Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images.

IF 2.1 4区医学

Japanese Journal of Radiology Pub Date : 2025-07-07 DOI:10.1007/s11604-025-01828-z

Haruka Higashibori, Wataru Fukumoto, Sayaka Kusuda, Kazushi Yokomachi, Hidenori Mitani, Yuko Nakamura, Kazuo Awai

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引用次数: 0

Abstract

Purpose: Artificial intelligence (AI) algorithms for lung nodule detection assist radiologists. As their performance using ultra-high-resolution CT (U-HRCT) images has not been evaluated, we investigated the usefulness of 0.25-mm slices at U-HRCT using the commercially available deep-learning-based lung nodule detection (DL-LND) system.

Materials and methods: We enrolled 63 patients who underwent U-HRCT for lung cancer and suspected lung cancer. Two board-certified radiologists identified nodules more than 4 mm in diameter on 1-mm HRCT slices and set the reference standard consensually. They recorded all lesions detected on 5-, 1-, and 0.25-mm slices by the DL-LND system. Unidentified nodules were included in the reference standard. To examine the performance of the DL-LND system, the sensitivity, and positive predictive value (PPV) and the number of false positive (FP) nodules were recorded.

Results: The mean number of lesions detected on 5-, 1-, and 0.25-mm slices was 5.1, 7.8 and 7.2 per CT scan. On 5-mm slices the sensitivity and PPV were 79.8% and 46.4%; on 1-mm slices they were 91.5% and 34.8%, and on 0.25-mm slices they were 86.7% and 36.1%. The sensitivity was significantly higher on 1- than 5-mm slices (p < 0.01) while the PPV was significantly lower on 1- than 5-mm slices (p < 0.01). A slice thickness of 0.25 mm failed to improve its performance. The mean number of FP nodules on 5-, 1-, and 0.25-mm slices was 2.8, 5.2, and 4.7 per CT scan.

Conclusion: We found that 1 mm was the best slice thickness for U-HRCT images using the commercially available DL-LND system.

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基于深度学习的0.25 mm厚超高分辨率CT图像肺结节检测系统的性能

目的：人工智能（AI）肺结节检测算法辅助放射科医生。由于它们在超高分辨率CT （U-HRCT）图像上的表现尚未得到评估，我们使用市售的基于深度学习的肺结节检测（DL-LND）系统研究了0.25 mm切片在U-HRCT上的实用性。材料与方法：63例肺癌及疑似肺癌患者行U-HRCT检查。两名委员会认证的放射科医生在1毫米HRCT切片上发现直径超过4毫米的结节，并协商一致地制定了参考标准。他们用DL-LND系统记录了在5毫米、1毫米和0.25毫米切片上检测到的所有病变。未确定的结节被纳入参考标准。为了检验DL-LND系统的性能，我们记录了灵敏度、阳性预测值（PPV）和假阳性结节（FP）的数量。结果：5-、1-和0.25-mm切片的平均病灶数分别为5.1、7.8和7.2个。在5mm切片上，灵敏度和PPV分别为79.8%和46.4%；1毫米的切片分别为91.5%和34.8%，0.25毫米的切片分别为86.7%和36.1%。结论：我们发现使用市售DL-LND系统，1mm是U-HRCT图像的最佳切片厚度。

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

Japanese Journal of Radiology Medicine-Radiology, Nuclear Medicine and Imaging

自引率

4.80%

发文量

133

期刊介绍： Japanese Journal of Radiology is a peer-reviewed journal, officially published by the Japan Radiological Society. The main purpose of the journal is to provide a forum for the publication of papers documenting recent advances and new developments in the field of radiology in medicine and biology. The scope of Japanese Journal of Radiology encompasses but is not restricted to diagnostic radiology, interventional radiology, radiation oncology, nuclear medicine, radiation physics, and radiation biology. Additionally, the journal covers technical and industrial innovations. The journal welcomes original articles, technical notes, review articles, pictorial essays and letters to the editor. The journal also provides announcements from the boards and the committees of the society. Membership in the Japan Radiological Society is not a prerequisite for submission. Contributions are welcomed from all parts of the world.