Avelumab, a PD-L1 Inhibitor, in Combination with Hypofractionated Radiotherapy and the Abscopal Effect in Relapsed Refractory Multiple Myeloma.

LESSONS LEARNED: Despite the initial optimism for using immune checkpoint inhibition in the treatment of multiple myeloma, subsequent clinical studies have been disappointing. Preclinical studies have suggested that priming the immune system with various modalities in addition to checkpoint inhibition may overcome the relative T-cell exhaustion or senescence; however, in this small data set, radiotherapy with checkpoint inhibition did not appear to activate the antitumor immune response. BACKGROUND: Extramedullary disease (EMD) is recognized as an aggressive subentity of multiple myeloma (MM) with a need for novel therapeutic approaches. We therefore designed a proof-of-principle pilot study to evaluate the synergy between the combination of the anti-PD-L1, avelumab, and concomitant hypofractionated radiotherapy. METHODS: This was a single-arm phase II Simon two-stage single center study that was prematurely terminated because of the COVID-19 pandemic after enrolling four patients. Key eligibility included patients with relapsed/refractory multiple myeloma (RRMM) who had exhausted or were not candidates for standard therapy and had at least one lesion amenable to radiotherapy. Patients received avelumab until progression or intolerable toxicity and hypofractionated radiotherapy to a focal lesion in cycle 2. Radiotherapy was delayed until cycle 2 to allow the avelumab to reach a study state, given the important observation from previous studies that concomitant therapy is needed for the abscopal effect. RESULTS: At a median potential follow-up of 10.5 months, there were no objective responses, one minimal response, and two stable disease as best response. The median progression-free survival (PFS) was 5.3 months (95% confidence interval [CI]: 2.5-7.1 months), and no deaths occurred. There were no grade ≥3 and five grade 1-2 treatment-related adverse events. CONCLUSION: Avelumab in combination with radiotherapy for patients with RRMM and EMD was associated with very modest systemic clinical benefit; however, patients did benefit as usual from local radiotherapy. Furthermore, the combination was very well tolerated compared with historical RRMM treatment regimens.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.