Dataset Information

Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors.

ABSTRACT: Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (mpMRI) has shown promise to improve detection and characterization of the disease. Regions that subdivide the tumor based on Dynamic Contrast Enhancement (DCE) of mpMRI are referred to as DCE-Habitats in this study. The DCE defined perfusion curve patterns on the identified tumor habitat region are used to assess clinical significance. These perfusion curves were systematically quantified using seven features in association with the patient biopsy outcome and classifier models were built to find the best discriminating characteristics between clinically significant and insignificant prostate lesions defined by Gleason score (GS). Multivariable analysis was performed independently on one institution and validated on the other, using a multi-parametric feature model, based on DCE characteristics and ADC features. The models had an intra institution Area under the Receiver Operating Characteristic (AUC) of 0.82. Trained on Institution I and validated on the cohort from Institution II, the AUC was also 0.82 (sensitivity 0.68, specificity 0.95).

SUBMITTER: Parra NA

PROVIDER: S-EPMC6324677 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors.

Parra N Andres NA Lu Hong H Li Qian Q Stoyanova Radka R Pollack Alan A Punnen Sanoj S Choi Jung J Abdalah Mahmoud M Lopez Christopher C Gage Kenneth K Park Jong Y JY Kosj Yamoah Y Pow-Sang Julio M JM Gillies Robert J RJ Balagurunathan Yoganand Y

Oncotarget 20181214 98

Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (mpMRI) has shown promise to improve detection and characterization of the disease. Regions that subdivide the tumor based on Dynamic Contrast Enhancement (DCE) of mpMRI are referred to as <i>DCE-Habita ...[more]

PMID: 30647849

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Project description:OBJECTIVE. The purpose of this study was to compare in a multireader manner the diagnostic accuracies of 3-T multiparametric MRI interpretation and serial prostate-specific antigen (PSA) measurement in predicting the presence of residual clinically significant prostate cancer after focal laser ablation. MATERIALS AND METHODS. Eighteen men had undergone focal laser ablation for low- or intermediate-risk prostate cancer as part of two National Cancer Institute-funded phase 1 clinical trials. Multiparametric MRI was performed immediately after and 6 and 12 months after focal laser ablation. Serial PSA measurements after focal laser ablation were recorded, and MRI-ultrasound fusion biopsy was performed 6 and 12 months after ablation and served as the reference standard. Multiparametric MRI was performed at 3 T with pelvic phased-array coils. T2-weighted, DW, and dynamic contrast-enhanced MR images were retrospectively assessed by two blinded radiologists using a 3-point Likert scale (0-2). Inter-reader agreement was assessed with the Cohen kappa statistic. The diagnostic accuracies of multiparametric MRI and PSA measurement were compared. RESULTS. Residual clinically significant prostate cancer was identified in 11 of 18 (61%) men. Logistic regression analysis of serial PSA measurements yielded a correct classification rate of 61.1% (p > 0.05). Using a multiparametric MRI threshold score of 4 or greater, both radiologists made correct classifications for 16 of 18 men (89%) at 6 months and 15 of 17 men (88%) at 12 months. Interreader agreement was substantial to excellent for T2-weighted imaging, DWI, and dynamic contrast-enhanced MRI and improved uniformly from 6 to 12 months. Logistic regression analysis of the retrospectively reviewed multiparametric MR images yielded AUCs greater than 0.90 for each radiologist 6 and 12 months after focal laser ablation (p < 0.001). CONCLUSION. Multiparametric MRI 6 and 12 months after focal laser ablation significantly outperformed serial PSA measurements for predicting the presence of residual clinically significant prostate cancer.

Project description:BackgroundTexture features based on the spatial relationship of pixels, known as the gray-level co-occurrence matrix (GLCM), may play an important role in providing the accurate classification of suspected prostate cancer. The purpose of this study was to use quantitative imaging parameters of pre-biopsy multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer.MethodsThis was a prospective study, recruiting 200 men suspected of having prostate cancer. Participants were imaged using a protocol-based 3T MRI in the pre-biopsy setting. Radiomics parameters were extracted from the T2WI and ADC texture features of the gray-level co-occurrence matrix were delineated from the region of interest. Radical prostatectomy histopathology was used as a reference standard. A Kruskal-Wallis test was applied first to identify the significant radiomic features between the three groups of Gleason scores (i.e., G1, G2 and G3). Subsequently, the Holm-Bonferroni method was applied to correct and control the probability of false rejections. We compared the probability of correctly predicting significant prostate cancer between the explanatory GLCM radiomic features, PIRADS and PSAD, using the area under the receiver operation characteristic curves.ResultsWe identified the significant difference in radiomic features between the three groups of Gleason scores. In total, 12 features out of 22 radiomics features correlated with the Gleason groups. Our model demonstrated excellent discriminative ability (C-statistic = 0.901, 95%CI 0.859-0.943). When comparing the probability of correctly predicting significant prostate cancer between explanatory GLCM radiomic features (Sum Variance T2WI, Sum Entropy T2WI, Difference Variance T2WI, Entropy ADC and Difference Variance ADC), PSAD and PIRADS via area under the ROC curve, radiomic features were 35.0% and 34.4% more successful than PIRADS and PSAD, respectively, in correctly predicting significant prostate cancer in our patients (p < 0.001). The Sum Entropy T2WI score had the greatest impact followed by the Sum Variance T2WI.ConclusionQuantitative GLCM texture analyses of pre-biopsy MRI has the potential to be used as a non-invasive imaging technique to predict clinically significant cancer in men suspected of having prostate cancer.

Project description:ObjectivesTo develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting.MethodsA consecutive cohort (n = 292) from a prospective database of low-risk patients eligible for the active surveillance was selected. A 3-T multi-parametric MRI at 3 months after inclusion was performed. Histopathology from biopsies was used as reference standard. MRI positivity was defined as PI-RADS score ≥ 3, histopathology positivity was defined as ISUP grade ≥ 2. The selected cohort contained four patient groups: (1) MRI-positive targeted biopsy-positive (n = 116), (2) MRI-negative systematic biopsy-negative (n = 55), (3) MRI-positive targeted biopsy-negative (n = 113), (4) MRI-negative systematic biopsy-positive (n = 8). Group 1 was further divided into three sets and a 3D convolutional neural network was trained using different combinations of these sets. Two MRI sequences (T2w, b = 800 DWI) and the ADC map were used as separate input channels for the model. After training, the model was evaluated on the remaining group 1 patients together with the patients of groups 2 and 3 to identify and segment clinically significant prostate cancer.ResultsThe average sensitivity achieved was 82-92% at an average specificity of 43-76% with an area under the curve (AUC) of 0.65 to 0.89 for different lesion volumes ranging from > 0.03 to > 0.5 cc.ConclusionsThe proposed deep learning computer-aided method yields promising results in identification and segmentation of clinically significant prostate cancer and in confirming low-risk cancer (ISUP grade ≤ 1) in patients on active surveillance.Key points• Clinically significant prostate cancer identification and segmentation on multi-parametric MRI is feasible in low-risk patients using a deep neural network. • The deep neural network for significant prostate cancer localization performs better for lesions with larger volumes sizes (> 0.5 cc) as compared to small lesions (> 0.03 cc). • For the evaluation of automatic prostate cancer segmentation methods in the active surveillance cohort, the large discordance group (MRI positive, targeted biopsy negative) should be included.

Dataset Information

Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors.

Publications

Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors.

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