Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

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Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks

Journal of Medical Imaging ◽

10.1117/1.jmi.6.1.011002 ◽

2018 ◽

Vol 6 (01) ◽

pp. 1 ◽

Cited By ~ 1

Keyword(s):

Magnetic Resonance ◽

Breast Lesion ◽

Short Term Memory ◽

Magnetic Resonance Images ◽

Short Term ◽

Term Memory ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced ◽

Long Short Term Memory ◽

Lesion Classification

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Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis

Acta Radiologica ◽

10.1177/0284185120956269 ◽

2020 ◽

pp. 028418512095626

Author(s):

Lu Yang ◽

Yuchuan Tan ◽

Hanli Dan ◽

Lin Hu ◽

Jiuquan Zhang

Keyword(s):

Magnetic Resonance Imaging ◽

Prostate Cancer ◽

Magnetic Resonance ◽

Likelihood Ratio ◽

Diffusion Weighted Imaging ◽

Diagnostic Performance ◽

Resonance Imaging ◽

Dce Mri ◽

Dynamic Contrast Enhanced ◽

Contrast Enhanced

Background The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. Purpose To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. Material and Methods A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. Results Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76–0.81), 0.85 (95% CI = 0.83–0.86), 6.58 (95% CI = 3.93–11.00), 0.24 (95% CI = 0.17–0.34), and 36.43 (95% CI = 14.41–92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. Conclusion Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.

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