Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.
Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling.
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DOI：
10.1259/bjro.20190021
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
El Naqa I
中科院分区：
文献类型：
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作者：
Luo Y;Tseng HH;Cui S;Wei L;Ten Haken RK;El Naqa I
放射治疗结局预测（ROP）在个体化处方和适应性放射治疗中起着重要作用。临床决策可能不仅取决于准确的辐射结果预测，而且还需要基于对患者特征、辐射反应和治疗计划之间关系的知情理解来做出。随着更多患者的生物物理信息变得可用，机器学习（ML）技术将具有改善ROP的巨大潜力。创建可解释的ML方法是临床实践的最终任务，但仍然是一项具有挑战性的任务。为了实现完全的可解释性，需要首先探索ML方法的可解释性。因此，本文重点关注ML技术在放射肿瘤学临床应用中的应用，平衡感兴趣预测模型的准确性和可解释性。ML算法通常可以分为可解释（IP）或不可解释（NIP）（“黑盒”）技术。虽然前者可以提供更清晰的解释以帮助临床决策，但其预测性能通常优于后者。因此，已经投入了大量的精力和资源来平衡ROP中ML方法的准确性和可解释性，但仍需要做更多的工作。在这篇综述中，目前的进展，以提高准确性的IP ML方法，并总结了主要的趋势，以提高可解释性和减轻“黑箱”的标记ML辐射结果建模。还讨论了集成IP和NIP ML方法以产生具有更高精度和可解释性的ROP预测模型的努力。
Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.