Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.

OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. MATERIALS AND METHODS: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. RESULTS: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). CONCLUSION: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy.

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

RATIONALE AND OBJECTIVES: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias. MATERIALS AND METHODS: This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa. RESULTS: The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18). CONCLUSION: The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.

Acceptability to and Engagement With a Virtual Sickle Cell Trait Education Program (SCTaware): Single-Center Prospective Study.

BACKGROUND: Public health programs are tasked with educating the community on health topics, but it is unclear whether these programs are acceptable to learners. Currently, these programs are delivered via a variety of platforms including in-person, virtually, and over the telephone. Sickle cell trait (SCT) education for parents of children with this trait is one of many education programs provided by the Ohio Department of Health. The novel SCTaware videoconference education program was developed by a research team after central Ohio's standard program transitioned from in-person to telephone-only education during the COVID-19 pandemic. OBJECTIVE: Our objectives were to investigate the acceptability of the format and engagement with the SCTaware education and assess parental worry about having a child with SCT before and after receiving SCTaware. METHODS: This was a single-center, prospective study of English-speaking parents of children <3 years of age identified to have hemoglobin S trait by newborn screening. Parents who previously received SCT education by telephone, were able to be contacted, and had access to an electronic device capable of videoconferencing were eligible to complete surveys after receiving the virtual SCTaware education program. The SCTaware educator also completed a survey to assess participant engagement. Data were summarized descriptively and a McNemar test was used to compare parental worry before and after receiving SCTaware. RESULTS: In total, 55 participants completed follow-up surveys after receiving standard SCT telephone education and then completing SCTaware. Most (n=51) participants reported that the SCTaware content and visuals were very easy to understand (n=47) and facilitated conversation with the educator (n=42). All of them said the visuals were respectful and trustworthy, helped them understand content better, and that their questions were addressed. Nearly two-thirds (62%, n=34) reported that the pictures appeared very personal and applied to them. The educator noted most participants (n=45) were engaged and asked questions despite having to manage distractions during their education sessions. Many participants (n=33) reported some level of worry following telephone-only education; this was significantly reduced after receiving SCTaware (P<.001). CONCLUSIONS: Our results suggest that SCTaware is acceptable and engaging to parents. While telephone education may make SCT education more accessible, these findings suggest that many parents experience significant worry about their child with SCT after these sessions. A study to evaluate SCTaware's effectiveness at closing parents' SCT knowledge gaps is ongoing.

A Health Literate Approach to Address Health Disparities: a Virtual Program for Parents of Children with Sickle Cell Trait.

Background: Approximately 8% of African Americans born annually have sickle cell trait (SCT), a public health concern that may contribute to health disparities if individuals with SCT do not know it and lack access to understandable information about reproductive implications. Pre-pandemic, Ohio offered in-person SCT education for parents of SCT-affected children but many did not attend. Those with limited health literacy (HL) were less likely to achieve high knowledge. We used a HL-focused evaluation of this education to develop a virtual program (SCTaware) to communicate clear, actionable information and promote knowledge retention. Methods: Seven English-speaking parents, three with limited HL, were recruited in 2019 for in-person session videotaping and SCT knowledge assessments. Clinicians, HL experts, educators, genetic counselors, and parent stakeholders (evaluators) reviewed sessions, assessments, and accompanying visuals. Results: Evaluators: observed parents asked few questions; noted undefined technical terms, closed questions, key concept omission, and limited explanation of visuals scoring low for understandability, actionability, and clarity; and developed SCTaware for individual videoconference delivery (knowledge objectives; plain language guide; HL-informed communication strategies; new visuals scoring highly for understandability, actionability, and clarity; narrated post-education version; standardized educator training). Conclusions: Using a HL-focused evaluation, our diverse team created a promising virtual SCT education program addressing a common issue affecting populations at risk for disparities. Given virtual education will likely continue post-pandemic and limited HL is common, this approach may be essential and replicable for other public health education programs, especially those transitioning to virtual formats, to convey clear, actionable information and promote health equity.