Artificial Intelligence in Radiation Therapy.

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Development and validation of prognostic models for anal cancer outcomes using distributed learning: protocol for the international multi-centre atomCAT2 study.

BACKGROUND: Anal cancer is a rare cancer with rising incidence. Despite the relatively good outcomes conferred by state-of-the-art chemoradiotherapy, further improving disease control and reducing toxicity has proven challenging. Developing and validating prognostic models using routinely collected data may provide new insights for treatment development and selection. However, due to the rarity of the cancer, it can be difficult to obtain sufficient data, especially from single centres, to develop and validate robust models. Moreover, multi-centre model development is hampered by ethical barriers and data protection regulations that often limit accessibility to patient data. Distributed (or federated) learning allows models to be developed using data from multiple centres without any individual-level patient data leaving the originating centre, therefore preserving patient data privacy. This work builds on the proof-of-concept three-centre atomCAT1 study and describes the protocol for the multi-centre atomCAT2 study, which aims to develop and validate robust prognostic models for three clinically important outcomes in anal cancer following chemoradiotherapy. METHODS: This is a retrospective multi-centre cohort study, investigating overall survival, locoregional control and freedom from distant metastasis after primary chemoradiotherapy for anal squamous cell carcinoma. Patient data will be extracted and organised at each participating radiotherapy centre (n = 18). Candidate prognostic factors have been identified through literature review and expert opinion. Summary statistics will be calculated and exchanged between centres prior to modelling. The primary analysis will involve developing and validating Cox proportional hazards models across centres for each outcome through distributed learning. Outcomes at specific timepoints of interest and factor effect estimates will be reported, allowing for outcome prediction for future patients. DISCUSSION: The atomCAT2 study will analyse one of the largest available cross-institutional cohorts of patients with anal cancer treated with chemoradiotherapy. The analysis aims to provide information on current international clinical practice outcomes and may aid the personalisation and design of future anal cancer clinical trials through contributing to a better understanding of patient risk stratification.

Repeatability of hypoxia dose painting by numbers based on EF5-PET in head and neck cancer.

BACKGROUND: Hypoxia dose painting is a radiotherapy technique to increase the dose to hypoxic regions of the tumour. Still, the clinical effect relies on the reproducibility of the hypoxic region shown in the medical image. 18F-EF5 is a hypoxia tracer for positron emission tomography (PET), and this study investigated the repeatability of 18F-EF5-based dose painting by numbers (DPBN) in head and neck cancer (HNC). MATERIALS AND METHODS: Eight HNC patients undergoing two 18F-EF5-PET/CT sessions (A and B) before radiotherapy were included. A linear conversion of PET signal intensity to radiotherapy dose prescription was employed and DPBN treatment plans were created using the image basis acquired at each PET/CT session. Also, plan A was recalculated on the image basis for session B. Voxel-by-voxel Pearson's correlation and quality factor were calculated to assess the DPBN plan quality and repeatability. RESULTS: The mean (SD) correlation coefficient between DPBN prescription and plan was 0.92 (0.02) and 0.93 (0.02) for sessions A and B, respectively, with corresponding quality factors of 0.02 (0.002) and 0.02 (0.003), respectively. The mean correlation between dose prescriptions at day A and B was 0.72 (0.13), and 0.77 (0.12) for the corresponding plans. A mean correlation of 0.80 (0.08) was found between plan A, recalculated on image basis B, and plan B. CONCLUSION: Hypoxia DPBN planning based on 18F-EF5-PET/CT showed high repeatability. This illustrates that 18F-EF5-PET provides a robust target for dose painting.

Predicting outcomes in anal cancer patients using multi-centre data and distributed learning - A proof-of-concept study.

BACKGROUND AND PURPOSE: Predicting outcomes is challenging in rare cancers. Single-institutional datasets are often small and multi-institutional data sharing is complex. Distributed learning allows machine learning models to use data from multiple institutions without exchanging individual patient-level data. We demonstrate this technique in a proof-of-concept study of anal cancer patients treated with chemoradiotherapy across multiple European countries. MATERIALS AND METHODS: atomCAT is a three-centre collaboration between Leeds Cancer Centre (UK), MAASTRO Clinic (The Netherlands) and Oslo University Hospital (Norway). We trained and validated a Cox proportional hazards regression model in a distributed fashion using data from 281 patients treated with radical, conformal chemoradiotherapy for anal cancer in three institutions. Our primary endpoint was overall survival. We selected disease stage, sex, age, primary tumour size, and planned radiotherapy dose (in EQD2) a priori as predictor variables. RESULTS: The Cox regression model trained across all three centres found worse overall survival for high risk disease stage (HR = 2.02), male sex (HR = 3.06), older age (HR = 1.33 per 10 years), larger primary tumour volume (HR = 1.05 per 10 cm3) and lower radiotherapy dose (HR = 1.20 per 5 Gy). A mean concordance index of 0.72 was achieved during validation, with limited variation between centres (Leeds = 0.72, MAASTRO = 0.74, Oslo = 0.70). The global model performed well for risk stratification for two out of three centres. CONCLUSIONS: Using distributed learning, we accessed and analysed one of the largest available multi-institutional cohorts of anal cancer patients treated with modern radiotherapy techniques. This demonstrates the value of distributed learning in outcome modelling for rare cancers.