COVID-19 Pandemic-Adapted Radiotherapy Guidelines: Are They Really Followed?

Covid-19 and radiotherapy: a systematic review after 2 years of pandemic

Introduction

Following the Covid-19 pandemic spread, changes in clinical practice were necessary to limit the pandemic diffusion. Also, oncological practice has undergone changes with radiotherapy (RT) treatments playing a key role.Although several experiences have been published, the aim of this review is to summarize the current evidence after 2 years of pandemic to provide useful conclusions for clinicians.

Methods

A Pubmed/MEDLINE and Embase systematic review was conducted. The search strategy was "Covid AND Radiotherapy" and only original articles in the English language were considered.

Results

A total of 2.733 papers were obtained using the mentioned search strategy. After the complete selection process, a total of 281 papers were considered eligible for the analysis of the results.

Discussion

RT has played a key role in Covid-19 pandemic as it has proved more resilient than surgery and chemotherapy. The impact of the accelerated use of hypofractionated RT and telemedicine will make these strategies central also in the post-pandemic period.

Change in the Use of Fractionation in Radiotherapy Used for Early Breast Cancer at the Start of the COVID-19 Pandemic: A Population-Based Cohort Study of Older Women in England and Wales

Aims

Adjuvant radiotherapy is recommended for most patients with early breast cancer (EBC) receiving breast-conserving surgery and those at moderate/high risk of recurrence treated by mastectomy. During the first wave of COVID-19 in England and Wales, there was rapid dissemination of randomised controlled trial-based evidence showing non-inferiority for five-fraction ultra-hypofractionated radiotherapy (HFRT) regimens compared with standard moderate-HFRT, with guidance recommending the use of five-fraction HFRT for eligible patients. We evaluated the uptake of this recommendation in clinical practice as part of the National Audit of Breast Cancer in Older Patients (NABCOP).

Materials and methods

Women aged ≥50 years who underwent surgery for EBC from January 2019 to July 2020 were identified from the Rapid Cancer Registration Dataset for England and from Wales Cancer Network data. Radiotherapy details were from linked national Radiotherapy Datasets. Multivariate mixed-effects logistic regression models were used to assess characteristics influential in the use of ultra-HFRT.

Results

Among 35 561 women having surgery for EBC, 71% received postoperative radiotherapy. Receipt of 26 Gy in five fractions (26Gy5F) increased from <1% in February 2020 to 70% in April 2020. Regional variation in the use of 26Gy5F during April to July 2020 was similar by age, ranging from 49 to 87% among women aged ≥70 years. Use of 26Gy5F was characterised by no known nodal involvement, no comorbidities and initial breast-conserving surgery. Of those patients receiving radiotherapy to the breast/chest wall, 85% had 26Gy5F; 23% had 26Gy5F if radiotherapy included regional nodes. Among 5139 women receiving postoperative radiotherapy from April to July 2020, nodal involvement, overall stage, type of surgery, time from diagnosis to start of radiotherapy were independently associated with fractionation choice.

Conclusions

There was a striking increase in the use of 26Gy5F dose fractionation regimens for EBC, among women aged ≥50 years, within a month of guidance published at the start of the COVID-19 pandemic in England and Wales.

Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond

In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.