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Background: Knowledge-based planning (KBP) is a method for automated radiotherapy treatment planning where appropriate optimization objectives for new patients are predicted based on a library of training plans. KBP can save time and improve organ at-risk sparing and inter-patient consistency compared to manual planning, but its performance depends on the quality of the training plans. We used another system for automated planning, which generates multi-criteria optimized (MCO) plans based on a wish list, to create training plans for the KBP model, to allow seamless integration of knowledge from a new system into clinical routine. Model performance was compared for KBP models trained with manually created and automatic MCO treatment plans. Material and Methods: Two RapidPlan models with the same 30 locally advanced non-small cell lung cancer patients included were created, one containing manually created clinical plans (RP_CLIN) and one containing fully automatic multi-criteria optimized plans (RP_MCO). For 15 validation patients, model performance was compared in terms of dose-volume parameters and normal tissue complication probabilities, and an oncologist performed a blind comparison of the clinical (CLIN), RP_CLIN, and RP_MCO plans. Results: The heart and esophagus doses were lower for RP_MCO compared to RP_CLIN, resulting in an average reduction in the risk of 2-year mortality by 0.9 percentage points and the risk of acute esophageal toxicity by 1.6 percentage points with RP_MCO. The oncologist preferred the RP_MCO plan for 8 patients and the CLIN plan for 7 patients, while the RP_CLIN plan was not preferred for any patients. Conclusion: RP_MCO improved OAR sparing compared to RP_CLIN and was selected for implementation in the clinic. Training a KBP model with clinical plans may lead to suboptimal output plans, and making an extra effort to optimize the library plans in the KBP model creation phase can improve the plan quality for many future patients. [ABSTRACT FROM AUTHOR]

Manual volumetric modulated arc therapy (VMAT) treatment planning for high-risk prostate cancer receiving whole pelvic radiotherapy (WPRT) with four integrated dose levels is complex and time consuming. We have investigated if the radiotherapy planning process and plan quality can be improved using a well-tuned model developed through a commercial system for knowledge-based planning (KBP). Treatment plans from 69 patients treated for high-risk prostate cancer with manually planned VMAT were used to develop an initial KBP model (RapidPlan, RP). Prescribed doses were 50, 60, 67.5, and 72.5 Gy in 25 fractions to the pelvic lymph nodes, prostate and seminal vesicles, prostate gland, and prostate tumour(s), respectively. This RP model was in clinical use from July 2019 to February 2020, producing another set of 69 clinically delivered treatment plans for a new patient group, which were used to develop a second RP model. Both models were validated on an independent group of 40 patients. Plan quality was compared by D98% and the Paddick conformity index for targets, mean dose (Dmean) and generalised equivalent uniform dose (gEUD) for bladder, bowel bag and rectum, and number of monitor units (MU). Target coverage and conformity was similar between manually created and RP treatment plans. Compared to the manually created treatment plans, the final RP model reduced average Dmean and gEUD with 2.7 Gy and 1.3 Gy for bladder, 1.2 Gy and 0.9 Gy for bowel bag, and 2.7 Gy and 0.8 Gy for rectum, respectively (p <.05). For rectum, the interpatient variation (i.e., 95% confidence interval) of DVHs was reduced by 23%. KBP improved plan quality and consistency among treatment plans for high-risk prostate cancer. Model tuning using KBP-based clinical plans further improved model outcome. [ABSTRACT FROM AUTHOR]

Simple Summary: In treatment planning for intensity-modulated radiotherapy (IMRT), optimization objectives and beam angle settings are individualized for the anatomy of each patient. This is a complex interactive process that is usually performed by a treatment planner. In this study, a novel system for automated optimization of IMRT plans with integrated beam angle optimization (BAO) was developed, and used to systematically investigate the impact of selected beam angles on treatment plan quality for locally advanced non-small cell lung cancer (LA-NSCLC). Automatically generated plans were of a higher quality than the manually generated, clinically delivered plans, while dramatically reducing the planning workload. The study demonstrates the potential for automated planning with integrated BAO to enhance radiotherapy for LA-NSCLC patients. In this study, the novel iCE radiotherapy treatment planning system (TPS) for automated multi-criterial planning with integrated beam angle optimization (BAO) was developed, and applied to optimize organ at risk (OAR) sparing and systematically investigate the impact of beam angles on radiotherapy dose in locally advanced non-small cell lung cancer (LA-NSCLC). iCE consists of an in-house, sophisticated multi-criterial optimizer with integrated BAO, coupled to a broadly used commercial TPS. The in-house optimizer performs fluence map optimization to automatically generate an intensity-modulated radiotherapy (IMRT) plan with optimal beam angles for each patient. The obtained angles and dose-volume histograms are then used to automatically generate the final deliverable plan with the commercial TPS. For the majority of 26 LA-NSCLC patients, iCE achieved improved heart and esophagus sparing compared to the manually created clinical plans, with significant reductions in the median heart Dmean (8.1 vs. 9.0 Gy, p = 0.02) and esophagus Dmean (18.5 vs. 20.3 Gy, p = 0.02), and reductions of up to 6.7 Gy and 5.8 Gy for individual patients. iCE was superior to automated planning using manually selected beam angles. Differences in the OAR doses of iCE plans with 6 beams compared to 4 and 8 beams were statistically significant overall, but highly patient-specific. In conclusion, automated planning with integrated BAO can further enhance and individualize radiotherapy for LA-NSCLC. [ABSTRACT FROM AUTHOR]

Purpose: Intensity modulated proton therapy (IMPT) could yield high linear energy transfer (LET) in critical structures and increased biological effect. For head and neck cancers at the skull base this could potentially result in radiation-associated brain image change (RAIC). The purpose of the current study was to investigate voxel-wise dose and LET correlations with RAIC after IMPT.Methods and Materials: For 15 patients with RAIC after IMPT, contrast enhancement observed on T1-weighted magnetic resonance imaging was contoured and coregistered to the planning computed tomography. Monte Carlo calculated dose and dose-averaged LET (LETd) distributions were extracted at voxel level and associations with RAIC were modelled using uni- and multivariate mixed effect logistic regression. Model performance was evaluated using the area under the receiver operating characteristic curve and precision-recall curve.Results: An overall statistically significant RAIC association with dose and LETd was found in both the uni- and multivariate analysis. Patient heterogeneity was considerable, with standard deviation of the random effects of 1.81 (1.30-2.72) for dose and 2.68 (1.93-4.93) for LETd, respectively. Area under the receiver operating characteristic curve was 0.93 and 0.95 for the univariate dose-response model and multivariate model, respectively. Analysis of the LETd effect demonstrated increased risk of RAIC with increasing LETd for the majority of patients. Estimated probability of RAIC with LETd = 1 keV/µm was 4% (95% confidence interval, 0%, 0.44%) and 29% (95% confidence interval, 0.01%, 0.92%) for 60 and 70 Gy, respectively. The TD15 were estimated to be 63.6 and 50.1 Gy with LETd equal to 2 and 5 keV/µm, respectively.Conclusions: Our results suggest that the LETd effect could be of clinical significance for some patients; LETd assessment in clinical treatment plans should therefore be taken into consideration. [ABSTRACT FROM AUTHOR]