CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice".

INTRODUCTION: Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. MATERIALS AND METHODS: In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. RESULTS: Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. CONCLUSIONS: The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

Clinical characteristics of lung cancer patients with COVID-19: Retrospective case series.

INTRODUCTION: One of the patient groups adversely affected during the COVID19 pandemic is those suffering with cancer. The aim of this study was to evaluate the clinical characteristics and outcomes of lung cancer (LC) patients with COVID-19. MATERIALS AND METHODS: Three thousand seven-hundred and fifty hospitalized patients with a presumptive diagnosis of COVID-19 in a tertiary referral hospital between March 2020-February 2021 were retrospectively evaluated. Among them, 36 hospitalized COVID-19 patients with a history of primary LC were included in the study. Univariate and multivariate analyses were carried out to assess the risk factors associated with severe disease. RESULT: Of the 36 patients included in the study, 28 (77%) were males and 8 (23%) were females. Median age was 67 years (min-max: 53-81 years). Six patients (17%) had a diagnosis of small cell LC, whereas 30 patients (83%) had a diagnosis of non-small cell LC. The most common symptoms were fever (n= 28, 77%), coughing and myalgia (n= 21, 58%) and dyspnea (n= 18, 50%). The most common radiological finding was ground glass opacity (GGO) (n= 30), of which 13 was bilateral and 17 was unilateral in distribution. Nearly 30% (n= 11) of LC patients with COVID-19 developed severe disease, 5% (n= 2) of the 36 patients were admitted to intensive care unit and all of these patients eventually expired. LC patients with COVID-19 and patchy consolidation on computed tomography of thorax (Th CT) on admission had a higher risk of developing severe disease in univariate (HR 2.41, 95%CI: 1.4- 4.4, p= 0.04) and multivariate Cox regression analysis (HR 0.48, 95%CI: 0.24-0.97, p= 0.03). CONCLUSIONS: Clinical characteristics, laboratory and radiographic findings were similar in LC patients with COVID-19 when compared with the general population, LC patients have a higher mortality rate than the general population, with a 5% mortality rate in our series. Our findings suggest that LC may be a risk factor associated with the prognosis of COVID-19 patients.

Oncological results in primary and secondary malignant chest wall tumors.

Background: This study aims to evaluate the oncological results of primary and secondary chest wall tumors treated with curative resections and to investigate possible prognostic factors. Methods: Between January 2010 and December 2021, a total of 77 patients (53 males, 24 females; median age: 59 years; range, 3 to 87 years) who underwent curative resection for malignant chest wall tumors were retrospectively analyzed. Each tumor was staged according to its histological type. Age, sex, tumor diameter, tumor type (primary/secondary), histological tumor type, grade, stage, complete resection, rib resection, reconstruction, neoadjuvant and adjuvant therapy, recurrence, and survival data were recorded. Results: Of the chest wall tumors, 33 (42.9%) were primary and 44 (57.1%) were secondary (local invasion, metastasis). Nine (11.7%) patients had positive surgical margins. Chest wall resection was most commonly performed due to lung cancer invasion (46.8%), followed by Ewing sarcoma (13%). Recurrence was observed in 34 (44.2%) patients. The five-year recurrence-free survival rate was 42.7% and the five-year overall survival rate was 58.6%. There was no significant difference between the primary and secondary tumors in terms of recurrence-free and overall survival (p=0.663 and p=0.313, respectively). In the multivariate analysis, tumor grade and rib resection were found to be independent prognostic factors for both recurrence-free survival (p=0.005 and p<0.001, respectively) and overall survival (p=0.048 and p=0.007, respectively). Conclusion: Successful oncological results can be achieved in wellselected patients with primary and secondary chest wall tumors. The grade of the tumor should be taken into account while determining the neoadjuvant or adjuvant treatment approach and surgical margin width. Rib resection should not be avoided when necessary.