150/jca.63370. Available from https://www.jcancer.org/v12p7079.htm

File import instruction


Graphic abstract

Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC).

Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test.

Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively.

Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.

Keywords: Hepatocellular Carcinoma, Transarterial Chemoembolization, Refractoriness, Predictive Model, Random Forest