Using Statistical Measures and Density Maps Generated From Chest Computed Tomography Scans to Identify and Monitor COVID-19 Cases in Radiation Oncology Rapidly.

Objectives This study aimed to evaluate quantitative and qualitative screening measures for anomalous computed tomography (CT) scans in cancer patients with potential coronavirus disease 2019 (COVID-19) as an automated detection tool in a radiation oncology treatment setting. Methods We identified a non-COVID-19 cohort and patients with suspected COVID-19 with chest CT scans from February 1, 2020 to June 30, 2020. Lungs were segmented, and a mean normal Hounsfield Unit (HU) histogram was generated for the non-COVID-19 CT scans; these were used to define thresholds for designating the COVID-19-suspected histograms as normal or abnormal. Statistical measures were computed and compared to the threshold levels, and density maps were generated to examine the difference between lungs with and without COVID-19 qualitatively. Results The non-COVID-19 cohort consisted of 70 patients with 70 CT scans, and the cohort of suspected COVID-19 patients consisted of 59 patients with 80 CT scans. Sixty-two patients were positive for COVID-19. The mean HUs and skewness of the intensity histogram discriminated between COVID-19 positive and negative cases, with an area under the curve of 0.948 for positive and 0.944 for negative cases. Skewness correctly identified 57 of 62 positive cases, whereas mean HUs correctly identified 17 of 18 negative cases. Density maps allowed for visualization of the temporal evolution of COVID-19 disease. Conclusions The statistical measures and density maps evaluated here could be employed in an automated screening algorithm for COVID-19 infection. The accuracy is high enough for a simple and rapid screening tool for early identification of suspected infection in patients treated with chemotherapy and radiation therapy already receiving CT scans as part of clinical care. This screening tool could also identify other infections that present critical risks for patients undergoing chemotherapy and radiation therapy, such as pneumonitis.

COVID-19 Incidentally Detected on PET/CT During Work-up for Locally Advanced Head and Neck Cancer.

AIM: To describe the incidental detection of COVID-19 disease on positron-emission tomography/computed tomography (PET/CT) in a patient with cancer despite initial negative swab by polymerase chain reaction (PCR). CASE REPORT: Clinical and radiographic data were obtained from the electronic medical record. Nasopharyngeal swabs were obtained and evaluated for COVID-19 by the Food and Drug Administration-approved reverse transcription-PCR assays. On radiographic examination, PET/CT was consistent with COVID-19-related pneumonia not seen on prior imaging. Initial nasopharyngeal swab 2 days after PET/CT imaging was negative for COVID-19. Subsequent repeat swab 10 days later was positive for COVID-19, while the patient was febrile on screening assessment. The patient remained COVID-19-positive until 1 month after abnormal PET/CT imaging. CONCLUSION: PET/CT can be sensitive for early COVID-19 detection, even in the setting of a negative confirmatory PCR test. This highlights the importance of continued patient surveillance and use of appropriate personal protective equipment to minimize COVID-19 transmission.

Extent of Prior Lung Irradiation and Mortality in COVID-19 Patients With a Cancer History.

PURPOSE: There have been nearly 200,000 deaths worldwide so far from coronavirus disease 2019 (COVID-19), which is caused by a coronavirus called SARS-CoV-2. Cancer history appears to be a poor prognostic factor for COVID-19 patients, although the reasons for this are unclear. In this report, we assess whether extent of prior lung irradiation is a risk factor for death as a result of COVID-19 infection. METHODS AND MATERIALS: Patients who tested positive for COVID-19 between March 14 and April 15, 2020, at our institution and who previously received radiation therapy for cancer in our department were included in this analysis. Patient characteristics and metrics describing the extent of lung irradiation were tabulated. Cox regression models were used to identify predictors of death after COVID-19 diagnosis. A logistic model was used to characterize the association between mean lung radiation therapy dose and 14-day mortality risk after COVID-19 diagnosis. RESULTS: For the study, 107 patients met the inclusion criteria. With a median follow-up of 7 days from COVID-19 diagnosis for surviving patients, 24 deaths have been observed. The actuarial survival rate 14 days after COVID-19 testing is 66%. Increasing mean lung dose (hazard ratio [HR] per Gy = 1.1, P = .002), lung cancer diagnosis (HR = 3.0, P = .034), and receiving radiation therapy between 1 month and 1 year before COVID-19 testing (HR = 3.4, P = .013) were associated with increased risk of death. Our survival model demonstrates a near linear relationship between mortality risk after COVID-19 diagnosis and mean lung radiation therapy dose. CONCLUSIONS: COVID-19 patients with a history of radiation therapy for cancer have a poor prognosis, and mortality risk appears to be associated with extent of lung irradiation. Validation of these findings will be critical as the COVID-19 pandemic continues.