Tips and tricks for a proper radiological assessment of abdominal and pelvic lymph nodes.

The assessment of lymph node dimensions is a commonly used criterion in analyzing lymphatic involvement related to inflammatory or neoplastic diseases. However, it is important to understand that the interpretation of lymph nodes goes beyond simply considering their size. A pathologic lymph node can present with enlarged dimensions, a heterogeneous appearance, increased cortex thickness, irregular contours, or a lobulated shape. In this context, it is essential to consider not only the dimensions but also the morphology, attenuation, and enhancement of lymph nodes on imaging exams. This article aims to demonstrate how characteristics of lymph nodes, beyond their size, can provide crucial insights that assist in diagnostic reasoning, focusing on computed tomography. By emphasizing different enhancement patterns, attenuation, and the potential contents related to these patterns, the study seeks to show how these features can indicate possible differential diagnoses and guide more accurate clinical assessments.

Preoperative Prostate Magnetic Resonance Imaging-based Anatomical Predictors of Early Urinary Continence Following Single-port Transvesical Robot-assisted Radical Prostatectomy.

BACKGROUND AND OBJECTIVE: The introduction of the single-port (SP) robotic system has led to new approaches in robot-assisted radical prostatectomy (RARP), such as the transvesical (TV) approach, offering high rates of early urinary continence. While previous studies of SP TV RARP have identified perioperative factors influencing continence outcomes, the impact of anatomical factors remains unexplored. This study aims to assess magnetic resonance imaging (MRI)-based anatomical predictors of urinary continence after SP TV RARP. METHODS: A retrospective analysis of consecutive SP TV RARP cases (November 2020 to June 2023) with preoperative prostate MRI was performed. Two urogenital radiologists independently evaluated ten anatomical parameters to distinguish patients achieving urinary continence within 1 wk and 3 mo. Nonparametric methods estimated receiver operating characteristic curves (area under the curve [AUC]) and inter-reader agreement. KEY FINDINGS AND LIMITATIONS: In 120 cases, 40% achieved continence within 1 wk, rising to 71.7% by 3 mo. Membranous urethra length (MUL) alone was significantly associated with continence at 3 mo (AUC: 0.67, p = 0.003). At 1 wk, several parameters, including anteroposterior diameter of the prostate, coronal membranous urethra length, prostate volume, and transverse diameter of the prostate, showed promise in predicting continence. CONCLUSIONS AND CLINICAL IMPLICATIONS: A longer preoperative MUL was significantly associated with better odds of an early return to urinary continence after SP TV RARP. Each 1-mm increase in coronal MUL was associated with a 27% increase in the odds of continence at 3 mo. This information can aid in patient counseling and expectations preoperatively. PATIENT SUMMARY: Urinary incontinence is a common outcome after prostate cancer surgery, particularly in the early months. Recently, the single-port (SP) robotic system has emerged, localizing surgery to the diseased area. With the SP robot, accessing the prostate via the bladder leads to high rates of early continence. Our study reveals that the longer the urethral portion beneath the prostate, the higher the likelihood of regaining continence within 3 mo after surgery.

Improving Prostate MR Image Quality in Practice-Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative.

OBJECTIVE: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. METHODS: Teams from five organizations in the United States were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of examinations meeting quality criteria (ie, PI-QUAL score ≥4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of examinations with PI-QUAL ≥4 to at least 85%. RESULTS: Across 2,380 examinations audited, the mean weekly rates of prostate MR examinations meeting image quality criteria increased from 67% (range: 60%-74%) at baseline to 87% (range: 80%-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personnel training, and development of an auditing process mechanism. CONCLUSION: A learning network model, in which organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a road map for improvement on a broader scale.

PI-QUAL version 2: an update of a standardised scoring system for the assessment of image quality of prostate MRI.

Multiparametric MRI is the optimal primary investigation when prostate cancer is suspected, and its ability to rule in and rule out clinically significant disease relies on high-quality anatomical and functional images. Avenues for achieving consistent high-quality acquisitions include meticulous patient preparation, scanner setup, optimised pulse sequences, personnel training, and artificial intelligence systems. The impact of these interventions on the final images needs to be quantified. The prostate imaging quality (PI-QUAL) scoring system was the first standardised quantification method that demonstrated the potential for clinical benefit by relating image quality to cancer detection ability by MRI. We present the updated version of PI-QUAL (PI-QUAL v2) which applies to prostate MRI performed with or without intravenous contrast medium using a simplified 3-point scale focused on critical technical and qualitative image parameters. CLINICAL RELEVANCE STATEMENT: High image quality is crucial for prostate MRI, and the updated version of the PI-QUAL score (PI-QUAL v2) aims to address the limitations of version 1. It is now applicable to both multiparametric MRI and MRI without intravenous contrast medium. KEY POINTS: High-quality images are essential for prostate cancer diagnosis and management using MRI. PI-QUAL v2 simplifies image assessment and expands its applicability to prostate MRI without contrast medium. PI-QUAL v2 focuses on critical technical and qualitative image parameters and emphasises T2-WI and DWI.

True-Positive 18F-Flotufolastat Lesions in Patients with Prostate Cancer Recurrence with Baseline-Negative Conventional Imaging: Results from the Prospective, Phase 3, Multicenter SPOTLIGHT Study.

18F-rhPSMA-7.3 (18F-flotufolastat) is a high-affinity prostate-specific membrane antigen-targeted diagnostic radiopharmaceutical for PET imaging in patients with prostate cancer. Here, we report findings from the SPOTLIGHT study (NCT04186845), assessing the performance of 18F-flotufolastat PET/CT for identifying prostate-specific membrane antigen-positive lesions confirmed by standard of truth (SoT) in men with biochemical recurrence of prostate cancer and negative conventional imaging at baseline. Methods: Men with biochemical recurrence received 296 MBq of 18F-flotufolastat intravenously and then underwent PET/CT 50-70 min later. 18F-flotufolastat PET/CT findings were evaluated by 3 masked central readers and verified using histopathology or follow-up confirmatory imaging (CT, MRI, bone scan, or 18F-fluciclovine PET/CT) as the SoT. The present analysis evaluated all patients who had negative conventional imaging at baseline, underwent 18F-flotufolastat PET/CT, and had SoT verification by histopathology or follow-up confirmatory imaging to report detection rate (DR), which is the number of patients with at least 1 PET-positive lesion, divided by the number of evaluable patients, and verified DR (VDR), which is the proportion of patients with at least 1 true-positive lesion as verified by SoT, of all patients scanned (PET-positive and PET-negative scans). DR and VDR were calculated and stratified according to prior therapy. Majority read data (agreement between ≥2 readers) are reported. Results: In total, 171 patients with negative baseline conventional imaging and SoT by histopathology or post-PET confirmatory imaging were evaluated. By majority read, the overall 18F-flotufolastat DR among these patients was 95% (163/171; 95% CI, 91.0%-98.0%), and 110 of 171 of these patients had at least 1 true-positive lesion identified (VDR, 64%; 95% CI, 56.7%-71.5%). In the postprostatectomy group (133/171), 8.3% of patients had at least 1 true-positive lesion in the prostate bed, 28% in pelvic lymph nodes, and 35% in other sites. Among those who had received radiotherapy (36/171), 50% of patients had true-positive detections in the prostate, 8.3% in pelvic lymph nodes, and 36% in other sites. Conclusion: 18F-flotufolastat frequently identified true-positive prostate cancer lesions in patients with negative conventional imaging. 18F-flotufolastat may help to better define sites of disease recurrence and inform salvage therapy decisions than does conventional imaging, potentially leading to improved outcomes.

New Prostate MRI Scoring Systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB): AJR Expert Panel Narrative Review.

Multiparametric MRI (mpMRI), interpreted using PI-RADS, improves the initial detection of clinically significant prostate cancer (PCa). Prostate MR image quality has increasingly recognized relevance to the use of mpMRI for PCa diagnosis. Additionally, mpMRI is increasingly used in scenarios beyond initial detection, including active surveillance and assessment for local recurrence after prostatectomy, radiation therapy, or focal therapy. Acknowledging these evolving demands, specialized prostate MRI scoring systems beyond PI-RADS have emerged, to address distinct scenarios and unmet needs. Examples include Prostate Imaging Quality (PI-QUAL) for assessment of image quality of mpMRI, Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) recommendations for evaluation of serial mpMRI examinations during active surveillance, Prostate Imaging for Recurrence Reporting System (PI-RR) for assessment for local recurrence after prostatectomy or radiation therapy, and Prostate Imaging after Focal Ablation (PI-FAB) for assessment for local recurrence after focal therapy. These systems' development and early uptake signal a compelling shift towards prostate MRI standardization in different scenarios, and ongoing research will help refine their roles in practice. This AJR Expert Panel Narrative Review critically examines these new prostate MRI scoring systems (PI-QUAL, PRECISE, PI-RR, and PI-FAB), analyzing the available evidence, delineating current limitations, and proposing solutions for improvement.

Enhancing prostate MRI expertise: educational strategies for radiologists.

The adoption of multiparametric MRI (mpMRI) and the Prostate Imaging Reporting and Data System has significantly changed prostate cancer diagnosis and management. These advancements, alongside novel biomarkers and updated International Society of Uropathology grade groups, have improved cancer detection and prognostication. Despite this progress, varying levels of expertise in mpMRI among radiologists have resulted in inconsistent assessments, potentially leading to unnecessary procedures and diminished confidence in the modality. This review assesses the educational landscape for prostate MRI, highlighting available resources for radiologists at all professional stages. It emphasizes the need for targeted educational strategies to bridge knowledge gaps and improve patient care outcomes in prostate cancer management.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings.

Objectives: To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE). Methods: Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC). Results: Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2). Conclusions: Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer.

BACKGROUND AND OBJECTIVE: The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty. METHODS: A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring. KEY FINDINGS AND LIMITATIONS: Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9). CONCLUSIONS AND CLINICAL IMPLICATIONS: The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS. PATIENT SUMMARY: The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations are used in clinical practice and research to guide the interpretation and reporting of magnetic resonance imaging for patients on active surveillance for prostate cancer. An international panel has updated these recommendations, clarified the areas of uncertainty, and highlighted the areas for further research.

PI-RADS Upgrading Rules: Impact on Prostate Cancer Detection and Biopsy Avoidance of MRI-Directed Diagnostic Pathways.

BACKGROUND. PI-RADS incorporates rules by which ancillary sequence findings upgrade a dominant score to a higher final category. Evidence on the upgrading rules' impact on diagnostic pathways remains scarce. OBJECTIVE. The purpose of this article was to evaluate the clinical net benefit of the PI-RADS upgrading rules in MRI-directed diagnostic pathways. METHODS. This study was a retrospective analysis of a prospectively maintained clinical registry. The study included patients without known prostate cancer who underwent prostate MRI followed by prostate biopsy from January 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade group 2 and higher. csPCa detection was compared between dominant (i.e., no upgrade rule applied) and upgraded lesions. Decision-curve analysis was used to compare the net benefit, considering the trade-off of csPCa detection and biopsy avoidance, of MRI-directed pathways in scenarios considering and disregarding PI-RADS upgrading rules. These included a biopsy-all pathway, MRI-focused pathway (no biopsy for PI-RADS ≤ 2), and risk-based pathway (use of PSA density ≥ 0.15 ng/mL2 to select patients with PI-RADS ≤ 3 for biopsy). RESULTS. The sample comprised 716 patients (mean age, 64.9 years; 93 with a PI-RADS ≤ 2 examination, 623 with total of 780 PI-RADS ≥ 3 lesions). Frequencies of csPCa were not significantly different between dominant and upgraded PI-RADS 3 transition zone lesions (20% vs 19%, respectively), dominant and upgraded PI-RADS 4 transition zone lesions (33% vs 26%), and dominant and upgraded PI-RADS 4 peripheral zone lesions (58% vs 45%) (p > .05). In the biopsy-all, per-guideline MRI-focused, MRI-focused disregarding upgrading rules, per-guideline risk-based, and risk-based disregarding upgrading rules pathways, csPCa frequency was 53%, 52%, 51%, 52%, and 48% and biopsy avoidance was 0%, 13%, 16%, 19%, and 25%, respectively. Disregarding upgrading rules yielded 5.5 and 1.9 biopsies avoided per missed csPCa for MRI-focused and risk-based pathways, respectively. At probability thresholds for biopsy selection of 7.5-30.0%, net benefit was highest for the per-guideline risk-based pathway. CONCLUSION. Disregarding PI-RADS upgrading rules reduced net clinical bene fit of the risk-based MRI-directed diagnostic pathway when considering trade-offs between csPCa detection and biopsy avoidance. CLINICAL IMPACT. This study supports the application of PI-RADS upgrading rules to optimize biopsy selection, particularly in risk-based pathways.

Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology.

BACKGROUND: Accurate delineations of regions of interest (ROIs) on multi-parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning-based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor-intensive and susceptible to inter-reader variability. Histopathology images from radical prostatectomy (RP) represent the "gold standard" in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co-registering digitized histopathology images onto pre-operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI-histopathology co-registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole-mount histopathology images (WMHs). Furthermore, the artifacts on WMHs, such as tissue loss, may introduce unrealistic deformation during co-registration. PURPOSE: This study presents a new registration pipeline, MSERgSDM, a multi-scale feature-based registration (MSERg) with a statistical deformation (SDM) constraint, which aims to improve accuracy of MRI-histopathology co-registration. METHODS: In this study, we collected 85 pairs of MRI and WMHs from 48 patients across three cohorts. Cohort 1 (D1), comprised of a unique set of 3D printed mold data from six patients, facilitated the generation of ground truth deformations between ex vivo WMHs and in vivo MRI. The other two clinically acquired cohorts (D2 and D3) included 42 patients. Affine and nonrigid registrations were employed to minimize the deformation between ex vivo WMH and ex vivo T2-weighted MRI (T2WI) in D1. Subsequently, ground truth deformation between in vivo T2WI and ex vivo WMH was approximated as the deformation between in vivo T2WI and ex vivo T2WI. In D2 and D3, the prostate anatomical annotations, for example, tumor and urethra, were made by a pathologist and a radiologist in collaboration. These annotations included ROI boundary contours and landmark points. Before applying the registration, manual corrections were made for flipping and rotation of WMHs. MSERgSDM comprises two main components: (1) multi-scale representation construction, and (2) SDM construction. For the SDM construction, we collected N = 200 reasonable deformation fields generated using MSERg, verified through visual inspection. Three additional methods, including intensity-based registration, ProsRegNet, and MSERg, were also employed for comparison against MSERgSDM. RESULTS: Our results suggest that MSERgSDM performed comparably to the ground truth (p > 0.05). Additionally, MSERgSDM (ROI Dice ratio = 0.61, landmark distance = 3.26 mm) exhibited significant improvement over MSERg (ROI Dice ratio = 0.59, landmark distance = 3.69 mm) and ProsRegNet (ROI Dice ratio = 0.56, landmark distance = 4.00 mm) in local alignment. CONCLUSIONS: This study presents a novel registration method, MSERgSDM, for mapping ex vivo WMH onto in vivo prostate MRI. Our preliminary results demonstrate that MSERgSDM can serve as a valuable tool to map ground truth disease annotations from histopathology images onto MRI, thereby assisting in the development of machine learning models for PCa detection on MRI.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.

PURPOSE: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality. METHOD: A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes. RESULTS: The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis. CONCLUSION: Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.

Background: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective: To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv. Conclusion: Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.

Optimization of non-endorectal prostate MR image quality using PI-QUAL: A multidisciplinary team approach.

PURPOSE: To evaluate the utility of the PI-QUAL score in assessing protocol changes aimed to improve image quality from a non-endorectal coil prostate MR imaging protocol during a 9-month quality improvement (QI) project and to quantify the inter-reader agreement of PI-QUAL scores between radiologists, technologists, and physicists. METHODS: This retrospective study audited 1,012 multiparametric prostate MRI examinations as part of a national QI project according to the PI-QUAL standard. PI-QUAL scores were used to inform MR protocol changes. Following the project, 4 radiologists, 2 technologists, and 1 medical physicist collectively audited an additional set of 150 examinations to identify statistical improvements in image quality using the two-tailed Wilcoxon rank sum test. The improvements due to individual protocol changes were assessed among subsets of the 1,012 examinations which compared examinations occurring before and after the isolated protocol change. Inter-reader variability was assessed using the percent majority agreement and the average standard deviation of PI-QUAL scores between evaluators. RESULTS: During this QI project, PI-QUAL scores improved from 3.67 ± 0.75 to 4.16 ± 0.59 (p < 0.01) after implementing a series of protocol changes. Among a subset of 451 cases, we found that adopting R/L rather than A/P phase encoding reduced distortion in diffusion-weighted imaging (DW) from 21.6% (41/190 A/P phase encoded cases) to 11.5% (30/261 R/L phase encoded cases) (p < 0.01). Similarly, in the same 451 cases, adopting R/L phase encoding in T2WI reduced breathing motion artifacts from 34.6% (94/272 A/P phase encoding cases) to 12.8% (23/179 R/L phase encoding cases) (p < 0.01). DWI wraparound artifact was mitigated by employing a full-pelvis shim and enabling the abdomen shim option. The occurrence of low signal-to-noise ratio was reduced from 19.4% (19/98 cases without a weight-based threshold) to 6.3% (10/160) by instituting a weight-based threshold for using an endorectal coil (p < 0.01). The percent majority agreement was similar between radiologists, technologists and physicists, and all evaluators combined (72%, 77%, and 67%, respectively). CONCLUSIONS: PI-QUAL can evaluate image quality changes resulting from protocol optimizations at both the exam- and series-levels. With training, radiologists, technologists, and physicists can perform PI-QUAL scoring with similar performance. Broadening the scope of the quality improvement team can result in meaningful and lasting change.

Taking PI-QUAL beyond the prostate: Towards a standardized radiological image quality score (RI-QUAL).

PURPOSE: To compare the interreader agreement of a novel quality score, called the Radiological Image Quality Score (RI-QUAL), to a slighly modified version of the existing Prostate Imaging Quality (mPI-QUAL) score for magnetic resonance imaging (MRI) of the prostate. METHODS: A total of 43 consecutive scans were evaluated by two subspecialized radiologists who assigned scores using both the RI-QUAL and mPI-QUAL methods. The interreader agreement was analyzed using three statistical methods: concordance correlation coefficient (CCC), intraclass correlation coefficient (ICC), and Cohen's kappa. Time needed to arrive at a quality judgment was measured and compared using the Wilcoxon signed rank test. RESULTS: The interreader agreement for RI-QUAL and mPI-QUAL scores was comparable, as evidenced by the high CCC (0.76 vs. 0.77, p = 0.93), ICC (0.86 vs. 0.87, p = 0.93), and moderate Cohen's kappa (0.61 vs. 0.64, p = 0.85) values. Moreover, RI-QUAL assessment was faster than mPI-QUAL (19 vs. 40 s, p = 0.001). CONCLUSION: RI-QUAL is a new quality score that has comparable interreader agreement to the mPI-QUAL score, but with the potential to be applied to different MRI protocols and even different modalities. Like PI-QUAL, RI-QUAL may also facilitate communication about quality to referring physicians, as it provides a standardized and easily interpretable score. Further studies are warranted to validate the usefulness of RI-QUAL in larger patient cohorts and for other imaging modalities.

American College of Radiology initiatives on prostate magnetic resonance imaging quality.

Magnetic resonance imaging (MRI) has become integral to diagnosing and managing patients with suspected or confirmed prostate cancer. However, the benefits of utilizing MRI can be hindered by quality issues during imaging acquisition, interpretation, and reporting. As the utilization of prostate MRI continues to increase in clinical practice, the variability in MRI quality and how it can negatively impact patient care have become apparent. The American College of Radiology (ACR) has recognized this challenge and developed several initiatives to address the issue of inconsistent MRI quality and ensure that imaging centers deliver high-quality patient care. These initiatives include the Prostate Imaging Reporting and Data System (PI-RADS), developed in collaboration with an international panel of experts and members of the European Society of Urogenital Radiology (ESUR), the Prostate MR Image Quality Improvement Collaborative, which is part of the ACR Learning Network, the ACR Prostate Cancer MRI Center Designation, and the ACR Appropriateness Criteria. In this article, we will discuss the importance of these initiatives in establishing quality assurance and quality control programs for prostate MRI and how they can improve patient outcomes.

ACR Appropriateness Criteria® Staging of Renal Cell Carcinoma: 2022 Update.

Renal cell carcinoma is a complex group of highly heterogenous renal tumors demonstrating variable biological behavior. Pretreatment imaging of renal cell carcinoma involves accurate assessment of the primary tumor, presence of nodal, and distant metastases. CT and MRI are the key imaging modalities used in the staging of renal cell carcinoma. Important imaging features that impact treatment include tumor extension into renal sinus and perinephric fat, involvement of pelvicalyceal system, infiltration into adrenal gland, involvement of renal vein and inferior vena cava, as well as the presence of metastatic adenopathy and distant metastases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study.

Introduction: For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema). Methods: This study presents the application of U-Net deep learning models that have been uniquely developed with region-specific context to automatically segment each of the outer rectal wall, lumen, and perirectal fat regions on post-treatment, T2-weighted MRI scans. Results: In multi-institutional evaluation, region-specific U-Nets (wall Dice = 0.920, lumen Dice = 0.895) were found to perform comparably to multiple readers (wall inter-reader Dice = 0.946, lumen inter-reader Dice = 0.873). Additionally, when compared to a multi-class U-Net, region-specific U-Nets yielded an average 20% improvement in Dice scores for segmenting each of the wall, lumen, and fat; even when tested on T2-weighted MRI scans that exhibited poorer image quality, or from a different plane, or were accrued from an external institution. Discussion: Developing deep learning segmentation models with region-specific context may thus enable highly accurate, detailed annotations for multiple rectal structures on post-chemoradiation T2-weighted MRI scans, which is critical for improving evaluation of tumor extent in vivo and building accurate image-based analytic tools for rectal cancers.