Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model

2021 ◽  
Vol 143 ◽  
pp. 109913
Author(s):  
Zhihua Li ◽  
Guangyu Dan ◽  
Vikram Tammana ◽  
Scott Johnson ◽  
Zheng Zhong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Model ◽  
Fractional Order ◽  
Peripheral Zone ◽  
Fractional Order Calculus

scholarly journals Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model

2020 ◽  
Vol 93 (1113) ◽  
pp. 20200052
Author(s):  
Wei Chen ◽  
Liu-Ning Zhu ◽  
Yong-Ming Dai ◽  
Jia-Suo Jiang ◽  
Shou-Shan Bu ◽  
...  
Keyword(s):  
Salivary Gland ◽  
Diffusion Model ◽  
Fractional Order ◽  
Diffusion Weighted Imaging ◽  
Diagnostic Performance ◽  
Salivary Gland Tumors ◽  
Characteristic Analysis ◽  
Fractional Order Calculus ◽  
Diffusion Weighted ◽  
Significant Difference

Objective: To evaluate the feasibility of using imaging parameters (D, β and μ) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors. Methods: 15 b-value (0–2000 s/mm2) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity) and a microstructural quantity μ of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin’s tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis. Results: None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; β, p = 0.967; μ, p = 0.693). WT showed significantly lower D (p < 0.001) and β (p < 0.001), while higher μ (p = 0.001) than PA. Combination of D, β and μ showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and β (p = 0.025) than PA, while no significant difference was found on μ (p = 0.064). Combination of D and β showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on β (p = 0.027) between MT and WT, while not on D (p = 0.806) and μ (p = 0.789). Setting a βof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806). Conclusion: A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors. Advances in knowledge: (1) PA showed higher D and β and lower μ than WT. (2) PA had higher D and β than MT. (3) WT demonstrated lower β than MT. (4) β, as a new FROC parameter, could offer an added value to the differentiation.

scholarly journals Diffusion Is Directional: Innovative Diffusion Tensor Imaging to Improve Prostate Cancer Detection

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 563
Author(s):  
Chen Shenhar ◽  
Hadassa Degani ◽  
Yaara Ber ◽  
Jack Baniel ◽  
Shlomit Tamir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Diffusion Tensor Imaging ◽  
Predictive Value ◽  
Breast Imaging ◽  
Normal Tissue ◽  
Diffusion Tensor ◽  
Peripheral Zone ◽  
Predictive Values ◽  
Directional Diffusion ◽  
Central Gland

In the prostate, water diffusion is faster when moving parallel to duct and gland walls than when moving perpendicular to them, but these data are not currently utilized in multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) detection. Diffusion tensor imaging (DTI) can quantify the directional diffusion of water in tissue and is applied in brain and breast imaging. Our aim was to determine whether DTI may improve PCa detection. We scanned patients undergoing mpMRI for suspected PCa with a DTI sequence. We calculated diffusion metrics from DTI and diffusion weighted imaging (DWI) for suspected lesions and normal-appearing prostate tissue, using specialized software for DTI analysis, and compared predictive values for PCa in targeted biopsies, performed when clinically indicated. DTI scans were performed on 78 patients, 42 underwent biopsy and 16 were diagnosed with PCa. The median age was 62 (IQR 54.4–68.4), and PSA 4.8 (IQR 1.3–10.7) ng/mL. DTI metrics distinguished PCa lesions from normal tissue. The prime diffusion coefficient (λ1) was lower in both peripheral-zone (p < 0.0001) and central-gland (p < 0.0001) cancers, compared to normal tissue. DTI had higher negative and positive predictive values than mpMRI to predict PCa (positive predictive value (PPV) 77.8% (58.6–97.0%), negative predictive value (NPV) 91.7% (80.6–100%) vs. PPV 46.7% (28.8–64.5%), NPV 83.3% (62.3–100%)). We conclude from this pilot study that DTI combined with T2-weighted imaging may have the potential to improve PCa detection without requiring contrast injection.

scholarly journals Is PSA density of the peripheral zone as a useful predictor for prostate cancer in patients with gray zone PSA levels?

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jaegeun Lee ◽  
Seung Woo Yang ◽  
Long Jin ◽  
Chung Lyul Lee ◽  
Ji Yong Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Prostate Biopsy ◽  
Peripheral Zone ◽  
Transitional Zone ◽  
Gray Zone ◽  
Prostate Cancer Diagnosis ◽  
Psa Density ◽  
Diagnosis Group ◽  
Predictive Rate

Abstract Background Serum prostate-specific antigen (PSA) is widely used in screening tests for prostate cancer. As the low specificity of PSA results in unnecessary and invasive prostate biopsies, we evaluated the clinical significance of various PSAs and PSA density (PSAD) related to peripheral zones in patients with gray zone PSA level (4–10 ng/mL). Methods A total of 1300 patients underwent transrectal ultrasonography-guided prostate biopsy from 2014 to 2019. Among them, 545 patients in the gray zone were divided into the prostate cancer diagnosis group and the non-prostate cancer diagnosis group, and PSA, relative extra transitional zone PSA (RETzPSA), estimated post holmium laser enucleation of the prostate PSA (EPHPSA), PSAD, peripheral zone PSA density (PZPSAD) and extra-transitional zone density (ETzD) were compared and analyzed using receiver-operating characteristics (ROC) analysis after 1:1 matching using propensity score. Results Area under the ROC curve values of PSA, EPHPSA, RETzPSA, PSA density, ETzD, and PZPSAD were 0.553 (95% CI: 0.495–0.610), 0.611 (95% CI: 0.554–0.666), 0.673 (95% CI: 0.617–0.725), 0.745 (95% CI: 0.693–0.793), 0.731 (95% CI: 0.677–0.780) and 0.677 (95% CI: 0.611–0.719), respectively. PSAD had 67.11% sensitivity, 71.71% specificity, and 70.34% positive predictive rate at 0.18 ng/mL/cc. ETzD had 69.08% sensitivity, 64.47% specificity, and 66.04% positive predictive rate at 0.04 ng/mL/cc. When the cut-off value of PSAD was increased to 0.18 ng/mL/cc, the best results were obtained with an odds ratio of 5.171 (95% CI: 3.171–8.432), followed by ETzD with 4.054 (95% CI: 2.513–6.540). Conclusions These results suggested that volume-adjusted parameters (ETzD and PSAD) might be more sensitive and accurate than various PSA in gray zone patients who required prostate biopsy to reduce unnecessary biopsy.

scholarly journals Studies of anomalous diffusion in the human brain using fractional order calculus

2010 ◽  
Vol 63 (3) ◽  
pp. 562-569 ◽  
Author(s):  
Xiaohong Joe Zhou ◽  
Qing Gao ◽  
Osama Abdullah ◽  
Richard L. Magin
Keyword(s):  
Human Brain ◽  
Fractional Order ◽  
Anomalous Diffusion ◽  
Fractional Order Calculus

scholarly journals Active Sliding Mode for Synchronization of a Wide Class of Four-Dimensional Fractional-Order Chaotic Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Wang ◽  
Yuangui Zhou ◽  
Jianyi Xue ◽  
Delan Zhu
Keyword(s):  
Sliding Mode Control ◽  
Fractional Order ◽  
Wide Class ◽  
Stability Theory ◽  
Chaotic Systems ◽  
Sliding Mode ◽  
Order System ◽  
Fractional Order Calculus ◽  
Simulation Results ◽  
The Stability

We focus on the synchronization of a wide class of four-dimensional (4-D) chaotic systems. Firstly, based on the stability theory in fractional-order calculus and sliding mode control, a new method is derived to make the synchronization of a wide class of fractional-order chaotic systems. Furthermore, the method guarantees the synchronization between an integer-order system and a fraction-order system and the synchronization between two fractional-order chaotic systems with different orders. Finally, three examples are presented to illustrate the effectiveness of the proposed scheme and simulation results are given to demonstrate the effectiveness of the proposed method.

Prostate Cancer: Comparison of Dynamic Contrast-Enhanced MRI Techniques for Localization of Peripheral Zone Tumor

2013 ◽  
Vol 201 (3) ◽  
pp. W471-W478 ◽  
Author(s):  
Andrew B. Rosenkrantz ◽  
Amy Sabach ◽  
James S. Babb ◽  
Brent W. Matza ◽  
Samir S. Taneja ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Peripheral Zone ◽  
Dynamic Contrast Enhanced Mri ◽  
Dynamic Contrast Enhanced ◽  
Enhanced Mri ◽  
Contrast Enhanced ◽  
Contrast Enhanced Mri
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