The clinical management of COVID-19 is challenging. Medical imaging plays a critical role in the early detection, clinical monitoring and outcomes assessment of this disease. Chest x-ray radiography and computed tomography) are the standard imaging modalities used for the structural assessment of the disease status, while functional imaging (namely, positron emission tomography) has had limited application. Artificial intelligence can enhance the predictive power and utilization of these imaging approaches and new approaches focusing on detection, stratification and prognostication are showing encouraging results. We review the current landscape of these imaging modalities and artificial intelligence approaches as applied in COVID-19 management.

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Background: Glycoprotein-A repetitions predominant (GARP) regulates membranebound transforming growth factor b1 (TGFb1), an immunosuppressive cytokine.ABBV-151 is a first-in-class monoclonal antibody (mAb) that binds to the GARP-TGFb1 complex and blocks TGFb1 release. Preclinical data demonstrated that targeting both GARP-TGFb1 and programmed cell death protein 1 (PD-1) improved antitumor effects compared with antiePD-1 alone. Combining ABBV-151 with the antiePD-1 mAb budigalimab (ABBV-181) may enable a more effective antitumor immune response by reducing the immunosuppressive effect of TGFb1.Trial design: This is a multicenter phase I, dose escalation and dose expansion study (NCT03821935) in patients (pts; !18 yr, Eastern Cooperative Oncology Group performance status 0e1) with locally advanced or metastatic solid tumors. The primary objective of dose escalation is to determine the recommended phase II dose (RP2D) of ABBV-151 as monotherapy or with budigalimab; dose expansion will assess the objective response rate of ABBV-151 AE budigalimab. Secondary/exploratory objectives include assessing preliminary efficacy, safety, tolerability, pharmacokinetics (PK), and evaluating potential pharmacodynamic and predictive biomarkers. Dose escalation of ABBV-151, guided by a Bayesian optimal interval design, will assess doselimiting toxicities during the first 28-day cycle and will be utilized until the RP2D is defined. ABBV-151 + budigalimab (fixed dose) will start !2 dose levels below that proven safe for ABBV-151. Adverse events will be evaluated per National Cancer Institute Common Terminology Criteria v5.0. Response will be assessed using Response Evaluation Criteria In Solid Tumors (RECIST) v1.1 and iRECIST every 8 weeks. PK of ABBV-151 will be characterized. Saturation of GARP-TGFb1 on platelets and PD-1 on CD4 T cells will be determined. Modulation of cytokines, chemokines, lymphocyte activity, and gene expression will be assessed in blood, while gene signatures and protein markers will be explored in tumor tissues. Baseline tumor characteristics will be retrospectively related to response. Enrollment initiated Mar 2019, with 37 pts enrolled as of May 2020.Clinical trial identification: NCT03821935.

BACKGROUND Modified Radiographic Response Assessment in Neuro-Oncology (“mRANO”) criteria based on SPDP form the basis for assessing treatment response in Glioblastoma but are subject to sampling bias and difficulty in differentiating between pseudo- and true disease progression. Volumetric image analysis using AI may overcome these limitations of standard techniques and improve our ability to detect changes earlier and more accurately. METHODS Images from eight reGBM patients enrolled in a Phase-2 reGBM study of Vivacitas Oncology’s drug, AR-67, were re-assessed using IAG’s AI-assisted volumetric measurements. A median of five MRI time points from each patient were included. The mRANO response was determined by central reading and tumor volumetric measurement using IAG’s proprietary platform. Statistical significance was set at p< .0001. RESULTS Four patients showed responses, two patients showed stable disease, and two patients showed progressive disease. Tumor volume was correlated (r=0.97) with SPDP, but was driven by high coefficients in large lesions. Standard SPDP overestimated tumor size in larger tumors using the Bland-Altman analysis (mean difference: 829; 95% CI: 704 to -2362) leading to discrepancies in response rates. For example, the mean response rate based on IAG’s volumetric criteria was +22% (1.29) compared with +17% using SPDP (0.81). Eight out of 45 time-points also differed in the directionality of responses (e.g., increase vs. decrease) with SPDP underestimating the positive effects of AR-67 compared to AI analysis. CONCLUSIONS IAG’s AI-assisted tumor volumetric analysis is feasible for clinical trials and may be more sensitive for evaluating treatment-related response rates vs. SPDP methodology. This is particularly true for measuring large lesions, and may also allow for more accurately differentiating between pseudo- and true disease progression. The data included eight patients’ MRI images from a Phase-2 reGBM study, showing that five patients achieved the primary end-point of six months Progression-Free Survival, suggesting AR-67’s therapeutic potential.