Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan, China, in late 2019 and created a global pandemic that overwhelmed healthcare systems. COVID-19, as of July 3, 2021, yielded 182 million confirmed cases and 3.9 million deaths globally according to the World Health Organization. Several patients who were initially diagnosed with mild or moderate COVID-19 later deteriorated and were reclassified to severe disease type. Objective The aim is to create a predictive model for COVID-19 ventilatory support and mortality early on from baseline (at the time of diagnosis) and routinely collected data of each patient (CXR, CBC, demographics, and patient history). Methods Four common machine learning algorithms, three data balancing techniques, and feature selection are used to build and validate predictive models for COVID-19 mechanical requirement and mortality. Baseline CXR, CBC, demographic, and clinical data were retrospectively collected from April 2, 2020, till June 18, 2020, for 5739 patients with confirmed PCR COVID-19 at King Abdulaziz Medical City in Riyadh. However, of those patients, only 1508 and 1513 have met the inclusion criteria for ventilatory support and mortalilty endpoints, respectively. Results In an independent test set, ventilation requirement predictive model with top 20 features selected with reliefF algorithm from baseline radiological, laboratory, and clinical data using support vector machines and random undersampling technique attained an AUC of 0.87 and a balanced accuracy of 0.81. For mortality endpoint, the top model yielded an AUC of 0.83 and a balanced accuracy of 0.80 using all features with balanced random forest. This indicates that with only routinely collected data our models can predict the outcome with good performance. The predictive ability of combined data consistently outperformed each data set individually for intubation and mortality. For the ventilator support, chest X-ray severity annotations alone performed better than comorbidity, complete blood count, age, or gender with an AUC of 0.85 and balanced accuracy of 0.79. For mortality, comorbidity alone achieved an AUC of 0.80 and a balanced accuracy of 0.72, which is higher than models that use either chest radiograph, laboratory, or demographic features only. Conclusion The experimental results demonstrate the practicality of the proposed COVID-19 predictive tool for hospital resource planning and patients’ prioritization in the current COVID-19 pandemic crisis.

Treatment of patients with lung cancer during the current COVID-19 pandemic is challenging. Lung cancer is a heterogenous disease with a wide variety of therapeutic options. Oncologists have to determine the risks and benefits of modifying the treatment plans of patients especially in situation where the disease biology and treatment are complex. Health care visits carry a risk of transmission of SARS-CoV-2 and the similarities of COVID-19 symptoms and lung cancer manifestations represent a dominant problem. Efforts to modify treatment of lung cancer during the current pandemic have been adapted by many healthcare institutes to reduce exposure of lung cancer patients to SARS-CoV-2. We summarized the implications of COVID-19 pandemic on the management of lung cancer from the perspective of different specialties of thoracic oncology multidisciplinary team.

Background Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged in 2019 and caused a global pandemic in 2020, manifesting in the coronavirus disease 2019 (COVID-19). The majority of patients exhibit a mild form of the disease with no major complications; however, moderate to severe and fatal cases are of public health concerns. Predicting the potential prognosis of COVID-19 could assist healthcare workers in managing the case and controlling the pandemic in an effective way. Methods Here, clinical data of COVID-19 patients admitted to two large centers in Saudi Arabia between April and June 2020 were retrospectively analysed. The objectives of the study were to search for biomarkers associated with COVID-19 mortality and predictors of the overall survival (OS) of the patients. Results More than 23% of the study subjects with available data have died, enabling the prediction of mortality in our cohort. Markers that were significantly associated with mortality in our study were older age, increased D-dimer in the blood, higher counts of WBCs, higher percentage of neutrophil, and a higher chest X-ray (CXR) score. The CXR scores were also positively associated with age, D-dimer, WBC count, and percentage of neutrophil. This supports the utility of CXR scores in the absence of blood testing. Predicting mortality based on Ct values of RT-PCR was not successful, necessitating a more quantitative RT-PCR to determine virus quantity in samples. Our work has also identified age, D-dimer concentration, leukocyte parameters and CXR score to be prognostic markers of the OS of COVID-19 patients. Conclusion Overall, this retrospective study on hospitalised cohort of COVID-19 patients presents that age, haematological, and radiological data at the time of diagnosis are of value and could be used to guide better clinical management of COVID-19 patients.

Purpose: PET/CT is a standard medical imaging used in the delineation of gross tumor volume (GTV) in case of radiation therapy for lung tumors. However, PET/CT could present some limitations such as resolution and standardized uptake value threshold. Moreover, chest MRI has shown good potential in diagnosis for thoracic oncology. Therefore, we investigated the influence of chest MRI on inter-observer variability of GTV delineation. Methods and Materials: Five observers contoured the GTV on CT for 14 poorly defined lung tumors during three contouring phases based on true daily clinical routine and acquisition: CT phase, with only CT images; PET phase, with PET/CT; and MRI phase, with both PET/CT and MRI. Observers waited at least 1 week between each phases to decrease memory bias. Contours were compared using descriptive statistics of volume, coefficient of variation (COV), and Dice similarity coefficient (DSC). Results: MRI phase volumes (median 4.8 cm 3 ) were significantly smaller than PET phase volumes (median 6.4 cm 3 , p = 0.015), but not different from CT phase volumes (median 5.7 cm 3 , p = 0.30). The mean COV was improved for the MRI phase (0.38) compared to the CT (0.58, p = 0.024) and PET (0.53, p = 0.060) phases. The mean DSC of the MRI phase (0.67) was superior to those of the CT and PET phases (0.56 and 0.60, respectively; p < 0.001 for both). Conclusions: The addition of chest MRI seems to decrease inter-observer variability of GTV delineation for poorly defined lung tumors compared to PET/CT alone and should be explored in further prospective studies.

The study cohort included 12,393 patients with M0 MPM. Patients were more likely to be treated at AC than NAC if they were younger (p<.0001), female (pZ.006), non-white (p<.0001), had private insurance over Medicare (p<.0001), and had biphasic (p<.0001), epithelioid (p<.0001), or fibrous (pZ.003) histology as compared to malignant mesothelioma NOS. On univariate frailty analysis, median annual case volume was associated with mortality. Patients seen at centers with 11 cases/ year had a 28.4% decrease in risk of death compared to centers with 1-2 cases/year (p<.0001). Further, controlling for all clinicodemographic and treatment covariates, patients seen at AC continued to have a decreased risk of mortality (HRZ0.93, 95% CI: 0.88-0.97; pZ.001). 2-year and 5year OS rates were 31.1% and 12.2% for AC and were 23.5% and 8.0% for NAC, respectively. Compared to NAC, AC were more likely to treat patients with certain regimens over chemotherapy alone. Patients at AC were 1.2 times more likely to receive surgery (pZ.01), 1.8 times more likely to receive surgery and chemotherapy (p<.0001), 2.0 times more likely to receive surgery and RT (pZ.001), and 2.0 times more likely to receive surgery and chemoradiotherapy (CRT) (p<.0001). Treatment with surgery and chemotherapy, with surgery and RT, and with surgery and CRT significantly improved OS compared to chemotherapy alone (p<0.0001), RT alone (p<0.0001), surgery alone (p<0.0001), and CRT alone (p<0.05). Conclusion: AC confer decreased mortality risk for patients with MPM, and they utilize surgery and combined modality therapy (CMT) more often than NAC. These treatment regimens yield better outcomes than single modality therapy or treatment without surgery. Facility case volume was also found to be associated with improved survival. Consideration should be given to refer MPM patients to centers with high case volume that emphasize surgical expertise and CMT.

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