Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort

被引:2
作者
Yun, Suyoung [1 ]
Park, Ji Eun [2 ,3 ]
Kim, Nakyoung [4 ]
Park, Seo Young [5 ]
Kim, Ho Sung [2 ,3 ]
机构
[1] Inje Univ, Coll Med, Busan Paik Hosp, Dept Radiol, Busan, South Korea
[2] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, 43 Olymp Ro 88, Seoul 05505, South Korea
[3] Univ Ulsan, Res Inst Radiol, Asan Med Ctr, Coll Med, 43 Olymp Ro 88, Seoul 05505, South Korea
[4] Dynapex LLC, Seoul, South Korea
[5] Korea Natl Open Univ, Dept Stat & Data Sci, Seoul, South Korea
关键词
Deep learning; Brain metastases; Spin-echo imaging; Early detection of disease; False positives; ARTIFICIAL-INTELLIGENCE; STEREOTACTIC RADIOSURGERY; SEGMENTATION; MANAGEMENT; IMAGES;
D O I
10.1007/s00330-023-10318-7
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
Objectives To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL).Materials and methods DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists.Results Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913).Conclusion The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists.Clinical relevance statementThe use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed.
引用
收藏
页码:2873 / 2884
页数:12
相关论文
共 30 条
[21]   Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging [J].
Park, Yae Won ;
Jun, Yohan ;
Lee, Yangho ;
Han, Kyunghwa ;
An, Chansik ;
Ahn, Sung Soo ;
Hwang, Dosik ;
Lee, Seung-Koo .
EUROPEAN RADIOLOGY, 2021, 31 (09) :6686-6695
[22]   Stereotactic radiosurgery (SRS) in the modern management of patients with brain metastases [J].
Soliman, Hany ;
Das, Sunit ;
Larson, David A. ;
Sahgal, Arjun .
ONCOTARGET, 2016, 7 (11) :12318-12330
[23]   The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis [J].
Suh, Chong Hyun ;
Jung, Seung Chai ;
Kim, Kyung Won ;
Pyo, Junhee .
JOURNAL OF NEURO-ONCOLOGY, 2016, 129 (02) :363-371
[24]  
Tatsuno Satoshi, 1996, Nippon Acta Radiologica, V56, P855
[25]   Treatment for Brain Metastases: ASCO-SNO-ASTRO Guideline [J].
Vogelbaum, Michael A. ;
Brown, Paul D. ;
Messersmith, Hans ;
Brastianos, Priscilla K. ;
Burri, Stuart ;
Cahill, Dan ;
Dunn, Ian F. ;
Gaspar, Laurie E. ;
Gatson, Na Tosha N. ;
Gondi, Vinai ;
Jordan, Justin T. ;
Lassman, Andrew B. ;
Maues, Julia ;
Mohile, Nimish ;
Redjal, Navid ;
Stevens, Glen ;
Sulman, Erik ;
van den Bent, Martin ;
Wallace, H. James ;
Weinberg, Jeffrey S. ;
Zadeh, Gelareh ;
Schiff, David .
JOURNAL OF CLINICAL ONCOLOGY, 2022, 40 (05) :492-+
[26]   Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery [J].
Wu, Siangruei ;
Wu, Yihong ;
Chang, Haoyun ;
Su, Florence T. ;
Liao, Hengchun ;
Tseng, Wanju ;
Liao, Chunchih ;
Lai, Feipei ;
Hsu, Fengming ;
Xiao, Furen .
APPLIED SCIENCES-BASEL, 2021, 11 (19)
[27]   Deep learning-based detection and segmentation-assisted management of brain metastases [J].
Xue, Jie ;
Wang, Bao ;
Ming, Yang ;
Liu, Xuejun ;
Jiang, Zekun ;
Wang, Chengwei ;
Liu, Xiyu ;
Chen, Ligang ;
Qu, Jianhua ;
Xu, Shangchen ;
Tang, Xuqun ;
Mao, Ying ;
Liu, Yingchao ;
Li, Dengwang .
NEURO-ONCOLOGY, 2020, 22 (04) :505-514
[28]   Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study [J].
Yin, Shaohan ;
Luo, Xiao ;
Yang, Yadi ;
Shao, Ying ;
Ma, Lidi ;
Lin, Cuiping ;
Yang, Qiuxia ;
Wang, Deling ;
Luo, Yingwei ;
Mai, Zhijun ;
Fan, Weixiong ;
Zheng, Dechun ;
Li, Jianpeng ;
Cheng, Fengyan ;
Zhang, Yuhui ;
Zhong, Xinwei ;
Shen, Fangmin ;
Shao, Guohua ;
Wu, Jiahao ;
Sun, Ying ;
Luo, Huiyan ;
Li, Chaofeng ;
Gao, Yaozong ;
Shen, Dinggang ;
Zhang, Rong ;
Xie, Chuanmiao .
NEURO-ONCOLOGY, 2022, 24 (09) :1559-1570
[29]   Deep-Learning Detection of Cancer Metastases to the Brain on MRI [J].
Zhang, Min ;
Young, Geoffrey S. ;
Chen, Huai ;
Li, Jing ;
Qin, Lei ;
McFaline-Figueroa, J. Ricardo ;
Reardon, David A. ;
Cao, Xinhua ;
Wu, Xian ;
Xu, Xiaoyin .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2020, 52 (04) :1227-1236
[30]   Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors [J].
Zhou, Zijian ;
Sanders, Jeremiah W. ;
Johnson, Jason M. ;
Gule-Monroe, Maria K. ;
Chen, Melissa M. ;
Briere, Tina M. ;
Wang, Yan ;
Son, Jong Bum ;
Pagel, Mark D. ;
Li, Jing ;
Ma, Jingfei .
RADIOLOGY, 2020, 295 (02) :407-415