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In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.

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Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.Methods: Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded.Cox PH with LASSO regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low-and high-volume lesions.Results: Radiomic models significantly outperformed models built on only clinical variables and volume.However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models.Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low-and high-volume models performed significantly better with the inclusion of *, affiliation at the time of the study.

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The feasibility of vein bypass surgery to the arteries of the foot was studied in a diabetic population with critical ischaemia; whether or not such reconstruction leads to an independent lifestyle was assessed 1 year later. Thirty-three reconstructions were performed on 32 limbs in 31 patients. The mortality and reoperation rates within 1 month were both 6 per cent. Primary and secondary patency, limb salvage and survival rates at 1 year were 76, 89, 89 and 82 per cent respectively. Good results in the form of combined survival, patency, limb salvage, walking ability, relief of pain and residence at home were achieved in 64 per cent of patients after 1 year. Reconstructive vascular surgery to the foot in diabetics is feasible and affords two-thirds of patients an independent lifestyle 1 year after surgery.

Background Italy experienced one of the world’s severest COVID-19 outbreak, with Lombardy being the most afflicted region. However, the imposed safety measures allowed to flatten the epidemic curve and hence to ease the restrictions and inaugurate, on the 4th of May 2020, the Italian phase (P) 2 of the pandemic. The present survey study, endorsed by CODRAL and AIRO-L, aimed to assess how radiotherapy (RT) departments in Lombardy have dealt with the recovery. Materials and methods A questionnaire dealing with the management of pandemic was developed online and sent to all CODRAL Directors on the 10th of June 2020. Answers were collected in full anonymity one week after. Results All the 33 contacted RT facilities (100%) responded to the survey. Despite the scale of the pandemic, during P1 14 (42.4%) centres managed to safely continue the activity (≤ 10% reduction). During P2, 10 (30.3%) centres fully recovered and 14 (42.4%) reported an increase. Nonetheless, 6 (18.2%) declared no changes and, interestingly, 3 (9.1%) reduced activities. Overall, 21 centres (63.6%) reported suspected or positive cases within healthcare workforce since the beginning of the pandemic. Staff units were quarantined in 19 (57.6%) and 6 (18.2%) centres throughout P1 and P2, respectively. In the two phases, about two thirds centres registered positive or suspected cases amongst patients. Conclusion The study revealed a particular attention to anti-contagion measures and a return to normal or even higher clinical workload in most RT centres in Lombardy, necessary to carry out current and previously deferred treatments. Electronic supplementary material The online version of this article (10.1007/s12032-020-01434-1) contains supplementary material, which is available to authorized users.