基于深度分割特征的放射组学改进了肝细胞癌复发预测。

IF 5 Q1 ENGINEERING, BIOMEDICAL
BME frontiers Pub Date : 2022-04-04 eCollection Date: 2022-01-01 DOI:10.34133/2022/9793716
Jifei Wang, Dasheng Wu, Meili Sun, Zhenpeng Peng, Yingyu Lin, Hongxin Lin, Jiazhao Chen, Tingyu Long, Zi-Ping Li, Chuanmiao Xie, Bingsheng Huang, Shi-Ting Feng
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引用次数: 2

摘要

目标和影响声明。本研究开发并验证了一种基于深度语义分割特征的放射组学(DSFR)模型,该模型基于术前对比增强计算机断层扫描(CECT)和临床信息,用于预测单肝细胞癌(HCC)根治性切除后的早期复发(ER)。ER预测对HCC的治疗决策和监测策略具有重要意义。介绍ER预测对HCC非常重要。然而,目前还不能充分确定。方法。共有208名根治性切除后的单发性HCC患者被回顾性纳入模型开发队列(n=180)和独立验证队列(n=28)。开发了基于不同CT阶段的DSFR模型。将最佳DSFR模型与临床信息相结合,建立DSFR-C模型。建立了基于Cox回归的综合列线图。DSFR特征用于对高危和低危ER组进行分层。后果选择基于门脉期的DSFR模型作为最佳模型(受试者工作特征曲线下面积(AUC):发育队列,0.740;验证队列,0.717)。DSFR-C模型在开发和验证队列中的AUC分别为0.782和0.744。在开发和验证队列中,综合列线图实现了无复发生存期(RFS)预测的C指数分别为0.748和0.741,时间相关AUC分别为0.823和0.822。风险组之间的RFS差异具有统计学意义(开发组和验证组分别为P0.001和P=0.045)。结论基于CECT的DSFR可以预测治愈性切除后单个HCC的ER,其与临床信息的结合进一步提高了ER预测的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma.

Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcinoma (HCC) after curative resection. ER prediction is of great significance to the therapeutic decision-making and surveillance strategy of HCC. Introduction. ER prediction is important for HCC. However, it cannot currently be adequately determined. Methods. Totally, 208 patients with single HCC after curative resection were retrospectively recruited into a model-development cohort (n=180) and an independent validation cohort (n=28). DSFR models based on different CT phases were developed. The optimal DSFR model was incorporated with clinical information to establish a DSFR-C model. An integrated nomogram based on the Cox regression was established. The DSFR signature was used to stratify high- and low-risk ER groups. Results. A portal phase-based DSFR model was selected as the optimal model (area under receiver operating characteristic curve (AUC): development cohort, 0.740; validation cohort, 0.717). The DSFR-C model achieved AUCs of 0.782 and 0.744 in the development and validation cohorts, respectively. In the development and validation cohorts, the integrated nomogram achieved C-index of 0.748 and 0.741 and time-dependent AUCs of 0.823 and 0.822, respectively, for recurrence-free survival (RFS) prediction. The RFS difference between the risk groups was statistically significant (P<0.0001 and P=0.045 in the development and validation cohorts, respectively). Conclusion. CECT-based DSFR can predict ER in single HCC after curative resection, and its combination with clinical information further improved the performance for ER prediction.

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CiteScore
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