Training radiology residents to evaluate deep myometrial invasion in endometrial cancer patients on MRI: A learning curve study

PURPOSE

To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI.

METHOD

Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions. Each subset consisted of 30 scans, the first and the last with the same cases, for a total of 150 reads. Diagnostic accuracy metrics, assessment time (rounded to the nearest minute), and confidence levels (using a 5-point Likert scale) were recorded. The learning curve was obtained plotting the diagnostic accuracy of the three trainees and the average over the subsets. Anatomopathological results served as the reference standard for DMI presence.

RESULTS

The three trainees exhibited heterogeneous starting point, with a learning curve and a trend to more homogeneous performance with training. The diagnostic accuracy of the average trainee raised from 64 % (56 %-76 %) to 88 % (80 %-94 %) across the five subsets (p < 0.001). Reductions in assessment time (5.92 to 4.63 min, p < 0.018) and enhanced confidence levels (3.58 to 3.97, p = 0.12) were observed. Improvements in sensitivity, specificity, positive predictive value, and negative predictive value were noted, particularly for specificity which raised from 56 % (41 %-68 %) in the first to 86 % (74 %-94 %) in the fifth subset (p = 0.16). Although not reaching statistical significance, these advancements aligned the trainees with literature performance benchmarks.

CONCLUSIONS

The structured training program significantly enhanced radiology residents' diagnostic accuracy in assessing DMI for EC on MRI, emphasizing the effectiveness of active case-based training in refining oncologic imaging skills within radiology residency curricula.

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

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.

Bridging Communication Gaps Between Radiologists, Referring Physicians, and Patients Through Standardized Structured Cancer Imaging Reporting: The Experience with Female Pelvic MRI Assessment Using O-RADS and a Simulated Cohort Patient Group

RATIONALE AND OBJECTIVES

This study aimed to evaluate whether implementing structured reporting based on Ovarian-Adnexal Reporting and Data System (O-RADS) magnetic resonance imaging (MRI) in women with sonographically indeterminate adnexal masses improves communication between radiologists, referrers, and patients/caregivers and enhances diagnostic performance for determining adnexal malignancy.

MATERIALS AND METHODS

We retrospectively analyzed prospectively issued MRI reports in 2019-2022 performed for characterizing adnexal masses before and after implementing O-RADS MRI; 56 patients/caregivers and nine gynecologic oncologists ("referrers") were surveyed about report interpretability/clarity/satisfaction; responses for pre- and post-implementation reports were compared using Fisher's exact and Chi-squared tests. Diagnostic performance was assessed using receiver operating characteristic curves.

RESULTS

A total of 123 reports from before and 119 reports from after O-RADS MRI implementation were included. Survey response rates were 35.7% (20/56) for patients/caregivers and 66.7% (6/9) for referrers. For patients/caregivers, O-RADS MRI reports were clearer (p < 0.001) and more satisfactory (p < 0.001) than unstructured reports, but interpretability did not differ significantly (p = 0.14), as 28.0% (28/100) of postimplementation and 38.0% (38/100) of preimplementation reports were considered difficult to interpret. For referrers, O-RADS MRI reports were clearer, more satisfactory, and easier to interpret (p < 0.001); only 1.3% (1/77) were considered difficult to interpret. For differentiating benign from malignant adnexal lesions, O-RADS MRI showed area under the curve of 0.92 (95% confidence interval [CI], 0.85-0.99), sensitivity of 0.81 (95% CI, 0.58-0.95), and specificity of 0.91 (95% CI, 0.83-0.96). Diagnostic performance of reports before implementation could not be calculated due to many different phrases used to describe the likelihood of malignancy.

CONCLUSION

Implementing standardized structured reporting using O-RADS MRI for characterizing adnexal masses improved clarity and satisfaction for patients/caregivers and referrers. Interpretability improved for referrers but remained limited for patients/caregivers.

Association between 3D membranous urethral parameters and urinary continence recovery after RARP

OBJECTIVES

To evaluate whether the urinary continence (UC) recovery after robotic-assisted radical prostatectomy (RARP) relates to the membranous urethral length (MUL) and the membranous urethral complex volume (MUV).

MATERIALS AND METHODS

120 patients who underwent RARP were enrolled according to the different times of UC recovery and examined using prostate magnetic resonance imaging (MRI) before surgery. The membranous urethral (MU) parameters were measured using the three-Dimensional (3D) model reconstructed by holographic technology, such as total MUV (tMUV), exposed MUV (eMUV), full MUL (fMUL) and exposed MUL (eMUL). Statistical software SPSS 26.0 was used to analyze the data and compare the MU parameters and baseline data in different groups.

RESULTS

Patients with larger tMUV (p = 0.038), eMUV (p = 0.003), longer fMUL (p = 0.025), eMUL (p = 0.044) had better UC after removal of the catheter, and eMUV (OR = 1.002, 95%CI = 1.001-1.004, p = 0.004) was a predictor; the patients with younger age (p = 0.021), lower VPSS score (p = 0.004) and larger eMUV (p = 0.012) and longer eMUL (p = 0.049) had better UC recovery one month after RARP while eMUV (OR = 1.002, 95% CI = 1.000-1.003, p = 0.008) and VPSS score (OR = 0.886, 95% CI = 0.806-0.973, p = 0.011) were independent risk factors; The patients with younger age (p = 0.018), larger tMUV (p = 0.029), eMUV (p = 0.016) had better UC recovery three months after RARP. eMUV (OR = 1.002, 95% CI = 1.000-1.004, p = 0.042) and age (OR = 0.904, 95% CI = 0.818-0.998, p = 0.046) were independent risk factors.

CONCLUSION

This clinical study shows that patients with larger MUV and longer MUL can return to UC earlier after surgery. Among that, eMUV is a better predictor.

PIRADS≥4 MRI lesion: Is performing systematic biopsies still essential for detecting clinically significant prostate cancer?

INTRODUCTION

In the era of targeted prostate biopsies, the necessity of performing randomized biopsies systematically is under question. Our objective is to evaluate the rate of clinically significant prostate cancer (csPCa), defined by presence of ISUP≥2 prostate cancer, diagnosed only on randomized cores in case of a PIRADS≥4 target lesion on MRI. The secondary objective is to evaluate whether specific variables can predict the presence of undetected csPCa in targeted biopsies.

METHODS

Retrospective data on targeted biopsies performed from 2015 to 2021 in our hospital were collected. Procedures were performed with MRI/Transrectal US fusion Trinity platform from Koelis®. All the MRI images were reviewed and the targets were classified using the PIRADS V2.1 classification. Inclusion criteria comprised procedures featuring at least one PIRADS≥4 targeted lesion were included. All procedures consisted 1-4 targeted cores and 12-core systematic biopsy.

RESULTS

We included 358 patients. In 44 patients (12.3%) csPCa was exclusively detected in randomized cores. Among these cases, only 12 patients (27.2%) showed no cancer on the targeted biopsies. Merely 4 patients (9.09%) lacked csPCa-positive cores on the same side as the index lesion. Factors such as PSA, PSA density, prostate volume, and digital rectal examination showed no significant association with the presence of csPCa exclusively on randomized cores. Likewise, the size, location, and PIRADS classification of the target demonstrated no significant impact.

CONCLUSION

Our findings indicate that in 12.3% of cases, targeted biopsies alone are insufficient for detecting the presence of csPCa. As such, systematic biopsies remain necessary to date.

LEVEL OF EVIDENCE: 4

Do we need MRI in all biopsy naïve patients? A multicenter cohort analysis

PURPOSE

The combined approach (CB) of magnetic resonance imaging (MRI)-guided biopsy (TB) and systematic biopsy (SB) is strongly recommended based on numerous studies in biopsy naïve men with suspicion of clinically significant prostate cancer (csPCA). However, the unbalanced accessibility of MRI, challenges related to reimbursement and the scarcity of specialized medical practitioners continue to impede a widespread implementation. Therefore, our objective was to determine a subset of men that could undergo SB without an increased risk of underdiagnosis at reduced expenses.

METHODS

A multicenter analysis of 2714 men with confirmed PCA and suspicious MRI who underwent CB were enrolled. Cancer detection rates were compared between the different biopsy routes SB, TB and CB using McNemar paired test. Additionally, Gleason grade up- and down-grading was determined.

RESULTS

CB detected more csPCA than TB and SB (p < 0.001), irrespective of MRI findings or biopsy route (transperineal vs. transrectal). Thereby, single biopsy approaches misgraded > 50% of csPCA. TB showed higher diagnostic efficiency, defined as csPCA detection per biopsy core than CB and SB (p < 0.001). For patients with abnormal DRE and PSA levels > 12.5 ng/ml, PSAD > 0.35 ng/ml/cm3, or > 75 years, SB and CB showed similar csPCA detection rates.

CONCLUSION

Conducting CB provides the highest level of diagnostic certainty and minimizes the risk of underdiagnosis in almost all biopsy-naive men. However, in patients with suspicious DRE and high PSA levels, PSAD, or advanced age solely using SB leads to similar csPCA detection rates. Thus, a reduced biopsy protocol may be considered for these men in case resources are limited.

Interactive training workshop to improve prostate mpMRI knowledge: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

PURPOSE

Prostate MRI is established for the investigation of patients presenting with suspected early prostate cancer. Outcomes are dependent on both image quality and interpretation. This study assessed the impact of an educational intervention on participants' theoretical knowledge of the technique.

METHODS

Eighty-one clinicians from two centers with varying experience in prostate MRI participated. Baseline knowledge was assessed with 10 written and image-based multiple-choice questions (MCQs) prior to a course including didactic lectures and hands-on interactive workshops on prostate MRI interpretation. Post-course, participants completed a second 10-question MCQ test, matched by format, themes, and difficulty, to assess for any improvement in knowledge and performance. Results were assessed using the Wilcoxon rank sum test, and the Wilcoxon signed-rank test for paired data.

RESULTS

Thirty-nine participants, including 25/49 (51.0%) and 14/32 (43.8%) at each center completed both assessments, with their results used for subsequent evaluation. Overall, there was a significant improvement from pre- (4.92 ± 2.41) to post-course scores (6.77 ± 1.46), p < 0.001 and at both Copenhagen (5.92 ± 2.25 to 7.36 ± 1.25) and Toronto (3.14 ± 1.51 to 5.71 ± 1.20); p = 0.005 and p = 0.002, respectively. Participants with no prostate MRI experience showed the greatest improvement (3.77 ± 1.97 to 6.18 ± 1.5, p < 0.001), followed by intermediate level (< 500 MRIs reported) experience (6.18 ± 1.99 to 7.46 ± 1.13, p = 0.058), then advanced (> 500 MRIs reported) experience (6.83 ± 2.48 to 7.67 ± 0.82, p = 0.339).

CONCLUSIONS

A dedicated prostate MRI teaching course combining didactic lectures and hands-on workshops significantly improved short-term theoretical knowledge of the technique for clinicians with differing levels of experience.

CRITICAL RELEVANCE STATEMENT

A dedicated teaching course significantly improved theoretical knowledge of the technique particularly for clinicians with less reporting experience and a lower baseline knowledge. The multiple-choice questions format mapped improved performance and may be considered as part of future MRI certification initiatives.

KEY POINTS

• Prostate MRI knowledge is important for image interpretation and optimizing acquisition sequences. • A dedicated teaching course significantly improved theoretical knowledge of the technique. • Improved performance was more apparent in clinicians with less reporting experience and a lower baseline knowledge.

Current magnetic resonance imaging-based diagnostic strategies for prostate cancer

Recent developments in multiparametric MRI and MRI-targeted biopsy have made it possible to detect clinically significant cancers more accurately and efficiently than ever before. Furthermore, software that enables easy MRI/US image fusion has been developed and is already available on the market, and this has provided a tailwind for the spread of MRI-based prostate cancer diagnostic strategies. Such precise diagnosis of prostate cancer localization is essential for highly accurate focal therapy. In addition, a recent large-scale study applying MRI to community screening for prostate cancer has reported its usefulness. By contrast, concerns about overdiagnosis and overtreatment, the existence of inter-reader variability in MRI diagnosis, and issues with current MRI-targeted biopsy have emerged. In this article, we review the development of multiparametric MRI and MRI-targeted biopsy to date and the current issues and discuss future directions.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

OBJECTIVE

To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history.

MATERIALS AND METHODS

This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles.

RESULTS

Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively.

CONCLUSION

The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Impact of prostate MRI central review over the diagnostic performance of MRI-targeted biopsy: should we routinely ask for an expert second opinion?

PURPOSE

There is substantial variability in multiparametric MRI (mpMRI) protocols and inter-readers' agreement. We tested the effect of a central mpMRI review on the detection of clinically significant PCa (csPCa) in a tertiary referral center.

METHODS

We retrospectively analyzed a cohort of 364 consecutive men with a positive externally performed mpMRI (PI-RADS ≥ 3) who underwent a targeted biopsy (TBx) plus a systematic biopsy at a single tertiary referral center (2018-2020). Of those mpMRIs, 32% (n = 116) were centrally reviewed. We compared the detection of csPCa between the non-central-reviewed vs reviewed group. Multivariable logistic regression models (MVA) tested the relationship between mpMRI central review and the detection of csPCa at TBx.

RESULTS

The detection of csPCa at TBx in non-central-reviewed vs central-reviewed group was 41 vs 63%, respectively (p = 0.001). The distribution of PI-RADS 2, 3, 4, and 5 at initial assessment vs after mpMRI central review was 0, 37, 47, and 16% vs 39, 9, 35, and 16%, respectively (p < 0.004). Of 43 patients with initial PI-RADS 3 score, respectively 67, 21, and 12, and 0% had a revised PI-RADS score of ≤ 2, 3, 4, and 5. At MVA, mpMRI central review (OR: 1.65, CI 0.85-0.98) was significantly associated with higher csPCa detection at TBx.

CONCLUSIONS

We demonstrated that a central review of external mpMRIs may decrease the overcall of equivocal lesions, namely PI-RADS 3, and should be considered to maximize the clinical benefit of TBx in terms of increasing the detection of csPCa and eventually decreasing the rate of unnecessary biopsies.

A scoping review of self-monitoring in graduate medical education

BACKGROUND

Physicians and physicians-in-training have repeatedly demonstrated poor accuracy of global self-assessments, which are assessments removed from the context of a specific task, regardless of any intervention. Self-monitoring, an in-the-moment self-awareness of one's performance, offers a promising alternative to global self-assessment. The purpose of this scoping review is to better understand the state of self-monitoring in graduate medical education.

METHODS

We performed a scoping review following Arksey and O'Malley's six steps: identifying a research question, identifying relevant studies, selecting included studies, charting the data, collating and summarising the results and consulting experts. Our search queried Ovid Medline, Web of Science, PsychINFO, Eric and EMBASE databases from 1 January 1999 to 12 October 2022.

RESULTS

The literature search yielded 5363 unique articles. The authors identified 77 articles for inclusion. The search process helped create a framework to identify self-monitoring based on time and context dependence. More than 20 different terms were used to describe self-monitoring, and only 13 studies (17%) provided a definition for the equivalent term. Most research focused on post-performance self-judgements of a procedural skill (n = 31, 42%). Regardless of task, studies focused on self-judgement (n = 66, 86%) and measured the accuracy or impact on performance of self-monitoring (n = 41, 71%). Most self-monitoring was conducted post-task (n = 65, 84%).

CONCLUSION

Self-monitoring is a time- and context-dependent phenomenon that seems promising as a research focus to improve clinical performance of trainees in graduate medical education and beyond. The landscape of current literature on self-monitoring is sparse and heterogeneous, suffering from a lack of theoretical underpinning, inconsistent terminology and insufficiently clear definitions.

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.

Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis

INTRODUCTION

Multiparametric MRI (mpMRI) has transformed the prostate cancer diagnostic pathway, allowing for improved risk stratification and more targeted subsequent management. However, concerns exist over the interobserver variability of images and the applicability of this model long term, especially considering the current shortage of radiologists and the growing ageing population. Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. The following report details a protocol for a systematic review and meta-analysis investigating the accuracy of AI in predicting primary prostate cancer on mpMRI.

METHODS AND ANALYSIS

A systematic search will be performed using PubMed, MEDLINE, Embase and Cochrane databases. All relevant articles published between January 2016 and February 2023 will be eligible for inclusion. To be included, articles must use AI to study MRI prostate images to detect prostate cancer. All included articles will be in full-text, reporting original data and written in English. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The QUADAS-2 score will assess the quality and risk of bias across selected studies.

ETHICS AND DISSEMINATION

Ethical approval will not be required for this systematic review. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42021293745.

The Impact of Prostate Volume on the Prostate Imaging and Reporting Data System (PI-RADS) in a Real-World Setting

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a new cornerstone in the diagnostic pathway of prostate cancer. However, mpMRI is not devoid of factors influencing its detection rate of clinically significant prostate cancer (csPCa). Amongst others, prostate volume has been demonstrated to influence the detection rates of csPCa. Particularly, increasing volume has been linked to a reduced cancer detection rate. However, information about the linkage between PI-RADS, prostate volume and detection rate is relatively sparse. Therefore, the current study aims to assess the association between prostate volume, PI-RADS score and detection rate of csP-Ca, representing daily practice and contemporary mpMRI expertise. Thus, 1039 consecutive patients with 1151 PI-RADS targets, who underwent mpMRI-guided prostate biopsy at our tertiary referral center, were included. Prior mpMRI had been assessed by a plethora of 111 radiology offices, including academic centers and private practices. mpMRI was not secondarily reviewed in house before biopsy. mpMRI-targeted biopsy was performed by a small group of a total of ten urologists, who had performed at least 100 previous biopsies. Using ROC analysis, we defined cut-off values of prostate volume for each PI-RADS score, where the detection rate drops significantly. For PI-RADS 4 lesions, we found a volume > 61.5 ccm significantly reduced the cancer detection rate (OR 0.24; 95% CI 0.16-0.38; p < 0.001). For PI-RADS 5 lesions, we found a volume > 51.5 ccm to significantly reduce the cancer detection rate (OR 0.39; 95% CI 0.25-0.62; p < 0.001). For PI-RADS 3 lesions, none of the evaluated clinical parameters had a significant impact on the detection rate of csPCa. In conclusion, we show that enlarged prostate volume represents a major limitation in the daily practice of mpMRI-targeted biopsy. This study is the first to define exact cut-off values of prostate volume to significantly impair the validity of PI-RADS assessed in a real-world setting. Therefore, the results of mpMRI-targeted biopsy should be interpreted carefully, especially in patients with prostate volumes above our defined thresholds.

The Applicability of Artificial Intelligence in Predicting the Depth of Myometrial Invasion on MRI Studies-A Systematic Review

(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.

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.

Future of prostate imaging: Artificial intelligence in assessing prostatic magnetic resonance imaging

Prostate cancer (Pca; adenocarcinoma) is one of the most common cancers in adult males and one of the leading causes of death in both men and women. The diagnosis of Pca requires substantial experience, and even then the lesions can be difficult to detect. Moreover, although the diagnostic approach for this disease has improved significantly with the advent of multiparametric magnetic resonance, that technology has certain unresolved limitations. In recent years artificial intelligence (AI) has been introduced to the field of radiology, providing new software solutions for prostate diagnostics. Precise mapping of the prostate has become possible through AI and this has greatly improved the accuracy of biopsy. AI has also allowed for certain suspicious lesions to be attributed to a given group according to the Prostate Imaging-Reporting & Data System classification. Finally, AI has facilitated the combination of data obtained from clinical, laboratory (prostate-specific antigen), imaging (magnetic resonance), and biopsy examinations, and in this way new regularities can be found which at the moment remain hidden. Further evolution of AI in this field is inevitable and it is almost certain to significantly expand the efficacy, accuracy and efficiency of diagnosis and treatment of Pca.

Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers

PURPOSE

The aim of the study was to determine the impact of using a semi-automatic commercially available AI-assisted software (Quantib® Prostate) on inter-reader agreement in PI-RADS scoring at different PI-QUAL ratings and grades of reader confidence and on reporting times among novice readers in multiparametric prostate MRI.

METHODS

A prospective observational study, with a final cohort of 200 patients undergoing mpMRI scans, was performed at our institution. An expert fellowship-trained urogenital radiologist interpreted all 200 scans based on PI-RADS v2.1. The scans were divided into four equal batches of 50 patients. Four independent readers evaluated each batch with and without the use of AI-assisted software, blinded to expert and individual reports. Dedicated training sessions were held before and after each batch. Image quality rated according to PI-QUAL and reporting times were recorded. Readers' confidence was also evaluated. A final evaluation of the first batch was conducted at the end of the study to assess for any changes in performance.

RESULTS

The overall kappa coefficient differences in PI-RADS scoring agreement without and with Quantib® were 0.673 to 0.736 for Reader 1, 0.628 to 0.483 for Reader 2, 0.603 to 0.292 for Reader 3 and 0.586 to 0.613 for Reader 4. Using PI-RADS ≥ 4 as cut-off for biopsy, the AUCs with AI ranged from 0.799 (95 % CI: 0.743, 0.856) to 0.820 (95 % CI: 0.765, 0.874). Inter-reader agreements at different PI-QUAL scores were higher with the use of Quantib, particularly for readers 1 and 4, with Kappa coefficient values showing moderate to slight agreement.

CONCLUSION

Quantib® Prostate could potentially be useful in improving inter-reader agreement among less experienced to completely novice readers if used as a supplement to PACS.

Comparison of prostate cancer detection rate at targeted biopsy of hub and spoke centers mpMRI: experience matters

BACKGROUND

Latest changes in European guidelines on prostate cancer determined a widespread of multiparametric magnetic resonance imaging (mpMRI) even in less experienced centers due to an increased demand. This could decrease diagnostic accuracy of targeted biopsy (TB) since image interpretation can be challenging and requires adequate and supervised training. Therefore we aimed to evaluate the prostate cancer (PCa) detection rate on TB according to mpMRI center's volume and experience.

METHODS

We retrospectively analyzed data of 737 patients who underwent mpMRI-TB at our institution. Patients were stratified according to mpMRI center: Hub (high volume >100 exams/year with dedicated radiologists and supervised training) and Spoke center (low volume <100 exams/year without dedicated radiologists and/or supervised training). Detection rate of PCa at TB and possible predictors of clinically significant PCa (csPCa) at TB. Differences in detection rate were explored using Chi-square test. Predictors of csPCa were evaluated through uni and multivariable logistic regression. The adjustment for casemix included: age, PSA, mpMRI center, lesion's location, PSA density, PI-RADS score and index lesion's size.

RESULTS

Four hundred forty-nine (60.9%) and 288 (39.1%) patients underwent mpMRI at a Hub or Spoke center, respectively. Hub group had higher detection rate for both any (60.3% vs. 48.1%) and csPCa (46.9% vs 38.7%; all P≤0.001). After stratifying for PI-RADS score, Hub group had higher detection rate for PI-RADS score 3 (csPCA 25.2% vs. 15.7%; p 0.04) and 4 (csPCa 65.7% vs. 45.7%; P=0.001). At multivariable analyses, receiving an mpMRI scan at a Spoke center was an independent predictor for csPCa on TB (OR 0.65; P=0.04).

CONCLUSIONS

mpMRI performed in Hub centers provided a significantly higher PCa yield on TB. A dedicated team of experienced radiologist, a supervised training for mpMRI and a central revision of mpMRI performed in non-experienced centres are essential to avoid unnecessary and potentially harmful procedures.

Promoting the use of the PI-QUAL score for prostate MRI quality: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

OBJECTIVES

The Prostate Imaging Quality (PI-QUAL) score is a new metric to evaluate the diagnostic quality of multiparametric magnetic resonance imaging (MRI) of the prostate. This study assesses the impact of an intervention, namely a prostate MRI quality training lecture, on the participant's ability to apply PI-QUAL.

METHODS

Sixteen participants (radiologists, urologists, physicists, and computer scientists) of varying experience in reviewing diagnostic prostate MRI all assessed the image quality of ten examinations from different vendors and machines. Then, they attended a dedicated lecture followed by a hands-on workshop on MRI quality assessment using the PI-QUAL score. Five scans assessed by the participants were evaluated in the workshop using the PI-QUAL score for teaching purposes. After the course, the same participants evaluated the image quality of a new set of ten scans applying the PI-QUAL score. Results were assessed using receiver operating characteristic analysis. The reference standard was the PI-QUAL score assessed by one of the developers of PI-QUAL.

RESULTS

There was a significant improvement in average area under the curve for the evaluation of image quality from baseline (0.59 [95 % confidence intervals: 0.50-0.66]) to post-teaching (0.96 [0.92-0.98]), an improvement of 0.37 [0.21-0.41] (p < 0.001).

CONCLUSIONS

A teaching course (dedicated lecture + hands-on workshop) on PI-QUAL significantly improved the application of this scoring system to assess the quality of prostate MRI examinations.

KEY POINTS

• A significant improvement in the application of PI-QUAL for the assessment of prostate MR image quality was observed after an educational intervention. • Appropriate training on image quality can be delivered to those involved in the acquisition and interpretation of prostate MRI. • Further investigation will be needed to understand the impact on improving the acquisition of high-quality diagnostic prostate MR examinations.

Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway

Multiparametric MRI of the prostate is now recommended as the initial diagnostic test for men presenting with suspected prostate cancer, with a negative MRI enabling safe avoidance of biopsy and a positive result enabling MRI-directed sampling of lesions. The diagnostic pathway consists of several steps, from initial patient presentation and preparation to performing and interpreting MRI, communicating the imaging findings, outlining the prostate and intra-prostatic target lesions, performing the biopsy and assessing the cores. Each component of this pathway requires experienced clinicians, optimized equipment, good inter-disciplinary communication between specialists, and standardized workflows in order to achieve the expected outcomes. Assessment of quality and mitigation measures are essential for the success of the MRI-directed prostate cancer diagnostic pathway. Quality assurance processes including Prostate Imaging-Reporting and Data System, template biopsy, and pathology guidelines help to minimize variation and ensure optimization of the diagnostic pathway. Quality control systems including the Prostate Imaging Quality scoring system, patient-level outcomes (such as Prostate Imaging-Reporting and Data System MRI score assignment and cancer detection rates), multidisciplinary meeting review and audits might also be used to provide consistency of outcomes and ensure that all the benefits of the MRI-directed pathway are achieved.

Temporal changes of PIRADS scoring by radiologists and correlation to radical prostatectomy pathological outcomes

Purpose

To assess temporal improvement of prostate image reporting and data system (PIRADS) 3-5 lesion correlation to histopathologic findings from radical prostatectomy (RP) in prostate cancer (PCa).

Materials and methods

A total of 1481 patients who underwent RP for biopsy-proven PCa between 2015 and 2019 were divided into 14 groups of 100 sequential readings for the evaluation of histopathological correlation with PIRADS readings. Temporal trends of PIRADS distribution and predictive performance for RP pathology were evaluated to assess underlying changes in prostate magnetic resonance imaging (MRI) interpretation by radiologists.

Results

PIRADS 4-5 lesions were significantly correlated with the increasing rates of Gleason Group (GG) upgrade (p = 0.044) and decreasing rate of GG downgrade (p = 0.016) over time. PIRADS ≥3 lesions read after median 2 years of experience were shown to independently predict intermediate-high-risk (GG ≥ 3) PCa (odds ratio 2.93, 95% confidence interval 1.00-8.54; P= 0.049) in RP pathology. Preoperative GG ≥ 3 biopsy lesions with PIRADS 4-5 lesions were significantly more susceptible to GG upgrade (P= 0.035) and GG ≥ 4 RP pathology (p = 0.003) in experienced reads, in contrast to insignificant findings in early readings (p = 0.588 and 0.248, respectively).

Conclusion

Preoperative MRI reports matched with RP pathology suggest an improved prediction of adverse pathology in PIRADS 3-5 lesions over time, suggesting a temporal change in PIRADS interpretation and predictive accuracy. Institutions with low volume experience should use caution in solely relying on MRI for predicting tumor characteristics. Future prospective trials and larger scale assessments are required to further validate our results.

Prostate MRI Qualification: AJR Expert Panel Narrative Review

Prostate MRI is now established as a first-line investigation for individuals presenting with suspected localized or locally advanced prostate cancer. Successful delivery of the MRI-directed pathway for prostate cancer diagnosis relies on high-quality imaging as well as the interpreting radiologist's experience and expertise. Radiologist certification in prostate MRI may help limit interreader variability, optimize outcomes, and provide individual radiologists with documentation of meeting predefined standards. This AJR Expert Panel Narrative Review summarizes existing certification proposals, recognizing variable progress across regions in establishing prostate MRI certification programs. To our knowledge, Germany is the only country with a prostate MRI certification process that is currently available for radiologists. However, prostate MRI certification programs have also recently been proposed in the United States and United Kingdom and by European professional society consensus panels. Recommended qualification processes entail a multifaceted approach, incorporating components such as minimum case numbers, peer learning, course participation, continuing medical education credits, and feedback from pathology results. Given the diversity in health care systems, including in the provision and availability of MRI services, national organizations will likely need to take independent approaches to certification and accreditation. The relevant professional organizations should begin developing these programs or continue existing plans for implementation.

Effect of Prostate MRI Interpretation Experience on PPV Using PI-RADS Version 2: A 6-Year Assessment Among Eight Fellowship-Trained Radiologists

BACKGROUND. Understanding the effect of specific experience in prostate MRI interpretation on diagnostic performance would help inform the minimum interpretation volume to establish proficiency. OBJECTIVE. The purpose of this article is to assess for an association between increasing experience in prostate MRI interpretation and change in radiologist-level PPVs for PI-RADS version 2 (v2) categories 3, 4, and 5. METHODS. This retrospective study included prostate MRI examinations performed between July 1, 2015, and August 13, 2021, that were assigned a PI-RADS v2 category of 3, 4, or 5 and with an MRI-ultrasound fusion biopsy available as the reference standard. All examinations were among the first 100-200 prostate MRI examinations interpreted using PI-RADS v2 by fellowship-trained abdominal radiologists. Radiologists received feedback through a quality assurance program. Radiologists' experience levels were classified using progressive subsets of 50 interpreted examinations. Change with increasing experience in distribution of individual radiologists' whole-gland PPVs for Gleason sum score 7 or greater prostate cancer, stratified by PI-RADS category, was assessed by hierarchic linear mixed models. RESULTS. The study included 1300 prostate MRI examinations in 1037 patients (mean age, 66 ± 7 [SD] years), interpreted by eight radiologists (median, 13 years of postfellow-ship experience; range, 5-22 years). Aggregate PPVs were 20% (68/340) for PI-RADS category 3, 49% (318/652) for category 4, and 71% (220/308) for category 5. Interquartile ranges (IQRs) of PPVs overlapped for category 4 (51%; IQR, 42-60%) and category 5 (70%; IQR, 54-75%) for radiologists' first 50 examinations. IQRs of PPVs did not overlap between categories of greater experience; for example, at the 101-150 examination level, PPV for category 3 was 24% (IQR, 20-29%), category 4 was 55% (IQR, 54-63%), and category 5 was 81% (IQR, 77-82%). Hierarchic modeling showed no change in radiologists' absolute PPV with increasing experience (category 3, p = .27; category 4, p = .71; category 5, p = .38). CONCLUSION. Absolute PPVs at specific PI-RADS categories did not change during radiologists' first 200 included examinations. However, resolution of initial overlap in IQRs indicates improved precision of PPVs after the first 50 examinations. CLINICAL IMPACT. If implementing a minimum training threshold for fellowship-trained abdominal radiologists, 50 prostate MRI examinations may be sufficient in the context of a quality assurance program with feedback.

Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study

Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).

Training of radiology specialists in local staging of primary rectal cancer on MRI: a prospective intervention study exploring the impact of various educational elements on the interpretive performance

Background

MRI interpretation and accurate radiological staging are crucial to the important treatment decisions and a consequent successful patient outcome in rectal cancer.

Aims

To investigate the effect of intensive training on rectal cancer MRI staging performance of radiologists and the impact of different course elements on learning outcomes.

Methods

In this prospective intervention study, 17 radiology specialists and 1 radiology registrar participated in a training programme including a 6-hour imaging workshop, a 3-hour session of individual feedback and independent MRI readings of primary rectal cancer cases. Their rectal MRI interpretive performance was evaluated through repeated readings of 30 training cases before and after each course element and a time interval with no educational intervention. A proforma template for MRI staging of primary rectal cancer was used and the results were compared with a reference standard of an expert panel. Participants repeatedly reported on confidence scores and self-assessed learning outcome. Outcomes were analysed using mixed-effects models.

Results

At baseline the quality of rectal MRI assessment varied significantly, with a higher interpretive performance among participants with shorter radiological experience (10.2 years vs 19.9 years, p=0.02). The ability to perform correct treatment allocation improved from 72% to 82% (adjusted OR=2.36, 95% CI 1.64 to 3.39). The improvement was largely driven by the participants with lower performance at baseline and by prevention of overstaging. Individual feedback had a significant impact on the improved interpretive performance (adjusted OR=1.82, 95% CI 1.27 to 2.63), whereas no significant change was seen after workshop or case readings only. Confidence scores increased significantly during training.

Conclusions

Targeted and individualised training improves the rectal cancer MRI interpretive performance essential to successful patient treatment, especially among radiology specialists with lower performance at baseline.

Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges

Artificial intelligence (AI) for prostate magnetic resonance imaging (MRI) is starting to play a clinical role for prostate cancer (PCa) patients. AI-assisted reading is feasible, allowing workflow reduction. A total of 3,369 multi-vendor prostate MRI cases are available in open datasets, acquired from 2003 to 2021 in Europe or USA at 3 T (n = 3,018; 89.6%) or 1.5 T (n = 296; 8.8%), 346 cases scanned with endorectal coil (10.3%), 3,023 (89.7%) with phased-array surface coils; 412 collected for anatomical segmentation tasks, 3,096 for PCa detection/classification; for 2,240 cases lesions delineation is available and 56 cases have matching histopathologic images; for 2,620 cases the PSA level is provided; the total size of all open datasets amounts to approximately 253 GB. Of note, quality of annotations provided per dataset highly differ and attention must be paid when using these datasets (e.g., data overlap). Seven grand challenges and commercial applications from eleven vendors are here considered. Few small studies provided prospective validation. More work is needed, in particular validation on large-scale multi-institutional, well-curated public datasets to test general applicability. Moreover, AI needs to be explored for clinical stages other than detection/characterization (e.g., follow-up, prognosis, interventions, and focal treatment).

Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

Objectives

The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging.

Methods

Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard.

Results

There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51–0.69] to post-teaching (AUC: 0.77 [0.70–0.84]). This was an improvement of 0.17 [0.016–0.28] (p = 0.004).

Conclusions

A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate.

Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression

OBJECTIVE

To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression.

METHODS

This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity.

RESULTS

60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (-11.7% and -9.5%) compared to non-progressors (-6.1% and -1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67.

CONCLUSION

AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression.

ADVANCES IN KNOWLEDGE

This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (-10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.

Second-opinion reads in prostate MRI: added value of subspecialty interpretation and review at multidisciplinary rounds

Purpose

This study evaluates the impact of second-opinion review of multiparametric prostate MRI for cancer detection by a multidisciplinary review board at a tertiary care center when compared with the initial community radiologist interpretation.

Methods

Cases were collected retrospectively from multidisciplinary prostate MRI rounds from 2017 to 2020 at a single tertiary care center. Patients with suspected prostate cancer or on active surveillance were referred for consideration of TRUS/MRI-fusion biopsy based on community-read prostate MRIs. All MRIs were re-read by subspecialized abdominal radiologists and a PI-RADS score assigned. Targeted fusion and 8–12 core systematic biopsy was performed in patients with PIRADS ≥ 3 lesions. Cohen kappa values were used to quantify interobserver agreement. Positive predictive value (PPV) was used to determine accuracy of PI-RADS score for detection of clinically significant prostate cancer (csPCa) (ISUP Grade Group ≥ 2).

Results

Three hundred and thirty-two lesions in 303 patients were reviewed and 252 lesions in 198 patients biopsied. The PI-RADS score was concordant in 60.5% of lesions, downgraded in 17.8%, and upgraded in 7.8%. Agreement between community and tertiary center interpretation was fair (κ = 0.354), with greater agreement for PI-RADS ≥ 4 (κ = 0.523) than PI-RADS ≥ 3 (κ = 0.456), and peripheral zone (κ = 0.419) than transition zone lesions (κ = 0.251). Prevalence of csPCa in biopsied lesions was 40.9%.

Conclusion

There is variability in community and tertiary care center interpretation of prostate MRI in cancer detection, with higher concordance rates for higher grade and peripheral zone lesions. These differences demonstrate the added value of multidisciplinary round review and highlight the need for ongoing education and feedback.

Graphical abstract

The learning curve in bladder MRI using VI-RADS assessment score during an interactive dedicated training program

OBJECTIVE

The purpose of the study was to evaluate the effect of an interactive training program on the learning curve of radiology residents for bladder MRI interpretation using the VI-RADS score.

METHODS

Three radiology residents with minimal experience in bladder MRI served as readers. They blindly evaluated 200 studies divided into 4 subsets of 50 cases over a 3-month period. After 2 months, the first subset was reassessed, resulting in a total of 250 evaluations. An interactive training program was provided and included educational lessons and case-based practice. The learning curve was constructed by plotting mean agreement as the ratio of correct evaluations per batch. Inter-reader agreement and diagnostic performance analysis were performed with kappa statistics and ROC analysis.

RESULTS

As for the VI-RADS scoring agreement, the kappa differences between pre-training and post-training evaluation of the same group of cases were 0.555 to 0.852 for reader 1, 0.522 to 0.695 for reader 2, and 0.481 to 0.794 for reader 3. Using VI-RADS ≥ 3 as cut-off for muscle invasion, sensitivity ranged from 84 to 89% and specificity from 91 to 94%, while the AUCs from 0.89 (95% CI:0.84, 0.94) to 0.90 (95% CI:0.86, 0.95). Mean evaluation time decreased from 5.21 ± 1.12 to 3.52 ± 0.69 min in subsets 1 and 5. Mean grade of confidence improved from 3.31 ± 0.93 to 4.21 ± 0.69, in subsets 1 and 5.

CONCLUSION

An interactive dedicated education program on bladder MRI and the VI-RADS score led to a significant increase in readers' diagnostic performance over time, with a general improvement observed after 100-150 cases.

KEY POINTS

• After the first educational lesson and 100 cases were interpreted, the concordance on VI-RADS scoring between the residents and the experienced radiologist was significantly higher. • An increase in the grade of confidence was experienced after 100 cases. • We found a decrease in the evaluation time after 150 cases.

Impact of double reading on NI-RADS diagnostic accuracy in reporting oral squamous cell carcinoma surveillance imaging - a single-center study

OBJECTIVES

The Neck Imaging Reporting and Data System (NI-RADS) is an increasingly utilized risk stratification tool for imaging surveillance after treatment for head and neck cancer. This study aims to measure the impact of supervision by subspecialized radiologists on diagnostic accuracy of NI-RADS when initial reading is performed by residents.

METHODS

150 CT and MRI datasets were initially read by two trained residents, and then supervised by two subspecialized radiologists. Recurrence rates by NI-RADS category were calculated, and receiver operating characteristic (ROC) curves were plotted. After dichotomization of the NI-RADS system (category 1 vs categories 2 + 3+4 and categories 1 + 2 vs 3 + 4), sensitivity, specificity, positive and negative predictive value were calculated.

RESULTS

26% of the reports were modified by the supervising radiologists. Area under the curve of ROC plots values of the supervision session were higher than those of the initial reading session for both the primary site (0.89 vs 0.86) and the neck (0.94 vs 0.91), but the difference was not statistically significant. For dichotomized NI-RADS category assignments, differences between the initial reading and the supervision session were statistically significant regarding specificity and PPV for the primary site (1 + 2 vs 3 + 4 and 1 vs 2 + 3+4) or even for both sites combined (1 vs 2 + 3+4).

CONCLUSION

NI-RADS enables trained resident radiologists to report surveillance imaging in patients with treated oral squamous cell carcinoma with high discriminatory power. Additional supervision by a subspecialized head and neck radiologist particularly improves specificity of radiological reports.

The urologist's learning curve of "in-bore" magnetic resonance-guided prostate biopsy

Background

The combination of multi-parametric MRI to locate and define suspected lesions together with their being targeted by an MRI-guided prostate biopsy has succeeded in increasing the detection rate of clinically significant disease and lowering the detection rate of non-significant prostate cancer. In this work we investigate the urologist’s learning curve of in-bore MRI-guided prostate biopsy which is considered to be a superior biopsy technique.

Materials and methods

Following Helsinki approval by The Chaim Sheba Medical Center ethics committee in accordance with The Sheba Medical Center institutional guidelines (5366-28-SMC) we retrospectively reviewed 110 IB-MRGpBs performed from 6/2016 to 1/2019 in a single tertiary center. All patients had a prostate multi-parametric MRI finding of at least 1 target lesion (prostate imaging reporting and data system [PI-RADS] score ≥ 3). We analyzed biopsy duration and clinically significant prostate cancer detection of targeted sampling in 2 groups of 55 patients each, once by a urologist highly trained in IB-MRGpBs and again by a urologist untrained in IB-MRGpBs. These two parameters were compared according to operating urologist and chronologic order.

Results

The patients’ median age was 68 years (interquartile range 62–72). The mean prostate-specific antigen level and prostate size were 8.6 ± 9.1 ng/d and 53 ± 27 cc, respectively. The mean number of target lesions was 1.47 ± 0.6. Baseline parameters did not differ significantly between the 2 urologists’ cohorts. Overall detection rates of clinically significant prostate cancer were 19%, 55%, and 69% for PI-RADS 3, 4 and 5, respectively. Clinically significant cancer detection rates did not differ significantly along the timeline or between the 2 urologists. The average duration of IB-MRGpB targeted sampling was 28 ± 15.8 min, correlating with the number of target lesions (p < 0.0001), and independent of the urologist’s expertise. Eighteen cases defined the cutoff for the procedure duration learning curve (p < 0.05).

Conclusions

Our data suggest a very short learning curve for IB-MRGpB-targeted sampling duration, and that clinically significant cancer detection rates are not influenced by the learning curve of this technique.

Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation

The classic prostate cancer (PCa) diagnostic pathway that is based on prostate-specific antigen (PSA) levels and the findings of digital rectal examination followed by systematic biopsy has shown multiple limitations. The use of multiparametric MRI (mpMRI) is now widely accepted in men with clinical suspicion for PCa. In addition, clinical information, PSA density, risk calculators, and genomic and other "omics" biomarkers are being used to improve risk stratification. On the basis of mpMRI and MRI-targeted biopsies (MRI-TBx), new diagnostic pathways have been established, aiming to improve the limitations of the classic diagnostic approach. However, these pathways still show limitations associated with mpMRI and MRI-TBx. Definitive PCa diagnosis is made on the basis of histopathologic Gleason grading, which has demonstrated an excellent correlation with clinical outcomes. However, Gleason grading is done subjectively by pathologists and involves poor reproducibility, and PCa may have a heterogeneous distribution of histologic patterns. Thus, important discrepancies persist between biopsy tumor grading and final whole-organ pathologic assessment after radical prostatectomy. PCa offers a unique opportunity to establish a real radiologic-pathologic correlation, as whole-mount radical prostatectomy specimens permit a complete spatial relationship with mpMRI. Artificial intelligence is increasingly being applied to radiologic and pathologic images to improve clinical accuracy and efficiency in PCa diagnosis. This review delineates current PCa diagnostic pathways, with a focus on the role of mpMRI, MRI-TBx, and pathologic analysis. An overview of the expected improvements in PCa diagnosis derived from the use of artificial intelligence, integrated radiologic-pathologic systems, and decision support tools for multidisciplinary teams is provided. An invited commentary by Purysko is available online. Online supplemental material is available for this article. ^©RSNA, 2021.

The Micro-ultrasound Guided Prostate Biopsy in Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

To compare the detection rate of micro-ultrasound with multiparametric magnetic resonance imaging targeted biopsy (mpMRI-TB) for prostate cancer diagnosis.

METHODS

The studies on micro-ultrasound prostate biopsy for prostate cancer diagnosis were searched in PubMed, Cochrane library and EMBASE databases from inception to April.2021. we performed a systematic review and cumulative meta-analysis based on search results using Software Rev-Man 5.3.

RESULTS

A total of 11 studies involving 1081 patients were included. The Meta-analysis showed that no significant difference was found between micro-ultrasound and mpMRI-TB in the total detection of prostate cancer(OR:1.01, 95%CI:0.85~1.21, p=0.89), of Grading Groups(GG)=1(OR: 0.92, 95%CI:0.68~1.25, p=0.59),of GG≥2(OR:1.01, 95%CI:0.83~1.22, p=0.92), and of GG≥3(OR: 1.31, 95%CI:0.95~1.81, p=0.10).

CONCLUSIONS

Micro-ultrasound guided prostate biopsy provides comparable detection rates for prostate cancer diagnosis with the mpMRI-TB, which is expected to challenge mpMRI-TB in the diagnosis of prostate cancer.

Optimal Number of Systematic Biopsy Cores Used in Magnetic Resonance Imaging/Transrectal Ultrasound Fusion Targeted Prostate Biopsy

Background

In recent years, the effectiveness of magnetic resonance imaging (MRI)-ultrasound fusion targeted biopsy (MRF-TB) has been widely reported. In this study, we assessed the effect of reduction of the number of systematic biopsy (SB) cores on the cancer detection rate (CDR).

Methods

Patients with a high prostate-specific antigen (PSA) level underwent prostate MRI. The Prostate Imaging-Reporting and Data System version 2 (PI-RADS) was then used to rate the lesions. The inclusion criteria were as follows: (1) PSA level between 4.0 and 30.0 ng/mL and (2) patients with one or more lesions on MRI and a PI-RADS score of 3 or more. All enrolled patients were SB naïve or had a history of one or more prior negative SBs. A total of 104 Japanese met this selection criterion. We have traditionally performed 14-core SB following the MRF-TB. In this study, the CDRs of 10-core SB methods, excluding biopsy results at the center of the base and mid-level on both sides, were compared with those of the conventional biopsy method.

Results

We compared CDRs of the 14-core and 10-core SBs used in combination. The overall CDR was 55.8% for the former and 55.8% for the latter, thereby indicating that there was no significant difference (P = 1.00) between the two. In addition, the CDRs of csPCa were 51.9% for the former and 51.1% for the latter, which indicated that there was no significant difference (P = 0.317).

Conclusion

There was no significant difference in the CDR when the number of SB cores to be used in combination was 14 and 10.

Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review

Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.

Reliability of Serial Prostate Magnetic Resonance Imaging to Detect Prostate Cancer Progression During Active Surveillance: A Systematic Review and Meta-analysis

CONTEXT

Although magnetic resonance imaging (MRI) is broadly implemented into active surveillance (AS) protocols, data on the reliability of serial MRI in order to help guide follow-up biopsy are inconclusive.

OBJECTIVE

To assess the diagnostic estimates of serial prostate MRI for prostate cancer (PCa) progression during AS.

EVIDENCE ACQUISITION

We systematically searched PubMed, Scopus, and Web of Science databases to select studies analyzing the association between changes on serial prostate MRI and PCa progression during AS. We included studies that provided data for MRI progression, which allowed us to calculate diagnostic estimates. We compared Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) accuracy with institution-specific definitions.

EVIDENCE SYNTHESIS

We included 15 studies with 2240 patients. Six used PRECISE criteria and nine institution-specific definitions of MRI progression. The pooled PCa progression rate, which included histological progression to Gleason grade ≥2, was 27%. The pooled sensitivity and specificity were 0.59 (95% confidence interval [CI] 0.44-0.73) and 0.75 (95% CI 0.66-0.84) respectively. There was significant heterogeneity between included studies. Depending on PCa progression prevalence, the pooled negative predictive value for serial prostate MRI ranged from 0.81 (95% CI 0.73-0.88) to 0.88 (95% CI 0.83-0.93) and the pooled positive predictive value ranged from 0.37 (95% CI 0.24-0.54) to 0.50 (95% CI 0.36-0.66). There were no significant differences in the pooled sensitivity (p = 0.37) and specificity (p = 0.74) of PRECISE and institution-specific schemes.

CONCLUSIONS

Serial MRI still should not be considered a sole factor for excluding PCa progression during AS, and changes on MRI are not accurate enough to indicate PCa progression. There was a nonsignificant trend toward improved diagnostic estimates of PRECISE recommendations. These findings highlight the need to further define the optimal triggers and timing of biopsy during AS, as well as the need for optimizing the quality, interpretation, and reporting of serial prostate MRI.

PATIENT SUMMARY

Our study suggests that serial prostate magnetic resonance imaging (MRI) alone in patients on active surveillance is not accurate enough to reliably rule out or rule in prostate cancer progression. Other clinical factors and biomarkers along with serial MRI are required to safely tailor the intensity of follow-up biopsies.

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

The role of multiparametric magnetic resonance imaging in the selection and follow-up of patients undergoing active surveillance for prostate cancer. An European Section of Uro-Technology (ESUT) review

INTRODUCTION

In recent years, active surveillance (AS) has gained popularity as a safe and reasonable option for patients with low-risk, clinically localized prostate cancer.

OBJECTIVE

To summarize the latest information regarding the use of mpMRI in the setting of active surveillance (AS) for the management of prostate cancer (PCa).

EVIDENCE ACQUISITION

A PubMed-based, English literature search was conducted through February 2020. We selected the most relevant original articles, meta-analyses and systematic reviews that could provide important information.

EVIDENCE SYNTHESIS

The great importance of mpMRI of the prostate in the setting of PCa diagnosis is its ability to visualize primarily high-grade cancerous lesions potentially missed on systematic biopsies. In several studies, mpMRI has shown an improved performance over clinically based models for identifying candidates which will benefit the most from AS. Although data on prostate mpMRI during follow-up of men under AS is sparse, it holds the probability to improve significantly AS programs by a more precise selection of optimal candidates, a more accurate identification of disease progression and a reduction in number of biopsies. The goal of reassessment of patients undergoing AS is to find the most effective moment to change attitude to active treatment.

CONCLUSION

The value of mpMRI has been recognized due to its high negative predictive value (NPV) for lesion upgrading in low-risk PCa patients. The improvement in imaging detection, and precise diagnosis with mpMRI could reduce misclassifications at initial diagnosis and during follow-up, reducing the number of biopsies.

Locoregional CT staging of colon cancer: does a learning curve exist?

PURPOSE

To evaluate the learning curve for locoreginal staging of colon cancer in radiologist trainees.

METHODS

Eighty-eight cases of colon cancer CT were included in this retrospective study. Four senior radiology residents staged the CTs according to TNM classification. Two out of four radiologists received feedback after reading every 20 cases. Radiologic staging was compared with pathologic staging and the learning curve, diagnostic performance, reader confidence and reading time were evaluated and compared between the two groups (feedback vs. no feedback). Generalized estimating equations logistic regression, QICu statistic, ANOVA and t test/Mann-Whitney test were utilized.

RESULTS

Radiologists demonstrated a significant increase in their performance to distinguish between ≤ T2 and ≥ T3 and reached an inflection point at 38 cases, with a significant association with increased number of cases reviewed (P < 0.001). Sensitivity (P < 0.001), specificity (P = 0.030) and NPV (P = 0.002) demonstrated significant associations with increased experience. The overall reader's confidence was significantly higher in the group which received feedback (P < 0.001). There was no significant improvement in performance nor in reader's confidence for N staging (N0 vs. ≥ N1) for all readers. Reading time decreased with experience and showed a significant negative association with experience (P < 0.001).

CONCLUSION

Diagnostic performance of senior radiology trainees in differentiating between T2 and T3 colon cancer on CTs improved with increased experience. In contrast, evaluation of lymph node involvement did not improve with more experience. Feedback had no significant effect on improvement of diagnostic performances.

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

Education of prostate MR imaging: commentary

Multiparametric MRI (mpMRI) of the prostate has been firmly established as a tool in the diagnosis, management, and treatment of prostate cancer. The growth in this field over the past decade has led to increased acceptance and demand for multiparametric prostate MRI across the world. However, with the rising demand for prostate mpMRI, it will become increasingly necessary to train a generation of dedicated prostate imagers to yield a high-quality product.

PI-RADS version 2.1 scoring system is superior in detecting transition zone prostate cancer: a diagnostic study

PURPOSE

The studies comparing the versions 2 vs. 2.1 of the Prostate Imaging Reporting and Data System (PI-RADS) are rare. This study aimed to evaluate whether PI-RADS version 2.1 is superior in detecting transition zone prostate cancer in comparison with PI-RADS version 2.

METHODS

This was a diagnostic study of patients with prostate diseases who visited the Urology Department of The Second Affiliated Hospital of Soochow University and underwent a magnetic resonance imaging (MRI) examination between 03-01-2016 and 10-31-2018. The images originally analyzed using PI-RADS version 2 were retrospectively re-analyzed and scored in 2019 according to the updated PI-RADS version 2.1. The kappa and receiver operating characteristic (ROC) curves were used.

RESULTS

For Reader 1, compared with PI-RADS version 2, version 2.1 had higher sensitivity (85% vs. 79%, P = 0.03), lower specificity (65% vs. 83%, P < 0.001), and lower area under the curve (AUC) (0.749 vs. 0.809, P < 0.001). For Reader 2 (first attempt), compared with PI-RADS version 2, version 2.1 had lower specificity (67% vs. 91%, P < 0.001) and lower AUC (0.702 vs. 0.844, P < 0.001). For Reader 2 (second attempt), compared with PI-RADS version 2, version 2.1 had higher sensitivity (88% vs. 78%, P < 0.001) and lower specificity (77% vs. 91%, P < 0.001). The kappa between the two attempts for Reader 2 was 0.321.

CONCLUSION

These results suggest that PI-RADS version 2.1 might improve the detection of prostate cancers in the transition zone compared with PI-RADS version 2 but that it might results in higher numbers of biopsies because of lower specificity.

PI-RADSv2.1: Current status

Multiparametric magnetic resonance imaging (mpMRI) has played an increasing role in the detection and local staging of prostate cancer over the last 15 years. Prostate mpMRI, due to various factors, is prone to high inter-reader variability necessitating standardized reporting guidelines that provide accurate and actionable information to the ordering clinician. The Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) was released in March 2019 as an update to PI-RADSv2.0 with the hope of further standardizing the reporting process of prostate mpMRI, improving the detection of clinically significant cancer, reducing the biopsy rate of indolent tumors, and decreasing inter-reader variability. Early data show an improved performance of PI-RADSv2.1 over PI-RADSv2.0. Updates included in PI-RADSv2.1 and its current experience in clinic will be reviewed in this review.

Fusion of high B-value diffusion-weighted and T2-weighted MR images increases sensitivity for identification of extraprostatic disease in prostate cancer

PURPOSE

To evaluate whether fusion of high b-value diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) increases radiologists' ability to detect pathologic features responsible for upstaging in prostate cancer patients prior to radical prostatectomy (RP).

BASIC PROCEDURES

This was a retrospective study including 103 patients who underwent RP and a prostate MRI performed at 3T. High b-value DWI and T2WI were fused and interpreted by three radiologists with different degrees of experience. Prior to and after fusion, readers answered questionnaires about cancer presence, extraprostatic extension (EPE), seminal vesicle (SV) invasion, lymph node (LN) involvement, and reader confidence. Pathology reports served as the reference standard.

MAIN FINDINGS

High b-value DWI-T2WI fusion increased sensitivity for detection of EPE from 65.6% to 77.4% (p < 0.05), SV invasion from 40.5% to 48.8% (p < 0.05), and LN metastasis by 23.8% to 44.4% (p < 0.05). Readers' confidence significantly improved with the use of fusion imaging. Across all readers, confidence of cancer detection increased by 12.5% (p < 0.05), EPE by 14.7% (p < 0.05), SV invasion by 8.1% (p < 0.05), and LN metastasis by 2.5% (p < 0.05) using Wilcoxon signed rank test.

PRINCIPAL CONCLUSIONS

Fusion overlay of high b-value DWI and T2WI increases sensitivity for detection of extraprostatic disease resulting in upstaging at the time of RP.