Trismus is caused by injury to the masticatory muscles resulting from cancer or its treatment. Contouring these muscles to reduce dose and radiation related trismus can be problematic due to interobserver variability. This study aimed to evaluate the reduction in interobserver variability achievable with a new contouring atlas. Materials/methods: The atlas included: medial and lateral pterygoids (MP, LP), masseter (M) and temporalis (T) muscles, and the temporo-mandibular joint (TMJ). Seven clinicians delineated five paired structures on CT scans from 5 patients without the atlas. After 5 weeks, contouring was repeated using the atlas. Using contours generated by the clinicians on the same 5 CT scans as reference, dice similarity coefficient (DSC), mean distance-to-agreement (DTA) and centre of mass (COM) difference were compared with and without the atlas. Comparison was also performed split by training grade. Mean and standard deviation (SD) values were measured. Results: The atlas reduced interobserver variability for all structures. Mean DTA significantly improved for MP (p = 0.01), M (p < 0.01), T (p < 0.01) and TMJ (p < 0.01). Mean DTA improved using the atlas for the trainees across all muscles, with the largest reduction in variability observed for the T (4.3 ± 7.1 v 1.2 ± 0.4 mm, p = 0.06) and TMJ (2.1 ± 0.7 v 0.8 ± 0.3 mm, p < 0.01). Distance between the COM and interobserver variability reduced in all directions for MP and T. Conclusion: A new atlas for contouring masticatory muscles during radiotherapy planning for head and neck cancer reduces interobserver variability and could be used as an educational tool.

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Higher mean radiation doses to the ipsilateral block, LP and M were significantly associated with deterioration in trismus. Limiting dose to these structures to ≤40 Gy for tumors not invading the masticatory muscles may improve treatment-related sequelae. The ipsilateral block, LP and M should be studied further as possible alternative avoidance structures in radiotherapy treatment planning.

The treatment of metastatic castrate-resistant prostate cancer (mCRPC) has grown over the past decade. The majority of patients develop bone metastases, which pose a significant burden on morbidity and mortality, especially skeletal-related events. Whilst demonstrating a favourable safety profile and improving symptoms, radiopharmaceuticals have until recently failed to show a survival benefit. However, since the large phase III randomized ALSYMPCA trial, the calcium mimetic properties of radium-223 (Ra223) have improved patients' quality of life and improved survival whilst keeping toxicities to a minimum. This review article summarizes the clinical data including our real life experience on the usage of the alpha emitter Ra223 in mCRPC, paying particular attention to how clinicians should best monitor response.

PURPOSE Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET). METHODS Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort. RESULTS The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation. CONCLUSION CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.