التفاصيل البيبلوغرافية
| العنوان: |
Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models |
| المؤلفون: |
Conlin, Christopher C, Feng, Christine H, Rodriguez‐Soto, Ana E, Karunamuni, Roshan A, Kuperman, Joshua M, Holland, Dominic, Rakow‐Penner, Rebecca, Hahn, Michael E, Seibert, Tyler M, Dale, Anders M |
| المصدر: |
Journal of Magnetic Resonance Imaging, vol 53, iss 2 |
| بيانات النشر: |
eScholarship, University of California |
| سنة النشر: |
2021 |
| المجموعة: |
University of California: eScholarship |
| مصطلحات موضوعية: |
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Urologic Diseases, Aging, Cancer, Biomedical Imaging, Prostate Cancer, Bayes Theorem, Diffusion Magnetic Resonance Imaging, Humans, Male, Prostatic Neoplasms, Reproducibility of Results, Retrospective Studies, restriction spectrum imaging, multishell diffusion weighted imaging, diffusion signal model, Physical Sciences, Engineering, Medical and Health Sciences, Nuclear Medicine & Medical Imaging |
| جغرافية الموضوع: |
628 - 639 |
| الوصف: |
BackgroundMulticompartmental 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.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study typeRetrospective.SubjectsForty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field strength/sequence3T multishell diffusion-weighted sequence.AssessmentMulticompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.Statistical testsAnalysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < ... |
| نوع الوثيقة: |
article in journal/newspaper |
| اللغة: |
unknown |
| العلاقة: |
qt1kn6b7kx; https://escholarship.org/uc/item/1kn6b7kxTest |
| الإتاحة: |
https://escholarship.org/uc/item/1kn6b7kxTest |
| حقوق: |
public |
| رقم الانضمام: |
edsbas.966389EC |
| قاعدة البيانات: |
BASE |
