Dataset Information

Prostate cancer bone metastases on staging prostate MRI: prevalence and clinical features associated with their diagnosis.

ABSTRACT: PURPOSEBone lesions on prostate MRI often raise concern about metastases. This study aimed to evaluate the prevalence of bone metastases on staging prostate MRI and evaluate associations between their MRI features and clinical/pathologic characteristics.METHODSRetrospective, IRB-approved study of 3765 patients undergoing prostate MRI for newly diagnosed PCa between 2000 and 2014. The reference standard to calculate the prevalence of bone metastases was bone biopsy and/or ?1-year follow-up after MRI. In a subsample of 228 patients, the MRI characteristics of bone lesions were recorded by two radiologists independently. Associations between MRI and clinical/pathologic findings, including National Comprehensive Cancer Network (NCCN) risk categories, were calculated.RESULTS57/3765 patients (1.5%, 95% CI 1.2-2.0%) had bone metastases. No patient with NCCN low-risk PCa (Gleason < 7, PSA < 10 ng/mL, cT1-2a) had bone metastases. In the subsample, ?1 bone lesion was present on MRI in 74% (95% CI 0.67-0.79) and 72% (95% CI 0.66-0.78) of patients (R1 and R2). Larger lesion diameter (OR 1.33/1.19; p < 0.001 for both readers) and the absence of intralesional fat (OR 0.07/0.11; p = 0.004/0.002 for R1/R2) were significantly associated with bone metastases.CONCLUSIONBone lesions are common in prostate MRI, but only rarely represent metastases. MRI should be interpreted in the context of clinical features that influence the likelihood of metastatic disease.

SUBMITTER: Vargas HA

PROVIDER: S-EPMC5247358 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Prostate cancer bone metastases on staging prostate MRI: prevalence and clinical features associated with their diagnosis.

Vargas Hebert Alberto HA Schor-Bardach Rachel R Long Niamh N Kirzner Anna N AN Cunningham Jane D JD Goldman Debra A DA Moskowitz Chaya S CS Sosa Ramon E RE Sala Evis E Panicek David M DM Hricak Hedvig H

Abdominal radiology (New York) 20170101 1

<h4>Purpose</h4>Bone lesions on prostate MRI often raise concern about metastases. This study aimed to evaluate the prevalence of bone metastases on staging prostate MRI and evaluate associations between their MRI features and clinical/pathologic characteristics.<h4>Methods</h4>Retrospective, IRB-approved study of 3765 patients undergoing prostate MRI for newly diagnosed PCa between 2000 and 2014. The reference standard to calculate the prevalence of bone metastases was bone biopsy and/or ≥1-yea ...[more]

PMID: 27480976

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Project description:PurposeTo examine the usefulness of rich diffusion protocols with high b-values and varying diffusion time for probing microstructure in bone metastases. Analysis techniques including biophysical and mathematical models were compared with the clinical apparent diffusion coefficient (ADC).MethodsFour patients were scanned using 13 b-values up to 3,000 s/mm2 and diffusion times ranging 18-52 ms. Data were fitted to mono-exponential ADC, intravoxel incoherent motion (IVIM), Kurtosis and Vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) models. Parameters from the models were compared using correlation plots.ResultsADC and IVIM did not fit the data well, failing to capture the signal at high b-values. The Kurtosis model best explained the data in many voxels, but in voxels exhibiting a more time-dependent signal, the VERDICT model explained the data best. The ADC correlated significantly (p < 0.004) with the intracellular diffusion coefficient (r = 0.48), intracellular volume fraction (r = -0.21), and perfusion fraction (r = 0.46) parameters from VERDICT, suggesting that these factors all contribute to ADC contrast. The mean kurtosis correlated with the intracellular volume fraction parameter (r = 0.26) from VERDICT, consistent with the hypothesis that kurtosis relates to cellularity, but also correlated weakly with the intracellular diffusion coefficient (r = 0.18) and cell radius (r = 0.16) parameters, suggesting that it may be difficult to attribute physical meaning to kurtosis.ConclusionBoth Kurtosis and VERDICT explained the diffusion signal better than ADC and IVIM, primarily due to poor fitting at high b-values in the latter two models. The Kurtosis and VERDICT models captured information at high b using parameters (Kurtosis or intracellular volume fraction and radius) that do not have a simple relationship with ADC and that may provide additional microstructural information in bone metastases.

Project description:PurposeBone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer.Methods and materialsOverall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors.ResultsThe best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit.ConclusionMultimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

Dataset Information

Prostate cancer bone metastases on staging prostate MRI: prevalence and clinical features associated with their diagnosis.

Publications

Prostate cancer bone metastases on staging prostate MRI: prevalence and clinical features associated with their diagnosis.

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