Literature DB >> 29117481

Advanced Diffusion-weighted Imaging Modeling for Prostate Cancer Characterization: Correlation with Quantitative Histopathologic Tumor Tissue Composition-A Hypothesis-generating Study.

Stefanie J Hectors1, Sahar Semaan1, Christopher Song1, Sara Lewis1, George K Haines1, Ashutosh Tewari1, Ardeshir R Rastinehad1, Bachir Taouli1.   

Abstract

Purpose To correlate quantitative diffusion-weighted imaging (DWI) parameters derived from conventional monoexponential DWI, stretched exponential DWI, diffusion kurtosis imaging (DKI), and diffusion-tensor imaging (DTI) with quantitative histopathologic tumor tissue composition in prostate cancer in a preliminary hypothesis-generating study. Materials and Methods This retrospective institutional review board-approved study included 24 patients with prostate cancer (mean age, 63 years) who underwent magnetic resonance (MR) imaging, including high-b-value DWI and DTI at 3.0 T, before prostatectomy. The following parameters were calculated in index tumors and nontumoral peripheral zone (PZ): apparent diffusion coefficient (ADC) obtained with monoexponential fit (ADCME), ADC obtained with stretched exponential modeling (ADCSE), anomalous exponent (α) obtained at stretched exponential DWI, ADC obtained with DKI modeling (ADCDKI), kurtosis with DKI, ADC obtained with DTI (ADCDTI), and fractional anisotropy (FA) at DTI. Parameters in prostate cancer and PZ were compared by using paired Student t tests. Pearson correlations between tumor DWI and quantitative histologic parameters (nuclear, cytoplasmic, cellular, stromal, luminal fractions) were determined. Results All DWI parameters were significantly different between prostate cancer and PZ (P < .012). ADCME, ADCSE, and ADCDKI all showed significant negative correlation with cytoplasmic and cellular fractions (r = -0.546 to -0.435; P < .034) and positive correlation with stromal fractions (r = 0.619-0.669; P < .001). ADCDTI and FA showed correlation only with stromal fraction (r = 0.512 and -0.413, respectively; P < .045). α did not correlate with histologic parameters, whereas kurtosis showed significant correlations with histopathologic parameters (r = 0.487, 0.485, -0.422 for cytoplasmic, cellular, and stromal fractions, respectively; P < .040). Conclusion Advanced DWI methods showed significant correlations with histopathologic tissue composition in prostate cancer. These findings should be validated in a larger study. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on November 10, 2017.

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Year:  2017        PMID: 29117481     DOI: 10.1148/radiol.2017170904

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  23 in total

1.  Characterization of focal liver lesions using the stretched exponential model: comparison with monoexponential and biexponential diffusion-weighted magnetic resonance imaging.

Authors:  Hyung Cheol Kim; Nieun Seo; Yong Eun Chung; Mi-Suk Park; Jin-Young Choi; Myeong-Jin Kim
Journal:  Eur Radiol       Date:  2019-02-22       Impact factor: 5.315

Review 2.  Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Authors:  Stephnie A Harmon; Sena Tuncer; Thomas Sanford; Peter L Choyke; Barış Türkbey
Journal:  Diagn Interv Radiol       Date:  2019-05       Impact factor: 2.630

3.  The impeded diffusion fraction quantitative imaging assay demonstrated in multi-exponential diffusion phantom and prostate cancer.

Authors:  Dariya I Malyarenko; Scott D Swanson; Sean D McGarry; Peter S LaViolette; Thomas L Chenevert
Journal:  Magn Reson Med       Date:  2021-11-14       Impact factor: 4.668

Review 4.  Diffusion and quantification of diffusion of prostate cancer.

Authors:  Yoshiko Ueno; Tsutomu Tamada; Keitaro Sofue; Takamichi Murakami
Journal:  Br J Radiol       Date:  2021-09-19       Impact factor: 3.039

5.  Synthesizing High-b-Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks.

Authors:  Lei Hu; Da-Wei Zhou; Yun-Fei Zha; Liang Li; Huan He; Wen-Hao Xu; Li Qian; Yi-Kun Zhang; Cai-Xia Fu; Hui Hu; Jun-Gong Zhao
Journal:  Radiol Artif Intell       Date:  2021-06-02

Review 6.  Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI.

Authors:  Durgesh Kumar Dwivedi; Naranamangalam R Jagannathan
Journal:  MAGMA       Date:  2022-07-22       Impact factor: 2.533

Review 7.  Imaging for Response Assessment in Cancer Clinical Trials.

Authors:  Anna G Sorace; Asser A Elkassem; Samuel J Galgano; Suzanne E Lapi; Benjamin M Larimer; Savannah C Partridge; C Chad Quarles; Kirsten Reeves; Tiara S Napier; Patrick N Song; Thomas E Yankeelov; Stefanie Woodard; Andrew D Smith
Journal:  Semin Nucl Med       Date:  2020-06-10       Impact factor: 4.446

8.  Histogram analysis from stretched exponential model on diffusion-weighted imaging: evaluation of clinically significant prostate cancer.

Authors:  EunJu Kim; Chan Kyo Kim; Hyun Soo Kim; Dong Pyo Jang; In Young Kim; Jinwoo Hwang
Journal:  Br J Radiol       Date:  2020-01-09       Impact factor: 3.039

9.  T2*-weighted imaging and diffusion kurtosis imaging (DKI) of rectal cancer: correlation with clinical histopathologic prognostic factors.

Authors:  Shan Hu; Yang Peng; Qiushi Wang; Bin Liu; Ihab Kamel; Zaiyi Liu; Changhong Liang
Journal:  Abdom Radiol (NY)       Date:  2021-12-27

10.  Spatial density and diversity of architectural histology in prostate cancer: influence on diffusion weighted magnetic resonance imaging.

Authors:  Stephanie A Harmon; G Thomas Brown; Thomas Sanford; Sherif Mehralivand; Joanna H Shih; Sheng Xu; Maria J Merino; Peter L Choyke; Peter A Pinto; Bradford J Wood; Jesse K McKenney; Baris Turkbey
Journal:  Quant Imaging Med Surg       Date:  2020-02
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