Predicting Pathological Response of Neoadjuvant Conversion Therapy for Hepatocellular Carcinoma Patients Using CT-Based Radiomics Model.

IF 4.2 3区 医学 Q2 ONCOLOGY Journal of Hepatocellular Carcinoma Pub Date : 2024-11-01 eCollection Date: 2024-01-01 DOI:10.2147/JHC.S487370
Haoxiang Wen, Ruiming Liang, Xiaofei Liu, Yang Yu, Shuirong Lin, Zimin Song, Yihao Huang, Xi Yu, Shuling Chen, Lili Chen, Baifeng Qian, Jingxian Shen, Han Xiao, Shunli Shen
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Abstract

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.

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利用基于CT的放射组学模型预测肝细胞癌患者对新辅助转换疗法的病理反应
目的:预测最初无法切除的肝细胞癌(HCC)新辅助转化治疗后的病理反应对于手术决策和生存结果至关重要,但仍是一项挑战。我们旨在开发一种放射组学模型来预测病理反应:我们纳入了 203 名在 2015 年至 2023 年间接受新辅助转换疗法后进行肝切除术的 HCC 患者,并将其分为训练集(100 名来自 A 中心的患者)和验证集(103 名来自 B 中心的患者)。病理完全反应(pCR)相关的放射学特征是从 CT 的动脉期和门静脉期的最大肿瘤层中提取的。使用合成少数群体过度取样技术(SMOTE)来平衡训练集中的少数群体。在 SMOTE 训练集中使用逻辑回归模型构建了 SMOTE 放射组学模型,并在验证集中验证了该模型的性能:结果:在训练集和验证集中,实体瘤术前改良反应评价标准(mRECIST)评估 pCR 的 AUC 分别为 0.656 和 0.589。SMOTE放射组学模型基于十个放射组学特征建立,在SMOTE训练集(AUC:0.889;准确率:87.7%)和验证集(AUC:0.843;准确率:86.4%)中显示出良好的pCR预测性能。在训练队列(P = 0.001,2 年 RFS 分别为 69.5%和 30.1%)和验证队列(P = 0.012,2 年 RFS 分别为 65.9%和 38.0%)中,放射组学预测-pCR 组的 RFS 明显优于预测-non-pCR 组:SMOTE放射组学模型在预测新辅助转换疗法后无法切除的HCC患者的病理反应和评估RFS方面具有巨大潜力。
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来源期刊
CiteScore
0.50
自引率
2.40%
发文量
108
审稿时长
16 weeks
期刊最新文献
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