TU‐CD‐BRA‐04: Evaluation of An Atlas‐Based Segmentation Method for Prostate and Peripheral Zone Regions On MRI

Nelson, A.; Piper, Jon; Curry, Kelly; Swallen, Amy; Padgett, Kyle R.; Pollack, Alan; Stoyanova, R.

doi:10.1118/1.4925601

Cited by 4 publications

(4 citation statements)

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“…The application of the thresholds also requires contours of PZ and TZ. The use of a prostate atlas will decrease significantly the need of manual contouring 20 . Finally, the generalizability of the determined thresholds to other MRI sequences, vendors, magnetic field strengths and coils (endorectal vs body) should be investigated.…”

Section: Discussionmentioning

confidence: 99%

Automatic Detection of Prostate Tumor Habitats using Diffusion MRI

Tschudi

Pollack

Punnen

et al. 2018

Sci Rep

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A procedure for identification of optimal Apparent Diffusion Coefficient (ADC) thresholds for automatic delineation of prostatic lesions with restricted diffusion at differing risk for cancer was developed. The relationship between the size of the identified Volumes of Interest (VOIs) and Gleason Score (GS) was evaluated. Patients with multiparametric (mp)MRI, acquired prior to radical prostatectomy (RP) (n = 18), mpMRI-ultrasound fused (MRI-US) (n = 21) or template biopsies (n = 139) were analyzed. A search algorithm, spanning ADC thresholds in 50 µm2/s increments, determined VOIs that were matched to RP tumor nodules. Three ADC thresholds for both peripheral zone (PZ) and transition zone (TZ) were identified for estimation of VOIs at low, intermediate, and high risk of prostate cancer. The determined ADC thresholds for low, intermediate and high risk in PZ/TZ were: 900/800; 1100/850; and 1300/1050 µm2/s. The correlation coefficients between the size of the high/intermediate/low risk VOIs and GS in the three cohorts were 0.771/0.778/0.369, 0.561/0.457/0.355 and 0.423/0.441/0.36 (p < 0.05). Low risk VOIs mapped all RP lesions; area under the curve (AUC) for intermediate risk VOIs to discriminate GS6 vs GS ≥ 7 was 0.852; for high risk VOIs to discriminate GS6,7 vs GS ≥ 8 was 0.952. In conclusion, the automatically delineated volumes in the prostate with restricted diffusion were found to strongly correlate with cancer aggressiveness.

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Section: Discussionmentioning

confidence: 99%

Automatic Detection of Prostate Tumor Habitats using Diffusion MRI

Tschudi

Pollack

Punnen

et al. 2018

Sci Rep

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“…However, HRS may be calibrated by utilizing the large array of user-defined parameters. In addition, the application of the thresholds requires contours of the PZ and TZ, for which a prostate atlas would decrease significantly the need of manual contouring(25).…”

Section: Disscusionmentioning

confidence: 99%

An Automated Multiparametric MRI Quantitative Imaging Prostate Habitat Risk Scoring System for Defining External Beam Radiation Therapy Boost Volumes

Stoyanova

Chinea

Kwon

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

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“…Efforts are underway for automation of this process by utilizing a prostate atlas. In a collaborative efforts with MIM, we developed an automated atlas-based segmentation method for generating prostate and PZ contours (26). …”

Section: Radiomics Pipelinementioning

confidence: 99%

Prostate cancer radiomics and the promise of radiogenomics

Stoyanova

Takhar

Tschudi

et al. 2016

Transl. Cancer Res.

113

104

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Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinicalpathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing Author Manuscript aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. HHS Public Access

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TU‐CD‐BRA‐04: Evaluation of An Atlas‐Based Segmentation Method for Prostate and Peripheral Zone Regions On MRI

Cited by 4 publications

References 0 publications

Automatic Detection of Prostate Tumor Habitats using Diffusion MRI

Automatic Detection of Prostate Tumor Habitats using Diffusion MRI

An Automated Multiparametric MRI Quantitative Imaging Prostate Habitat Risk Scoring System for Defining External Beam Radiation Therapy Boost Volumes

Prostate cancer radiomics and the promise of radiogenomics

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