COVID-19 pandemic and patients with cancer: The protocol of a Clinical Oncology center in Tehran, Iran

AimTo provide recommendations for the management of patients with cancer in the COVID-19 era.BackgroundThe current global pandemic of COVID-19 has severely impacted global healthcare systems. Several groups of people are considered high-risk for SARS-CoV-2 infection, including patients with cancer. Therefore, protocols for the better management of these patients during this viral pandemic are necessary. So far, several protocols have been presented regarding the management of patients with cancer during the COVID-19 pandemic. However, none of them points to a developing country with limited logistics and facilities.MethodsIn this review, we have provided a summary of recommendations on the management of patients with cancer during the COVID-19 pandemic based on our experience in Shohada-e Tajrish Hospital, Iran.ResultsWe recommend that patients with cancer should be managed in an individualized manner during the COVID-19 pandemic.ConclusionsOur recommendation provides a guide for oncology centers of developing countries for better management of cancer

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study, 49 HNCs treated with curative intensity modulated radiotherapy (IMRT) were enrolled. Baseline CT images (i.e., CT simulation), dosimetric, and clinical features were collected. RIOM was assessed using CTCAE v.5.0. RFs were extracted from manually-contoured oral mucosa structures. Minimum-redundancy-maximum-relevance (mRMR) method was applied to select the most informative radiomics, dosimetric, and clinical features. Then, binary prediction models were constructed for predicting acute RIOM based on the top mRMR-ranked radiomics, dosimetric, and clinical features alone or in combination, using random forest classifier algorithm. The predictive performance of models was assessed using the area under the receiver operating curve (AUC), accuracy, weighted-average based sensitivity, precision, and F1-measure. Results: Among extracted features, the top 10 RFs, the top 5 dose-volume features, and the top 5 clinical features were selected using mRMR method. The model exploiting the integrated features (10-radiomics + 5-dosimetric + 5-clinical) achieved the best prediction with AUC, accuracy, sensitivity, precision, and F1-measure values of 91.7 %, 90.0 %, 83.0 % 100.0 %, and 91.0 %, respectively. The model developed using baseline CT RFs alone provided the best performance compared to dose-volume features or clinical features alone, with an AUC of 87.0 %. Conclusion: Our results suggest that the integration of baseline CT radiomic features with dosimetric and clinical features showed promising potential to improve the performance of machine learning models in early prediction of RIOM. The ultimate goal is to personalize radiotherapy for HNC patients

Cross-Cultural Adaptation and Validation of the Persian Version of the M. D. Anderson Dysphagia Inventory

Introduction Dysphagia is a common issue in patients with head and neck cancer (HNC) and is known to negatively impact their quality of life. To evaluate the impact of dysphagia on the quality of life of HNC patients, the M. D. Anderson Dysphagia Inventory (MDADI) questionnaire was developed