Multi-centre real-world validation of automated treatment planning for breast radiotherapy.

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 heart Dmean, 16.7 % for ipsilateral lung Dmean, and 101.9 %, 45.5 %, and 35.7 % for contralateral breast D0.03cc, D5% and Dmean, 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 ± 2.9 (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 planning, while also achieving an overall superiority. However, fine-tuning of the autoplanning configuration prior to clinical implementation may be necessary in some centres to enhance clinicians' satisfaction with the generated autoplans.

Access to bedding and outdoor runs for growing-finishing pigs: is it possible to improve welfare without increasing environmental impacts?

Providing bedding or access to an outdoor run are husbandry aspects intended to improve pig welfare, which is currently financially supported through animal welfare schemes in several European countries. However, they may significantly affect the environment through changes in feed efficiency and manure management. Therefore, the aim of this paper was to compare farms differing in animal welfare relevant husbandry aspects regarding (1) the welfare of growing-finishing pigs and (2) environmental impact categories such as global warming (GW), acidification (AC), and freshwater (FE) and marine eutrophication (ME), by employing an attributional Life Cycle Assessment. We collected data on 50 farms with growing-finishing pigs in seven European countries. Ten animal-based welfare indicators were aggregated into three pig welfare indices using principal component analysis. Cluster analysis of farms based on husbandry aspects resulted in three clusters: NOBED (31 farms without bedding or outdoor run), BED (11 farms with bedding only) and BEDOUT (eight farms with bedding and outdoor run). Pigs on farms with bedding (BED and BEDOUT) manipulated enrichment more often (P < 0.001), pen fixtures less frequently (P = 0.003) and showed fewer oral stereotypies (P < 0.001) than pigs on NOBED farms. There were fewer pigs with a short(er) tail on farms with than without bedding (P < 0.001). Acidification of BEDOUT and BED farms was significantly higher (compared to NOBED farms P = 0.002) due to higher ammonia emissions related to farmyard manure. Also, BEDOUT farms had higher ME than NOBED farms (P = 0.035). There were no significant differences regarding GW and FE between husbandry clusters, due to the large variability within clusters regarding feed composition and conversion. Therefore, both husbandry aspects associated with improved animal welfare have a significant influence on some environmental impacts, such as acidification and marine eutrophication. Nevertheless, the large variation within clusters suggests that trade-offs may be minimised through e.g. AC and ME.