Combination of proton- or X-irradiation with anti-PDL1 immunotherapy in two murine oral cancers.

Combining radiation therapy with immunotherapy is a strategy to improve both treatments. The purpose of this study was to compare responses for two syngeneic head and neck cancer (HNC) tumor models in mice following X-ray or proton irradiation with or without immune checkpoint inhibition (ICI). MOC1 (immunogenic) and MOC2 (less immunogenic) tumors were inoculated in the right hind leg of each mouse (C57BL/6J, n = 398). Mice were injected with anti-PDL1 (10 mg/kg, twice weekly for 2 weeks), and tumors were treated with single-dose irradiation (5-30 Gy) with X-rays or protons. MOC2 tumors grew faster and were more radioresistant than MOC1 tumors, and all mice with MOC2 tumors developed metastases. Irradiation reduced the tumor volume in a dose-dependent manner. ICI alone reduced the tumor volume for MOC1 with 20% compared to controls, while no reduction was seen for MOC2. For MOC1, there was a clear treatment synergy when combining irradiation with ICI for radiation doses above 5 Gy and there was a tendency for X-rays being slightly more biologically effective compared to protons. For MOC2, there was a tendency of protons being more effective than X-rays, but both radiation types showed a small synergy when combined with ICI. Although the responses and magnitudes of the therapeutic effect varied, the optimal radiation dose for maximal synergy appeared to be in the order of 10-15 Gy, regardless of tumor model.

Using immunotherapy to enhance the response of a C3H mammary carcinoma to proton radiation.

BACKGROUND: The benefit of combining immunotherapy with photon irradiation has been shown pre-clinically and clinically. This current pre-clinical study was designed to investigate the anti-tumour action of combining immunotherapy with protons. MATERIALS AND METHODS: Male CDF1 mice, with a C3H mammary carcinoma inoculated on the right rear foot, were locally irradiated with single radiation doses when tumours reached 200mm3. Radiation was delivered with an 83-107MeV pencil scanning proton beam in the centre of a 3 cm spread out Bragg peak. Following irradiation (day 0), mice were injected intraperitoneal with anti-CTLA-4, anti-PD-1, or anti-PD-L1 (10 mg/kg) twice weekly for two weeks. Endpoints were tumour growth time (TGT3; time to reach 3 times treatment volume) or local tumour control (percent of mice showing tumour control at 90 days). A Student's T-test (tumour growth) or Chi-squared test (tumour control) were used for statistical analysis; significance levels of p < 0.05. RESULTS: Untreated tumours had a mean (± 1 S.E.) TGT3 of 4.6 days (± 0.4). None of the checkpoint inhibitors changed this TGT3. A linear increase in TGT3 was seen with increasing radiation doses (5-20 Gy), reaching 17.2 days (± 0.7) with 20 Gy. Anti-CTLA-4 had no effect on radiation doses up to 15 Gy, but significantly enhanced 20 Gy; the TGT3 being 23.0 days (± 1.3). Higher radiation doses (35-60 Gy) were investigated using a tumour control assay. Logit analysis of the dose response curve, resulted in a TCD50 value (radiation dose causing 50% tumour control; with 95% confidence intervals) of 48 Gy (44-53) for radiation only. This significantly decreased to 43 Gy (38-49) when mice were treated with anti-CTLA-4. Neither anti-PD-1 nor anti-PD-L1 significantly affected tumour control. CONCLUSION: Checkpoint inhibitors enhanced the response of this C3H mammary carcinoma to proton irradiation. However, this enhancement depended on the checkpoint inhibitor and radiation dose.

Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs.

BACKGROUND: Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED). MATERIALS AND METHODS: We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other - Infectious, Other - Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement. RESULTS: 1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result. CONCLUSION: At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.

A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals' PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners' examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.