Voxel-level Classification of Prostate Cancer on Magnetic Resonance Imaging: Improving Accuracy Using Four-Compartment Restriction Spectrum Imaging.

Published on Mar 31, 2021in Journal of Magnetic Resonance Imaging3.954
· DOI :10.1002/JMRI.27623
Christine H. Feng5
Estimated H-index: 5
(UCSD: University of California, San Diego),
Christopher C. Conlin3
Estimated H-index: 3
(UCSD: University of California, San Diego)
+ 8 AuthorsTyler M. Seibert18
Estimated H-index: 18
(UCSD: University of California, San Diego)
Sources
Abstract
BACKGROUND Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity. PURPOSE To evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa. STUDY TYPE Retrospective. SUBJECTS Forty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa. FIELD STRENGTH/SEQUENCE A 3 T, multishell diffusion-weighted and axial T2-weighted sequences. ASSESSMENT High-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated. STATISTICAL TESTS Voxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05. RESULTS RSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P < 0.05). DATA CONCLUSION RSI4 -C1 yielded a quantitative, voxel-level classifier of PCa that was superior to conventional ADC. RSI classifier images with a low false-positive rate might improve PCa detection and facilitate clinical applications like targeted biopsy and treatment planning. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
References40
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#1Christopher C. Conlin (UCSD: University of California, San Diego)H-Index: 3
#2Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
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BACKGROUND Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. PURPOSE To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. STUDY TYPE Retrospective. SUBJECTS Forty-six patients who underwent MRI examination for suspected prostate cancer; 2...
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