Retinoic acid enhances HIV-1 reverse transcription and transcription in macrophages via mTOR-modulated mechanisms.

The intestinal environment facilitates HIV-1 infection via mechanisms involving the gut-homing vitamin A-derived retinoic acid (RA), which transcriptionally reprograms CD4+ T cells for increased HIV-1 replication/outgrowth. Consistently, colon-infiltrating CD4+ T cells carry replication-competent viral reservoirs in people with HIV-1 (PWH) receiving antiretroviral therapy (ART). Intriguingly, integrative infection in colon macrophages, a pool replenished by monocytes, represents a rare event in ART-treated PWH, thus questioning the effect of RA on macrophages. Here, we demonstrate that RA enhances R5 but not X4 HIV-1 replication in monocyte-derived macrophages (MDMs). RNA sequencing, gene set variation analysis, and HIV interactor NCBI database interrogation reveal RA-mediated transcriptional reprogramming associated with metabolic/inflammatory processes and HIV-1 resistance/dependency factors. Functional validations uncover post-entry mechanisms of RA action including SAMHD1-modulated reverse transcription and CDK9/RNA polymerase II (RNAPII)-dependent transcription under the control of mammalian target of rapamycin (mTOR). These results support a model in which macrophages residing in the intestine of ART-untreated PWH contribute to viral replication/dissemination in an mTOR-sensitive manner.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Caspase cleavage of gasdermin E causes neuronal pyroptosis in HIV-associated neurocognitive disorder.

Despite effective antiretroviral therapies, 20-30% of persons with treated HIV infection develop a neurodegenerative syndrome termed HIV-associated neurocognitive disorder (HAND). HAND is driven by HIV expression coupled with inflammation in the brain but the mechanisms underlying neuronal damage and death are uncertain. The inflammasome-pyroptosis axis coordinates an inflammatory type of regulated lytic cell death that is underpinned by the caspase-activated pore-forming gasdermin proteins. The mechanisms driving neuronal pyroptosis were investigated herein in models of HAND, using multi-platform molecular and morphological approaches that included brain tissues from persons with HAND and simian immunodeficiency virus (SIV)-infected non-human primates as well as cultured human neurons. Neurons in the frontal cortices from persons with HAND showed increased cleaved gasdermin E (GSDME), which was associated with ß-III tubulin degradation and increased HIV levels. Exposure of cultured human neurons to the HIV-encoded viral protein R (Vpr) elicited time-dependent cleavage of GSDME and Ninjurin-1 (NINJ1) induction with associated cell lysis that was inhibited by siRNA suppression of both proteins. Upstream of GSDME cleavage, Vpr exposure resulted in activation of caspases-1 and 3. Pretreatment of Vpr-exposed neurons with the caspase-1 inhibitor, VX-765, reduced cleavage of both caspase-3 and GSDME, resulting in diminished cell death. To validate these findings, we examined frontal cortical tissues from SIV-infected macaques, disclosing increased expression of GSDME and NINJ1 in cortical neurons, which was co-localized with caspase-3 detection in animals with neurological disease. Thus, HIV infection of the brain triggers the convergent activation of caspases-1 and -3, which results in GSDME-mediated neuronal pyroptosis in persons with HAND. These findings demonstrate a novel mechanism by which a viral infection causes pyroptotic death in neurons while also offering new diagnostic and therapeutic strategies for HAND and other neurodegenerative disorders.

Non-Isocentric Geometry for Next-Generation Tomosynthesis With Super-Resolution.

Our lab at the University of Pennsylvania (UPenn) is investigating novel designs for digital breast tomosynthesis. We built a next-generation tomosynthesis system with a non-isocentric geometry (superior-to-inferior detector motion). This paper examines four metrics of image quality affected by this design. First, aliasing was analyzed in reconstructions prepared with smaller pixelation than the detector. Aliasing was assessed with a theoretical model of r -factor, a metric calculating amplitudes of alias signal relative to input signal in the Fourier transform of the reconstruction of a sinusoidal object. Aliasing was also assessed experimentally with a bar pattern (illustrating spatial variations in aliasing) and 360°-star pattern (illustrating directional anisotropies in aliasing). Second, the point spread function (PSF) was modeled in the direction perpendicular to the detector to assess out-of-plane blurring. Third, power spectra were analyzed in an anthropomorphic phantom developed by UPenn and manufactured by Computerized Imaging Reference Systems (CIRS), Inc. (Norfolk, VA). Finally, calcifications were analyzed in the CIRS Model 020 BR3D Breast Imaging Phantom in terms of signal-to-noise ratio (SNR); i.e., mean calcification signal relative to background-tissue noise. Image quality was generally superior in the non-isocentric geometry: Aliasing artifacts were suppressed in both theoretical and experimental reconstructions prepared with smaller pixelation than the detector. PSF width was also reduced at most positions. Anatomic noise was reduced. Finally, SNR in calcification detection was improved. (A potential trade-off of smaller-pixel reconstructions was reduced SNR; however, SNR was still improved by the detector-motion acquisition.) In conclusion, the non-isocentric geometry improved image quality in several ways.

Is the Central Nervous System Reservoir a Hurdle for an HIV Cure?

There is currently no cure for HIV infection although adherence to effective antiretroviral therapy (ART) suppresses replication of the virus in blood, increases CD4+ T-cell counts, reverses immunodeficiency, and increases life expectancy. Despite these substantial advances, ART is a lifelong treatment for people with HIV (PWH) and upon cessation or interruption, the virus quickly rebounds in plasma and anatomic sites, including the central nervous system (CNS), resulting in disease progression. With recent advances in quantifying viral burden, detection of genetically intact viral genomes, and isolation of replication-competent virus from brain tissues of PWH receiving ART, it has become apparent that the CNS viral reservoir (largely comprised of macrophage type cells) poses a substantial challenge for HIV cure strategies. Other obstacles impacting the curing of HIV include ageing populations, substance use, comorbidities, limited antiretroviral drug efficacy in CNS cells, and ART-associated neurotoxicity. Herein, we review recent findings, including studies of the proviral integration sites, reservoir decay rates, and new treatment/prevention strategies in the context of the CNS, together with highlighting the next steps for investigations of the CNS as a viral reservoir.

Donating One's Body to HIV Cure Research Through Canadian Medical Assistance in Dying: A Case Study.

BACKGROUND: Finding a cure for HIV is challenged by persisting reservoirs, the mapping of which necessitates invasive procedures. Inviting people with HIV (PWHIV) at the end of life to donate body specimens post-mortem through research autopsies is a novel approach, raising ethical concerns. OBJECTIVE: This case study aims to explore the motivations, barriers, and facilitators of a terminally-ill Canadian PWHIV who requested medical assistance in dying (MAID) and expressed interest in donating his body for HIV cure research. CASE PRESENTATION: An in-depth 3-hour and semi-structured interview was conducted with the participant. The interview transcription was thematically coded to identify motivations and perceived barriers and facilitators to participate in end-of-life HIV cure research. Our analysis identified six themes. Two themes expressed motivations: Collaboration in progress in health and science, seeing cure research as collaboration with professionals; and Opportunity to learn more, mostly about science and health. One theme expressed a barrier: Losing interest in or identification with long-term care research matters, especially those related to the management of long-term care. Three themes expressed by facilitators: Receiving information from professionals one trusts and knows, especially clinical and research teams; Perceiving research procedures as simple, useful, and embedded in care, perceiving clinical, educational, and interpersonal benefits that surpass costs of participation; and Perceiving research as one last way to contribute, that is, feeling useful or give back. CONCLUSION: Several circumstances facilitated the patient's participation: being a single man, having time to participate, having no strong religious belief, and valuing clear, direct communication. His motivations to participate in HIV cure research were altruistic, and also an experience of working with clinical and research teams. Finally, this perspective highlights HIV cure research participant candidates' need for education about research procedures.

MicroRNA-25/93 induction by Vpu as a mechanism for counteracting MARCH1-restriction on HIV-1 infectivity in macrophages.

IMPORTANCE: In order to efficiently produce infectious viral particles, HIV must counter several restrictions exerted by host cell antiviral proteins. MARCH1 is a member of the MARCH protein family that restricts HIV infection by limiting the incorporation of viral envelope glycoproteins into nascent virions. Here, we identified two regulatory RNAs, microRNAs-25 and -93, induced by the HIV-1 accessory protein Vpu, that downregulate MARCH1 mRNA. We also show that Vpu induces these cellular microRNAs in macrophages by hijacking the cellular ß-catenin pathway. The notion that HIV-1 has evolved a mechanism to counteract MARCH1 restriction on viral infectivity underlines the importance of MARCH1 in the host antiviral response.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy.

BACKGROUND: Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS). METHODS: A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components. RESULTS: We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002). CONCLUSIONS: These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

Early changes in tumor properties during treatment may tell us whether or not a patient's tumor is responding to treatment. Such changes may be seen on imaging. Here, changes in breast cancer properties are identified on imaging and are used in combination with gene markers to investigate whether response to treatment can be predicted using mathematical models. We demonstrate that tumor properties seen on imaging early on in treatment can help to predict patient outcomes. Our approach may allow clinicians to better inform patients about their prognosis and choose appropriate and effective therapies.

Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening.

PURPOSE: Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. MATERIALS AND METHODS: We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. RESULTS: PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. CONCLUSIONS: Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.

Generation of functional human T cell development in NOD/SCID/IL2rγnull humanized mice without using fetal tissue: Application as a model of HIV infection and persistence.

Humanization of mice with functional T cells currently relies on co-implantation of hematopoietic stem cells from fetal liver and autologous fetal thymic tissue (so-called BLT mouse model). Here, we show that NOD/SCID/IL2rγnull mice humanized with cord blood- derived CD34+ cells and implanted with allogeneic pediatric thymic tissues excised during cardiac surgeries (CCST) represent an alternative to BLT mice. CCST mice displayed a strong immune reconstitution, with functional T cells originating from CD34+ progenitor cells. They were equally susceptible to mucosal or intraperitoneal HIV infection and had significantly higher HIV-specific T cell responses. Antiretroviral therapy (ART) robustly suppressed viremia and reduced the frequencies of cells carrying integrated HIV DNA. As in BLT mice, we observed a complete viral rebound following ART interruption, suggesting the presence of HIV reservoirs. In conclusion, CCST mice represent a practical alternative to BLT mice, broadening the use of humanized mice for research.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women.

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64−0.72) for all women, 0.67 (0.61−0.72) for White women, and 0.70 (0.65−0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

The Ongoing Saga of the Evolution of Percutaneous Coronary Intervention: From Balloon Angioplasty to Recent Innovations to Future Prospects.

The advances in percutaneous coronary intervention (PCI) have been, above all, dependent on the work of pioneers in surgery, radiology, and interventional cardiology. From Grüntzig's first balloon angioplasty, PCI has expanded through technology development, improved protocols, and dissemination of best-practice techniques. We can nowadays treat more complex lesions in higher-risk patients with favourable results. Guide wires, balloon types and profiles, debulking techniques such as atherectomy or lithotripsy, stents, and scaffolds all represent evolutions that have allowed us to tackle complex lesions such as an unprotected left main coronary artery, complex bifurcations, or chronic total occlusions. Best-practice PCI, including physiology assessment, imaging, and optimal lesion preparation are now the gold standard when performing PCI for sound indications, and new technologies such as intravascular lithotripsy for lesion preparation, or artificial intelligence, are innovations in the steps of 4 decades of pioneers to improve patient care in interventional cardiology. In the present review, major innovations in PCI since the first balloon angioplasty and also uncertainties and obstacles inherent to such medical advances are described.

Development of a robust radiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first-line immunotherapy.

We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.

Lentiviral Infections Persist in Brain despite Effective Antiretroviral Therapy and Neuroimmune Activation.

HIV infection persists in different tissue reservoirs among people with HIV (PWH) despite effective antiretroviral therapy (ART). In the brain, lentiviruses replicate principally in microglia and trafficking macrophages. The impact of ART on this viral reservoir is unknown. We investigated the activity of contemporary ART in various models of lentivirus brain infection. HIV-1 RNA and total and integrated DNA were detected in cerebral cortex from all PWH (n = 15), regardless of ART duration or concurrent plasma viral quantity and, interestingly, integrated proviral DNA levels in brain were significantly higher in the aviremic ART-treated group (P < 0.005). Most ART drugs tested (dolutegravir, ritonavir, raltegravir, and emtricitabine) displayed significantly lower 50% effective concentration (EC50) values in lymphocytes than in microglia, except tenofovir, which showed 1.5-fold greater activity in microglia (P < 0.05). In SIV-infected Chinese rhesus macaques, despite receiving suppressive (n = 7) or interrupted (n = 8) ART, brain tissues had similar SIV-encoded RNA and total and integrated DNA levels compared to brains from infected animals without ART (n = 3). SIV and HIV-1 capsid antigens were immunodetected in brain, principally in microglia/macrophages, regardless of ART duration and outcome. Antiviral immune responses were comparable in the brains of ART-treated and untreated HIV- and SIV-infected hosts. Both HIV-1 and SIV persist in brain tissues despite contemporary ART, with undetectable virus in blood. ART interruption exerted minimal effect on the SIV brain reservoir and did not alter the neuroimmune response profile. These studies underscore the importance of augmenting ART potency in different tissue compartments. IMPORTANCE Antiretroviral therapy (ART) suppresses HIV-1 in plasma and CSF to undetectable levels. However, the impact of contemporary ART on HIV-1 brain reservoirs remains uncertain. An active viral reservoir in the brain during ART could lead to rebound systemic infection after cessation of therapy, development of drug resistance mutations, and neurological disease. ART's impact, including its interruption, on brain proviral DNA remains unclear. The present studies show that in different experimental platforms, contemporary ART did not suppress viral burden in the brain, regardless of ART component regimen, the duration of therapy, and its interruption. Thus, new strategies for effective HIV-1 suppression in the brain are imperative to achieve sustained HIV suppression.

Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns-a woman's left and right breasts. From 341 features, we identified "robust" features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross-validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.

HIV-1 Vpu Promotes Phagocytosis of Infected CD4+ T Cells by Macrophages through Downregulation of CD47.

Human immunodeficiency virus (HIV) remodels the cell surface of infected cells to facilitate viral dissemination and promote immune evasion. The membrane-associated viral protein U (Vpu) accessory protein encoded by HIV-1 plays a key role in this process by altering cell surface levels of multiple host proteins. Using an unbiased quantitative plasma membrane profiling approach, we previously identified CD47 as a putative host target downregulated by Vpu. CD47 is a ubiquitously expressed cell surface protein that interacts with the myeloid cell inhibitory receptor signal regulatory protein-alpha (SIRPα) to deliver a "don't-eat-me" signal, thus protecting cells from phagocytosis. In this study, we investigate whether CD47 modulation by HIV-1 Vpu might promote the susceptibility of macrophages to viral infection via phagocytosis of infected CD4+ T cells. Indeed, we find that Vpu downregulates CD47 expression on infected CD4+ T cells, leading to enhanced capture and phagocytosis by macrophages. We further provide evidence that this Vpu-dependent process allows a C-C chemokine receptor type 5 (CCR5)-tropic transmitted/founder (T/F) virus, which otherwise poorly infects macrophages in its cell-free form, to efficiently infect macrophages. Importantly, we show that HIV-1-infected cells expressing a Vpu-resistant CD47 mutant are less prone to infecting macrophages through phagocytosis. Mechanistically, Vpu forms a physical complex with CD47 through its transmembrane domain and targets the latter for lysosomal degradation. These results reveal a novel role of Vpu in modulating macrophage infection, which has important implications for HIV-1 transmission in early stages of infection and the establishment of viral reservoir. IMPORTANCE Macrophages play critical roles in human immunodeficiency virus (HIV) transmission, viral spread early in infection, and as a reservoir of virus. Selective capture and engulfment of HIV-1-infected T cells was shown to drive efficient macrophage infection, suggesting that this mechanism represents an important mode of infection notably for weakly macrophage-tropic T/F viruses. In this study, we provide insight into the signals that regulate this process. We show that the HIV-1 accessory protein viral protein U (Vpu) downregulates cell surface levels of CD47, a host protein that interacts with the inhibitory receptor signal regulatory protein-alpha (SIRPα), to deliver a "don't-eat-me" signal to macrophages. This allows for enhanced capture and phagocytosis of infected T cells by macrophages, ultimately leading to their productive infection even with transmitted/founder (T/F) virus. These findings provide new insights into the mechanisms governing the intercellular transmission of HIV-1 to macrophages with implications for the establishment of the macrophage reservoir and early HIV-1 dissemination in vivo.

Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.