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1.
Urology ; 2019 Nov 08.
Artigo em Inglês | MEDLINE | ID: mdl-31711982

RESUMO

OBJECTIVE: To study if prostatic ductal adenocarcinoma (PDA) controlled by Grade Group (GG), PSA, and tumor volume (TV) is an independent predictor of adverse radical prostatectomy (RP) outcomes. MATERIALS: One-hundred-and-twenty-eight PDA and 1,141 acinar continuous RPs were studied. Each tumor nodule (TN) was individually graded, staged, and its TV measured. Univariate analysis (UVA) identified features associated with lymph node metastasis (LN+), extraprostatic extension (EPE), positive surgical margins (SM+), and seminal vesicle invasion (SV+). We then assessed PDA effect on RP outcomes in a multivariate analysis (MVA). RESULTS: In 127 cases PDA was present in 1 TN and no TN was pure PDA. One-hundred-and-twenty-three cases had PDA in TNs with highest grade, stage, and TV. Patients with PDA were older (65 vs. 63 years, p<0.001), had higher GG (p<0.001), and LN+ (6.3% vs 2.7%, p=0.049). Controlling these variables by GG eliminated statistical significance. Overall, there were 3,249 separate TNs (129 PDA and 3,120 acinar). In UVA, PDA predicted EPE (92/124 vs 517/3,045), SV+ (28/1129 vs 116/3,120), and SM+ (51/129 vs 296/3,120), all p<0.001. In MVA, PDA lost its effect on EPE (OR=0.88, p=0.64), SM+ (OR=0.86, p=0.5), and SV+ (OR=0.99, p=0.98). CONCLUSION: Controlled for grade and TV, PDA was not an independent predictor of adverse RP outcomes, but former two were. Hence, higher GG and TV associated with PDA TNs may be predictive of adverse RP outcomes rather than PDA by itself. These conclusions may be used in preoperative risk stratification and definitive therapy planning when PDA is identified on needle biopsy.

2.
J Magn Reson Imaging ; 2019 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-31654463

RESUMO

BACKGROUND: The manual segmentation of intact blood-brain barrier (BBB) regions in the stroke brain is cumbersome, due to the coexistence of infarction, large blood vessels, ventricles, and intact BBB regions, specifically in areas with weak signal enhancement following contrast agent injection. HYPOTHESIS: That from dynamic susceptibility contrast (DSC)-MRI alone, without user intervention, regions of weak BBB damage can be segmented based on the leakage-related parameter K 2 and the extent of intact BBB regions, needed to estimate K 2 values, determined. STUDY TYPE: Feasibility. ANIMAL MODEL: Ten female Sprague-Dawley rats (SD, 200-250g) underwent 1-hour middle carotid artery occlusion (MCAO) and 1-day reperfusion. Two SD rats underwent 1-hour MCAO with 3-day and 5-day reperfusion. FIELD STRENGTH/SEQUENCE: 7T; ADC and T1 maps using diffusion-weighted echo planar imaging (EPI) and relaxation enhancement (RARE) with variable repetition time (TR), respectively. dynamic contrast-enhanced (DCE)-MRI using FLASH. DSC-MRI using gradient-echo EPI. ASSESSMENT: Constrained nonnegative matrix factorization (cNMF) was applied to the dynamic Δ R 2 * -curves of DSC-MRI (<4 min) in a BBB-disrupted rat model. Areas of voxels with intact BBB, classified by automated cNMF analyses, were then used in estimating K 1 and K 2 values, and compared with corresponding values from manually-derived areas. STATISTICAL TESTS: Mean ± standard deviation of ΔT1 -differences between ischemic and healthy areas were displayed with unpaired Student's t-tests. Scatterplots were displayed with slopes and intercepts and Pearson's r values were evaluated between K 2 maps obtained with automatic (cNMF)- and manually-derived regions of interest (ROIs) of the intact BBB region. RESULTS: Mildly BBB-damaged areas (indistinguishable from DCE-MRI (10 min) parameters) were automatically segmented. Areas of voxels with intact BBB, classified by automated cNMF, matched closely the corresponding, manually-derived areas when respective areas were used in estimating K 2 maps (Pearson's r = 0.97, 12 slices). DATA CONCLUSION: Automatic segmentation of short DSC-MRI data alone successfully identified areas with intact and compromised BBB in the stroke brain and compared favorably with manual segmentation. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019.

3.
Can J Urol ; 26(3): 9763-9768, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31180306

RESUMO

INTRODUCTION: To assess the secondary sequence rule in The Prostate Imaging Reporting Data System (PI-RADS) version 2 by comparing the detection of Grade group 1+ (GG1+) and 2+ (GG2+) cancers in PI-RADS 3, an upgraded PI-RADS 4, and true (non-upgraded) PI-RADS 4 targets. MATERIALS AND METHODS: We analyzed a total of 589 lesions scored as PI-RADS 3 or 4 obtained from 434 men who underwent mpMRI-US fusion biopsy from September 2015 to November 2017 for evaluation of GG1+ and GG2+ prostate cancer. PI-RADS 4 lesions were differentiated into those that were 'upgraded' to PI-RADS 4 based on the secondary sequence and those that were 'true' PI-RADS 4 based on the dominant sequence. RESULTS: The odds of detecting a GG2+ cancer was significantly higher for an upgraded 4 (peripheral zone (PZ): OR 5.06, 95%CI 2.04-12.54, p < 0.001, transitional zone (TZ): OR 3.08, 95%CI 1.04-9.08, p = 0.042) and true 4 (PZ: OR 5.82, 95%CI 3.10-10.94, p < 0.0001, TZ: OR 2.43, 95%CI 1.14-5.18, p = 0.022) lesions compared to PI-RADS 3 lesions. Additionally, we found no difference in the odds of detecting a GG2+ prostate cancer between a true PI-RADS 4 (OR 1.15, 95%CI 0.49-2.71 p = 0.746) and upgraded 4 (referent) in the PZ. Similar non-significance was noted between true 4 (OR 0.79, 95%CI 0.26-2.38 p = 0.674) and upgraded 4 lesions in the TZ for detection of GG2+ cancers. CONCLUSIONS: Upgraded PI-RADS 4 and true 4 targets have a higher odds of detecting GG1+ and GG2+ compared to PI-RADS 3 in the PZ and TZ. Our findings validate the revised scoring system for PI-RADS.

4.
J Urol ; 202(3): 505, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31166884
6.
Sci Rep ; 8(1): 16801, 2018 Nov 14.
Artigo em Inglês | MEDLINE | ID: mdl-30429515

RESUMO

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.

7.
Transl Androl Urol ; 7(Suppl 4): S443-S452, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30363524

RESUMO

Radiogenomics is a field that amalgamates data from genomics and imaging techniques in order to derive clinically meaningful trends. In this article, we discuss the importance of prostate cancer risk classification and how data derived from genomic testing and multi-parametric magnetic resonance imaging (mpMRI) can be integrated into clinical decision-making processes with a focus on active surveillance (AS). Finally, we describe an ongoing prospective trial (Miami MAST trial) which incorporates imaging (mpMRI) and radiomics data in patients who are on AS for prostate cancer.

8.
Strahlenther Onkol ; 2018 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-30140944

RESUMO

BACKGROUND AND PURPOSE: The aim of this study was to evaluate an automatic multi-atlas-based segmentation method for generating prostate, peripheral (PZ), and transition zone (TZ) contours on MRIs with and without fat saturation (±FS), and compare MRIs from different vendor MRI systems. METHODS: T2-weighted (T2) and fat-saturated (T2FS) MRIs were acquired on 3T GE (GE, Waukesha, WI, USA) and Siemens (Erlangen, Germany) systems. Manual prostate and PZ contours were used to create atlas libraries. As a test MRI is entered, the procedure for atlas segmentation automatically identifies the atlas subjects that best match the test subject, followed by a normalized intensity-based free-form deformable registration. The contours are transformed to the test subject, and Dice similarity coefficients (DSC) and Hausdorff distances between atlas-generated and manual contours were used to assess performance. RESULTS: Three atlases were generated based on GE_T2 (n = 30), GE_T2FS (n = 30), and Siem_T2FS (n = 31). When test images matched the contrast and vendor of the atlas, DSCs of 0.81 and 0.83 for T2 ± FS were obtained (baseline performance). Atlases performed with higher accuracy when segmenting (i) T2FS vs. T2 images, likely due to a superior contrast between prostate vs. surrounding tissue; (ii) prostate vs. zonal anatomy; (iii) in the mid-gland vs. base and apex. Atlases performance declined when tested with images with differing contrast and MRI vendor. Conversely, combined atlases showed similar performance to baseline. CONCLUSION: The MRI atlas-based segmentation method achieved good results for prostate, PZ, and TZ compared to expert contoured volumes. Combined atlases performed similarly to matching atlas and scan type. The technique is fast, fully automatic, and implemented on commercially available clinical platform.

9.
PLoS One ; 13(8): e0201384, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30092002

RESUMO

OBJECTIVES: To evaluate the performance of mpMRI and the 4Kscore test together for the detection of significant prostate cancer. Material and methods We selected a consecutive series of men who were referred for evaluation of prostate cancer at an academic institution and underwent mpMRI and the 4Kscore test. The primary outcome was the presence of Gleason 7 or higher cancer on biopsy of the prostate. We used logistic regression and Decision Curve Analysis to report the discrimination and clinical utility of using mpMRI and the 4Kscore test for prostate cancer detection. We modeled the probability of harboring a Gleason 7 or higher prostate cancer based on the 4Kscore test and mpMRI findings. Finally, we examined various combinations and sequences of mpMRI and the 4Kscore test and assessed the impact on biopsies avoided and cancers missed. RESULTS: Among 300 men who underwent a 4Kscore test and mpMRI, 149 (49%) underwent a biopsy. Among those, 73 (49%) had cancer, and 49 (33%) had Gleason 7 cancer. The area under the curve (AUC) for using the 4Kscore test and mpMRI together 0.82 (0.75-0.89) was superior to using the 4Kscore 0.70 (0.62-0.79) or mpMRI 0.74 (0.66-0.81) individually (p = 0.001). Similarly, decision analysis revealed the highest net benefit was achieved using both tests. CONCLUSIONS: The 4Kscore test and mpMRI results provide independent, but complementary, information that enhances the prediction of higher-grade prostate cancer and improves patient's selection for a prostate biopsy. Prospective trials are required to confirm these findings.

10.
Transl Androl Urol ; 7(3): 399-413, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30050800

RESUMO

While radical prostatectomy (RP) has provided long-term disease control for the majority of patients with localized prostate cancer (CaP), nearly 30% of all surgical patients have disease progression. For high-risk patients, more than half of men experience disease recurrence within 10 years. Postoperative radiotherapy is the only known potentially curative treatment for a large number of patients following prostatectomy. Lately, there have been several advances with the potential to improve outcomes for patients undergoing postoperative radiotherapy. This article will give an overview of the existing literature and current controversies on: (I) timing of postoperative radiation; (II) use of concomitant androgen deprivation therapy; (III) optimal dose to the prostate bed; (IV) use of hypofractionation; (V) elective treatment of the pelvic lymph nodes; (VI) novel imaging modalities, and (VII) genomic biomarkers.

11.
Transl Androl Urol ; 7(3): 445-458, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30050803

RESUMO

In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

12.
Abdom Radiol (NY) ; 2018 Jun 20.
Artigo em Inglês | MEDLINE | ID: mdl-29926137

RESUMO

Radiomics and radiogenomics are attractive research topics in prostate cancer. Radiomics mainly focuses on extraction of quantitative information from medical imaging, whereas radiogenomics aims to correlate these imaging features to genomic data. The purpose of this review is to provide a brief overview summarizing recent progress in the application of radiomics-based approaches in prostate cancer and to discuss the potential role of radiogenomics in prostate cancer.

13.
Phys Med ; 50: 26-36, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29891091

RESUMO

The purpose of this study was to examine the dependence of image texture features on MR acquisition parameters and reconstruction using a digital MR imaging phantom. MR signal was simulated in a parallel imaging radiofrequency coil setting as well as a single element volume coil setting, with varying levels of acquisition noise, three acceleration factors, and four image reconstruction algorithms. Twenty-six texture features were measured on the simulated images, ground truth images, and clinical brain images. Subtle algorithm-dependent errors were observed on reconstructed phantom images, even in the absence of added noise. Sources of image error include Gibbs ringing at image edge gradients (tissue interfaces) and well-known artifacts due to high acceleration; two of the iterative reconstruction algorithms studied were able to mitigate these image errors. The difference of the texture features from ground truth, and their variance over reconstruction algorithm and parallel imaging acceleration factor, were compared to the clinical "effect size", i.e., the feature difference between high- and low-grade tumors on T1- and T2-weighted brain MR images of twenty glioma patients. The measured feature error (difference from ground truth) was small for some features, but substantial for others. The feature variance due to reconstruction algorithm and acceleration factor were generally smaller than the clinical effect size. Certain texture features may be preserved by MR imaging, but adequate precautions need to be taken regarding their validity and reliability. We present a general simulation framework for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios.


Assuntos
Processamento de Imagem Assistida por Computador , Imagem por Ressonância Magnética , Modelos Teóricos , Glioma/diagnóstico por imagem , Humanos , Imagens de Fantasmas , Projetos de Pesquisa , Razão Sinal-Ruído
14.
Int J Radiat Oncol Biol Phys ; 102(4): 821-829, 2018 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-29908220

RESUMO

PURPOSE: To develop a prostate tumor habitat risk scoring (HRS) system based on multiparametric magnetic resonance imaging (mpMRI) referenced to prostatectomy Gleason score (GS) for automatic delineation of gross tumor volumes. A workflow for integration of HRS into radiation therapy boost volume dose escalation was developed in the framework of a phase 2 randomized clinical trial (BLaStM). METHODS AND MATERIALS: An automated quantitative mpMRI-based 10-point pixel-by-pixel method was optimized to prostatectomy GSs and volumes using referenced dynamic contrast-enhanced and apparent diffusion coefficient sequences. The HRS contours were migrated to the planning computed tomography scan for boost volume generation. RESULTS: There were 51 regions of interest in 12 patients who underwent radical prostatectomy (26 with GS ≥7 and 25 with GS 6). The resultant heat maps showed inter- and intratumoral heterogeneity. The HRS6 level was significantly associated with radical prostatectomy regions of interest (slope 1.09, r = 0.767; P < .0001). For predicting the likelihood of cancer, GS ≥7 and GS ≥8 HRS6 area under the curve was 0.718, 0.802, and 0.897, respectively. HRS was superior to the Prostate Imaging, Reporting and Diagnosis System 4/5 classification, wherein the area under the curve was 0.62, 0.64, and 0.617, respectively (difference with HR6, P < .0001). HRS maps were created for the first 37 assessable patients on the BLaStM trial. There were an average of 1.38 habitat boost volumes per patient at a total boost volume average of 3.6 cm3. CONCLUSIONS: An automated quantitative mpMRI-based method was developed to objectively guide dose escalation to high-risk habitat volumes based on prostatectomy GS.

15.
Cureus ; 10(3): e2385, 2018 Mar 28.
Artigo em Inglês | MEDLINE | ID: mdl-29850380

RESUMO

Magnetic resonance-guided radiotherapy (MRgRT) is a new and evolving treatment modality that allows unprecedented visualization of the tumor and surrounding anatomy. MRgRT includes daily 3D magnetic resonance imaging (MRI) for setup and rapidly repeated near real-time MRI scans during treatment for target tracking. One of the more exciting potential benefits of MRgRT is the ability to analyze serial MRIs to monitor treatment response or predict outcomes. A typical radiation treatment (RT) over the span of 10-15 minutes on the MRIdian system (ViewRay, Cleveland, OH) yields thousands of "cine" images, each acquired in 250 ms. This unique data allows for a glimpse in image intensity changes during RT delivery. In this report, we analyze cine images from a single fraction RT of a glioblastoma patient on the ViewRay platform in order to characterize the dynamic signal changes occurring during RT therapy. The individual frames in the cines were saved into DICOM format and read into an MIM image analysis platform (MIM Software, Cleveland, OH) as a time series. The three possible states of the three Cobalt-60 radiation sources-OFF, READY, and ON-were also recorded. An in-house Java plugin for MIM was created in order to perform principal component analysis (PCA) on each of the datasets. The analysis resulted in first PC, related to monotonous signal increase over the course of the treatment fraction. We found several distortion patterns in the data that we postulate result from the perturbation of the magnetic field due to the moving metal parts in the platform while treatment was being administered. The largest variations were detected when all Cobalt-60 sources were OFF. During this phase of the treatment, the gantry and multi-leaf collimators (MLCs) are moving. Conversely, when all Cobalt-60 sources were in the ON position, the image signal fluctuations were minimal, relating to very little mechanical motion. At this phase, the gantry, the MLCs, and sources are fixed in their positions. These findings were confirmed in a study with the daily quality assurance (QA) phantom. While the identified variations were not related to physiological processes, our findings confirm the sensitivity of the developed approach to identify very small fluctuations. Relating these variations to the physical changes that occur during treatment shows the methodical ability of the technique to uncover their underlying sources.

16.
Cureus ; 10(3): e2346, 2018 Mar 19.
Artigo em Inglês | MEDLINE | ID: mdl-29796358

RESUMO

Radiation therapy (RT) plays a critical role in the treatment of glioblastoma. Studies of brain imaging during RT for glioblastoma have demonstrated changes in the brain during RT. However, frequent or daily utilization of standalone magnetic resonance imaging (MRI) scans during RT have limited feasibility. The recent release of the tri-cobalt-60 MRI-guided RT (MR-IGRT) device (ViewRay MRIdian, Cleveland, OH) allows for daily brain MRI for the RT setup. Daily MRI of three postoperative patients undergoing RT and temozolomide for glioblastoma over a six-week course allowed for the identification of changes to the cavity, edema, and visible tumor on a daily basis. The volumes and dimensions of the resection cavities, edema, and T2-hyperintense tumor were measured. A general trend of daily decreases in cavity measurements was observed in all patients. For the one patient with edema, a trend of daily increases followed by a trend of daily decreases were observed. These results suggest that daily MRI could be used for onboard resimulation and adaptive RT for future fluctuations in the sizes of brain tumors, cavities, or cystic components. This could improve tumor targeting and reduce RT of healthy brain tissue.

17.
J Appl Clin Med Phys ; 19(2): 258-264, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-29476603

RESUMO

PURPOSE: Validating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic-MRI and radiation treatment planning-CT by utilizing a planning-MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain. METHODS: For thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic-MRI) and the second on the same day as the planning-CT (planning-MRI). The diagnostic-MRI was deformed to the planning-CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning-MRI provided an independent surrogate for the planning-CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas. RESULTS: The planning-MRI provided an excellent surrogate for the planning-CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013). CONCLUSION: By utilizing the planning-MRI as a surrogate for the planning-CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.


Assuntos
Algoritmos , Processamento de Imagem Assistida por Computador/métodos , Imagem por Ressonância Magnética/métodos , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/radioterapia , Planejamento da Radioterapia Assistida por Computador/métodos , Tomografia Computadorizada por Raios X/métodos , Idoso , Feminino , Marcadores Fiduciais , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Dosagem Radioterapêutica , Radioterapia de Intensidade Modulada/métodos
18.
J Clin Oncol ; 36(6): 581-590, 2018 Feb 20.
Artigo em Inglês | MEDLINE | ID: mdl-29185869

RESUMO

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

19.
Magn Reson Med ; 79(6): 2886-2895, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29130515

RESUMO

PURPOSE: Estimation of brain metabolite concentrations by MR spectroscopic imaging (MRSI) is complicated by partial volume contributions from different tissues. This study evaluates a method for increasing tissue specificity that incorporates prior knowledge of tissue distributions. METHODS: A spectral decomposition (sDec) technique was evaluated for separation of spectra from white matter (WM) and gray matter (GM), and for measurements in small brain regions using whole-brain MRSI. Simulation and in vivo studies compare results of metabolite quantifications obtained with the sDec technique to those obtained by spectral fitting of individual voxels using mean values and linear regression against tissue fractions and spectral fitting of regionally integrated spectra. RESULTS: Simulation studies showed that, for GM and the putamen, the sDec method offers < 2% and 3.5% error, respectively, in metabolite estimates. These errors are considerably reduced in comparison to methods that do not account for partial volume effects or use regressions against tissue fractions. In an analysis of data from 197 studies, significant differences in mean metabolite values and changes with age were found. Spectral decomposition resulted in significantly better linewidth, signal-to-noise ratio, and spectral fitting quality as compared to individual spectral analysis. Moreover, significant partial volume effects were seen on correlations of neurometabolite estimates with age. CONCLUSION: The sDec analysis approach is of considerable value in studies of pathologies that may preferentially affect WM or GM, as well as smaller brain regions significantly affected by partial volume effects. Magn Reson Med 79:2886-2895, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

20.
Magn Reson Med ; 79(3): 1736-1744, 2018 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-28727185

RESUMO

PURPOSE: To automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity. METHODS: Three steps were automated. First, the arrival time of the contrast agent at the tumor was determined, including a calculation of the precontrast signal. Second, four criteria-based algorithms for the slice-specific selection of number of patterns (NP) were validated using 109 tumor slices from subcutaneous flank tumors of five different tumor models. The criteria were: half area under the curve, standard deviation thresholding, percent signal enhancement, and signal-to-noise ratio (SNR). The performance of these criteria was assessed by comparing the calculated NP with the visually determined NP. Third, spatial assignment of single patterns and/or pattern mixtures was obtained by way of constrained nonnegative matrix factorization. RESULTS: The determination of the contrast agent arrival time at the tumor slice was successfully automated. For the determination of NP, the SNR-based approach outperformed other selection criteria by agreeing >97% with visual assessment. The spatial localization of single patterns and pattern mixtures, the latter inferring tumor vascular heterogeneity at subpixel spatial resolution, was established successfully by automated assignment from DCE-MRI signal-versus-time curves. CONCLUSION: The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. Magn Reson Med 79:1736-1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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