Financial toxicity and mental well-being of the oral cancer survivors residing in a developing country in the era of COVID 19 pandemic - A cross-sectional study.

OBJECTIVES: The primary outcome measures evaluated the financial toxicity and mental well-being of the oral cancer survivors. METHODS: A cross-sectional study of oral cancer survivors who were disease-free for more than 6 months after treatment and visited the hospital for a routine follow-up is included in the study. Mental well-being and financial toxicity were evaluated using the Depression, Anxiety, and Stress Scale - 21 (DASS 21) and Comprehensive Score for financial Toxicity (COST- Functional Assessment of Chronic Illness Therapy) questionnaires. A literature review was done to compare the results with financial toxicity and mental health in cancer patients from the pre-pandemic era. RESULTS: A total of 79 oral cancer survivors were included in the study, predominantly males (M: F = 10:1). The age ranged from 26 to 75 years (The median age is 49). The full-time employment dropped from 83.5% in the pre-treatment period to 21.5% post-treatment. Depression was observed in 58.2% and anxiety in 72.2%. Unemployed survivors were observed to have more depression (OR = 1.3, 95% confidence interval (CI) = 0.3-5.4, p = 0.6), anxiety (OR = 3.5, 95% CI = 0.3-21.2, p = 0.1) and stress (OR = 1.6, 95% CI = 0.3-6.6, p = 0.5) than rest of the cohort. On univariate analysis, unemployed survivors (M = 11.8 ± 3.8, p = 0.01) had significantly poorer financial toxicity scores. Survivors with depression (M = 16.4 ± 7.1, p = 0.06) and stress (M = 14.4 ± 6.8, p = 0.002) had poor financial toxicity scores. On multifactorial analysis of variance, current employment (p = 0.04) and treatment modality (p = 0.05) were significant factors impacting the financial toxicity. CONCLUSION: There is a trend towards increased incidence of depression, anxiety, and stress among oral cancer survivors compared to the literature from the pre-COVID era. There is significant financial toxicity among either unemployed or part-time workers. This calls for urgent public/government intervention to prevent the long-term impact of financial toxicity on survival and quality of life.

A novel abnormality annotation database for COVID-19 affected frontal lung X-rays.

Consistent clinical observations of characteristic findings of COVID-19 pneumonia on chest X-rays have attracted the research community to strive to provide a fast and reliable method for screening suspected patients. Several machine learning algorithms have been proposed to find the abnormalities in the lungs using chest X-rays specific to COVID-19 pneumonia and distinguish them from other etiologies of pneumonia. However, despite the enormous magnitude of the pandemic, there are very few instances of public databases of COVID-19 pneumonia, and to the best of our knowledge, there is no database with annotation of abnormalities on the chest X-rays of COVID-19 affected patients. Annotated databases of X-rays can be of significant value in the design and development of algorithms for disease prediction. Further, explainability analysis for the performance of existing or new deep learning algorithms will be enhanced significantly with access to ground-truth abnormality annotations. The proposed COVID Abnormality Annotation for X-Rays (CAAXR) database is built upon the BIMCV-COVID19+ database which is a large-scale dataset containing COVID-19+ chest X-rays. The primary contribution of this study is the annotation of the abnormalities in over 1700 frontal chest X-rays. Further, we define protocols for semantic segmentation as well as classification for robust evaluation of algorithms. We provide benchmark results on the defined protocols using popular deep learning models such as DenseNet, ResNet, MobileNet, and VGG for classification, and UNet, SegNet, and Mask-RCNN for semantic segmentation. The classwise accuracy, sensitivity, and AUC-ROC scores are reported for the classification models, and the IoU and DICE scores are reported for the segmentation models.

Surviving COVID-19 and multiple complications post total laryngectomy.

The clinical manifestation of novel COVID-19 is variable. Pre-existing carcinoma and other comorbidities have been associated with increased COVID-19-related morbidity and mortality. Surgical intervention for advanced laryngeal carcinoma in old age during the COVID-19 pandemic may pose multiple challenges to the patient and the treatment team. We report a case of a 67-year-old elderly man who developed SARS-CoV-2 infection on the 21st day following total laryngectomy and neck dissection. The postoperative period was complicated by sequential development of pulmonary embolism, neck infection, pharyngeal leak and COVID-19 which were managed successfully. No close contacts were positive on the reverse transcription-PCR test for SARS-CoV-2. The patient is in follow-up for the past 7 months without any recurrence or COVID-19-related morbidity. The successful recovery and no cross-infection may be attributed to early diagnosis, immediate intervention and properly implemented institutional infection control policy.