利用 18F-FDG PET/CT 评估肺癌患者放疗后复发情况的深度学习模型的诊断性能

IF 2.5 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Changhwan Sung, Jungsu S. Oh, Byung Soo Park, Su Ssan Kim, Si Yeol Song, Jong Jin Lee
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引用次数: 0

摘要

方法 我们回顾性地招募了308名肺癌患者,这些患者在接受放疗(RT)后进行的18F-氟脱氧葡萄糖正电子发射断层扫描-计算机断层扫描(18F-FDG PET/CT)中观察到了与放疗相关的变化。通过组织学诊断或18F-FDG PET/CT后的临床随访,患者被标记为肿瘤复发阳性或阴性。结果在五个独立的测试集中,接受者操作特征曲线下面积(AUC)、灵敏度和特异性分别在0.98-0.99、95-98%和87-95%之间。结论基于二维切片的 CNN 模型使用 18F-FDG PET 成像能够很好地区分肺癌患者 RT 相关变化和肿瘤复发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer

Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer

Diagnostic performance of a deep-learning model using 18F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer

Objective

We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.

Methods

We retrospectively recruited 308 patients with lung cancer with RT-related changes observed on 18F-fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) performed after RT. Patients were labelled as positive or negative for tumour recurrence through histologic diagnosis or clinical follow-up after 18F-FDG PET/CT. A two-dimensional (2D) slice-based convolutional neural network (CNN) model was created with a total of 3329 slices as input, and performance was evaluated with five independent test sets.

Results

For the five independent test sets, the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, and specificity were in the range of 0.98–0.99, 95–98%, and 87–95%, respectively. The region determined by the model was confirmed as an actual recurred tumour through the explainable artificial intelligence (AI) using gradient-weighted class activation mapping (Grad-CAM).

Conclusion

The 2D slice-based CNN model using 18F-FDG PET imaging was able to distinguish well between RT-related changes and tumour recurrence in patients with lung cancer.

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来源期刊
Annals of Nuclear Medicine
Annals of Nuclear Medicine 医学-核医学
CiteScore
4.90
自引率
7.70%
发文量
111
审稿时长
4-8 weeks
期刊介绍: Annals of Nuclear Medicine is an official journal of the Japanese Society of Nuclear Medicine. It develops the appropriate application of radioactive substances and stable nuclides in the field of medicine. The journal promotes the exchange of ideas and information and research in nuclear medicine and includes the medical application of radionuclides and related subjects. It presents original articles, short communications, reviews and letters to the editor.
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