High-resolution anoscopy predictive modeling of anal canal cancer response after definitive chemoradiotherapy in COVID19 era.

PURPOSE: To develop a predictive index model, integrating both clinical and high-resolution anoscopy (HRA) features to further personalize the decision making process in anal canal carcinoma in COVID19 era. METHODS AND MATERIALS: We assess HRA parameters after definitive chemoradiotherapy in patients with anal canal malignant lesions. RESULTS: HRA features could be important to assess the effect of CRT and a risk stratification system should be introduced in clinical practice to better allocate therapeutic interventions. CONCLUSION: To our knowledge this is the first proposal for HRA findings in anal canal cancer after definitive CRT. We believe that a risk score can be useful to estimate the risk of treatment failure (in term of persistence disease and/or recurrence) and its clinical relevance should not to be underestimated.

An Expectation-Maximization Algorithm for Including Oncological COVID-19 Deaths in Survival Analysis.

We address the problem of how COVID-19 deaths observed in an oncology clinical trial can be consistently taken into account in typical survival estimates. We refer to oncological patients since there is empirical evidence of strong correlation between COVID-19 and cancer deaths, which implies that COVID-19 deaths cannot be treated simply as non-informative censoring, a property usually required by the classical survival estimators. We consider the problem in the framework of the widely used Kaplan-Meier (KM) estimator. Through a counterfactual approach, an algorithmic method is developed allowing to include COVID-19 deaths in the observed data by mean-imputation. The procedure can be seen in the class of the Expectation-Maximization (EM) algorithms and will be referred to as Covid-Death Mean-Imputation (CoDMI) algorithm. We discuss the CoDMI underlying assumptions and the convergence issue. The algorithm provides a completed lifetime data set, where each Covid-death time is replaced by a point estimate of the corresponding virtual lifetime. This complete data set is naturally equipped with the corresponding KM survival function estimate and all available statistical tools can be applied to these data. However, mean-imputation requires an increased variance of the estimates. We then propose a natural extension of the classical Greenwood's formula, thus obtaining expanded confidence intervals for the survival function estimate. To illustrate how the algorithm works, CoDMI is applied to real medical data extended by the addition of artificial Covid-death observations. The results are compared with the estimates provided by the two naïve approaches which count COVID-19 deaths as censoring or as deaths by the disease under study. In order to evaluate the predictive performances of CoDMI an extensive simulation study is carried out. The results indicate that in the simulated scenarios CoDMI is roughly unbiased and outperforms the estimates obtained by the naïve approaches. A user-friendly version of CoDMI programmed in R is freely available.

Survival Analysis Using the Covid-death Mean-imputation (CoDMI) Algorithm: A First Clinical Application in Radiation Oncology.

BACKGROUND/AIM: To report long-term survival results after trimodal approach for locally advanced rectal cancer (LARC) in the Covid-19 era. We herein illustrate a clinical application of Covid-death mean-imputation (CoDMI) algorithm in LARC patients with Covid-19 infection. PATIENTS AND METHODS: We analyzed 94 patients treated for primary LARC. Overall survival was calculated in months from diagnosis to first event (last follow-up/death). Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events, the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using the CoDMI algorithm. Patients who died of Covid-19 (DoC) are mean-imputed by the Kaplan-Meier estimator. Under this approach, the observed lifetime of each DoC patient is considered as an "incomplete data" and is extended by an additional expected lifetime computed using the classical Kaplan-Meier model. RESULTS: Sixteen patients were dead of disease (DoD), 1 patient was DoC and 77 cases were censored (Cen). The DoC patient died of Covid-19 52 months after diagnosis. The CoDMI algorithm computed the expected future lifetime provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, the CoDMI algorithm estimated that this patient would have died after 79.5 months of follow-up. CONCLUSION: The CoDMI algorithm leads to "unbiased" probability of overall survival in LARC patients with Covid-19 infection, compared to that provided by a naïve application of Kaplan-Meier approach. This allows for a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.

Safety and efficacy of combining afatinib and whole-brain radiation therapy in treating brain metastases from EGFR-mutated NSCLC: a case report and literature review.

Combining EGFR-tyrosine kinase inhibitors (TKIs) to whole brain radiation therapy (WBRT) has been shown to be more effective than EGFR-TKIs or WBRT alone in treating brain metastases (BMs) from EGFR-mutated Non Small-Cell Lung Cancer (NSCLC). However, despite the combination results well tolerated, EGFR-TKIs are often discontinued before WBRT, to reduce the risk of possible side effects, potentially resulting in reduced treatment efficacy and possible progression of intra- and extra-cranial disease. Afatinib, an irreversible inhibitor of EGFR-TK, has been shown to radiosensitize NSCLC in pre-clinical models and, compared to the other EGFR-TKIs, more efficiently penetrates the blood-brain barrier. However, nowadays, only two case reports describe the therapeutic efficiency and safety of combining afatinib with WBRT. Herein, we report on a 58-year-old woman patient with symptomatic BMs from NSLCL, treated with afatinib and concomitant WBRT, 30 Gy in 10 fractions. Treatment induced a remarkable and persistent radiological regression of BMs and the disappearance of neurological symptoms. However, the patient experienced severe skin toxicity of G3, corresponding to the irradiation area. Toxicity was successfully treated pharmacologically, and the patient did not experience any BMs-related symptoms for the next 10 months. She died of COVID-19-related respiratory failure. The association of afatinib with WBRT appears to be a successful strategy in the control of BMs from EGFR-mutated NSCLC. However, it should be considered that the combination could be responsible for serious dermatological toxicity.

A snapshot on radiotherapy for head and neck cancer patients during the COVID-19 pandemic: a survey of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) head and neck working group.

OBJECTIVES: The objective of the paper was to assess real-life experience in the management of head and neck cancer (HNC) patients during the COVID-19 pandemic in radiotherapy departments and to evaluate the variability in terms of adherence to American Society of Radiation Oncology (ASTRO) and European Society for Radiotherapy and Oncology (ESTRO) recommendations. MATERIALS AND METHODS: In May 2020, an anonymous 30-question online survey, comparing acute phase of outbreak and pre-COVID-19 period, was conducted. Two sections exploited changes in general management of HNC patients and different HNC primary tumors, addressing specific statements from ASTRO ESTRO consensus statement as well. RESULTS: Eighty-eight questionnaires were included in the demographic/clinical workflow analysis, and 64 were analyzed for treatment management. Forty-eight percent of radiotherapy departments became part of oncologic hubs. First consultations reduced, and patients were addressed to other centers in 33.8 and 18.3% of cases, respectively. Telematic consultations were used in 50% of follow-up visits and 73.9% of multidisciplinary tumor board discussions. There were no practical changes in the management of patients affected by different primitive HNCs. Hypofractionation was not favored over conventional schedules. CONCLUSIONS: Compared to pre-COVID era, the clinical workflow was highly re-organized, whereas there were no consistent changes in RT indications and schedules.

Head and neck radiotherapy amid the COVID-19 pandemic: practice recommendations of the Italian Association of Radiotherapy and Clinical Oncology (AIRO).

Management of patients with head and neck cancers (HNCs) is challenging for the Radiation Oncologist, especially in the COVID-19 era. The Italian Society of Radiotherapy and Clinical Oncology (AIRO) identified the need of practice recommendations on logistic issues, treatment delivery and healthcare personnel's protection in a time of limited resources. A panel of 15 national experts on HNCs completed a modified Delphi process. A five-point Likert scale was used; the chosen cut-offs for strong agreement and agreement were 75% and 66%, respectively. Items were organized into two sections: (1) general recommendations (10 items) and (2) special recommendations (45 items), detailing a set of procedures to be applied to all specific phases of the Radiation Oncology workflow. The distribution of facilities across the country was as follows: 47% Northern, 33% Central and 20% Southern regions. There was agreement or strong agreement across the majority (93%) of proposed items including treatment strategies, use of personal protection devices, set-up modifications and follow-up re-scheduling. Guaranteeing treatment delivery for HNC patients is well-recognized in Radiation Oncology. Our recommendations provide a flexible tool for management both in the pandemic and post-pandemic phase of the COVID-19 outbreak.

Coronavirus Disease (COVID-19): A Machine Learning Bibliometric Analysis.

BACKGROUND/AIM: To evaluate the research trends in coronavirus disease (COVID-19). MATERIALS AND METHODS: A bibliometric analysis was performed using a machine learning bibliometric methodology. Information regarding publication outputs, countries, institutions, journals, keywords, funding and citation counts was retrieved from Scopus database. RESULTS: A total of 1883 eligible papers were returned. An exponential increase in the COVID-19 publications occurred in the last months. As expected, China produced the majority of articles, followed by the United States of America, the United Kingdom and Italy. There is greater collaboration between highly contributing authors and institutions. The "BMJ" published the highest number of papers (n=129) and "The Lancet" had the most citations (n=1439). The most ubiquitous topic was COVID-19 clinical features. CONCLUSION: This bibliometric analysis presents the most influential references related to COVID-19 during this time and could be useful to improve understanding and management of COVID-19.