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| Computed tomography radiomic features and clinical factors predicting the response to first transarterial chemoembolization in intermediate-stage hepatocellular carcinoma |
| Zhong-Xing Shi a , Chang-Fu Li b , Li-Feng Zhao c , Zhong-Qi Sun d , Li-Ming Cui a , Yan-Jie Xin d , Dong-Qing Wang a , Tan-Rong Kang a , Hui-Jie Jiang d , ∗ |
a Department of Interventional Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
b Department of Digestive Medicine, Daqing Longnan Hospital, Daqing 163453, China
c Department of Radiology, Daqing Longnan Hospital, Daqing 163453, China
d Department of Radiology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
∗Corresponding author.
E-mail address: jianghuijie@hrbmu.edu.cn (H.-J. Jiang). |
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Abstract Background: According to clinical practice guidelines, transarterial chemoembolization (TACE) is the standard treatment modality for patients with intermediate-stage hepatocellular carcinoma (HCC). Early prediction of treatment response can help patients choose a reasonable treatment plan. This study aimed to investigate the value of the radiomic-clinical model in predicting the efficacy of the first TACE treatment for HCC to prolong patient survival.
Methods: A total of 164 patients with HCC who underwent the first TACE from January 2017 to September 2021 were analyzed. The tumor response was assessed by modified response evaluation criteria in solid tumors (mRECIST), and the response of the first TACE to each session and its correlation with overall survival were evaluated. The radiomic signatures associated with the treatment response were identified by the least absolute shrinkage and selection operator (LASSO), and four machine learning models were built with different types of regions of interest (ROIs) (tumor and corresponding tissues) and the model with the best performance was selected. The predictive performance was assessed with receiver operating characteristic (ROC) curves and calibration curves.
Results: Of all the models, the random forest (RF) model with peritumor ( + 10 mm) radiomic signatures had the best performance [area under ROC curve (AUC) = 0.964 in the training cohort, AUC = 0.949 in the validation cohort]. The RF model was used to calculate the radiomic score (Rad-score), and the optimal cutoffvalue (0.34)was calculatedaccordingto theYouden’sindex. Patientswere thendivided intoa high-risk group (Rad-score > 0.34) and a low-risk group (Rad-score ≤0.34), and a nomogram model was successfully established to predict treatment response. The predicted treatment response also allowed for significant discrimination of Kaplan-Meier curves. Multivariate Cox regression identified six independent prognostic factors for overall survival, including male [hazard ratio (HR) = 0.500, 95% confidence interval (CI): 0.260–0.962, P = 0.038], alpha-fetoprotein (HR = 1.003, 95% CI: 1.002–1.004, P < 0.001), alanine aminotransferase (HR = 1.003, 95% CI: 1.0 01–1.0 05, P = 0.025), performance status (HR = 2.400, 95% CI: 1.20 0–4.80 0, P = 0.013), the number of TACE sessions (HR = 0.870, 95% CI: 0.780–0.970, P = 0.012) and Rad-score (HR = 3.480, 95% CI: 1.416–8.552, P = 0.007).
Conclusions: The radiomic signatures and clinical factors can be well-used to predict the response of HCC patients to the first TACE and may help identify the patients most likely to benefit from TACE.
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Mazzarella G, Muttillo EM, Coletta D, Picardi B, Rossi S, Rossi Del Monte S, Gomes V, Muttillo IA. Solid pseudopapillary tumor of the pancreas: A systematic review of clinical, surgical and oncological characteristics of 1384 patients underwent pancreatic surgery[J]. Hepatobiliary Pancreat Dis Int, 2024, 23(4): 331-338. |
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2024, Vol. 23 
