Chen Kun, Sui Chunxiao, Wang Ziyang, Liu Zifan, Qi Lisha, Li Xiaofeng
Department of Nuclear Medicine, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China; Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
Transl Oncol. 2025 Feb;52:102260. doi: 10.1016/j.tranon.2024.102260. Epub 2025 Jan 2.
Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).
A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms.
A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUC of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model.
The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.
尽管一些临床病理特征被确定为预后指标,但有望通过术前非侵入性的潜在预后放射组学模型预测肝癌患者的生存期。传统放射组学模型未考虑肿瘤内部区域异质性。本研究旨在建立并验证多栖放射组学模型预测肝细胞癌(HCC)预后的能力。
回顾性纳入232例HCC患者,包括一个训练/验证队列以及来自4个中心的两个外部测试队列。对于多栖放射组学,首先利用大津阈值法基于CT图像进行肿瘤内部栖区划分。其次,构建350个多栖放射组学模型以选择最优模型。然后,应用ROC曲线分析和Kaplan-Meier生存曲线分析评估预测性能。最终,基于生物信息学分析和多重免疫组化(mIHC)检测进行免疫状态分析以揭示潜在机制。
共分割出4个栖区,并基于每个栖区以及所有4个栖区的整合构建了相应的多栖放射组学模型。总体而言,在对HCC预后分层方面,多栖放射组学模型优于传统放射组学模型。基于分割出的栖区4构建的、涉及决策树(DT)筛选和随机森林(RF)分类器的多栖放射组学模型被确定为最优模型,AUC为0.806。不同的静息自然杀伤(NK)细胞浸润显著有助于最优多栖放射组学模型对HCC预后进行分层。
基于CT图像的多栖放射组学模型可能预测HCC的总生存期,优于传统放射组学模型。多栖放射组学模型的预后预测能力部分归因于CT图像中捕捉到的不同免疫状态。