scholarly journals Deep neural network analysis employing diffusion basis spectrum imaging metrics as classifiers improves prostate cancer detection and grading

2021 ◽  
Author(s):  
Zezhong Ye ◽  
Qingsong Yang ◽  
Joshua Lin ◽  
Peng Sun ◽  
Chengwei Shao ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
Tumor Grade ◽  
Noninvasive Detection ◽  
Spectrum Imaging ◽  
Cellular Components

AbstractStructural and cellular complexity of prostatic histopathology limits the accuracy of noninvasive detection and grading of prostate cancer (PCa). We addressed this limitation by employing a novel diffusion basis spectrum imaging (DBSI) to derive structurally-specific diffusion fingerprints reflecting various underlying prostatic structural and cellular components. We further developed diffusion histology imaging (DHI) by combining DBSI-derived structural fingerprints with a deep neural network (DNN) algorithm to more accurately classify different histopathological features and predict tumor grade in PCa. We examined 243 patients suspected with PCa using in vivo DBSI. The in vivo DBSI-derived diffusion metrics detected coexisting prostatic pathologies distinguishing inflammation, PCa, and benign prostatic hyperplasia. DHI distinguished PCa from benign peripheral and transition zone tissues with over 95% sensitivity and specificity. DHI also demonstrated over 90% sensitivity and specificity for Gleason score noninvasively. We present DHI as a novel diagnostic tool capable of noninvasive detection and grading of PCa.One sentence summaryDiffusion histology imaging noninvasively and accurately detects and grades prostate cancer.

scholarly journals Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems

2021 ◽  
pp. 104743
Author(s):  
L. Duran-Lopez ◽  
Juan P. Dominguez-Morales ◽  
D. Gutierrez-Galan ◽  
A. Rios-Navarro ◽  
A. Jimenez-Fernandez ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Network Model ◽  
Cancer Detection ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Prostate Cancer Detection ◽  
Detection Systems

scholarly journals In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI

2016 ◽  
Vol 19 (2) ◽  
pp. 168-173 ◽  
Author(s):  
K C McCammack ◽  
C J Kane ◽  
J K Parsons ◽  
N S White ◽  
N M Schenker-Ahmed ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Prostate Cancer Detection ◽  
Spectrum Imaging

scholarly journals Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neda Gholizadeh ◽  
Peter B. Greer ◽  
John Simpson ◽  
Jonathan Goodwin ◽  
Caixia Fu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Transition Zone ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Spectroscopic Imaging ◽  
Multiparametric Mri ◽  
Low Risk ◽  
Intermediate Risk

Abstract Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.

Deep neural network for multiparametric ultrasound imaging of prostate cancer

2021 ◽  
Author(s):  
Derek Y. Chan ◽  
D. Cody Morris ◽  
Theresa Lye ◽  
Thomas J. Polascik ◽  
Mark L. Palmeri ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Ultrasound Imaging ◽  
Deep Neural Network ◽  
Multiparametric Ultrasound

scholarly journals Accuracy of high b-value diffusion-weighted MRI for prostate cancer detection: a meta-analysis

2017 ◽  
Vol 59 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Keith Craig Godley ◽  
Tom Joseph Syer ◽  
Andoni Paul Toms ◽  
Toby Oliver Smith ◽  
Glyn Johnson ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Diagnostic Performance ◽  
Visual Assessment ◽  
B Value ◽  
B Values ◽  
Prostate Cancer Detection ◽  
High B Value

Background The diagnostic accuracy of diffusion-weighted imaging (DWI) to detect prostate cancer is well-established. DWI provides visual as well as quantitative means of detecting tumor, the apparent diffusion coefficient (ADC). Recently higher b-values have been used to improve DWI’s diagnostic performance. Purpose To determine the diagnostic performance of high b-value DWI at detecting prostate cancer and whether quantifying ADC improves accuracy. Material and Methods A comprehensive literature search of published and unpublished databases was performed. Eligible studies had histopathologically proven prostate cancer, DWI sequences using b-values ≥ 1000 s/mm2, less than ten patients, and data for creating a 2 × 2 table. Study quality was assessed with QUADAS-2 (Quality Assessment of diagnostic Accuracy Studies). Sensitivity and specificity were calculated and tests for statistical heterogeneity and threshold effect performed. Results were plotted on a summary receiver operating characteristic curve (sROC) and the area under the curve (AUC) determined the diagnostic performance of high b-value DWI. Results Ten studies met eligibility criteria with 13 subsets of data available for analysis, including 522 patients. Pooled sensitivity and specificity were 0.59 (95% confidence interval [CI], 0.57–0.61) and 0.92 (95% CI, 0.91–0.92), respectively, and the sROC AUC was 0.92. Subgroup analysis showed a statistically significant ( P = 0.03) improvement in accuracy when using tumor visual assessment rather than ADC. Conclusion High b-value DWI gives good diagnostic performance for prostate cancer detection and visual assessment of tumor diffusion is significantly more accurate than ROI measurements of ADC.

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