Medicine 분야 (2): Organs at Risk; Radiotherapy, Intensity-Modulated; Knowledge-based planning

Purpose
Automated and knowledge-based planning techniques aim to reduce variations in plan quality. RapidPlan uses a library consisting of different patient plans to make a model that can predict achievable dose-volume histograms (DVHs) for new patients and uses those models for setting optimization objectives. We benchmarked RapidPlan versus clinical plans for 2 patient groups, using 3 different libraries.

Methods and Materials
Volumetric modulated arc therapy plans of 60 recent head and neck cancer patients that included sparing of the salivary glands, swallowing muscles, and oral cavity were evenly divided between 2 models, Model30A and Model30B, and were combined in a third model, Model60. Knowledge-based plans were created for 2 evaluation groups: evaluation group 1 (EG1), consisting of 15 recent patients, and evaluation group 2 (EG2), consisting of 15 older patients in whom only the salivary glands were spared. RapidPlan results were compared with clinical plans (CP) for boost and/or elective planning target volume homogeneity index, using HIB/HIE = 100 × (D2% − D98%)/D50%, and mean dose to composite salivary glands, swallowing muscles, and oral cavity (Dsal, Dswal, and Doc, respectively).

Results
For EG1, RapidPlan improved HIB and HIE values compared with CP by 1.0% to 1.3% and 1.0% to 0.6%, respectively. Comparable Dsal and Dswal values were seen in Model30A, Model30B, and Model60, decreasing by an average of 0.1, 1.0, and 0.8 Gy and 4.8, 3.7, and 4.4 Gy, respectively. However, differences were noted between individual organs at risk (OARs), with Model30B increasing Doc by 0.1, 3.2, and 2.8 Gy compared with CP, Model30A, and Model60. Plan quality was less consistent when the patient was flagged as an outlier. For EG2, RapidPlan decreased Dsal by 4.1 to 4.9 Gy on average, whereas HIB and HIE decreased by 1.1% to 1.5% and 2.3% to 1.9%, respectively.

Conclusions
RapidPlan knowledge-based treatment plans were comparable to CP if the patient's OAR-planning target volume geometry was within the range of those included in the models. EG2 results showed that a model including swallowing-muscle and oral-cavity sparing can be applied to patients with only salivary gland sparing. This may allow model library sharing between institutes. Optimal detection of inadequate plans and population of model libraries requires further investigation.

Purpose
To evaluate the performance of a model-based optimisation process for volumetric modulated arc therapy applied to advanced lung cancer and to low risk prostate carcinoma patients.

Methods and materials
Two sets each of 27 previously treated patients, were selected to train models for the prediction of dose–volume constraints. The models were validated on the same sets of plans (closed-loop) and on further two sets each of 25 patients not used for the training (open-loop).

Results
Quantitative improvements (statistically significant for the majority of the analysed dose–volume parameters) were observed between the benchmark and the test plans. In the pass–fail analysis, the rate of criteria not fulfilled was reduced in the lung patient group from 11% to 7% in the closed-loop and from 13% to 10% in the open-loop studies; in the prostate patient group it was reduced from 4% to 3% in the open-loop study.

Conclusions
Plans were optimised using a knowledge-based model to determine the dose–volume constraints. The results showed dosimetric improvements when compared to the benchmark data, particularly in the sparing of organs at risk. The data suggest that the new engine is reliable and could encourage its application to clinical practice.

Purpose
To develop and evaluate fully automated volumetric modulated arc therapy (VMAT) treatment planning for prostate cancer patients, avoiding manual trial-and-error tweaking of plan parameters by dosimetrists.

Methods and Materials
A system was developed for fully automated generation of VMAT plans with our commercial clinical treatment planning system (TPS), linked to the in-house developed Erasmus-iCycle multicriterial optimizer for preoptimization. For 30 randomly selected patients, automatically generated VMAT plans (VMATauto) were compared with VMAT plans generated manually by 1 expert dosimetrist in the absence of time pressure (VMATman). For all treatment plans, planning target volume (PTV) coverage and sparing of organs-at-risk were quantified.

Results
All generated plans were clinically acceptable and had similar PTV coverage (V95% > 99%). For VMATauto and VMATman plans, the organ-at-risk sparing was similar as well, although only the former plans were generated without any planning workload.

Conclusions
Fully automated generation of high-quality VMAT plans for prostate cancer patients is feasible and has recently been implemented in our clinic.

Background and purpose
To implement a robust and fast stereotactic MR-guided adaptive radiation therapy (SMART) online strategy in locally advanced pancreatic cancer (LAPC).

Material and methods
SMART strategy for plan adaptation was implemented with the MRIdian system (ViewRay Inc.). At each fraction, OAR (re-)contouring is done within a distance of 3 cm from the PTV surface. Online plan re-optimization is based on robust prediction of OAR dose and optimization objectives, obtained by building an artificial neural network (ANN). Proposed limited re-contouring strategy for plan adaptation (SMART3CM) is evaluated by comparing 50 previously delivered fractions against a standard (re-)planning method using full-scale OAR (re-)contouring (FULLOAR). Plan quality was assessed using PTV coverage (V95%, Dmean, D1cc) and institutional OAR constraints (e.g. V33Gy).

Results
SMART3CM required a significant lower number of optimizations than FULLOAR (4 vs 18 on average) to generate a plan meeting all objectives and institutional OAR constraints. PTV coverage with both strategies was identical (mean V95% = 89%). Adaptive plans with SMART3CM exhibited significant lower intermediate and high doses to all OARs than FULLOAR, which also failed in 36% of the cases to adhere to the V33Gy dose constraint.

Conclusions
SMART3CM approach for LAPC allows good OAR sparing and adequate target coverage while requiring only limited online (re-)contouring from clinicians.

Purpose
Over the past decade, intensity-modulated radiation therapy (IMRT) has replaced conventional radiation techniques in the management of head-and-neck cancers (HNCs). We conducted this population-based study to evaluate the influence of radiation oncologist experience on outcomes in patients with HNC treated with IMRT compared with patients with HNC treated with conventional radiation therapy.

Methods
We identified radiation providers from Medicare claims of 6,212 Medicare beneficiaries with HNC treated between 2000 and 2009. We analyzed the impact of provider volume on all-cause mortality, HNC mortality, and toxicity end points after treatment with either conventional radiation therapy or IMRT. All analyses were performed by using either multivariable Cox proportional hazards or Fine-Gray regression models controlling for potential confounding variables.

Results
Among patients treated with conventional radiation, we found no significant relationship between provider volume and patient survival or any toxicity end point. Among patients receiving IMRT, those treated by higher-volume radiation oncologists had improved survival compared with those treated by low-volume providers. The risk of all-cause mortality decreased by 21% for every additional five patients treated per provider per year (hazard ratio [HR], 0.79; 95% CI, 0.67 to 0.94). Patients treated with IMRT by higher-volume providers had decreased HNC-specific mortality (subdistribution HR, 0.68; 95% CI, 0.50 to 0.91) and decreased risk of aspiration pneumonia (subdistribution HR, 0.72; 95% CI, 0.52 to 0.99).

Conclusion
Patients receiving IMRT for HNC had improved outcomes when treated by higher-volume providers. These findings will better inform patients and providers when making decisions about treatment, and emphasize the critical importance of high-quality radiation therapy for optimal treatment of HNC.