Purpose: For timely treatment of extrahepatic metastasis and macrovascular invasion (aggressive progressive disease [PD]) in hepatocellular carcinoma, models aimed at stratifying the risks of subsequent aggressive PD should be constructed.
Patients and Methods: After dividing 332 patients from five hospitals into training (n = 236) and validation (n = 96) datasets, non-invasive models, including clinical/semantic factors (Model^CS), deep learning radiomics (Model^D), and both (Model^CSD), were constructed to stratify patients according to the risk of aggressive PD. We examined the discrimination and calibration; similarly, we plotted a decision curve and devised a nomogram. Furthermore, we performed analyses of subgroups who received different treatments or those in different disease stages and compared time to aggressive PD and overall survival in the high- and low-risk subgroups.
Results: Among the constructed models, Model^CSD, combining clinical/semantic factors and deep learning radiomics, outperformed Model^CS and Model^D (areas under the curve [AUCs] for the training dataset: 0.741, 0.815, and 0.856; validation dataset: 0.780, 0.836, and 0.862), with statistical difference per the net reclassification improvement, the integrated discrimination improvement, and/or the DeLong test in both datasets. Besides, Model^CSD had the best calibration and decision curves. The performance of Model^CSD was not affected by treatment types (AUC: resection = 0.839; transarterial chemoembolization = 0.895; p = 0.183) or disease stages (AUC: BCLC [Barcelona Clinic Liver Cancer] stage 0 and A = 0.827; BCLC stage AB &B = 0.861; p = 0.537). Moreover, the high-risk group had a significantly shorter median time to aggressive PD than the low-risk group (training dataset hazard ratio [HR] = 0.108, p < 0.001; validation dataset HR = 0.058, p < 0.001) and poorer overall survival (training dataset HR = 0.357, p < 0.001; validation dataset HR = 0.204, p < 0.001).
Conclusion: Our deep learning-based model successfully stratified the risks of aggressive PD. In the high-risk population, current guideline indicates that first-line treatments are insufficient to prevent extrahepatic metastasis and macrovascular invasion and ensure survival benefits, so more therapies may be explored for these patients.

Keywords: aggressive disease progression, deep learning radiomics, clinical factors, high-risk, risk prediction


中文翻译：

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基于深度学习的肝细胞癌未来肝外转移和大血管浸润预测

目的：为了及时治疗肝细胞癌的肝外转移和大血管侵袭（侵袭性进展性疾病 [PD]），应构建旨在对后续侵袭性 PD 风险进行分层的模型。
患者和方法：将来自 5 家医院的 332 名患者分为训练（n = 236）和验证（n = 96）数据集、非侵入性模型，包括临床/语义因素（模型^CS）、深度学习放射组学（模型^D）、和两者（型号^CSD)，根据侵袭性 PD 的风险对患者进行分层。我们检查了辨别和校准；同样，我们绘制了一条决策曲线并设计了一个列线图。此外，我们对接受不同治疗或处于不同疾病阶段的亚组进行了分析，并比较了高风险和低风险亚组的侵袭性 PD 时间和总生存期。
结果：在构建的模型中，模型^CSD结合临床/语义因素和深度学习放射组学，优于模型^CS和模型^D（训练数据集的曲线下面积 [AUC]：0.741、0.815 和 0.856；验证数据集：0.780、0.836 和 0.862），每个净重分类改进、综合辨别改进和/或 DeLong 的统计差异在两个数据集中进行测试。此外，模型^CSD具有最佳的校准和决策曲线。^CSD模型的性能不受治疗类型（AUC：切除 = 0.839；经动脉化疗栓塞 = 0.895；p = 0.183）或疾病分期（AUC：BCLC [巴塞罗那临床肝癌] 0 期和 A = 0.827；BCLC AB 期）的影响&B = 0.861; p= 0.537)。此外，与低风险组相比，高风险组发生侵袭性 PD 的中位时间显着缩短（训练数据集风险比 [HR] = 0.108，p < 0.001；验证数据集 HR = 0.058，p < 0.001）并且总体较差生存（训练数据集 HR = 0.357，p < 0.001；验证数据集 HR = 0.204，p < 0.001）。
结论：我们基于深度学习的模型成功地对侵袭性 PD 的风险进行了分层。在高危人群中，目前的指南表明，一线治疗不足以预防肝外转移和大血管侵袭并确保生存获益，因此可以为这些患者探索更多的治疗方法。

关键词：侵袭性疾病进展，深度学习放射组学，临床因素，高风险，风险预测