Earlier discharge from follow-up for lung cancer screening using artificial intelligence in computed tomography

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-06-17 DOI:10.1016/j.ejrad.2025.112253

I.A. Gimbel , M. Bergsma , M.A.J. van de Weijer , A. Welling , A. Olijve , P.R. Algra

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引用次数: 0

Abstract

Background

Lung cancer is the leading cause of cancer death worldwide. Effective screening and early detection are critical in reducing mortality. Artificial intelligence (AI) methods have been proved useful in the diagnosis of pulmonary nodules and early diagnosis of lung cancer. However, the implementation of lung cancer screening and frequent detection of incidental pulmonary nodules lead to more computed tomography scans resulting in increased costs. Therefore, determining the cost-effectiveness of AI is important for implementing these methods in routine clinical practice. Based on volume measurements of pulmonary nodules performed by AI, patients could potentially be discharged earlier from incidental lung nodule follow-up.

Objective

To determine whether using AI volume measurements of pulmonary nodules on CT scan results in shorter follow-up time of incidental lung nodule follow-up.

Methods

For this retrospective cohort study patients with follow-up chest computed tomography for incidental pulmonary nodules were included. The primary outcome was the proportion of patients that could have been discharged earlier from follow-up based on the current BTS guidelines using AI volume measurements.

Results

A total of 252 patients were included, of which 49 (19,4 %; 95 % confidence interval [CI], 14.7–24.9) patients could have been earlier discharged from follow-up using AI volume measurements.

Conclusion

Based on current BTS guidelines using AI volume measurements of pulmonary nodules leads to shorter follow-up time period for incidental lung nodule follow-up and therefore a reduction of unnecessary computed tomography imaging, appointments and cost reduction.

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利用计算机断层扫描中的人工智能进行肺癌筛查的早期随访出院

背景肺癌是全球癌症死亡的主要原因。有效的筛查和早期发现对于降低死亡率至关重要。人工智能（AI）方法在肺结节的诊断和肺癌的早期诊断中已被证明是有用的。然而，肺癌筛查的实施和偶然肺结节的频繁检测导致更多的计算机断层扫描，从而增加了成本。因此，确定人工智能的成本效益对于在常规临床实践中实施这些方法非常重要。基于人工智能对肺结节的体积测量，患者可能会从偶发肺结节随访中提前出院。目的探讨在CT扫描中应用人工智能测量肺结节体积是否能缩短偶发肺结节随访时间。方法回顾性队列研究纳入随访胸部ct检查偶发性肺结节的患者。主要结果是根据目前的BTS指南使用人工智能体积测量可以更早出院的患者比例。结果共纳入患者252例，其中49例(19.4%；95%可信区间[CI]， 14.7-24.9)采用人工智能容积测量可使患者更早出院。结论基于目前的BTS指南，使用人工智能测量肺结节的体积可以缩短偶发肺结节随访的随访时间，从而减少不必要的计算机断层成像，预约和降低成本。

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

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.