The study aims to develop and validate a predictive tool for assessing the risk of in-breast tumor recurrence (IBTR) in breast cancer patients considered candidates for intraoperative radiotherapy using electrons (IOERT). This study included 3397 breast cancer patients treated with IOERT at a single institution between 2000 and 2016. The primary endpoint was IBTR, with or without nodal or distant metastasis. Fine and Gray regression models were used to identify predictors of IBTR. A nomogram predicting the 5- and 10-year probability of IBTR was developed based on the multivariable model and was validated both internally and externally using data from the IOERT arm of the ELIOT phase III trial (585 patients). With a median follow-up of 6.1 years (interquartile range 4.3–8.0), 265 IBTRs (7.8

Background and purpose: Robustness evaluation (RE) is vital for proton treatment planning, but lacks international consensus or guidelines, with clinics using varied, self-developed methods focused on selected uncertainties. This ESTRO project surveys expert opinions on clinical RE methods to inform future treatment planning system (TPS) development. Materials and methods: A study within the European Particle Therapy Network (EPTN) involved 24 European proton therapy centres, with one radiation oncologist and one medical physicist per centre. The goal was to reach a consensus on transitioning from Planning Target Volume (PTV)-based planning to robustly optimized planning, including uncertainties, methods, and reporting of robustness evaluations. An internal committee drafted 39 statements, reviewed by an independent committee. Following a two-round Delphi procedure, consensus was set at a 75% agreement threshold. Results: Twenty of 24 contacted centers (83.0%) responded to both questionnaire rounds. Consensus was reached on 26 of 39 statements (66.7%), with 5 being high-priority. Strong agreement emerged regarding which uncertainties to include in RE (range, setup, intra-fraction, anatomy changes), methodologies (e.g., for moving targets, combining setup and range), and how to report RE results clinically. Disagreement was found on using the PTV for both planning and dose reporting. The results also offer important implications for TPS vendors and future software development. Conclusions: The ESTRO Delphi consensus may serve as practical guidance on points where a clear consensus was achieved. For remaining points, the development of guidelines is recommended to standardize methodologies and reporting. Furthermore, TPS vendors are encouraged to align their developments with the community’s articulated requirements.

PURPOSE/OBJECTIVE:To perform a dosimetric and a normal tissue complication probability (NTCP) comparison between intensity modulated proton therapy and photon volumetric modulated arc therapy in a cohort of patients with parotid gland cancers in a post-operative or radical setting. MATERIALS AND METHODS:From May 2011 to September 2021, 37 parotid gland cancers patients treated at two institutions were eligible. Inclusion criteria were as follows: patients aged ⩾ 18 years, diagnosis of parotid gland cancers candidate for postoperative radiotherapy or definitive radiotherapy, presence of written informed consent for the use of anonymous data for research purposes. Organs at risk (OARs) were retrospectively contoured. Target coverage goal was defined as D95 > 98%. Six NTCP models were selected. NTCP profiles were calculated for each patient using an internally-developed Python script in RayStation TPS. Average differences in NTCP between photon and proton plans were tested for significance with a two-sided Wilcoxon signed-rank test. RESULTS:Seventy-four plans were generated. A lower Dmean to the majority of organs at risk (inner ear, cochlea, oral cavity, pharyngeal constrictor muscles, contralateral parotid and submandibular gland) was obtained with intensity modulated proton therapy vs volumetric modulated arc therapy with statistical significance (p < .05). Ten (27%) patients had a difference in NTCP (photon vs proton plans) greater than 10% for hearing loss and tinnitus: among them, seven qualified for both endpoints, two patients for hearing loss only, and one for tinnitus. CONCLUSIONS:In the current study, nearly one-third of patients resulted eligible for proton therapy and they were the most likely to benefit in terms of prevention of hearing loss and tinnitus.

Purpose: To present the results of the first multi-centre real-world validation of autoplanning for whole breast irradiation after breast-sparing surgery, encompassing high complexity cases (e.g. with a boost or regional lymph nodes) and a wide range of clinical practices. Methods: The 24 participating centers each included 10 IMRT/VMAT/Tomotherapy patients, previously treated with a manually generated plan ('manplan'). There were no restrictions regarding case complexity, planning aims, plan evaluation parameters and criteria, fractionation, treatment planning system or treatment machine/ technique. In addition to dosimetric comparisons of autoplans with manplans, blinded plan scoring/ranking was conducted by a clinician from the treating center. Autoplanning was performed using a single configuration for all patients in all centres. Deliverability was verified through measurements at delivery units. Results: Target dosimetry showed comparability, while reductions in OAR dose parameters were 21.4 % for D mean , 16.7 % for ipsilateral lung D mean , and 101.9 %, 45.5 %, and 35.7 % for contralateral breast D 0.03cc and D mean , respectively (all p < 0.001). Among the 240 patients included, the clinicians preferred the autoplan for 119 patients, with manplans preferred for 96 cases (p = 0.01). Per centre there were on average 5.0 (1SD) patients with a preferred autoplan (range [0-10]), compared to 4.0 +/- 2.7 with a preferred manplan ([0,9]). No differences were observed regarding deliverability. Conclusion: The automation significantly reduced the hands-on planning workload compared to manual ning, while also achieving an overall superiority. However, fine-tuning of the autoplanning configuration clinical implementation may be necessary in some centres to enhance clinicians' satisfaction with the generated autoplans.

AIMS:Aim of the present analysis was to report results of a systematic review of the literature in the setting of patients treated with hypoF PT for benign lesions of the central nervous system (CNS).METHODS:The methodology complied with the PRISMA recommendations. PubMed, EMBASE and Scopus databases were interrogated in September 2022.RESULTS:Twelve papers have been selected including patients treated for base of the skull meningiomas (6 papers), vestibular schwannoma (3 papers) and pituitary adenomas (3 papers). Clinical outcomes were evaluated with both radiologic images and clinical parameters. Long-term toxicity was reported in all but one series with an incidence ranging from 2 % to 7 % in patients treated for base of skull meningioma and 1-9 % for schwannoma.CONCLUSIONS:HypoF PT is a safe and effective treatment in selected benign tumors of the CNS. Further dosimetric and clinical comparisons are required to better refine the patients' selection criteria.

Purpose: The objective of this work was to investigate the ability of machine learning models to use treatment plan dosimetry for prediction of clinician approval of treatment plans (no further planning needed) for left-sided whole breast radiation therapy with boost.Methods and Materials: Investigated plans were generated to deliver a dose of 40.05 Gy to the whole breast in 15 fractions over 3 weeks, with the tumor bed simultaneously boosted to 48 Gy. In addition to the manually generated clinical plan of each of the 120 patients from a single institution, an automatically generated plan was included for each patient to enhance the number of study plans to 240. In random order, the treating clinician retrospectively scored all 240 plans as (1) approved without further planning to seek improvement or (2) further planning needed, while being blind for type of plan generation (manual or automated). In total, 2 x 5 classifiers were trained and evaluated for ability to correctly predict the clinician's plan evaluations: random forest (RF) and constrained logistic regression (LR) classifiers, each trained for 5 different sets of dosimetric plan parameters (feature sets [FS]). Importances of included features for predictions were investigated to better understand clinicians' choices.Results: Although all 240 plans were in principle clinically acceptable for the clinician, only for 71.5% was no further planning required. For the most extensive FS, accuracy, area under the receiver operating characteristic curve, and Cohen's K for generated RF/ LR models for prediction of approval without further planning were 87.2 & PLUSMN; 2.0/86.7 & PLUSMN; 2.2, 0.80 & PLUSMN; 0.03/0.86 & PLUSMN; 0.02, and 0.63 & PLUSMN; 0.05/ 0.69 & PLUSMN; 0.04, respectively. In contrast to LR, RF performance was independent of the applied FS. For both RF and LR, whole breast excluding boost PTV (PTV40.05Gy) was the most important structure for predictions, with importance factors of 44.6% and 43%, respectively, dose recieved by 95% volume of PTV40.05 (D95%) as the most important parameter in most cases.Conclusions: The investigated use of machine learning to predict clinician approval of treatment plans is highly promising. Including nondosimetric parameters could further increase classifiers' performances. The tool could become useful for aiding treatment planners in generating plans with a high probability of being directly approved by the treating clinician.