Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings.
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SUBMITTER: Bailey C
PROVIDER: S-EPMC5820304 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings.
Bailey Colleen C Collins David J DJ Tunariu Nina N Orton Matthew R MR Morgan Veronica A VA Feiweier Thorsten T Hawkes David J DJ Leach Martin O MO Alexander Daniel C DC Panagiotaki Eleftheria E
Frontiers in oncology 20180216
<h4>Purpose</h4>To examine the usefulness of rich diffusion protocols with high <i>b</i>-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).<h4>Methods</h4>Four patients were scanned using 13 <i>b</i>-values up to 3,000 s/mm<sup>2</sup> and diffusion times ranging 18-52 ms. Data were fitted to mono-exponential ADC, intravoxel incoher ...[more]
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Project description:BACKGROUND:Radiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE:To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE:Retrospective. SUBJECTS:In all, 120 PCa patients from two institutions, I1 and I2 , partitioned into training set D1 (N?=?70) from I1 and independent validation set D2 (N?=?50) from I2 . All patients were followed for ?3 years. SEQUENCE:3T, T2 -weighted (T2 WI) and apparent diffusion coefficient (ADC) maps derived from diffusion-weighted sequences. ASSESSMENT:PCa regions of interest (ROIs) on T2 WI were annotated by two experienced radiologists. Radiomic features from bpMRI (T2 WI and ADC maps) were extracted from the ROIs. A machine-learning classifier (CBCR ) was trained with the best discriminating set of radiomic features to predict BCR (pBCR ). STATISTICAL TESTS:Wilcoxon rank-sum tests with P?<?0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using pBCR was assessed using the Kaplan-Meier method and compared with Gleason Score (GS), PSA, and PIRADS-v2. RESULTS:Distribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T2 WI and ADC were associated with BCR (P?<?0.05) on D1 . CBCR predictions resulted in a mean AUC?=?0.84 on D1 and AUC?=?0.73 on D2 . A significant difference in BCR-free survival between the predicted classes (BCR?+?and BCR-) was observed (P?=?0.02) on D2 compared to those obtained from GS (P?=?0.8), PSA (P?=?0.93) and PIRADS-v2 (P?=?0.23). DATA CONCLUSION:Radiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.
| S-EPMC6222024 | biostudies-literature
Project description:ObjectivesWe sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases).MethodsThis single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions.ResultsMRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018).ConclusionsMRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI.Key points• MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.
| S-EPMC6684343 | biostudies-literature
Project description:For many pathologies, early structural tissue changes occur at the cellular level, on the scale of micrometers or tens of micrometers. Magnetic resonance imaging (MRI) is a powerful non-invasive imaging tool used for medical diagnosis, but its clinical hardware is incapable of reaching the cellular length scale directly. In spite of this limitation, microscopic tissue changes in pathology can potentially be captured indirectly, from macroscopic imaging characteristics, by studying water diffusion. Here we focus on water diffusion and NMR relaxation in the human prostate, a highly heterogeneous organ at the cellular level. We present a physical picture of water diffusion and NMR relaxation in the prostate tissue, that is comprised of a densely-packed cellular compartment (composed of stroma and epithelium), and a luminal compartment with almost unrestricted water diffusion. Transverse NMR relaxation is used to identify fast and slow T 2 components, corresponding to these tissue compartments, and to disentangle the luminal and cellular compartment contributions to the temporal evolution of the overall water diffusion coefficient. Diffusion in the luminal compartment falls into the short-time surface-to-volume (S/V) limit, indicating that only a small fraction of water molecules has time to encounter the luminal walls of healthy tissue; from the S/V ratio, the average lumen diameter averaged over three young healthy subjects is measured to be 217.7±188.7 ?m. Conversely, the diffusion in the cellular compartment is highly restricted and anisotropic, consistent with the fibrous character of the stromal tissue. Diffusion transverse to these fibers is well described by the random permeable barrier model (RPBM), as confirmed by the dynamical exponent ? = 1/2 for approaching the long-time limit of diffusion, and the corresponding structural exponent p = -1 in histology. The RPBM-derived fiber diameter and membrane permeability were 19.8±8.1 ?m and 0.044±0.045 ?m/ms, respectively, in agreement with known values from tissue histology and membrane biophysics. Lastly, we revisited 38 prostate cancer cases from a recently published study, and found the same dynamical exponent ? = 1/2 of diffusion in tumors and benign regions. Our results suggest that a multi-parametric MRI acquisition combined with biophysical modeling may be a powerful non-invasive complement to prostate cancer grading, potentially foregoing biopsies.
| S-EPMC6296484 | biostudies-literature
Project description: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.
| S-EPMC5247358 | biostudies-literature
Project description:Bone is the most common metastatic site for prostate cancer, and osseous metastases are the leading cause of morbidity from this disease. Recent autopsy studies prove that 100% of men who die of prostate cancer have bone involvement. Understanding the biology of prostate cancer and its evolution to an incurable androgen-independent phenotype requires an understanding of the genetic and cellular alterations that lead to the seeding and proliferation of tumor foci in bone, as well as the microenvironment in which these metastases arise. No intensive studies, however, have been conducted on osseous metastatic tissues from patients with metastatic prostate cancer due to lack of access to such tissues for profiling and other research.We show, for the first time, a reproducible methodology to obtain high quality clinical tumor tissues metastatic to the bone. This technique allowed the procurement of viable metastatic tumor tissue from involved bones in 13 recent autopsies conducted at the University of Michigan and analyzed the gene expression of these tissues using real-time PCR and microarrays.We present here the discovery of nonossified bone metastases from multiple patients with advanced prostate cancer and their subsequent characterization and comparison to nonosseous metastases from the same patients.This represents a versatile and practical approach that may be employed to characterize the steps in metastasis and the phenotypic characteristics of osseous metastasis of prostate cancer and to profile RNA, DNA, and cDNA from tumor samples metastatic to the bone.
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Project description:Approximately 90% of patients who die of Prostate Cancer (PCa) have bone metastases, which promote a spectrum of osteoblastic, osteolytic or mixed bone responses. Numerous secreted proteins have been reported to promote osteoblastic or osteolytic bone responses. We determined whether previously identified and/or novel proteins were associated with the osteoblastic or osteolytic response in clinical specimens of PCa bone metastases.Gene expression was analyzed on 14 PCa metastases from 11 patients by microarray profiling and qRT-PCR, and protein expression was analyzed on 33 PCa metastases from 30 patients by immunohistochemistry on highly osteoblastic and highly osteolytic bone specimens.Transcript and protein levels of BMP-2, BMP-7, DKK-1, ET-1, and Sclerostin were not significantly different between osteoblastic and osteolytic metastases. However, levels of OPG, PGK1, and Substance P proteins were increased in osteoblastic samples. In addition, Emu1, MMP-12, and sFRP-1 were proteins identified with a novel role of being associated with either the osteoblastic or osteolytic bone response.This is the first detailed analysis of bone remodeling proteins in human specimens of PCa bone metastases. Three proteins not previously shown to be involved may have a role in the PCa bone response. Furthermore, our data suggests that the relative expression of numerous, rather than a single, bone remodeling proteins determine the bone response in PCa bone metastases.
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Project description:BackgroundMetastasis to the bone is one clinically important features of prostate cancer (PCa). Current diagnostic methods cannot predict metastatic PCa at a curable stage of the disease. Identification of metabolic pathways involved in the growth of bone metastases therefore has the potential to improve PCa prognostication as well as therapy.Methodology/principal findingsMetabolomics was applied for the study of PCa bone metastases (n = 20) in comparison with corresponding normal bone (n = 14), and furthermore of malignant (n = 13) and benign (n = 17) prostate tissue and corresponding plasma samples obtained from patients with (n = 15) and without (n = 13) diagnosed metastases and from men with benign prostate disease (n = 30). This was done using gas chromatography-mass spectrometry for sample characterization, and chemometric bioinformatics for data analysis. Results were verified in a separate test set including metastatic and normal bone tissue from patients with other cancers (n = 7). Significant differences were found between PCa bone metastases, bone metastases of other cancers, and normal bone. Furthermore, we identified metabolites in primary tumor tissue and in plasma which were significantly associated with metastatic disease. Among the metabolites in PCa bone metastases especially cholesterol was noted. In a test set the mean cholesterol level in PCa bone metastases was 127.30 mg/g as compared to 81.06 and 35.85 mg/g in bone metastases of different origin and normal bone, respectively (P = 0.0002 and 0.001). Immunohistochemical staining of PCa bone metastases showed intense staining of the low density lipoprotein receptor and variable levels of the scavenger receptor class B type 1 and 3-hydroxy-3-methylglutaryl-coenzyme reductase in tumor epithelial cells, indicating possibilities for influx and de novo synthesis of cholesterol.Conclusions/significanceWe have identified metabolites associated with PCa metastasis and specifically identified high levels of cholesterol in PCa bone metastases. Based on our findings and the previous literature, this makes cholesterol a possible therapeutic target for advanced PCa.
| S-EPMC2997052 | biostudies-literature
Project description:Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10-3, 2.8 × 10-3, and >3.0 ×10-2 mm2/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.
| S-EPMC9896230 | biostudies-literature
Project description:PURPOSE:To assess which microstructural models best explain the diffusion-weighted MRI signal in the human placenta. METHODS:The placentas of nine healthy pregnant subjects were scanned with a multishell, multidirectional diffusion protocol at 3T. A range of multicompartment biophysical models were fit to the data, and ranked using the Bayesian information criterion. RESULTS:Anisotropic extensions to the intravoxel incoherent motion model, which consider the effect of coherent orientation in both microvascular structure and tissue microstructure, consistently had the lowest Bayesian information criterion values. Model parameter maps and model selection results were consistent with the physiology of the placenta and surrounding tissue. CONCLUSION:Anisotropic intravoxel incoherent motion models explain the placental diffusion signal better than apparent diffusion coefficient, intravoxel incoherent motion, and diffusion tensor models, in information theoretic terms, when using this protocol. Future work will aim to determine if model-derived parameters are sensitive to placental pathologies associated with disorders, such as fetal growth restriction and early-onset pre-eclampsia. Magn Reson Med 80:756-766, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
| S-EPMC5947291 | biostudies-literature
Project description:PurposeTo develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy.Material and methodsConsecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI).Results195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features.ConclusionWhole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.
| S-EPMC10264289 | biostudies-literature