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Wada T, Kikuchi K, Obara M, Tokunaga C, Yamashita K, Kobayashi K, Kato T, Ishigami K, Togao O. Improvement of image quality for bright-blood image in VISIBLE (volume isotropic simultaneous interleaved bright- and black-blood examination) by using k-space reordering and startup echoes. Magn Reson Imaging 2024; 112:144-150. [PMID: 39029602 DOI: 10.1016/j.mri.2024.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 07/13/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
PURPOSE A volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) can simultaneously acquire images with suppressed vascular signals (black-blood images) and images without suppression (bright-blood images). We aimed to improve of the bright-blood images by adjusting the k-space filling and using startup echo. METHODS The k-space arrangement of bright-blood images in the conventional VISIBLE followed a low-to-high frequency order, whereas that in the proposed VISIBLE sequence was in the reversed order, and a startup echo was added. The effects of startup echo on the signal-to-noise ratio (SNR) were evaluated using phantoms, considering both white matter (WM) and post-contrast blood. Data from copper sulfate phantoms were acquired in 1D Fourier transform mode using both the conventional and proposed methods of the two VISIBLE sequences. The signal behavior with each sequence was evaluated. Fourteen patients with a total of 21 metastases were included in the study. For each patient, VISIBLE images of both conventional and proposed methods were obtained consecutively after the contrast agent administration. Using clinical images, we conducted a comparison of the SNR and contrast-to-noise ratio (CNR) for tumors, normal WM, and blood vessels between the conventional and proposed VISIBLE sequences. RESULTS There was no significant difference in SNRs for both black- and bright-blood images between the conventional sequence and the proposed sequence with different number of startup echoes, however, the SNR of the proposed sequence decreased with increasing number of startup echoes in both black- and bright-images. The signal behavior of the bright-blood image reached a "steady state" when the startup echo exceeded 20. The SNRs of blood vessels in the bright-blood images did not differ significantly between conventional and proposed VISIBLE sequences. The SNRs of WM in the bright-blood images was significantly larger in the conventional sequence than in the proposed sequence. The SNRs of tumors in bright blood images was significantly larger in the proposed sequence than in the conventional sequence. The CNRs between tumors and WM, vessels and WM in the bright-blood images were significantly higher in the proposed sequence than in the conventional sequence. CONCLUSION The use of the startup echo in combination with the high-to-low frequency k-space ordering method resulted in improved CNR of the bright-blood images in the VISIBLE sequence.
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Affiliation(s)
- Tatsuhiro Wada
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan.
| | - Kazufumi Kikuchi
- Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Japan
| | | | - Chiaki Tokunaga
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Koji Kobayashi
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Toyoyuki Kato
- Division of Radiology, Department of Medical Technology, Kyushu University Hospital, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Japan
| | - Osamu Togao
- Department of Molecular Imaging & Diagnosis, Graduate School of Medical Sciences, Kyushu University, Japan
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Yun S, Park JE, Kim N, Park SY, Kim HS. 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. Eur Radiol 2024; 34:2873-2884. [PMID: 37891415 DOI: 10.1007/s00330-023-10318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/08/2023] [Accepted: 08/18/2023] [Indexed: 10/29/2023]
Abstract
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 STATEMENT The 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. KEY POINTS •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.
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Affiliation(s)
- Suyoung Yun
- Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | | | - Seo Young Park
- Department of Statistics and Data Science, Korea National Open University, Seoul, Republic of Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea
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Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Yuhei S, Fujioka Y, Narutomi F, Obara M, Yoshimoto K, Ishigami K. Comparison of diagnostic performance of radiologist- and AI-based assessments of T2-FLAIR mismatch sign and quantitative assessment using synthetic MRI in the differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, IDH-mutant and 1p/19q-codeleted. Neuroradiology 2024; 66:333-341. [PMID: 38224343 PMCID: PMC10859342 DOI: 10.1007/s00234-024-03288-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/07/2024] [Indexed: 01/16/2024]
Abstract
PURPOSE This study aimed to compare assessments by radiologists, artificial intelligence (AI), and quantitative measurement using synthetic MRI (SyMRI) for differential diagnosis between astrocytoma, IDH-mutant and oligodendroglioma, and IDH-mutant and 1p/19q-codeleted and to identify the superior method. METHODS Thirty-three cases (men, 14; women, 19) comprising 19 astrocytomas and 14 oligodendrogliomas were evaluated. Four radiologists independently evaluated the presence of the T2-FLAIR mismatch sign. A 3D convolutional neural network (CNN) model was trained using 50 patients outside the test group (28 astrocytomas and 22 oligodendrogliomas) and transferred to evaluate the T2-FLAIR mismatch lesions in the test group. If the CNN labeled more than 50% of the T2-prolonged lesion area, the result was considered positive. The T1/T2-relaxation times and proton density (PD) derived from SyMRI were measured in both gliomas. Each quantitative parameter (T1, T2, and PD) was compared between gliomas using the Mann-Whitney U-test. Receiver-operating characteristic analysis was used to evaluate the diagnostic performance. RESULTS The mean sensitivity, specificity, and area under the curve (AUC) of radiologists vs. AI were 76.3% vs. 94.7%; 100% vs. 92.9%; and 0.880 vs. 0.938, respectively. The two types of diffuse gliomas could be differentiated using a cutoff value of 2290/128 ms for a combined 90th percentile of T1 and 10th percentile of T2 relaxation times with 94.4/100% sensitivity/specificity with an AUC of 0.981. CONCLUSION Compared to the radiologists' assessment using the T2-FLAIR mismatch sign, the AI and the SyMRI assessments increased both sensitivity and objectivity, resulting in improved diagnostic performance in differentiating gliomas.
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Affiliation(s)
- Kazufumi Kikuchi
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Koji Yamashita
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daichi Momosaka
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshitomo Kikuchi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Daisuke Kuga
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Sangatsuda Yuhei
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yutaka Fujioka
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Fumiya Narutomi
- Department of Anatomic Pathology, Pathological Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Makoto Obara
- Philips Japan Ltd., 2-13-37, Konan, Minato-Ku, Tokyo, 108-8507, Japan
| | - Koji Yoshimoto
- Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
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Jeong H, Park JE, Kim N, Yoon SK, Kim HS. Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study. Eur Radiol 2024; 34:2062-2071. [PMID: 37658885 PMCID: PMC10873231 DOI: 10.1007/s00330-023-10120-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 06/25/2023] [Accepted: 07/01/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs. METHODS The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated. RESULTS Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk. CONCLUSION DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs. CLINICAL RELEVANCE STATEMENT For patients with newly diagnosed brain metastasis, deep learning-based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes. KEY POINTS • Deep learning-based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.
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Affiliation(s)
- Hana Jeong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea.
| | | | - Shin-Kyo Yoon
- Department of Oncology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
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Wang TW, Hsu MS, Lee WK, Pan HC, Yang HC, Lee CC, Wu YT. Brain metastasis tumor segmentation and detection using deep learning algorithms: A systematic review and meta-analysis. Radiother Oncol 2024; 190:110007. [PMID: 37967585 DOI: 10.1016/j.radonc.2023.110007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/15/2023] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
BACKGROUND Manual detection of brain metastases is both laborious and inconsistent, driving the need for more efficient solutions. Accordingly, our systematic review and meta-analysis assessed the efficacy of deep learning algorithms in detecting and segmenting brain metastases from various primary origins in MRI images. METHODS We conducted a comprehensive search of PubMed, Embase, and Web of Science up to May 24, 2023, which yielded 42 relevant studies for our analysis. We assessed the quality of these studies using the QUADAS-2 and CLAIM tools. Using a random-effect model, we calculated the pooled lesion-wise dice score as well as patient-wise and lesion-wise sensitivity. We performed subgroup analyses to investigate the influence of factors such as publication year, study design, training center of the model, validation methods, slice thickness, model input dimensions, MRI sequences fed to the model, and the specific deep learning algorithms employed. Additionally, meta-regression analyses were carried out considering the number of patients in the studies, count of MRI manufacturers, count of MRI models, training sample size, and lesion number. RESULTS Our analysis highlighted that deep learning models, particularly the U-Net and its variants, demonstrated superior segmentation accuracy. Enhanced detection sensitivity was observed with an increased diversity in MRI hardware, both in terms of manufacturer and model variety. Furthermore, slice thickness was identified as a significant factor influencing lesion-wise detection sensitivity. Overall, the pooled results indicated a lesion-wise dice score of 79%, with patient-wise and lesion-wise sensitivities at 86% and 87%, respectively. CONCLUSIONS The study underscores the potential of deep learning in improving brain metastasis diagnostics and treatment planning. Still, more extensive cohorts and larger meta-analysis are needed for more practical and generalizable algorithms. Future research should prioritize these areas to advance the field. This study was funded by the Gen. & Mrs. M.C. Peng Fellowship and registered under PROSPERO (CRD42023427776).
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Affiliation(s)
- Ting-Wei Wang
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ming-Sheng Hsu
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Kai Lee
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan
| | - Hung-Chuan Pan
- Department of Neurosurgery, Taichung Veterans General Hospital, Taichung, Taiwan; Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Huai-Che Yang
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cheng-Chia Lee
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yu-Te Wu
- Institute of Biophotonics, National Yang Ming Chiao Tung University, 155, Sec. 2, Li-Nong St. Beitou Dist., Taipei 112304, Taiwan; National Yang Ming Chiao Tung University, Brain Research Center, Taiwan; National Yang Ming Chiao Tung University, College Medical Device Innovation and Translation Center, Taiwan.
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Chen J, Meng L, Bu C, Zhang C, Wu P. Feature pyramid network-based computer-aided detection and monitoring treatment response of brain metastases on contrast-enhanced MRI. Clin Radiol 2023; 78:e808-e814. [PMID: 37573242 DOI: 10.1016/j.crad.2023.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/06/2023] [Accepted: 07/12/2023] [Indexed: 08/14/2023]
Abstract
AIM To investigate the value of feature pyramid network (FPN)-based computer-aided detection (CAD) of brain metastases (BMs) before and after non-surgical treatment, and to evaluate its performance in monitoring treatment response of BM on contrast-enhanced (CE) magnetic resonance imaging (MRI). MATERIAL AND METHODS Eighty-five cancer patients newly diagnosed with BM who had undergone initial and follow-up three-dimensional (3D) CE MRI at Liaocheng People's Hospital were included retrospectively in this study. Manual detection (MD) was performed by reviewer 1. Computer-aided detection (CAD) was performed by reviewer 2 using uAI Discover-BMs software. The treatment response was assessed by the two reviewers for each patient separately. A paired chi-square test was used to compare the differences in the detection of BM between MD and CAD. Agreement between MD and CAD in monitoring treatment response was assessed by kappa test. RESULTS The sensitivities of MD and CAD on initial 3D CE MRI were 78.65% and 99.13%, respectively. The sensitivities of MD and CAD on follow-up 3D CE MRI were 76.32% and 98.24%, respectively. There was a very good agreement between Reviewer 1 and Reviewer 2 in evaluating the treatment response of BM. CONCLUSION FPN-based CAD has a higher sensitivity of close to 100% and lower false negatives (FNs) for BM detection, compared to MD. Although CAD had a few shortcomings in reflecting changes of BMs after treatment, it had high performance in monitoring treatment response of BM on CE MRI.
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Affiliation(s)
- J Chen
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China.
| | - L Meng
- Department of Radiotherapy, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - C Bu
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - C Zhang
- Department of MR, Liaocheng People's Hospital, Liaocheng, Shandong Province, 252000, China
| | - P Wu
- Philips Healthcare, Shanghai, 200072, China
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Qu J, Zhang W, Shu X, Wang Y, Wang L, Xu M, Yao L, Hu N, Tang B, Zhang L, Lui S. Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation. Eur Radiol 2023; 33:6648-6658. [PMID: 37186214 DOI: 10.1007/s00330-023-09648-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/23/2023] [Accepted: 02/08/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVES To construct and evaluate a gated high-resolution convolutional neural network for detecting and segmenting brain metastasis (BM). METHODS This retrospective study included craniocerebral MRI scans of 1392 patients with 14,542 BMs and 200 patients with no BM between January 2012 and April 2022. A primary dataset including 1000 cases with 11,686 BMs was employed to construct the model, while an independent dataset including 100 cases with 1069 BMs from other hospitals was used to examine the generalizability. The potential of the model for clinical use was also evaluated by comparing its performance in BM detection and segmentation to that of radiologists, and comparing radiologists' lesion detecting performances with and without model assistance. RESULTS Our model yielded a recall of 0.88, a dice similarity coefficient (DSC) of 0.90, a positive predictive value (PPV) of 0.93 and a false positives per patient (FP) of 1.01 in the test set, and a recall of 0.85, a DSC of 0.89, a PPV of 0.93, and a FP of 1.07 in dataset from other hospitals. With the model's assistance, the BM detection rates of 4 radiologists improved significantly, ranging from 5.2 to 15.1% (all p < 0.001), and also for detecting small BMs with diameter ≤ 5 mm (ranging from 7.2 to 27.0%, all p < 0.001). CONCLUSIONS The proposed model enables accurate BM detection and segmentation with higher sensitivity and less time consumption, showing the potential to augment radiologists' performance in detecting BM. CLINICAL RELEVANCE STATEMENT This study offers a promising computer-aided tool to assist the brain metastasis detection and segmentation in routine clinical practice for cancer patients. KEY POINTS • The GHR-CNN could accurately detect and segment BM on contrast-enhanced 3D-T1W images. • The GHR-CNN improved the BM detection rate of radiologists, including the detection of small lesions. • The GHR-CNN enabled automated segmentation of BM in a very short time.
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Affiliation(s)
- Jiao Qu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Wenjing Zhang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Xin Shu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Ying Wang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Lituan Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Mengyuan Xu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Li Yao
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Na Hu
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Biqiu Tang
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China
| | - Lei Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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Luo X, Yang Y, Yin S, Li H, Zhang W, Xu G, Fan W, Zheng D, Li J, Shen D, Gao Y, Shao Y, Ban X, Li J, Lian S, Zhang C, Ma L, Lin C, Luo Y, Zhou F, Wang S, Sun Y, Zhang R, Xie C. False-negative and false-positive outcomes of computer-aided detection on brain metastasis: Secondary analysis of a multicenter, multireader study. Neuro Oncol 2023; 25:544-556. [PMID: 35943350 PMCID: PMC10013637 DOI: 10.1093/neuonc/noac192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Indexed: 11/14/2022] Open
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.
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Affiliation(s)
- Xiao Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yadi Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shaohan Yin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Hui Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weijing Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Guixiao Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Weixiong Fan
- Department of Radiology, Meizhou People's Hospital, Meizhou, China
| | - Dechun Zheng
- Department of Radiology, Fujian Cancer Hospital, Fujian Medical University Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jianpeng Li
- Department of Radiology, Affiliated Dongguan Hospital, Southern Medical University, Guangzhou, China
| | - Dinggang Shen
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Yaozong Gao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Ying Shao
- R&D Department, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Xiaohua Ban
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Jing Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shanshan Lian
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cheng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Lidi Ma
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Cuiping Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yingwei Luo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Fan Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Shiyuan Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Rong Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Chuanmiao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Department of Radiology, Sun Yat-Sen University Cancer Center, Guangzhou, China
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10
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Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis. Cancers (Basel) 2023; 15:cancers15020334. [PMID: 36672286 PMCID: PMC9857123 DOI: 10.3390/cancers15020334] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 12/31/2022] [Accepted: 12/31/2022] [Indexed: 01/06/2023] Open
Abstract
Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.
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11
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Buchner JA, Kofler F, Etzel L, Mayinger M, Christ SM, Brunner TB, Wittig A, Menze B, Zimmer C, Meyer B, Guckenberger M, Andratschke N, El Shafie RA, Debus J, Rogers S, Riesterer O, Schulze K, Feldmann HJ, Blanck O, Zamboglou C, Ferentinos K, Wolff R, Eitz KA, Combs SE, Bernhardt D, Wiestler B, Peeken JC. Development and external validation of an MRI-based neural network for brain metastasis segmentation in the AURORA multicenter study. Radiother Oncol 2023; 178:109425. [PMID: 36442609 DOI: 10.1016/j.radonc.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Stereotactic radiotherapy is a standard treatment option for patients with brain metastases. The planning target volume is based on gross tumor volume (GTV) segmentation. The aim of this work is to develop and validate a neural network for automatic GTV segmentation to accelerate clinical daily routine practice and minimize interobserver variability. METHODS We analyzed MRIs (T1-weighted sequence ± contrast-enhancement, T2-weighted sequence, and FLAIR sequence) from 348 patients with at least one brain metastasis from different cancer primaries treated in six centers. To generate reference segmentations, all GTVs and the FLAIR hyperintense edematous regions were segmented manually. A 3D-U-Net was trained on a cohort of 260 patients from two centers to segment the GTV and the surrounding FLAIR hyperintense region. During training varying degrees of data augmentation were applied. Model validation was performed using an independent international multicenter test cohort (n = 88) including four centers. RESULTS Our proposed U-Net reached a mean overall Dice similarity coefficient (DSC) of 0.92 ± 0.08 and a mean individual metastasis-wise DSC of 0.89 ± 0.11 in the external test cohort for GTV segmentation. Data augmentation improved the segmentation performance significantly. Detection of brain metastases was effective with a mean F1-Score of 0.93 ± 0.16. The model performance was stable independent of the center (p = 0.3). There was no correlation between metastasis volume and DSC (Pearson correlation coefficient 0.07). CONCLUSION Reliable automated segmentation of brain metastases with neural networks is possible and may support radiotherapy planning by providing more objective GTV definitions.
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Affiliation(s)
- Josef A Buchner
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.
| | - Florian Kofler
- Department of Informatics, Technical University of Munich, Munich, Germany; Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany; Helmholtz AI, Helmholtz Zentrum Munich, Munich, Germany
| | - Lucas Etzel
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Michael Mayinger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian M Christ
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Thomas B Brunner
- Department of Radiation Oncology, University Hospital Magdeburg, Magdeburg, Germany
| | - Andrea Wittig
- Department of Radiotherapy and Radiation Oncology, University Hospital Jena, Friedrich-Schiller University, Jena, Germany
| | - Björn Menze
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Bernhard Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolaus Andratschke
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Rami A El Shafie
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany; Department of Radiation Oncology, University Medical Center Göttingen, Göttingen, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany; Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Oncology (NCRO), Heidelberg, Germany
| | - Susanne Rogers
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Oliver Riesterer
- Radiation Oncology Center KSA-KSB, Kantonsspital Aarau, Aarau, Switzerland
| | - Katrin Schulze
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Horst J Feldmann
- Department of Radiation Oncology, General Hospital Fulda, Fulda, Germany
| | - Oliver Blanck
- Department of Radiation Oncology, University Medical Center Schleswig Holstein, Kiel, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, University of Freiburg - Medical Center, Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany; Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Konstantinos Ferentinos
- Department of Radiation Oncology, German Oncology Center, European University of Cyprus, Limassol, Cyprus
| | - Robert Wolff
- Saphir Radiosurgery Center Frankfurt and Northern Germany, Guestrow, Germany; Department of Neurosurgery, University Hospital Frankfurt, Frankfurt, Germany
| | - Kerstin A Eitz
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Stephanie E Combs
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; TranslaTUM - Central Institute for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Jan C Peeken
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany; Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany; Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Center Munich, Munich, Germany
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12
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Pflüger I, Wald T, Isensee F, Schell M, Meredig H, Schlamp K, Bernhardt D, Brugnara G, Heußel CP, Debus J, Wick W, Bendszus M, Maier-Hein KH, Vollmuth P. Automated detection and quantification of brain metastases on clinical MRI data using artificial neural networks. Neurooncol Adv 2022; 4:vdac138. [PMID: 36105388 PMCID: PMC9466273 DOI: 10.1093/noajnl/vdac138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Reliable detection and precise volumetric quantification of brain metastases (BM) on MRI are essential for guiding treatment decisions. Here we evaluate the potential of artificial neural networks (ANN) for automated detection and quantification of BM.
Methods
A consecutive series of 308 patients with BM was used for developing an ANN (with a 4:1 split for training/testing) for automated volumetric assessment of contrast-enhancing tumors (CE) and non-enhancing FLAIR signal abnormality including edema (NEE). An independent consecutive series of 30 patients was used for external testing. Performance was assessed case-wise for CE and NEE and lesion-wise for CE using the case-wise/lesion-wise DICE-coefficient (C/L-DICE), positive predictive value (L-PPV) and sensitivity (C/L-Sensitivity).
Results
The performance of detecting CE lesions on the validation dataset was not significantly affected when evaluating different volumetric thresholds (0.001–0.2 cm3; P = .2028). The median L-DICE and median C-DICE for CE lesions were 0.78 (IQR = 0.6–0.91) and 0.90 (IQR = 0.85–0.94) in the institutional as well as 0.79 (IQR = 0.67–0.82) and 0.84 (IQR = 0.76–0.89) in the external test dataset. The corresponding median L-Sensitivity and median L-PPV were 0.81 (IQR = 0.63–0.92) and 0.79 (IQR = 0.63–0.93) in the institutional test dataset, as compared to 0.85 (IQR = 0.76–0.94) and 0.76 (IQR = 0.68–0.88) in the external test dataset. The median C-DICE for NEE was 0.96 (IQR = 0.92–0.97) in the institutional test dataset as compared to 0.85 (IQR = 0.72–0.91) in the external test dataset.
Conclusion
The developed ANN-based algorithm (publicly available at www.github.com/NeuroAI-HD/HD-BM) allows reliable detection and precise volumetric quantification of CE and NEE compartments in patients with BM.
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Affiliation(s)
- Irada Pflüger
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Tassilo Wald
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Fabian Isensee
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Kai Schlamp
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
| | - Denise Bernhardt
- Department of Radiation Oncology, Klinikum rechts der Isar, Technical University Munich , Munich , Germany
| | - Gianluca Brugnara
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Clinic for Thoracic Diseases (Thoraxklinik), Heidelberg University Hospital , Heidelberg , Germany
- Member of the Cerman Center for Lung Research (DZL), Translational Lung Research Center (TLRC) , Heidelberg , Germany
| | - Juergen Debus
- Department of Radiation Oncology, Heidelberg University Hospital , Heidelberg , Germany
- Heidelberg Institute for Radiation Oncology (HIRO), Heidelberg University Hospital , Heidelberg , Germany
- German Cancer Consotium (DKTK), National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ) , Heidelberg , Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Wolfgang Wick
- Neurology Clinic, Heidelberg University Hospital , Heidelberg , Germany
- Clinical Cooperation Unit Neurooncology, German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
| | - Klaus H Maier-Hein
- Medical Image Computing, German Cancer Research Center (DKFZ) , Heidelberg , Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital , Heidelberg , Germany
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