Clinical Impact of Radiologist's Alert System on Patient Care for High-risk Incidental CT Findings: A Machine Learning-Based Risk Factor Analysis.

IF 3.8 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Academic Radiology Pub Date : 2024-10-03 DOI:10.1016/j.acra.2024.09.034

Seitaro Oda, Akira Chikamoto, Zaw Aung Khant, Hiroyuki Uetani, Masafumi Kidoh, Yasunori Nagayama, Takeshi Nakaura, Toshinori Hirai

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引用次数: 0

Abstract

Rationale and objectives: Efficient communication between radiologists and clinicians ordering computed tomography (CT) examinations is crucial for managing high-risk incidental CT findings (ICTFs). Herein, we introduced a Radiologist's Alert and Patient Care Follow-up System (APCFS) for high-risk ICTFs. This study aimed to analyze the ICTFs detected by this system and the factors associated with them.

Materials and methods: This retrospective study was approved by the institutional review board. We analyzed 52,331 CT examinations conducted between 2019 and 2021. In cases where high-risk ICTFs were identified, radiologists utilized APCFS to prompt ordering clinicians for further patient care. We assessed the frequency, affected body organs, presence or absence of therapeutic interventions, and diagnoses of high-risk ICTFs. An automated machine learning platform was employed to analyze the factors associated with high-risk ICTFs.

Results: Among the 52,331 CT examinations, 507 (0.96%) revealed high-risk ICTFs, primarily affecting the lung (18.0%). Of these 507 high-risk ICTFs, 117 (23.1%) underwent therapeutic interventions, while 362 (71.4%) required only follow-up. Of the 117 cases undergoing interventions, 61 (52.1%) required surgery. Of the 219 high-risk ICTFs leading to a confirmed diagnosis, 146 (66.7%) were neoplastic lesions, including 88 (60.3%) malignancies, and 73 (33.3%) were non-neoplastic lesions. The top three risk factors associated with high-risk ICTFs in the regularized logistic regression model were the imaging protocol (especially aortic valve implantation planning protocol), imaging area (especially whole-body imaging), and clinical department (especially cardiology).

Conclusion: Utilizing APCFS, high-risk ICTFs were detected in approximately 1% of all CT examinations, likely associated with specific imaging protocols, areas, and clinical departments.

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放射科医生警报系统对高风险偶然 CT 检查结果患者护理的临床影响：基于机器学习的风险因素分析。

理由和目标：放射科医生与下达计算机断层扫描 (CT) 检查单的临床医生之间的有效沟通对于管理高风险的偶然 CT 检查结果 (ICTF) 至关重要。在此，我们引入了针对高风险 ICTF 的放射医师警报和患者护理随访系统 (APCFS)。本研究旨在分析该系统检测出的 ICTFs 及其相关因素：这项回顾性研究获得了机构审查委员会的批准。我们分析了2019年至2021年期间进行的52331例CT检查。在发现高风险ICTF的病例中，放射科医生利用APCFS提示下单的临床医生对患者进行进一步治疗。我们评估了高风险 ICTF 的频率、受影响的身体器官、有无治疗干预以及诊断。我们采用了一个自动化机器学习平台来分析与高风险ICTFs相关的因素：在52331例CT检查中，507例（0.96%）发现了高风险ICTF，主要影响肺部（18.0%）。在这 507 例高风险 ICTF 中，117 例（23.1%）接受了治疗干预，362 例（71.4%）仅需随访。在接受干预治疗的 117 例病例中，61 例（52.1%）需要手术治疗。在确诊的219例高风险ICTF中，146例（66.7%）为肿瘤性病变，其中88例（60.3%）为恶性肿瘤，73例（33.3%）为非肿瘤性病变。在正则化逻辑回归模型中，与高风险ICTF相关的前三个风险因素分别是成像方案（尤其是主动脉瓣植入计划方案）、成像区域（尤其是全身成像）和临床科室（尤其是心内科）：利用 APCFS，所有 CT 检查中约有 1% 发现了高风险 ICTF，这可能与特定的成像方案、区域和临床科室有关。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Radiology 医学-核医学

CiteScore

7.60

自引率

10.40%

发文量

432

审稿时长

18 days

期刊介绍： Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.