Search

PurposeIn a phase III randomized trial, adding a radiation boost to tumor(s) visible on MRI improved prostate cancer (PCa) disease-free and metastasis-free survival without additional toxicity. Radiation oncologists' ability to identify prostate tumors is critical to widely adopting intraprostatic tumor radiotherapy boost for patients. A diffusion MRI biomarker, called the Restriction Spectrum Imaging restriction score (RSIrs), has been shown to improve radiologists' identification of clinically significant PCa. We hypothesized that (1) radiation oncologists would find accurately delineating PCa tumors on conventional MRI challenging and (2) using RSIrs maps would improve radiation oncologists' accuracy for PCa tumor delineation.Methods and materialsIn this multi-institutional, international, prospective study, 44 radiation oncologists (participants) and 2 expert radiologists (experts) contoured prostate tumors on 39 total patient cases using conventional MRI with or without RSIrs maps. Participant volumes were compared to the consensus expert volumes. Contouring accuracy metrics included percent overlap with expert volume, Dice coefficient, conformal number, and maximum distance beyond expert volume.Results1604 participant volumes were produced. 40 of 44 participants (91%) completely missed ≥1 expert-defined target lesion without RSIrs, compared to 13 of 44 (30%) with RSIrs maps. On conventional MRI alone, 134 of 762 contour attempts (18%) completely missed the target, compared to 18 of 842 (2%) with RSIrs maps. Use of RSIrs maps improved all contour accuracy metrics by approximately 50% or more. Mixed effects modeling confirmed that RSIrs maps were the main variable driving improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p

BackgroundMultiparametric magnetic resonance imaging (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the subjective Prostate Imaging Reporting and Data System (PI-RADS) system and quantitative apparent diffusion coefficient (ADC) are inconsistent. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted MRI technique that yields a quantitative imaging biomarker for csPCa called the RSI restriction score (RSIrs).ObjectiveTo evaluate RSIrs for automated patient-level detection of csPCa.Design setting and participantsWe retrospectively studied all patients (n = 151) who underwent 3 T mpMRI and RSI (a 2-min sequence on a clinical scanner) for suspected prostate cancer at University of California San Diego during 2017-2019 and had prostate biopsy within 180 d of MRI.InterventionWe calculated the maximum RSIrs and minimum ADC within the prostate, and obtained PI-RADS v2.1 from medical records.Outcome measurements and statistical analysisWe compared the performance of RSIrs, ADC, and PI-RADS for the detection of csPCa (grade group ≥2) on the best available histopathology (biopsy or prostatectomy) using the area under the curve (AUC) with two-tailed α = 0.05. We also explored whether the combination of PI-RADS and RSIrs might be superior to PI-RADS alone and performed subset analyses within the peripheral and transition zones.Results and limitationsAUC values for ADC, RSIrs, and PI-RADS were 0.48 (95% confidence interval: 0.39, 0.58), 0.78 (0.70, 0.85), and 0.77 (0.70, 0.84), respectively. RSIrs and PI-RADS were each superior to ADC for patient-level detection of csPCa (p

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.

Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm2) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C1 compartment and product of the restricted and intermediate diffusion compartments (C1 and C2). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

Diffusion-weighted magnetic resonance imaging (DWI) of the musculoskeletal system has various applications, including visualization of bone tumors. However, DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. This study aimed to estimate spatial displacements of bone and to examine whether distortion corrected DWI images more accurately reflect underlying anatomy. Whole-body MRI data from 127 prostate cancer patients were analyzed. The reverse polarity gradient (RPG) technique was applied to DWI data to estimate voxel-level distortions and to produce a distortion corrected DWI dataset. First, an anatomic landmark analysis was conducted, in which corresponding vertebral landmarks on DWI and anatomic T2-weighted images were annotated. Changes in distance between DWI- and T2-defined landmarks (i.e., changes in error) after distortion correction were calculated. In secondary analyses, distortion estimates from RPG were used to assess spatial displacements of bone metastases. Lastly, changes in mutual information between DWI and T2-weighted images of bone metastases after distortion correction were calculated. Distortion correction reduced anatomic error of vertebral DWI up to 29 mm. Error reductions were consistent across subjects (Wilcoxon signed-rank p

BackgroundDiffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded greatest tumor conspicuity.PurposeTo evaluate the slowest diffusion compartment of a four-compartment spectrum imaging model (RSI4 -C1 ) as a quantitative voxel-level classifier of PCa.Study typeRetrospective.SubjectsForty-six men who underwent an extended MRI acquisition protocol for suspected PCa. Twenty-three men had benign prostates, and the other 23 men had PCa.Field strength/sequenceA 3 T, multishell diffusion-weighted and axial T2-weighted sequences.AssessmentHigh-confidence cancer voxels were delineated by expert consensus, using imaging data and biopsy results. The entire prostate was considered benign in patients with no detectable cancer. Diffusion images were used to calculate RSI4 -C1 and conventional ADC. Classifier images were also generated.Statistical testsVoxel-level discrimination of PCa from benign prostate tissue was assessed via receiver operating characteristic (ROC) curves generated by bootstrapping with patient-level case resampling. RSI4 -C1 was compared to conventional ADC for two metrics: area under the ROC curve (AUC) and false-positive rate for a sensitivity of 90% (FPR90 ). Statistical significance was assessed using bootstrap difference with two-sided α = 0.05.ResultsRSI4 -C1 outperformed conventional ADC, with greater AUC (mean 0.977 [95% CI: 0.951-0.991] vs. 0.922 [0.878-0.948]) and lower FPR90 (0.032 [0.009-0.082] vs. 0.201 [0.132-0.290]). These improvements were statistically significant (P

AbstractPurposeMultiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa.Materials and MethodsRetrospective analysis of men who underwent mpMRI with RSI and prostate biopsy for suspected prostate cancer from 2017-2019. Maximum RSIrs within the prostate was assessed by area under the receiver operating characteristic curve (AUC) for discriminating csPCa (grade group ≥2) from benign or grade group 1 biopsies. Performance of RSIrs was compared to minimum ADC and PI-RADS v2-2.1via bootstrap confidence intervals and bootstrap difference (two-tailed α=0.05). We also tested whether the combination of PI-RADS and RSIrs (PI-RADS+RSIrs) was superior to PI-RADS, alone.Results151 patients met criteria for inclusion. AUC values for ADC, RSIrs, and PI-RADS were 0.50 [95% confidence interval: 0.41, 0.60], 0.76 [0.68, 0.84], and 0.78 [0.71, 0.85], respectively. RSIrs (p=0.0002) and PI-RADS (p

BackgroundMulticompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer.PurposeTo optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo.Study typeRetrospective.SubjectsForty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer.Field strength/sequence3T multishell diffusion-weighted sequence.AssessmentMulticompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps.Statistical testsAnalysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models.ResultsThe lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P

Background Prostate MRI is used widely in clinical care for guiding tissue sampling, active surveillance, and staging. The Prostate Imaging Reporting and Data System (PI-RADS) helps provide a standardized probabilistic approach for identifying clinically significant prostate cancer. Despite widespread use, the variability in performance of prostate MRI across practices remains unknown. Purpose To estimate the positive predictive value (PPV) of PI-RADS for the detection of high-grade prostate cancer across imaging centers. Materials and Methods This retrospective cross-sectional study was compliant with the HIPAA. Twenty-six centers with members in the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel submitted data from men with suspected or biopsy-proven untreated prostate cancer. MRI scans were obtained between January 2015 and April 2018. This was followed with targeted biopsy. Only men with at least one MRI lesion assigned a PI-RADS score of 2-5 were included. Outcome was prostate cancer with Gleason score (GS) greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2). A mixed-model logistic regression with institution and individuals as random effects was used to estimate overall PPVs. The variability of observed PPV of PI-RADS across imaging centers was described by using the median and interquartile range. Results The authors evaluated 3449 men (mean age, 65 years ± 8 [standard deviation]) with 5082 lesions. Biopsy results showed 1698 cancers with GS greater than or equal to 3+4 (International Society of Urological Pathology grade group ≥2) in 2082 men. Across all centers, the estimated PPV was 35% (95% confidence interval [CI]: 27%, 43%) for a PI-RADS score greater than or equal to 3 and 49% (95% CI: 40%, 58%) for a PI-RADS score greater than or equal to 4. The interquartile ranges of PPV at these same PI-RADS score thresholds were 27%-44% and 27%-48%, respectively. Conclusion The positive predictive value of the Prostate Imaging and Reporting Data System was low and varied widely across centers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

Cancer treatments have evolved from indiscriminate cytotoxic agents to selective genome- and immune-targeted drugs that have transformed the outcomes of some malignancies1. Tumor complexity and heterogeneity suggest that the 'precision medicine' paradigm of cancer therapy requires treatment to be personalized to the individual patient2-6. To date, precision oncology trials have been based on molecular matching with predetermined monotherapies7-14. Several of these trials have been hindered by very low matching rates, often in the 5-10% range15, and low response rates. Low matching rates may be due to the use of limited gene panels, restrictive molecular matching algorithms, lack of drug availability, or the deterioration and death of end-stage patients before therapy can be implemented. We hypothesized that personalized treatment with combination therapies would improve outcomes in patients with refractory malignancies. As a first test of this concept, we implemented a cross-institutional prospective study (I-PREDICT, NCT02534675 ) that used tumor DNA sequencing and timely recommendations for individualized treatment with combination therapies. We found that administration of customized multidrug regimens was feasible, with 49% of consented patients receiving personalized treatment. Targeting of a larger fraction of identified molecular alterations, yielding a higher 'matching score', was correlated with significantly improved disease control rates, as well as longer progression-free and overall survival rates, compared to targeting of fewer somatic alterations. Our findings suggest that the current clinical trial paradigm for precision oncology, which pairs one driver mutation with one drug, may be optimized by treating molecularly complex and heterogeneous cancers with combinations of customized agents.

Purpose: The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). Methods: This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C‐maps of TE90 to the reference C‐maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). Results: Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non‐csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to ‐0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to ‐1.03SI, a 55% reduction in absolute error. At the Youden index for patient‐level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. Conclusions: The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE‐induced error by 72% and 55% for non‐csPCa and csPCa, respectively. [ABSTRACT FROM AUTHOR]

Introduction: The current work probes the effectiveness of multimodal touch screen tablet electronic devices in conveying science, technology, engineering, and mathematics graphics via vibrations and sounds to individuals who are visually impaired (i.e., blind or low vision) and compares it with similar graphics presented in an embossed format. Method: A volunteer sample of 22 participants who are visually impaired, selected from a summer camp and local schools for blind students, were recruited for the current study. Participants were first briefly (~30 min) trained on how to explore graphics via a multimodal touch screen tablet. They then explored six graphic types (number line, table, pie chart, bar chart, line graph, and map) displayed via embossed paper and tablet. Participants answered three content questions per graphic type following exploration. Results: Participants were only 6% more accurate when answering questions regarding an embossed graphic as opposed to a tablet graphic. A paired-samples t test indicated that this difference was not significant, t(14) = 1.91, p = 0.07. Follow-up analyses indicated that presentation medium did not interact with graphic type, F(5, 50) = 0.43, p =0.83, nor visual ability, F(1, 13) = 0.00, p = 0.96. Discussion: The findings demonstrate that multimodal touch screen tablets may be comparable to embossed graphics in conveying iconographic science and mathematics content to individuals with visual impairments, regardless of the severity of impairment. The relative equivalence in response accuracy between mediums was unexpected, given that most students who participated were braille readers and had experience reading embossed graphics, whereas they were introduced to the tablet the day of testing. Implications for practitioners: This work illustrates that multimodal touch screen tablets may be an effective option for general education teachers or teachers of students with visual impairments to use in their educational practices. Currently, preparation of accessible graphics is time consuming and requires significant preparation, but such tablets provide solutions for offering "real-time" displays of these graphics for presentation in class.

A Gd3+-coordinated polymerizable analogue of the MRI contrast agent Gd-DOTA was used to prepare amphiphilic block copolymers, with hydrophilic blocks composed entirely of the polymerized contrast agent. The resulting amphiphilic block copolymers assemble into nanoparticles (NPs) of spherical- or fibril-shape, each demonstrating enhanced relaxivity over Gd-DOTA. As an initial examination of their behavior in vivo, intraperitoneal (IP) injection of NPs into live mice was performed, showing long IP residence times, observed by MRI. Extended residence times for particles of well-defined morphology may represent a valuable design paradigm for treatment or diagnosis of peritoneal malignances.

Noninvasive assessment of muscle mechanical properties in clinical and performance settings tends to rely on manual palpation and emphasizes examination of musculotendinous stiffness. However, measurement standards are highly subjective. The purpose of the study was to compare musculotendinous stiffness in adult women with varying resistance training history while exploring the use of multiple tissue compliance measures. We identified relationships between tissue stiffness and morphology, and tested the hypothesis that combining objective measures of mor-phology and stiffness would better predict indices of contractile performance. Resistance-trained (RT) women (n = 11) and nontrained (NT) women (n = 10) participated in the study. Muscle echogenicity and morphology were measured using B-mode ultrasonography (US). Vastus lateralis (VL) and patellar tendon (PT) stiffness were measured using digital palpation and US across submaximal isometric contractions. Muscle function was evaluated during maximal voluntary isometric contraction (MVIC) of the knee extensors (KEs). Resistance trained had significantly greater PT stiffness and reduced echogenicity (p < 0.01). Resistance trained also had greater strength per body mass (p < 0.05). Muscle echogenicity was strongly associated with strength and rate of torque development (RTD). Patellar tendon passive stiffness was associated with RTD normalized to MVIC (RTDrel; r = 0.44, p < 0.05). Patellar tendon stiffness was greater in RT young women. No predictive models of muscle function incorporated both stiffness and echogenicity. Because RTDrel is a clinically relevant measure of rehabilitation in athletes and can be predicted by digital palpation, this might represent a practical and objective measure in settings where RTD may not be easy to measure directly. [ABSTRACT FROM AUTHOR]

Objective: To identify vaginal morphology and position factors associated with prolapse recurrence following vaginal surgery. Design: Secondary analysis of magnetic resonance images (MRI) of the Defining Mechanisms of Anterior Vaginal Wall Descent cross‐sectional study. Setting: Eight clinical sites in the US Pelvic Floor Disorders Network. Population or Sample: Women who underwent vaginal mesh hysteropexy (hysteropexy) with sacrospinous fixation or vaginal hysterectomy with uterosacral ligament suspension (hysterectomy) for uterovaginal prolapse between April 2013 and February 2015. Methods: The MRI (rest, strain) obtained 30–42 months after surgery, or earlier for participants with recurrence who desired reoperation before 30 months, were analysed. MRI‐based prolapse recurrence was defined as prolapse beyond the hymen at strain on MRI. Vaginal segmentations (at rest) were used to create three‐dimensional models placed in a morphometry algorithm to quantify and compare vaginal morphology (angulation, dimensions) and position. Main outcome measures: Vaginal angulation (upper, lower and upper–lower vaginal angles in the sagittal and coronal plane), dimensions (length, maximum transverse width, surface area, volume) and position (apex, mid‐vagina) at rest. Results: Of the 82 women analysed, 12/41 (29%) in the hysteropexy group and 22/41 (54%) in the hysterectomy group had prolapse recurrence. After hysteropexy, women with recurrence had a more laterally deviated upper vagina (p = 0.02) at rest than women with successful surgery. After hysterectomy, women with recurrence had a more inferiorly (lower) positioned vaginal apex (p = 0.01) and mid‐vagina (p = 0.01) at rest than women with successful surgery. Conclusions: Vaginal angulation and position were associated with prolapse recurrence and suggestive of vaginal support mechanisms related to surgical technique and potential unaddressed anatomical defects. Future prospective studies in women before and after prolapse surgery may distinguish these two factors. [ABSTRACT FROM AUTHOR]

The editorial in the Journal of Musculoskeletal Research discusses the increasing prevalence of degenerative joint diseases and the limitations of pharmacologic treatments and joint replacements. Regenerative medicine, including techniques like stem cell therapies and platelet-rich plasma injections, offers potential benefits such as tissue repair, pain management, enhanced surgical outcomes, and reduced dependency on opioids. The editorial highlights the role of regenerative medicine in promoting healing and improving outcomes for musculoskeletal conditions, with contributions from researchers in the United States and Taiwan. [Extracted from the article]