Zeeuw Michiel, Bereska Jacqueline, Strampel Marius, Wagenaar Luuk, Janssen Boris, Marquering Henk, Kemna Ruby, van Waesberghe Jan Hein, van den Bergh Janneke, Nota Irene, Moos Shira, Nio Yung, Kop Marnix, Kist Jakob, Struik Femke, Wesdorp Nina, Nelissen Jules, Rus Katinka, de Sitter Alexandra, Stoker Jaap, Huiskens Joost, Verpalen Inez, Kazemier Geert
Department of Surgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Cancer Center Amsterdam, Amsterdam, The Netherlands.
Insights Imaging. 2025 May 23;16(1):110. doi: 10.1186/s13244-025-01984-w.
For patients with colorectal liver metastases (CRLM), total tumor volume (TTV) is prognostic. A deep-learning segmentation model for CRLM to assess TTV called COlorectal cAncer Liver metastases Assessment (COALA) has been developed. This study evaluated COALA's performance and practical utility in the radiological picture archiving and communication system (PACS). A secondary aim was to provide lessons for future researchers on the implementation of artificial intelligence (AI) models.
Patients discussed between January and December 2023 in a multidisciplinary meeting for CRLM were included. In those patients, CRLM was automatically segmented in portal-venous phase CT scans by COALA and integrated with PACS. Eight expert abdominal radiologists completed a questionnaire addressing segmentation accuracy and PACS integration. They were also asked to write down general remarks.
In total, 57 patients were evaluated. Of those patients, 112 contrast-enhanced portal-venous phase CT scans were analyzed. Of eight radiologists, six (75%) evaluated the model as user-friendly in their radiological workflow. Areas of improvement of the COALA model were the segmentation of small lesions, heterogeneous lesions, and lesions at the border of the liver with involvement of the diaphragm or heart. Key lessons for implementation were a multidisciplinary approach, a robust method prior to model development and organizing evaluation sessions with end-users early in the development phase.
This study demonstrates that the deep-learning segmentation model for patients with CRLM (COALA) is user-friendly in the radiologist's PACS. Future researchers striving for implementation should have a multidisciplinary approach, propose a robust methodology and involve end-users prior to model development.
Many segmentation models are being developed, but none of those models are evaluated in the (radiological) workflow or clinically implemented. Our model is implemented in the radiological work system, providing valuable lessons for researchers to achieve clinical implementation.
Developed segmentation models should be implemented in the radiological workflow. Our implemented segmentation model provides valuable lessons for future researchers. If implemented in clinical practice, our model could allow for objective radiological evaluation.
对于结直肠癌肝转移（CRLM）患者，总肿瘤体积（TTV）具有预后价值。已开发出一种用于评估CRLM患者TTV的深度学习分割模型，称为结直肠癌肝转移评估（COALA）。本研究评估了COALA在放射图像存档与通信系统（PACS）中的性能及实际应用价值。第二个目的是为未来研究人员在人工智能（AI）模型实施方面提供经验教训。
纳入2023年1月至12月在CRLM多学科会议上讨论的患者。在这些患者中，COALA在门静脉期CT扫描中自动分割CRLM，并与PACS整合。八位腹部放射学专家完成了一份关于分割准确性和PACS整合的问卷。他们还被要求写下总体意见。
共评估了57例患者。对这些患者的112次对比增强门静脉期CT扫描进行了分析。八位放射科医生中，六位（75%）在其放射学工作流程中将该模型评估为用户友好型。COALA模型需要改进的方面包括小病灶、异质性病灶以及累及膈肌或心脏的肝边缘病灶的分割。实施的关键经验教训包括多学科方法、模型开发前的稳健方法以及在开发阶段早期组织与最终用户的评估会议。
本研究表明，用于CRLM患者的深度学习分割模型（COALA）在放射科医生的PACS中对用户友好。未来致力于实施的研究人员应采用多学科方法，提出稳健的方法，并在模型开发前让最终用户参与。
许多分割模型正在开发中，但这些模型均未在（放射学）工作流程中进行评估或临床实施。我们的模型在放射学工作系统中实施，为研究人员实现临床实施提供了宝贵经验。
已开发的分割模型应在放射学工作流程中实施。我们实施的分割模型为未来研究人员提供了宝贵经验。如果在临床实践中实施，我们的模型可实现客观的放射学评估。
