Delineation of tumors and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive proton therapy (APT). Although AI-based auto-contouring technologies have sped up this process, the time needed to perform the quality assessment (QA) of the generated contours remains a bottleneck, taking clinicians between several minutes up to an hour to complete. This paper introduces a fast contouring workflow suitable for time-critical APT, enabling detection of anatomical changes in shorter time frames and with a lower demand of clinical resources. The proposed human-centered AI-infused workflow follows two principles uncovered after reviewing the APT literature and conducting several interviews and an observational study in two radiotherapy centers in the Netherlands. First, enable targeted inspection of the generated contours by leveraging AI uncertainty and clinically-relevant features such as the proximity of the organs-at-risk to the tumor. Second, minimize the number of interactions needed to edit faulty delineations with redundancy-aware editing tools that provide the user a sense of predictability and control. We use a proof of concept that we validated with clinicians to demonstrate how current and upcoming AI capabilities support the workflow and how it would fit into clinical practice.
翻译:肿瘤和高危器官的脱线允许在整个治疗过程中发现和纠正病人解剖过程的变化,使之成为适应质子疗法的核心步骤。 虽然AI型自动调试技术加快了这一过程,但对生成的轮廓进行质量评估所需的时间仍然是瓶颈,临床医师在数分钟到一小时的时间内完成。本文件引入了适合时间紧迫的APT的快速调试工作流程,从而能够探测出较短时间框架内的解剖变化和临床资源需求较低。拟议的以人为中心的人工自毁工作流程遵循了在审查APT文献并在荷兰的两个放射治疗中心进行若干次访谈和观察研究之后发现的两项原则。首先,通过利用AI不确定性和临床相关特征,例如器官与肿瘤的距离,能够有针对性地检查生成的轮廓。第二,最大限度地减少与我们意识到的冗余编辑工具编辑错误的相互作用次数,这些工具能为用户提供一种可预测性感和监控性能,从而证明用户如何适应当前的临床工作流程概念。我们使用一种证据来证明它是如何适应当前的临床工作流程。