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Objectives: To assess the effectiveness of fluorine-18 fluorodeoxyglucose (FDG) positron-emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI) for response assessment post curative-intent chemoradiotherapy (CRT) in anal squamous cell carcinoma (ASCC)., Methods: Consecutive ASCC patients treated with curative-intent CRT at a single centre between January 2018 and April 2020 were retrospectively identified. Clinical meta-data including progression-free survival (PFS) and overall survival (OS) outcomes were collated. Three radiologists evaluated PET-CT and MRI using qualitative response assessment criteria and agreed in consensus. Two-proportion z test was used to compare diagnostic performance metrics (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy). Kaplan-Meier analysis (Mantel-Cox log-rank) was performed., Results: MRI (accuracy 76%, PPV 44.8%, NPV 95.7%) and PET-CT (accuracy 69.3%, PPV 36.7%, NPV 91.1%) performance metrics were similar; when combined, there were statistically significant improvements (accuracy 94.7%, PPV 78.9%, NPV 100%). Kaplan-Meier analysis demonstrated significant differences in PFS between responders and non-responders at PET-CT (p = 0.007), MRI (p = 0.005), and consensus evaluation (p < 0.001). Cox regression analysis of PFS demonstrated a lower hazard ratio (HR) and narrower 95% confidence intervals for consensus findings (HR = 0.093, p < 0.001). Seventy-five patients, of which 52 (69.3%) were females, with median follow-up of 17.8 months (range 5-32.6) were included. Fifteen of the 75 (20%) had persistent anorectal and/or nodal disease after CRT. Three patients died, median time to death 6.2 months (range 5-18.3)., Conclusion: Combined PET-CT and MRI response assessment post-CRT better predicts subsequent outcome than either modality alone. This could have valuable clinical benefits by guiding personalised risk-adapted patient follow-up., Key Points: • MRI and PET-CT performance metrics for assessing response following chemoradiotherapy (CRT) in patients with anal squamous cell carcinoma (ASCC) were similar. • Combined MRI and PET-CT treatment response assessment 3 months after CRT in patients with ASCC was demonstrated to be superior to either modality alone. • A combined MRI and PET-CT assessment 3 months after CRT in patients with ASCC has the potential to improve accuracy and guide optimal patient management with a greater ability to predict outcome than either modality alone., (© 2022. The Author(s).)

Aims: Anal cancer is primarily treated using concurrent chemoradiotherapy (CRT), with conformal techniques such as intensity modulated radiotherapy (IMRT) and volumetric arc therapy (VMAT) now being the standard techniques utilised across the world. Despite this, there is still very limited consensus on prognostic factors for outcome following conformal CRT. This systematic review aims to evaluate the existing literature to identify prognostic factors for a variety of oncological outcomes in anal cancer, focusing on patients treated with curative intent using contemporary conformal radiotherapy techniques., Materials and Methods: A literature search was conducted using Medline and Embase to identify studies reporting on prognostic factors for survival and cancer-related outcomes after conformal CRT for anal cancer. The prognostic factors which were identified as significant in univariable and multivariable analysis, along with their respective factor effects (where available) were extracted. Only factors reported as prognostic in more than one study were included in the final results., Results: The results from 19 studies were analysed. In both univariable and multivariable analysis, N stage, T stage, and sex were found to be the most prevalent and reliable clinical prognostic factors for the majority of outcomes explored. Only a few biomarkers have been identified as prognostic by more than one study - pre-treatment biopsy HPV load, as well as the presence of leukocytosis, neutrophilia and anaemia at baseline measurement. The results also highlight the lack of studies with large cohorts exploring the prognostic significance of imaging factors., Conclusion: Establishing a set of prognostic and potentially predictive factors for anal cancer outcomes can guide the risk stratification of patients, aiding the design of future clinical trials. Such trials will in turn provide us with greater insight into how to effectively treat this disease using a more personalised approach., (© 2022. The Author(s).)

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 cm 3 ) 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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)