已发表论文

基于CT的影像组学模型预测肝细胞癌患者新辅助转化治疗的病理反应

 

Authors Wen H, Liang R, Liu X, Yu Y, Lin S, Song Z, Huang Y, Yu X, Chen S, Chen L, Qian B, Shen J , Xiao H, Shen S

Received 17 July 2024

Accepted for publication 18 October 2024

Published 1 November 2024 Volume 2024:11 Pages 2145—2157

DOI https://doi.org/10.2147/JHC.S487370

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Ali Hosni

Haoxiang Wen,1,2,* Ruiming Liang,3,* Xiaofei Liu,4 Yang Yu,1 Shuirong Lin,1 Zimin Song,1 Yihao Huang,1 Xi Yu,1 Shuling Chen,5 Lili Chen,6 Baifeng Qian,1 Jingxian Shen,7 Han Xiao,8 Shunli Shen1 

1Center of Hepato-Pancreatico-Biliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 2Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong Province, People’s Republic of China; 3Department of Medical Statistics, Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 4Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-senUniversity, Guangzhou, Guangdong Province, People’s Republic of China; 5Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 6Department of Pathology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong Province, People’s Republic of China; 7Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, People’s Republic of China; 8Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Shunli Shen, Center of Hepato-Pancreatico-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhong-Shan Road2, Guangzhou, Guangdong Province, People’s Republic of China, Email Shenshli@mail.sysu.edu.cn Han Xiao, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, No. 58 Zhongshan Road2, Guangzhou, Guang- Dong Province, People’s Republic of China, Email xiaoh69@mail.sysu.edu.cn

Purpose: Predicting the pathological response after neoadjuvant conversion therapy for initially unresectable hepatocellular carcinoma (HCC) is essential for surgical decision-making and survival outcomes but remains a challenge. We aimed to develop a radiomics model to predict pathological responses.
Methods: We included 203 patients with HCC who underwent hepatectomy after neoadjuvant conversion therapy between 2015 and 2023 and separated them into a training set (100 patients from Center A) and a validation set (103 patients from Center B). Pathological complete response (pCR)-related radiomic features were extracted from the largest tumor layer in the arterial and portal vein phases of the CT. A synthetic minority oversampling technique (SMOTE) was used to balance the minority groups in the training set. The SMOTE radiomics model was constructed using a logistic regression model in the SMOTE training set and its performance was verified in the validation set.
Results: The AUC of the preoperative modified response evaluation criteria in solid tumors (mRECIST) assessment for pCR was 0.656 and 0.589 in the training and validation sets, respectively. The SMOTE radiomics model was established based on ten radiomic features and showed good pCR-predictive performance in the SMOTE training set (AUC, 0.889; accuracy, 87.7%) and the validation set (AUC: 0.843, accuracy: 86.4%). The RFS of the radiomics-predicted-pCR group was significantly better than that of the predicted-non-pCR group in the training cohort (P = 0.001, 2-year RFS: 69.5% and 30.1% respectively) and the validation cohort (P = 0.012, 2-year RFS: 65.9% and 38.0% respectively).
Conclusion: The SMOTE radiomics model has great potential for predicting pathological response and evaluating RFS in patients with unresectable HCC after neoadjuvant conversion therapy.

Keywords: hepatocellular carcinoma, radiomics, pathological complete response, neoadjuvant conversion therapy