Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Role of gp120 Trimerization on HIV Binding Elucidated with Brownian Adhesive Dynamics

We simulated the docking of human immunodeficiency virus (HIV) with a cell membrane using Brownian adhesive dynamics. The main advance in the current version of Brownian adhesive dynamics is that we use a simple bead-spring model to coarsely approximate the role of gp120 trimerization on HIV docking. We used our simulations to elucidate the effect of env spike density on the rate and probability of HIV binding, as well as the probability that each individual gp120 trimer is fully engaged. We found that for typical CD4 surface densities, viruses expressing as few as 8 env spikes will dock with binding rate constants comparable to viruses expressing 72 spikes. We investigated the role of cellular receptor diffusion on the degree of binding achieved by the virus on both short timescales (where binding has reached steady state but before substantial receptor accumulation in the viral-cell contact zone has occurred) and long timescales (where the system has reached steady state). On short timescales, viruses with 10–23 env trimers most efficiently form fully engaged trimers. On long timescales, all gp120 in the contact area will become bound to CD4. We found that it takes seconds for engaged trimers to cluster CD4 molecules in the contact zone, which partially explains the deleay in viral entry

Discriminating Survival Outcomes in Patients with Glioblastoma Using a Simulation-Based, Patient-Specific Response Metric

<div><p>Accurate clinical assessment of a patient's response to treatment for glioblastoma multiforme (GBM), the most malignant type of primary brain tumor, is undermined by the wide patient-to-patient variability in GBM dynamics and responsiveness to therapy. Using computational models that account for the unique geometry and kinetics of individual patients' tumors, we developed a method for assessing treatment response that discriminates progression-free and overall survival following therapy for GBM. Applying these models as untreated virtual controls, we generate a patient-specific “Days Gained” response metric that estimates the number of days a therapy delayed imageable tumor progression. We assessed treatment response in terms of Days Gained scores for 33 patients at the time of their first MRI scan following first-line radiation therapy. Based on Kaplan-Meier analyses, patients with Days Gained scores of 100 or more had improved progression-free survival, and patients with scores of 117 or more had improved overall survival. Our results demonstrate that the Days Gained response metric calculated at the routinely acquired first post-radiation treatment time point provides prognostic information regarding progression and survival outcomes. Applied prospectively, our model-based approach has the potential to improve GBM treatment by accounting for patient-to-patient heterogeneity in GBM dynamics and responses to therapy.</p> </div

Comparisons between T1Gd MRI data and untreated virtual control (UVC) prediction at post-treatment time point.

<p>Patient was 58 years old and underwent biopsy followed by conformal radiation therapy with concurrent temozolomide chemotherapy. Top row: MRI data. Middle row: Actual tumor perimeter (red) with superimposed UVC-predicted tumor perimeter (cyan). Bottom row: full distribution of UVC cell densities showing invasion peripheral to abnormality. Outermost blue cell density profile represents a very low, but non-zero, threshold. Perimeter of actual tumor outlined in white.</p

Enhancing career paths for tomorrow's radiation oncologists

The purpose for this manuscript is to enhance career opportunities for radiation oncologists (ROs) by expanding the scope of work as a prelude to redefining the scope of our contributions at this critical inflection point in our history. The direct stimulus is the speculation and debate over the ROs' future, a logical issue in today's rapidly changing world of health care economics, cancer biology, artificial intelligence, and global resource disparities. 123456 To be proactive and effective in adapting to—and with—these external factors, the data upon which decisions are based should be well understood. Yet accuracy of workforce forecasts for ROs are notoriously inconsistent, partly because of the imperfect assumptions inherent in such complex models. 1234 Nonetheless, over 50% of ROs are concerned about a future oversupply, 56 and the downstream effects already appear to have negatively affected specialty choice among highly talented and pragmatic medical students. Discussions of practitioner supply and demand imbalance often focus on the numerator—are there too many? Better solutions may reside in a broadening of the denominator—the talent and contributions that ROs bring to cancer care and greater society. 78 Regardless of how one views these complex issues, this is a critical juncture for exploring how to evolve ROs' skills and ensure that our contributions continue to help solve the challenges facing health care and patients