False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study. Issue 3 (9th August 2022)
- Record Type:
- Journal Article
- Title:
- False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study. Issue 3 (9th August 2022)
- Main Title:
- False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study
- Authors:
- Luo, Xiao
Yang, Yadi
Yin, Shaohan
Li, Hui
Zhang, Weijing
Xu, Guixiao
Fan, Weixiong
Zheng, Dechun
Li, Jianpeng
Shen, Dinggang
Gao, Yaozong
Shao, Ying
Ban, Xiaohua
Li, Jing
Lian, Shanshan
Zhang, Cheng
Ma, Lidi
Lin, Cuiping
Luo, Yingwei
Zhou, Fan
Wang, Shiyuan
Sun, Ying
Zhang, Rong
Xie, Chuanmiao - Abstract:
- Abstract: Background: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. Methods: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1, 066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. Results: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees ( P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. Conclusions:Abstract: Background: Errors have seldom been evaluated in computer-aided detection on brain metastases. This study aimed to analyze false negatives (FNs) and false positives (FPs) generated by a brain metastasis detection system (BMDS) and by readers. Methods: A deep learning-based BMDS was developed and prospectively validated in a multicenter, multireader study. Ad hoc secondary analysis was restricted to the prospective participants (148 with 1, 066 brain metastases and 152 normal controls). Three trainees and 3 experienced radiologists read the MRI images without and with the BMDS. The number of FNs and FPs per patient, jackknife alternative free-response receiver operating characteristic figure of merit (FOM), and lesion features associated with FNs were analyzed for the BMDS and readers using binary logistic regression. Results: The FNs, FPs, and the FOM of the stand-alone BMDS were 0.49, 0.38, and 0.97, respectively. Compared with independent reading, BMDS-assisted reading generated 79% fewer FNs (1.98 vs 0.42, P < .001); 41% more FPs (0.17 vs 0.24, P < .001) but 125% more FPs for trainees ( P < .001); and higher FOM (0.87 vs 0.98, P < .001). Lesions with small size, greater number, irregular shape, lower signal intensity, and located on nonbrain surface were associated with FNs for readers. Small, irregular, and necrotic lesions were more frequently found in FNs for BMDS. The FPs mainly resulted from small blood vessels for the BMDS and the readers. Conclusions: Despite the improvement in detection performance, attention should be paid to FPs and small lesions with lower enhancement for radiologists, especially for less-experienced radiologists. … (more)
- Is Part Of:
- Neuro-oncology. Volume 25:Issue 3(2023)
- Journal:
- Neuro-oncology
- Issue:
- Volume 25:Issue 3(2023)
- Issue Display:
- Volume 25, Issue 3 (2023)
- Year:
- 2023
- Volume:
- 25
- Issue:
- 3
- Issue Sort Value:
- 2023-0025-0003-0000
- Page Start:
- 544
- Page End:
- 556
- Publication Date:
- 2022-08-09
- Subjects:
- brain neoplasms -- deep learning -- magnetic resonance imaging -- radiographic image interpretation -- ROC curve
Brain Neoplasms -- Periodicals
Brain -- Tumors -- Periodicals
Brain -- Cancer -- Periodicals
Nervous system -- Cancer -- Periodicals
616.99481 - Journal URLs:
- http://neuro-oncology.dukejournals.org/ ↗
http://neuro-oncology.oxfordjournals.org/ ↗
http://www.oxfordjournals.org/content?genre=journal&issn=1522-8517 ↗
http://ukcatalogue.oup.com/ ↗ - DOI:
- 10.1093/neuonc/noac192 ↗
- Languages:
- English
- ISSNs:
- 1522-8517
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6081.288000
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