Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging

Litjens, Geert; Elliott, Robin; Shih, Natalie; Feldman, Michael D.; Kobus, Thiele; Kaa, Christina Hulsbergen–van de; Barentsz, Jelle O.; Huisman, Henkjan; Madabhushi, Anant

doi:10.1148/radiol.2015142856

Cited by 44 publications

(42 citation statements)

References 28 publications

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“…(48,49) where a new classifier was introduced to separate prostate cancer and benign confounders on MRI. The pathology annotations were propagated to MR images by registration of whole-mount slides and manual annotation of cancer, benign prostatic hyperplasia (BPH), prostatic intraepithelial neoplasia (PIN), inflammation.…”

Section: Review Of Applications Of Radiomics To Prostate Cancermentioning

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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Section: Review Of Applications Of Radiomics To Prostate Cancermentioning

confidence: 99%

Prostate cancer radiomics and the promise of radiogenomics

Stoyanova

Takhar

Tschudi

et al. 2016

Transl. Cancer Res.

113

104

View full text Add to dashboard Cite

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“…Nonetheless, inter-observer variability in interpreting prostate mpMRI and existence of benign confounders on imaging [12, 13] still limit accurate detection and diagnosis of CaP. Recent studies [11, 14–16] have shown radiomics based classifiers can improve the accuracy and reproducibility in localizing prostate cancer lesions on mpMRI (which includes T2w, diffusion weighted (DWI) and dynamic contrast enhanced (DCE)).…”

Section: Introductionmentioning

confidence: 99%

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

et al. 2016

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BackgroundRadiomics or computer – extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans.MethodsThe Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT.ResultsRad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous () which is the current clinical standard, radiomics based focal (), and whole gland with a radiomics based focal boost (). Comparison of against conventional revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. resulted in only a marginal increase in dosage to the OARs compared to . A similar trend was observed in case of EBRT with and compared to .ConclusionsA radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.

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“…Another study found that intravoxel incoherent motion (IVIM) parameters (the pure diffusion coefficient D t , the pseudo-diffusion fraction F p and the pseudo-diffusion coefficient D p ) derived from a biexponential fit to DW-MRI [14,15] correlate with the cancer's aggressiveness [16]. In addition, parameters derived from high b value DW-MRI (as acquired or following Hessian focality filtering [17]) were found to be useful to distinguish noncancerous disease from prostatic adenocarcinoma [18]. Although all previous studies showed promising results, the association between lesion aggressiveness and values of DW-MRI parameters is not sufficiently specific to allow application to individual patients.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

et al. 2016

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Computer-extracted Features Can Distinguish Noncancerous Confounding Disease from Prostatic Adenocarcinoma at Multiparametric MR Imaging

Abstract: The best features with which to discriminate prostate cancer from noncancerous benign disease depend on the type of benign disease and cancer grade. Use of the best features may result in better diagnostic performance.

Cited by 44 publications

References 28 publications

Prostate cancer radiomics and the promise of radiogenomics

Prostate cancer radiomics and the promise of radiogenomics

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI

Differentiation of prostate cancer lesions with high and with low Gleason score by diffusion-weighted MRI

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