Brain metastases detection on MR by means of three‐dimensional tumor‐appearance template matching

Pérez-Ramírez, Úrsula; Arana, Estanislao; Moratal, David

doi:10.1002/jmri.25207

Cited by 37 publications

(25 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The sensitivities of BM detection in previous CAD studies ranged from 30.2% to 93.5% [ 24 – 27 ], which are comparable to that of our study. However, the FP per patient in previous studies ranged from 5.18 to 34.8 [ 24 – 26 ], which are lower than that of our study. In contrast to all but one of these studies [ 25 ], we enrolled consecutive patients to minimize selection bias.…”

Section: Discussioncontrasting

confidence: 93%

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

Sunwoo¹,

Kim²,

Choi³

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

PurposeTo assess the effect of computer-aided detection (CAD) of brain metastasis (BM) on radiologists’ diagnostic performance in interpreting three-dimensional brain magnetic resonance (MR) imaging using follow-up imaging and consensus as the reference standard.Materials and methodsThe institutional review board approved this retrospective study. The study cohort consisted of 110 consecutive patients with BM and 30 patients without BM. The training data set included MR images of 80 patients with 450 BM nodules. The test set included MR images of 30 patients with 134 BM nodules and 30 patients without BM. We developed a CAD system for BM detection using template-matching and K-means clustering algorithms for candidate detection and an artificial neural network for false-positive reduction. Four reviewers (two neuroradiologists and two radiology residents) interpreted the test set images before and after the use of CAD in a sequential manner. The sensitivity, false positive (FP) per case, and reading time were analyzed. A jackknife free-response receiver operating characteristic (JAFROC) method was used to determine the improvement in the diagnostic accuracy.ResultsThe sensitivity of CAD was 87.3% with an FP per case of 302.4. CAD significantly improved the diagnostic performance of the four reviewers with a figure-of-merit (FOM) of 0.874 (without CAD) vs. 0.898 (with CAD) according to JAFROC analysis (p < 0.01). Statistically significant improvement was noted only for less-experienced reviewers (FOM without vs. with CAD, 0.834 vs. 0.877, p < 0.01). The additional time required to review the CAD results was approximately 72 sec (40% of the total review time).ConclusionCAD as a second reader helps radiologists improve their diagnostic performance in the detection of BM on MR imaging, particularly for less-experienced reviewers.

show abstract

Section: Discussioncontrasting

confidence: 93%

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

Sunwoo¹,

Kim²,

Choi³

et al. 2017

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…However, because detecting ⌬MBT os is equivalent to detecting MBTs on a single scan, this limitation can be addressed by combining our algorithm with previously described MBT computer-aided detection tools. [9][10][11][12][13][14] Further enhancement of the clinical utility of our algorithm rests on improving its FPR. Like other studies concerned with automatic MBT detection on 3D-T1-Gad, 9-13 bright vessels were a major source of false-positives.…”

Section: Discussionmentioning

confidence: 99%

“…Like other studies concerned with automatic MBT detection on 3D-T1-Gad, 9-13 bright vessels were a major source of false-positives. Differentiating vessels and MBTs could be achieved by incorporating 3D template matching-based algorithms for MBT segmentation [9][10][11][12][13] in place of our current method. This may also improve the sensitivity of our algorithm because some of the false-negatives of our technique were a direct result of poor MBT segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…42 The use of black-blood MR imaging can also address this problem 14 because this technique has been shown to be superior in capturing smaller MBTs compared with other MR images. 43,44 More recently, Pérez-Ramírez et al 13 proposed the use of a degree of anisotropy to distinguish blood vessels and MBTs.…”

Section: Discussionmentioning

confidence: 99%

“…7 Moreover, the inherent limitations of viewing scans section by section, changes in head position from one scan to another, and user subjectivity result in the potential for increased inter-and intraobserver variability in both detection and volume assessment, especially with small MBTs or subtle volume changes. 8 Although several studies have investigated the efficiency of computer-aided detection techniques in MBTs on a single MR scan, [9][10][11][12][13][14] the literature is limited in studies evaluating the efficacy of computer algorithms in the follow-up of MBTs 15 and detection of volume-changing MBTs (⌬MBTs) as an indicator of treatment response. Tracking volumetric changes is of high clinical value, and implementing computer-aided techniques in the follow-up of MBTs can improve diagnostic accuracy and efficiency [16][17][18][19] and complement current single-scan detection algorithms.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

Shearkhani¹,

Khademi

Eilaghi

et al. 2017

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

show abstract

Deep‐Learning Detection of Cancer Metastases to the Brain on MRI

Zhang

Young

Chen

et al. 2020

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Approximately one‐fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, planning of radiotherapy, and the monitoring of treatment response. Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective. Purpose To develop a deep‐learning‐based approach for finding brain metastasis on MRI. Study Type Retrospective. Sequence Axial postcontrast 3D T1‐weighted imaging. Field Strength 1.5T and 3T. Population A total of 361 scans of 121 patients were used to train and test the Faster region‐based convolutional neural network (Faster R‐CNN): 1565 lesions in 270 scans of 73 patients for training; 488 lesions in 91 scans of 48 patients for testing. From the 48 outputs of Faster R‐CNN, 212 lesions in 46 scans of 18 patients were used for training the RUSBoost algorithm (MatLab) and 276 lesions in 45 scans of 30 patients for testing. Assessment Two radiologists diagnosed and supervised annotation of metastases on brain MRI as ground truth. This data were used to produce a 2‐step pipeline consisting of a Faster R‐CNN for detecting abnormal hyperintensity that may represent brain metastasis and a RUSBoost classifier to reduce the number of false‐positive foci detected. Statistical Tests The performance of the algorithm was evaluated by using sensitivity, false‐positive rate, and receiver's operating characteristic (ROC) curves. The detection performance was assessed both per‐metastases and per‐slice. Results Testing on held‐out brain MRI data demonstrated 96% sensitivity and 20 false‐positive metastases per scan. The results showed an 87.1% sensitivity and 0.24 false‐positive metastases per slice. The area under the ROC curve was 0.79. Conclusion Our results showed that deep‐learning‐based computer‐aided detection (CAD) had the potential of detecting brain metastases with high sensitivity and reasonable specificity. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2020;52:1227–1236.

show abstract

Brain metastases detection on MR by means of three‐dimensional tumor‐appearance template matching

Abstract: This method demonstrates that 3D template matching applying DA technique has high sensitivity and low FPR for detecting brain metastases in MR images. J. Magn. Reson. Imaging 2016;44:642-652.

Cited by 37 publications

References 29 publications

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

Computer-aided detection of brain metastasis on 3D MR imaging: Observer performance study

Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field

Deep‐Learning Detection of Cancer Metastases to the Brain on MRI

Contact Info

Product

Resources

About