图像纹理分析几十年来一直代表着癌症评估和疾病进展评估的一个有前途的机会，发展成为一个学科，即放射组学。然而，完全将其转化为临床实践的道路仍然受到固有限制的阻碍。由于纯监督分类模型无法设计强大的基于成像的生物标志物以进行预测，因此癌症亚型分类方法需要受到远程监督的帮助，例如利用生存/复发信息。在这项工作中，我们评估了之前提出的远程监督癌症亚型分类模型在霍奇金淋巴瘤中的领域一般性、测试性和验证性。我们评估了两个来自两家医院的独立数据集上的模型表现，进行比较和分析。尽管成功和一致，但比较确认了放射组学的不稳定性，因为跨中心重复性不足，在一个中心中可以解释结果，在另一个中心中解释性差。因此，我们提出了一种基于随机森林的可解释的转移模型，用于测试从回顾性癌症亚型分型中提取的成像生物标志物的领域不变性。这样做，我们测试了癌症亚型分类在验证和前瞻设置中的预测能力，取得了成功的结果，并支持所提出方法的领域普适性。另一方面，决策规则的提取使得能够绘制出风险因素和强大的生物标志物以了解临床决策。本研究展示了远程监督癌症亚型分类模型在更大的多中心数据集中进一步评估的潜力，以可靠地将放射组学转化为医疗实践。此代码可在 GitHub 存储库中获得，版权所有 © 2023 Elsevier B.V.。

Image texture analysis has for decades represented a promising opportunity for cancer assessment and disease progression evaluation, evolving in a discipline, i.e., radiomics. However, the road to a complete translation into clinical practice is still hampered by intrinsic limitations. As purely supervised classification models fail in devising robust imaging-based biomarkers for prognosis, cancer subtyping approaches would benefit from the employment of distant supervision, for instance exploiting survival/recurrence information. In this work, we assessed, tested, and validated the domain-generality of our previously proposed Distant Supervised Cancer Subtyping model on Hodgkin Lymphoma. We evaluate the model performance on two independent datasets coming from two hospitals, comparing and analyzing the results. Although successful and consistent, the comparison confirmed the instability of radiomics due to an across-center lack of reproducibility, leading to explainable results in one center and poor interpretability in the other. We thus propose a Random Forest-based Explainable Transfer Model for testing the domain-invariance of imaging biomarkers extracted from retrospective cancer subtyping. In doing so, we tested the predictive ability of cancer subtyping in a validation and perspective setting, which led to successful results and supported the domain-generality of the proposed approach. On the other hand, the extraction of decision rules enables to draw of risk factors and robust biomarkers to inform clinical decisions. This work shows the potentialities of the Distant Supervised Cancer Subtyping model to be further evaluated in larger multi-center datasets, to reliably translate radiomics into medical practice. The code is available at this GitHub repository.Copyright © 2023 Elsevier B.V. All rights reserved.