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GENITOURINARY IMAGING R enal masses are commonly encountered as incidental findings in patients undergoing cross-sectional imaging (1,2). The majority of solid small (4 cm [cT1a]) renal masses (SRMs) are malignant, but up to 20% are benign (3,4). Moreover, even when malignant, cT1a renal cell carcinoma (RCC) is frequently indolent and, when treated, rarely recurs locally or metastasizes (5). Indeed, cT1a RCC is an uncommon cause of patient mortality, particularly in older patients who may have other competing comorbidities (6). Among the various RCC subtypes, clear cell RCC (ccRCC) is the most common and is often aggressive; on the basis of this combination of characteristics, ccRCC is the most common cause of disease progression and metastasis in active surveillance populations (7).Various methods have been proposed to differentiate benign from malignant SRMs and indolent from aggressive SRMs in clinical practice. Renal mass biopsy can be used to differentiate between the various histologic subtypes of solid SRMs (8). However, a biopsy represents an additional diagnostic procedure, is invasive (median complication rate 8%) (8), is not feasible in all patients, and is nondiagnostic in up to 20% of renal masses (9). Moreover, renal mass biopsy is not widely used in clinical practice due to the perception among many urologists that its results do not alter management (10).Use of noninvasive imaging to diagnose renal mass subtype among solid SRMs with CT or MRI represents an alternative to biopsy, has been studied extensively, and has been shown, to some extent, to be accurate in the diagnosis of some renal mass histopathologic subtypes (11)(12)(13)(14)(15). However, studies on this topic have been mainly limited to single-center retrospective case-control series and therefore show selection bias (13,14,16-19). To date, unlike for cystic renal masses, where imaging assessment is performed using Background: Solid small renal masses (SRMs) (4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation.Purpose: To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods:This retrospective multicenter cross-sectional study included patients with consecutive solid (25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for...

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We tested the plasma of 51 HIV-1-infected children (23 naïve and 28 ART treated) for neutralization against five primary isolates (PIs) generated from adult Indian HIV-1-infected patients. The plasma exhibited neutralization potential with significantly higher neutralizing antibody titers in ART-treated children than naïve children against three out of five PIs (p<0.0001). Further, in treated children, neutralizing antibody titers were higher in those children with suppressed viremia (<1000 RNA copies/mL) than non-suppressors against two of the three PIs. We report here for the first time the neutralization potential of the plasma of HIV-1-infected Indian children.

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BackgroundDemand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2‐weighted imaging (T2WI).PurposeTo compare conventional bpMRIs (CL‐bpMRI) with bpMRIs including a deep learning‐accelerated T2WI (DL‐bpMRI) in diagnosing prostate cancer.Study TypeRetrospective.PopulationEighty consecutive men, mean age 66 years (47–84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow‐up included prostate biopsy or stability of prostate‐specific antigen (PSA) for 1 year.Field Strength and SequencesA 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL‐T2), 3‐fold deep learning‐accelerated axial and coronal T2‐weighted sequence (DL‐T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2, 1000 sec/mm2, calculated b = 1500 sec/mm2.AssessmentCL‐bpMRI and DL‐bpMRI including the same conventional diffusion‐weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer‐assisted detection algorithm (DL‐CAD). The readers evaluated image quality using a 4‐point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI‐RADS) v2.1. DL‐CAD identified and assigned lesions of PI‐RADS 3 or greater.Statistical TestsQuality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. Significance: P = 0.05.ResultsEighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL‐T2, DL‐T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient‐based analysis, the reader results of AUC are (CL‐bpMRI, DL‐bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL‐CAD (CL‐bpMRI, DL‐bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48).ConclusionDeep learning‐accelerated T2‐weighted imaging may potentially be used to decrease acquisition time for bpMRI.Evidence Level3.Technical EfficacyStage 2.