The diagnostic performance and clinical value of deep learning-based nodule detection system concerning influence of location of pulmonary nodule

被引：4

作者：

You, Seulgi ^{[1
]}

Park, Ji Hyun ^{[2
]}

Park, Bumhee ^{[2
,3
]}

Shin, Han-Bit ^{[2
]}

Ha, Taeyang ^{[1
]}

Yun, Jae Sung ^{[1
]}

Park, Kyoung Joo ^{[1
]}

Jung, Yongjun ^{[1
]}

Kim, You Na ^{[1
]}

Kim, Minji ^{[1
]}

Sun, Joo Sung ^{[1
]}

机构：

[1] Ajou Univ, Dept Radiol, Sch Med, Suwon, South Korea

[2] Ajou Univ, Ajou Res Inst Innovat Med, Off Biostat, Med Ctr, Suwon, South Korea

[3] Ajou Univ, Ajou Res Inst Innovat Med, Dept Biomed Informat, Sch Med, Suwon, South Korea

来源：

INSIGHTS INTO IMAGING | 2023年 / 14卷 / 01期

关键词：

Deep learning; Chest radiography; Solitary pulmonary nodule; COMPUTER-AIDED DETECTION; LUNG-CANCER; CHEST RADIOGRAPHS;

D O I：

10.1186/s13244-023-01497-4

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Background The deep learning-based nodule detection (DLD) system improves nodule detection performance of observers on chest radiographs (CXRs). However, its performance in different pulmonary nodule (PN) locations remains unknown. Methods We divided the CXR intrathoracic region into non-danger zone (NDZ) and danger zone (DZ). The DZ included the lung apices, paramediastinal areas, and retrodiaphragmatic areas, where nodules could be missed. We used a dataset of 300 CXRs (100 normal and 200 abnormal images with 216 PNs [107 NDZ and 109 DZ nodules]). Eight observers (two thoracic radiologists [ TRs], two non-thoracic radiologists [NTRs], and four radiology residents [RRs]) interpreted each radiograph with and without the DLD system. The metric of lesion localization fraction (LLF; the number of correctly localized lesions divided by the total number of true lesions) was used to evaluate the diagnostic performance according to the nodule location. Results The DLD system demonstrated a lower LLF for the detection of DZ nodules (64.2) than that of NDZ nodules (83.2, p = 0.008). For DZ nodule detection, the LLF of the DLD system (64.2) was lower than that of TRs (81.7, p < 0.001), which was comparable to that of NTRs (56.4, p = 0.531) and RRs (56.7, p = 0.459). Nonetheless, the LLF of RRs significantly improved from 56.7 to 65.6 using the DLD system (p = 0.021) for DZ nodule detection. Conclusion The performance of the DLD system was lower in the detection of DZ nodules compared to that of NDZ nodules. Nonetheless, RR performance in detecting DZ nodules improved upon using the DLD system. Critical relevance statement Despite the deep learning-based nodule detection system's limitations in detecting danger zone nodules, it proves beneficial for less-experienced observers by providing valuable assistance in identifying these nodules, thereby advancing nodule detection in clinical practice. Key points The deep learning-based nodule detection (DLD) system can improve the diagnostic performance of observers in nodule detection. The DLD system shows poor diagnostic performance in detecting danger zone nodules. For less-experienced observers, the DLD system is helpful in detecting danger zone nodules.

引用

页数：11