Functional Lung Avoidance Planning Using Multicriteria Optimization.

PURPOSE: Functional lung avoidance (FLA) radiation therapy is an evolving field. The aim of FLA planning is to reduce dose to areas of functioning lung, with comparable target coverage and dose to organs at risk. Multicriteria optimization (MCO) is a planning tool that may assist with FLA planning. This study assessed the feasibility of using MCO to adapt radiation therapy plans to avoid functional regions of lung that were identified using a 68Ga-4D-V/Q positron emission tomography/computed tomography. METHODS AND MATERIALS: A prospective clinical trial U1111-1138-4421 was performed in which patients had a 68Ga-4D-V/Q positron emission tomography/computed tomography before radiation treatment. Of the 72 patients enrolled in this trial, 38 patients had stage III non-small cell lung cancer and were eligible for selection into this planning study. Functional lung target volumes HF lung (highly functioning lung) and F lung (functional lung) were defined using the ventilated and perfused lung. Using knowledge-based planning, a baseline anatomic plan was created, and then a functional adapted plan was generated using multicriteria optimization. The primary aim was to spare dose to HF lung. Using the MCO tools, a clinician selected the final FLA plan. Dose to functional lung, target volumes, organs at risk and measures of plan quality were compared using standard statistical methods. RESULTS: The HF lung volume was successfully spared in all patients. The F lung volume was successfully spared in 36 of the 38 patients. There were no clinically significant differences in dose to anatomically defined organs at risk. There were differences in the planning target volume near maximum and minimum doses. Across the entire population, there was a statistically significant reduction in the functional mean lung dose but not in the functional volume receiving 20 Gy. All trade-off decisions were made by the clinician. CONCLUSIONS: Using MCO for FLA was achievable but did result in changes to planning target volume coverage. A distinct advantage in using MCO was that all decisions regarding the cost and benefits of FLA could be made in real time.

[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program.

Background and purpose: The [18]F-fluoroethyl-l-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TVMR) and hybrid target volumes (TVMR+FET) incorporating pre-defined FET PET biological tumour volumes (BTVs). Materials and methods: Central review and analysis of TVMR and TVMR+FET was undertaken across three benchmarking cases. BTVs were pre-defined by a sole nuclear medicine expert. Intraclass correlation coefficient (ICC) confidence intervals (CIs) evaluated volume agreement. RO contour spatial and boundary agreement were evaluated (Dice similarity coefficient [DSC], Jaccard index [JAC], overlap volume [OV], Hausdorff distance [HD] and mean absolute surface distance [MASD]). Dose plan generation (one case per site) was assessed. Results: Data from 19 ROs across 10 trial sites (54 initial submissions, 8 resubmissions requested, 4 conditional passes) was assessed with an initial pass rate of 77.8 %; all resubmissions passed. TVMR+FET were significantly larger than TVMR (p < 0.001) for all cases. RO gross tumour volume (GTV) agreement was moderate-to-excellent for GTVMR (ICC = 0.910; 95 % CI, 0.708-0.997) and good-to-excellent for GTVMR+FET (ICC = 0.965; 95 % CI, 0.871-0.999). GTVMR+FET showed greater spatial overlap and boundary agreement compared to GTVMR. For the clinical target volume (CTV), CTVMR+FET showed lower average boundary agreement versus CTVMR (MASD: 1.73 mm vs. 1.61 mm, p = 0.042). All sites passed the planning exercise. Conclusions: The credentialing program demonstrated feasibility in successful credentialing of 19 ROs across 10 sites, increasing national expertise in TVMR+FET delineation.

Transformer CycleGAN with uncertainty estimation for CBCT based synthetic CT in adaptive radiotherapy.

Objective. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit (HU) accuracy, and image quality. To achieve these goals, a vision-transformer and anatomically sensitive loss functions are described. Better quantification of image quality is achieved using the alignment-invariant Fréchet inception distance (FID), and uncertainty estimation for sCT risk prediction is implemented in a scalable plug-and-play manner.Approach. Baseline U-Net, generative adversarial network (GAN), and CycleGAN models were trained to identify shortcomings in each approach. The proposed CycleGAN-Best model was empirically optimized based on a large ablation study and evaluated using classical image quality metrics, FID, gamma index, and a segmentation analysis. Two uncertainty estimation methods, Monte-Carlo Dropout (MCD) and test-time augmentation (TTA), were introduced to model epistemic and aleatoric uncertainty.Main results. FID was correlated to blind observer image quality scores with a Correlation Coefficient of -0.83, validating the metric as an accurate quantifier of perceived image quality. The FID and mean absolute error (MAE) of CycleGAN-Best was 42.11 ± 5.99 and 25.00 ± 1.97 HU, compared to 63.42 ± 15.45 and 31.80 HU for CycleGAN-Baseline, and 144.32 ± 20.91 and 68.00 ± 5.06 HU for the CBCT, respectively. Gamma 1%/1 mm pass rates were 98.66 ± 0.54% for CycleGAN-Best, compared to 86.72 ± 2.55% for the CBCT. TTA and MCD-based uncertainty maps were well spatially correlated with poor synthesis outputs.Significance. Anatomical accuracy was achieved by suppressing CycleGAN-related artefacts. FID better discriminated image quality, where alignment-based metrics such as MAE erroneously suggest poorer outputs perform better. Uncertainty estimation for sCT was shown to correlate with poor outputs and has clinical relevancy toward model risk assessment and quality assurance. The proposed model and accompanying evaluation and risk assessment tools are necessary additions to achieve clinically robust sCT generation models.

Stereotactic body radiotherapy for early-stage lung cancer: a systematic review on the choice of photon energy and linac flattened/unflattened beams.

SBRT is an effective local treatment for patients with early-stage non-small cell lung cancer (NSCLC). This treatment is currently used in patients who have poor lung function or who decline surgery. As SBRT usually has small PTV margins, reducing the beam-on-time (BOT) is beneficial for accurate dose delivery by minimising intrafraction motion as well as improved patient comfort. Removal of the linear accelerator flattening filter can provide a higher dose rate which results in a faster treatment. In addition, the choice of photon energy can also affect the dose distribution to the target and the organs-at-risk (OAR). In this systematic review, studies analysing the choice of various photon beam energies, with a flattening filter or flattening filter free (FFF), were compared for their overall dosimetric benefit in the SBRT treatment for early-stage NSCLC. It was found that FFF treatment delivers a comparatively more conformal dose distribution, as well as a better homogeneity index and conformity index, and typically reduces BOT by between 30 and 50%. The trade-off may be a minor increase in monitor units for FFF treatment found in some studies but not others. Target conformity and OAR sparing, particularly lung doses appear better with 6MV FFF, but 10MV FFF was marginally more advantageous for skin sparing and BOT reduction. The favourable beam modality for clinical use would depend on the individual case, for which tumour size and depth, radiotherapy technique, as well as fractionation scheme need to be taken into account.