VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient

Johnston, Edward; Bonet‐Carne, Elisenda; Ferizi, Uran; Yvernault, Ben; Pye, Hayley; Patel, Dominic; Clemente, Joey; Piga, Wivijin; Heavey, Susan; Sidhu, Harbir; Giganti, Francesco; O’Callaghan, James M.; Appayya, Mrishta Brizmohun; Grey, Alistair; Saborowska, A; Ourselin, Sébastien; Hawkes, David J.; Moore, Caroline M.; Emberton, Mark; Ahmed, Hashim U.; Whitaker, Hayley C.; Rodriguez‐Justo, Manuel; Freeman, A; Atkinson, David; Alexander, Daniel C.; Panagiotaki, Eleftheria; Punwani, Shonit

doi:10.1148/radiol.2019181749

Cited by 66 publications

(69 citation statements)

References 21 publications

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“…To further enhance the specificity of dMRI‐derived parameters to the underlying microstructure, recently proposed higher‐order dMRI techniques employ biophysical compartment models of various (assumed) tissue features to fit the dMRI measurements, usually acquired at multiple b ‐values and diffusion times. Such approaches showed enhanced ability over ADC to explain the diffusion measurements and differentiate between benign and malignant tumors in various cancer types such as xenograft colorectal tumors, prostate cancer, breast cancer, and gliomas . Moreover, the estimated dMRI parameters for restriction size and volume fraction correlated well with histology measurements .…”

Section: Introductionmentioning

confidence: 92%

Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

Ianuş

Santiago

Galzerano

et al. 2019

Magnetic Resonance in Med

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Purpose Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher‐order diffusion MRI models accounting for non‐Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging. Methods The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM‐Kurtosis. Results Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM‐Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm2/ms) and malignant (1.02 ± 0.41 μm2/ms, P = .03) nodes. IVIM‐Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T2‐weighted imaging. Conclusion Higher‐order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.

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Section: Introductionmentioning

confidence: 92%

Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

Ianuş

Santiago

Galzerano

et al. 2019

Magnetic Resonance in Med

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“…Repeatability of prostate DWI has been studied with measurements of raw signal using intra‐class correlation coefficient (ICC)(3,1) . In addition, repeatability of prostate DWI has already been assessed for advanced DWI models such as VERDICT model, which was evaluated in 14 PCa patients who underwent 2 repeated prostate MRI examinations. Fedorov et al calculated ICC values for apparent diffusion coefficient maps calculated using mono‐exponential function for tumor region in 15 PCa patients scanned with 2 different scanners.…”

Section: Introductionmentioning

confidence: 99%

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

Merisaari

Taimen

Shiradkar

et al. 2019

Magnetic Resonance in Med

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Purpose To evaluate repeatability of prostate DWI‐derived radiomics and machine learning methods for prostate cancer (PCa) characterization. Methods A total of 112 patients with diagnosed PCa underwent 2 prostate MRI examinations (Scan1 and Scan2) performed on the same day. DWI was performed using 12 b‐values (0–2000 s/mm2), post‐processed using kurtosis function, and PCa areas were annotated using whole mount prostatectomy sections. A total of 1694 radiomic features including Sobel, Kirch, Gradient, Zernike Moments, Gabor, Haralick, CoLIAGe, Haar wavelet coefficients, 3D analogue to Laws features, 2D contours, and corner detectors were calculated. Radiomics and 4 feature pruning methods (area under the receiver operator characteristic curve, maximum relevance minimum redundancy, Spearman’s ρ, Wilcoxon rank‐sum) were evaluated in terms of Scan1‐Scan2 repeatability using intraclass correlation coefficient (ICC)(3,1). Classification performance for clinically significant and insignificant PCa with Gleason grade groups 1 versus >1 was evaluated by area under the receiver operator characteristic curve in unseen random 30% data split. Results The ICC(3,1) values for conventional radiomics and feature pruning methods were in the range of 0.28–0.90. The machine learning classifications varied between Scan1 and Scan2 with % of same class labels between Scan1 and Scan2 in the range of 61–81%. Surface‐to‐volume ratio and corner detector‐based features were among the most represented features with high repeatability, ICC(3,1) >0.75, consistently high ranking using all 4 feature pruning methods, and classification performance with area under the receiver operator characteristic curve >0.70. Conclusion Surface‐to‐volume ratio and corner detectors for prostate DWI led to good classification of unseen data and performed similarly in Scan1 and Scan2 in contrast to multiple conventional radiomic features.

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“…By using this most restricted compartment, the vast majority of benign prostate tissue signal is suppressed, and output images have noticeably less noise (Figure 4) than conventional ADC maps. Prior studies have also investigated the performance and utility of advanced DWI techniques, including RSI, in prostate cancer detection and characterization 15,[26][27][28][29] . However, many of the other studies conducted analysis at the lesion level rather than the voxel level.…”

Section: Resultsmentioning

confidence: 99%

Voxel-level Classification of Prostate Cancer Using a Four-Compartment Restriction Spectrum Imaging Model

Feng

Conlin

Batra

et al. 2020

Preprint

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Purpose: Diffusion MRI is integral to detection of prostate cancer (PCa), but conventional ADC cannot capture the complexity of prostate tissues. 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. In this study, RSI4-C1 was evaluated as a quantitative voxel-level classifier of PCa. Methods: This was a retrospective analysis of 46 men who underwent an expanded-acquisition pelvic MRI for suspected PCa. Twenty-three men had no detectable cancer on biopsy or clinical follow-up; the other 23 had biopsy-proven PCa corresponding to a lesion on MRI (PI-RADS category 3-5). 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. 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. Specifically, we compared RSI4-C1 and conventional ADC on mean (and 95% CI) for two metrics: area under the curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90). Classifier images were also compared. Results: RSI4-C1 outperformed conventional ADC, with greater AUC [0.977 (0.951-0.991) vs. 0.921 (0.873-0.949)] and lower FPR90 [0.033 (0.009-0.083) vs. 0.201 (0.131-0.300)]. 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.

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VERDICT MRI for Prostate Cancer: Intracellular Volume Fraction versus Apparent Diffusion Coefficient

Cited by 66 publications

References 21 publications

Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

Repeatability of radiomics and machine learning for DWI: Short‐term repeatability study of 112 patients with prostate cancer

Voxel-level Classification of Prostate Cancer Using a Four-Compartment Restriction Spectrum Imaging Model

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