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
{"title":"Predicting Pathological Response of Neoadjuvant Conversion Therapy for Hepatocellular Carcinoma Patients Using CT-Based Radiomics Model.","authors":"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","doi":"10.2147/JHC.S487370","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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 (<i>P =</i> 0.001, 2-year RFS: 69.5% and 30.1% respectively) and the validation cohort (<i>P =</i> 0.012, 2-year RFS: 65.9% and 38.0% respectively).</p><p><strong>Conclusion: </strong>The SMOTE radiomics model has great potential for predicting pathological response and evaluating RFS in patients with unresectable HCC after neoadjuvant conversion therapy.</p>","PeriodicalId":15906,"journal":{"name":"Journal of Hepatocellular Carcinoma","volume":"11 ","pages":"2145-2157"},"PeriodicalIF":4.2000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11537151/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hepatocellular Carcinoma","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2147/JHC.S487370","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

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.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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
Construction of a 2.5D Deep Learning Model for Predicting Early Postoperative Recurrence of Hepatocellular Carcinoma Using Multi-View and Multi-Phase CT Images. Unlocking the Potential of Phyto Nanotherapeutics in Hepatocellular Carcinoma Treatment: A Review. Preoperative Noninvasive Prediction of Recurrence-Free Survival in Hepatocellular Carcinoma Using CT-Based Radiomics Model. Radiofrequency Ablation Therapy versus Stereotactic Body Radiation Therapy for Naive Hepatocellular Carcinoma (≤5cm): A Retrospective Multi-Center Study. 2,2'- Bipyridine Derivatives Exert Anticancer Effects by Inducing Apoptosis in Hepatocellular Carcinoma (HepG2) Cells.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1