Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Percentage of Gleason pattern 4 and tumor volume predict adverse pathological stage and margin status at radical prostatectomy in grade Group 2 and grade Group 3 prostate cancers.

BACKGROUND: Increasing percentages of Gleason pattern 4 (GP4%) in radical prostatectomy (RP) correlate with an increased likelihood of nonorgan-confined disease and earlier biochemical recurrence (BCR). However, there are no detailed RP studies assessing the impact of GP4% and corresponding tumor volume (TV) on extraprostatic extension (EPE), seminal vesicle (SV) invasion (SV+), and positive surgical margin (SM) status (SM+). METHODS: In 1301 consecutive RPs, we analyzed each tumor nodule (TN) for TV, Grade Group (GG), presence of focal versus nonfocal EPE, SV+ , and SM+. Using GG1 (GP4% = 0) TNs as a reference, we recorded GP4% for all GG2 or GG3 TNs. We performed a multivariable analysis (MVA) using a mixed effects logistic regression that tested significant variables for risk of EPE, SV+, and SM+, as well as a multinomial logistic regression model that tested significant variables for risks of nonorgan-confined disease (pT2+, pT3a, and pT3b) versus organ-confined disease (pT2). RESULTS: We identified 3231 discrete TNs ranging from 1 to 7 (median: 2.5) per RP. These included GG1 (n = 2115), GG2 (n = 818), GG3 (n = 274), and GG4 (n = 24) TNs. Increasing GP4% weakly paralleled increasing TV (tau = 0.07, p < .001). In MVA, increasing GP4% and TV predicted a greater likelihood of EPE (odds ratio [OR]: 1.03 and 4.41), SV+ (OR: 1.03 and 3.83), and SM+ (1.01, p = .01 and 2.83), all p < .001. Our multinomial logistic regression model demonstrated an association between GP4% and the risk of EPE (i.e., pT3a and pT3b disease), as well as an association between TV and risk of upstaging (all p < .001). CONCLUSIONS: Both GP4% and TV are independent predictors of adverse pathological stage and margin status at RP. However, the risks for adverse outcomes associated with GP4% are marginal, while those for TV are strong. The prognostic significance of GP4% on BCR-free survival has not been studied controlling for TV and other adverse RP findings. Whether adverse pathological stage and margin status associated with larger TV could decrease BCR-free survival to a greater extent than increasing RP GP4% remains to be studied.

Heterogeneity in Genomic Risk Assessment from Tissue Based Prognostic Signatures Used in the Biopsy Setting and the Impact of Magnetic Resonance Imaging Targeted Biopsy.

PURPOSE: Genomic prognostic signatures are used on prostate biopsy tissue for cancer risk assessment, but tumor heterogeneity and multifocality may be an issue. We evaluated the variability in genomic risk assessment from different biopsy cores within the prostate using 3 prognostic signatures (Decipher, CCP, GPS). MATERIALS AND METHODS: Men in this study came from 2 prospective prostate cancer trials of patients undergoing multiparametric magnetic resonance imaging and magnetic resonance imaging targeted biopsy with genomic profiling of positive biopsy cores. We explored the relationship among tumor grade, magnetic resonance imaging risk and genomic risk for each signature. We evaluated the variability in genomic risk assessment between different biopsy cores and assessed how often magnetic resonance imaging targeted biopsy or the current standard of care (profiling the core with the highest grade) resulted in the highest genomic risk level. RESULTS: In all, 224 positive biopsy cores from 78 men with prostate cancer were profiled. For each signature, higher biopsy grade (p <0.001) and magnetic resonance imaging risk level (p <0.001) were associated with higher genomic scores. Genomic scores from different biopsy cores varied with risk categories changing by 21% to 62% depending on which core or signature was used. Magnetic resonance imaging targeted biopsy and profiling the core with the highest grade resulted in the highest genomic risk level in 72% to 84% and 75% to 87% of cases, respectively, depending on the signature used. CONCLUSIONS: There is variation in genomic risk assessment from different biopsy cores regardless of the signature used. Magnetic resonance imaging directed biopsy or profiling the highest grade core resulted in the highest genomic risk level in most cases.

Radiomics on radiography predicts giant cell tumor histologic response to denosumab.

OBJECTIVE: Denosumab is an established targeted systemic therapy for treatment of giant cell tumor of bone (GCTB). We sought to determine whether treatment response could be quantified from radiomics analysis of radiographs taken longitudinally during treatment. MATERIALS AND METHODS: Pre- and post-treatment radiographs of 10 GCTB tumors from 10 patients demonstrating histologic response after treatment with denosumab were analyzed. Intensity- and texture-based radiomics features for each manually segmented tumor were calculated. Radiomics features were compared pre- and post-treatment in tumors. RESULTS: Mean intensity (p = 0.033) significantly increased while skewness (p = 0.028) significantly decreased after treatment. Post-treatment increases in fractal dimensions (p = 0.057) and abundance (p = 0.065) approached significance. A potential linear correlation in mean (p = 0.005; ΔMean = 0.022 * duration - 0.026) with treatment duration was observed. CONCLUSION: Radiomics analysis of plain radiographs quantifies time-dependent matrix mineralization and trabecular reconstitution that mark positive response of giant cell tumors of bone to denosumab.

A personalized decision aid for prostate cancer shared decision making.

BACKGROUND: A shared decision-making model is preferred for engaging prostate cancer patients in treatment decisions. However, the process of assessing an individual's preferences and values is challenging and not formalized. The purpose of this study is to develop an automated decision aid for patient-centric treatment decision-making using decision analysis, preference thresholds and value elicitations to maximize the compatibility between a patient's treatment expectations and outcome. METHODS: A template for patient-centric medical decision-making was constructed. The inputs included prostate cancer risk group, pre-treatment health state, treatment alternatives (primarily focused on radiation in this model), side effects (erectile dysfunction, urinary incontinence, nocturia and bowel incontinence), and treatment success (5-year freedom from biochemical failure). A linear additive value function was used to combine the values for each attribute (side effects, success and the alternatives) into a value for all prospects. The patient-reported toxicity probabilities were derived from phase II and III trials. The probabilities are conditioned on the starting state for each of the side effects. Toxicity matrices for erectile dysfunction, urinary incontinence, nocturia and bowel incontinence were created for the treatment alternatives. Toxicity probability thresholds were obtained by identifying the patient's maximum acceptable threshold for each of the side effects. Results are represented as a visual. R and Rstudio were used to perform analyses, and R Shiny for application creation. RESULTS: We developed a web-based decision aid. Based on preliminary use of the application, every treatment alternative could be the best choice for a decision maker with a particular set of preferences. This result implies that no treatment has determinist dominance over the remaining treatments and that a preference-based approach can help patients through their decision-making process, potentially affecting compliance with treatment, tolerance of side effects and satisfaction with the decision. CONCLUSIONS: We present a unique patient-centric prostate cancer treatment decision aid that systematically assesses and incorporates a patient's preferences and values to rank treatment options by likelihood of achieving the preferred outcome. This application enables the practice and study of personalized medicine. This model can be expanded to include additional inputs, such as genomics, as well as competing, concurrent or sequential therapies.

The role of radiomics in prostate cancer radiotherapy.

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.

Segmentation of prostate and prostate zones using deep learning : A multi-MRI vendor analysis.

PURPOSE: Develop a deep-learning-based segmentation algorithm for prostate and its peripheral zone (PZ) that is reliable across multiple MRI vendors. METHODS: This is a retrospective study. The dataset consisted of 550 MRIs (Siemens-330, General Electric[GE]-220). A multistream 3D convolutional neural network is used for automatic segmentation of the prostate and its PZ using T2-weighted (T2-w) MRI. Prostate and PZ were manually contoured on axial T2­w. The network uses axial, coronal, and sagittal T2­w series as input. The preprocessing of the input data includes bias correction, resampling, and image normalization. A dataset from two MRI vendors (Siemens and GE) is used to test the proposed network. Six different models were trained, three for the prostate and three for the PZ. Of the three, two were trained on data from each vendor separately, and a third (Combined) on the aggregate of the datasets. The Dice coefficient (DSC) is used to compare the manual and predicted segmentation. RESULTS: For prostate segmentation, the Combined model obtained DSCs of 0.893 ± 0.036 and 0.825 ± 0.112 (mean ± standard deviation) on Siemens and GE, respectively. For PZ, the best DSCs were from the Combined model: 0.811 ± 0.079 and 0.788 ± 0.093. While the Siemens model underperformed on the GE dataset and vice versa, the Combined model achieved robust performance on both datasets. CONCLUSION: The proposed network has a performance comparable to the interexpert variability for segmenting the prostate and its PZ. Combining images from different MRI vendors on the training of the network is of paramount importance for building a universal model for prostate and PZ segmentation.

Pattern recognition analysis of dynamic susceptibility contrast (DSC)-MRI curves automatically segments tissue areas with intact blood-brain barrier in a rat stroke model: A feasibility and comparison study.

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 ΔR2* -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 2020;51:1369-1381.

Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters.

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.

Validation of dominant and secondary sequence utilization in PI-RADS v2 for classifying prostatic lesions.

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.

Spectral decomposition for resolving partial volume effects in MRSI.

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.

Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity.

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.

Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis.

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.

Dynamic contrast-enhanced MRI for automatic detection of foci of residual or recurrent disease after prostatectomy.

PURPOSE: This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. MATERIALS AND METHODS: Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the "tumor" pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0, …, DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. RESULTS: Principal component analysis determined 2-4 significant patterns in patients' DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). Ktrans for DCE2.5 was statistically higher than the GTV's Ktrans (p < 0.05), indicating that the automatic volume better captures areas of malignancy. CONCLUSION: A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system.

Denoising of MR spectroscopic imaging data using statistical selection of principal components.

OBJECTIVES: To evaluate a new denoising method for MR spectroscopic imaging (MRSI) data based on selection of signal-related principal components (SSPCs) from principal components analysis (PCA). MATERIALS AND METHODS: A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original data set utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSIs of human brain, from a normal subject and a patient with a brain tumor, using variable signal-to-noise ratios (SNRs), metabolite peak areas, Cramer-Rao bounds (CRBs) of fitted metabolite peak areas and metabolite linewidth. RESULTS: In simulated data, SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra. CONCLUSION: The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.

Quantification of the margin required for treating intraprostatic lesions.

Advances in magnetic resonance imaging (MRI) sequences allow physicians to define the dominant intraprostatic lesion (IPL) in prostate radiation therapy treat-ments allowing for dose escalation and potentially increased tumor control. This work quantifies the margin required around the MRI-defined IPL accounting for both prostate motion and deformation. Ten patients treated with a simultaneous integrated intraprostatic boost (SIIB) were retrospectively selected and replanned with incremental 1 mm margins from 0-5 mm around the IPL to determine if there were any significant differences in dosimetric parameters. Sensitivity analysis was then performed accounting for random and systematic uncertainties in both prostate motion and deformation to ensure adequate dose was delivered to the IPL. Prostate deformation was assessed using daily CBCT imaging and implanted fiducial markers. The average IPL volume without margin was 2.3% of the PTV volume and increased to 11.8% with a 5 mm margin. Despite these changes in vol-ume, the only statistically significant dosimetric difference was found for the PTV maximum dose, which increased with increasing margin. The sensitivity analysis demonstrated that a 3.0 mm margin ensures > 95% IPL coverage accounting for both motion and deformation. We found that a margin of 3.0 mm around the MRI defined IPL is sufficient to account for random and systematic errors in IPL posi-tion for the majority of cases.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system.

PURPOSE: MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac. METHODS AND MATERIALS: The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. RESULTS: The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. CONCLUSION: Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.

Molecular and diffusion features for identification of clinically significant prostate cancer in PI-RADS 3 lesions.

BACKGROUND: The recommendation to perform biopsy of PIRADS 3 lesions has not been adopted with strength as compared to higher scored lesions on multiparametric MRI. This represents a challenging scenario and an unmet need for clinicians to apply a risk adapted approach in these cases. In the present study, we examined clinical and radiologic characteristics in men with PI-RADS 3 index lesions that can predict csPCa on mpMRI-target biopsy. METHODS: Revision of a prospective database with patients who underwent targeted and systematic biopsies from 2015 to 2023 for PI-RADS 3 lesions identified on mpMRI. Baseline variables were collected, such as PSA density (PSAd), 4Kscore, prostate size, and the apparent diffusion coefficient (ADC) value of the lesion on mpMRI. Logistic regression, receiver operating characteristic (ROC) and decision curve analyses (DCA) assessing the association between clinic-radiologic factors and csPCa were performed. RESULTS: Overall, 230 patients were included in the study and the median age was 65 years. The median prostate size and PSA were 50 g and 6.26 ng/mL, respectively. 17.4% of patients had csPCa, while 27.5% had Gleason group 1. In univariable logistic analyses, we found that age, BMI, prostate size, PSAd, ADC, and 4Kscore were significant csPCa predictors (P < 0.05). PSAd showed the best prediction performance in terms of AUC (= 0.679). On multivariable analysis, PSAd and 4Kscore were associated with csPCa. The net benefit of PSAd combined with clinical features was superior to those of other parameters. Within patients with PSAd < 0.15, 4Kscore was a statistically significant predictor of csPCa (OR = 3.25, P = 0.032). CONCLUSION: PSAd and 4Kscore are better predictors of csPCa in patients with PIRADS 3 lesions compared to ADC. The predictive role of 4Kscore is higher in patients with low PSAd. These results can assist practitioners in the risk stratification of patients with equivocal lesions to determine the need of biopsy.