Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide.

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

Assessment of Consistency Between Peer-Reviewed Publications and Clinical Trial Registries.

Importance: Clinical trial registries are intended to increase clinical research transparency by nonselectively identifying and documenting clinical trial designs and outcomes. Inconsistencies in reported data undermine the utility of such registries and have previously been noted in general medical literature. Objective: To assess whether inconsistencies in reported data exist between ophthalmic literature and clinical trial registries. Design, Setting, and Participants: In this retrospective, cross-sectional study, interventional clinical trials published from January 1, 2014, to December 31, 2014, in the American Journal of Ophthalmology, JAMA Ophthalmology, and Ophthalmology were reviewed. Observational, retrospective, uncontrolled, and post hoc reports were excluded, yielding a sample size of 106 articles. Data collection was performed from January through September 2016. Data review and adjudication continued through January 2017. Main Outcomes and Measures: If possible, articles were matched to registry entries listed in the ClinicalTrials.gov database or in 1 of 16 international registries indexed by the World Health Organization International Clinical Trials Registry Platform version 3.2 search engine. Each article-registry pair was assessed for inconsistencies in design, results, and funding (each of which was further divided into subcategories) by 2 reviewers and adjudicated by a third. Results: Of 106 trials that met the study criteria, matching registry entries were found for 68 (64.2%), whereas no matching registry entries were found for 38 (35.8%). Inconsistencies were identified in study design, study results, and funding sources, including specific interventions in 8 (11.8%), primary outcome measure (POM) designs in 32 (47.1%), and POM results in 48 (70.6%). In addition, numerous data pieces were unreported, including analysis methods in 52 (76.5%) and POM results in 38 (55.9%). Conclusions and Relevance: Clinical trial registries were underused in this sample of ophthalmology clinical trials. For studies with registry data, inconsistency rates between published and registered data were similar to those previously reported for general medical literature. In most cases, inconsistencies involved missing data, but explicit discrepancies in methods and/or data were also found. Transparency and credibility of published trials may be improved by closer attention to their registration and reporting.

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board-approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.