Concerns for Active Breathing Control (ABC) With Breast Cancer in the Era of COVID-19: Maximizing Infection Control While Minimizing Heart Dose

Improving motion management in radiation therapy: findings from a workshop and survey in Australia and New Zealand

Motion management has become an integral part of radiation therapy. Multiple approaches to motion management have been reported in the literature. To allow the sharing of experiences on current practice and emerging technology, the University of Sydney and the New South Wales/Australian Capital Territory branch of the Australasian College of Physical Scientists and Engineers in Medicine (ACPSEM) held a two-day motion management workshop. To inform the workshop program, participants were invited to complete a survey prior to the workshop on current use of motion management techniques and their opinion on the effectiveness of each approach. A post-workshop survey was also conducted, designed to capture changes in opinion as a result of workshop participation. The online workshop was the most well attended ever hosted by the ACPSEM, with over 300 participants and a response to the pre-workshop survey was received from at least 60% of the radiation therapy centres in Australia and New Zealand. Motion management is extensively used in the region with use of deep inspiration breath-hold (DIBH) reported by 98% of centres for left-sided breast treatments and 91% for at least some right-sided breast treatments. Surface guided radiation therapy (SGRT) was the most popular session at the workshop and survey results showed that the use of SGRT is likely to increase. The workshop provided an excellent opportunity for the exchange of knowledge and experience, with most survey respondents indicating that their participation would lead to improvements in the quality of delivery of treatments at their centres.

Shallow kinetics induced by a metronome (SKIM): A novel contactless respiratory motion management

OBJECTIVES

As an alternative to conventional compression amidst the COVID-19 pandemic, we developed a contactless motion management strategy. By increasing the patient's breathing rate to induce shallow breathing with the aid of a metronome, our hypothesis is that the motion magnitude of the target may be minimized without physical contact or compression.

METHODS

Fourteen lung stereotactic body radiation therapy (SBRT) patients treated under fast shallow-breathing (FSB) were selected for inclusion in this retrospective study. Our proposed method is called shallow kinetics induced by a metronome (SKIM). We induce FSB by setting the beats-per-minute (BPM) high (typically in the range of 50-60). This corresponded to a patient breathing rate of 25-30 (breathing) cycles per minute. The magnitude of target motion in 3D under SKIM was evaluated using 4DCT datasets. Comparison with free breathing (FB) 4DCT was also made for a subset for which FB data available.

RESULTS

The overall effectiveness of SKIM was evaluated with 18 targets (14 patients). Direct comparison with FB was performed with 12 targets (10 patients). The vector norm mean ± SD value of motion magnitude under SKIM for 18 targets was 8.2 ± 4.1 mm. The mean ± SD metronome BPM was 54.9 ± 4.0 in this group. The vector norm means ± SD values of target motion for FB and SKIM in the 12 target sub-group were 14.6 ± 8.5 mm and 9.3 ± 3.7 mm, respectively. The mean ± SD metronome BPM for this sub-group was 56.3 ± 2.5.

CONCLUSION

Compared with FB, SKIM can significantly reduce respiratory motion magnitude of thoracic targets. The difference in maximum motion reduction in the overall vector norm, S-I, and A-P directions was significant (p = 0.033, 0.042, 0.011). Our proposed method can be an excellent practical alternative to conventional compression due to its flexibility and ease of implementation.

Dosimetric Comparisons of Simulation Techniques for Left-Sided Breast Cancer in the COVID-19 Era: Techniques to Reduce Viral Transmission and Respect the Therapeutic Ratio

Background

The COVID-19 pandemic challenges our ability to safely treat breast cancer patients and requires revisiting current techniques to evaluate optimal strategies. Potential long-term sequelae of breast radiation have been addressed by deep inspiration breath-hold (DIBH), prone positioning, and four-dimensional computed tomography (4DCT) average intensity projection (AveIP)-based planning techniques. Dosimetric comparisons to determine the optimal technique to minimize the normal tissue dose for left-sided breast cancers have not been performed.

Methods

Ten patients with left-sided, early-stage breast cancer undergoing whole breast radiation were simulated in the prone position, supine with DIBH, and with a free-breathing 4DCT scan. The target and organs at risk (OAR) contours were delineated in all scans. Target volume coverage and OAR doses were assessed. One-way analysis of variance (ANOVA) and Kruskal-Wallis one-way ANOVA were used to detect differences in dosimetric parameters among the different treatment plans. Significance was set as p < 0.05.

Results

We demonstrate differences in heart and lung dose by the simulation technique. The mean heart doses in the prone, DIBH, and AveIP plans were 129 cGy, 154 cGy, and 262 cGy, respectively (p=0.02). The lung V20 in the prone, DIBH, and AveIP groups was 0.5%, 10.3% and 9.5%, respectively (p <0.001). Regardless of technique, lumpectomy planning target volume (PTV) coverage did not differ between the three plans with 95% of the lumpectomy PTV volume covered by 100.4% in prone plans, 98.5% in AveIP plans, and 99.3% in DIBH plans (p=0.7).

Conclusions

Prone positioning provides dosimetric advantages as compared to DIBH. When infection risks are considered as in the current coronavirus disease 2019 (COVID-19) pandemic, prone plans have advantages in reducing the risk of disease transmission. In instances where prone positioning is not feasible, obtaining an AveIP simulation may be useful in more accurately assessing heart and lung toxicity and informing a risk/benefit discussion of DIBH vs free breath-hold techniques.

Deep learning-based survival analysis for brain metastasis patients with the national cancer database

PURPOSE

Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional survival analysis techniques such as Cox proportional hazards (CPH) and recursive partitioning analysis (RPA). Previous studies have shown that by evaluating CPH risk with a nonlinear deep neural network, DeepSurv, patient survival can be modeled more accurately. In this work, we apply DeepSurv to a test case: breast cancer patients with brain metastases who have received stereotactic radiosurgery.

METHODS

Survival times, censorship status, and 27 covariates including age, staging information, and hormone receptor status were provided for 1673 patients by the NCDB. Monte Carlo cross-validation with 50 samples of 1400 patients was used to train and validate the DeepSurv, CPH, and RPA models independently. DeepSurv was implemented with L2 regularization, batch normalization, dropout, Nesterov momentum, and learning rate decay. RPA was implemented as a random survival forest (RSF). Concordance indices of test sets of 140 patients were used for each sample to assess the generalizable predictive capacity of each model.

RESULTS

Following hyperparameter tuning, DeepSurv was trained at 32 min per sample on a 1.33 GHz quad-core CPU. Test set concordance indices of 0.7488 ± 0.0049, 0.6251 ± 0.0047, and 0.7368 ± 0.0047, were found for DeepSurv, CPH, and RSF, respectively. A Tukey HSD test demonstrates a statistically significant difference between the mean concordance indices of the three models.

CONCLUSION

Our results suggest that deep learning-based survival prediction can outperform traditional models, specifically in a case where an accurate prognosis is highly clinically relevant. We recommend that where appropriate data are available, deep learning-based prognostic indicators should be used to supplement classical statistics.

Mitigating disruptions, and scalability of radiation oncology physics work during the COVID-19 pandemic

PURPOSE

The COVID-19 pandemic has led to disorder in work and livelihood of a majority of the modern world. In this work, we review its major impacts on procedures and workflow of clinical physics tasks, and suggest alternate pathways to avoid major disruption or discontinuity of physics tasks in the context of small, medium, and large radiation oncology clinics. We also evaluate scalability of medical physics under the stress of "social distancing".

METHODS

Three models of facilities characterized by the number of clinical physicists, daily patient throughput, and equipment were identified for this purpose. For identical objectives of continuity of clinical operations, with constraints such as social distancing and unavailability of staff due to system strain, however with the possibility of remote operations, the performance of these models was investigated. General clinical tasks requiring on-site personnel presence or otherwise were evaluated to determine the scalability of the three models at this point in the course of disease spread within their surroundings.

RESULTS

The clinical physics tasks within three models could be divided into two categories. The former, which requires individual presence, include safety-sensitive radiation delivery, high dose per fraction treatments, brachytherapy procedures, fulfilling state and nuclear regulatory commission's requirements, etc. The latter, which can be handled through remote means, include dose planning, physics plan review and supervision of quality assurance, general troubleshooting, etc. CONCLUSION: At the current level of disease in the United States, all three models have sustained major system stress in continuing reduced operation. However, the small clinic model may not perform if either the current level of infections is maintained for long or staff becomes unavailable due to health issues. With abundance, and diversity of innovative resources, medium and large clinic models can sustain further for physics-related radiotherapy services.

Management of primary hepatic malignancies during the COVID-19 pandemic: recommendations for risk mitigation from a multidisciplinary perspective

Around the world, recommendations for cancer treatment are being adapted in real time in response to the pandemic of COVID-19. We, as a multidisciplinary team, reviewed the standard management options, according to the Barcelona Clinic Liver Cancer classification system, for hepatocellular carcinoma. We propose treatment recommendations related to COVID-19 for the different stages of hepatocellular carcinoma (ie, 0, A, B, and C), specifically in relation to surgery, locoregional therapies, and systemic therapy. We suggest potential strategies to modify risk during the pandemic and aid multidisciplinary treatment decision making. We also review the multidisciplinary management of intrahepatic cholangiocarcinoma as a potentially curable and incurable diagnosis in the setting of COVID-19.

Quantifying the Impact of COVID-19 on Cancer Patients: A Technical Report of Patient Experience During the COVID-19 Pandemic at a High-volume Radiation Oncology Proton Center in New York City

The COVID-19 pandemic has rapidly spread across the world and now affects more people within the United States than any other country. New York City has emerged as the epicenter of the outbreak in the United States. Both locally and across the country, there is great concern in our ability to deliver appropriate medical care during this time. Radiation therapy is another essential clinical service that cannot afford to suffer prolonged delays without compromising patient outcomes. Early action and guidance are therefore critical to minimize transmission events and ensure safe and timely delivery of radiation therapy. The New York Proton Center (NYPC) is a high-volume free-standing multi-institutional proton center located in Manhattan. The purpose of this report is to describe the institutional patient experience and quantify the impact of treatment delays and interruptions over the first month of the COVID-19 outbreak. We also quantify the incidence of COVID-19 positive patients on census and provide guidance on proactive institutional policies to mitigate patient risk.