A traffic light protocol workflow for image-guided adaptive radiotherapy in lung cancer patients.

BACKGROUND AND PURPOSE: Image-guided radiotherapy using cone beam-CT (CBCT) images is used to evaluate patient anatomy and positioning before radiotherapy. In this study we analyzed and optimized a traffic light protocol (TLP) used in lung cancer patients to identify patients requiring treatment adaptation. MATERIALS AND METHODS: First, CBCT review requests of 243 lung cancer patients were retrospectively analyzed and divided into 6 pre-defined categories. Frequencies and follow-up actions were scored. Based on these results, the TLP was optimized and evaluated in the same way on 230 patients treated in 2018. RESULTS: In the retrospective study, a total of 543 CBCT review requests were created during treatment in 193/243 patients due to changed anatomy of lung (24%), change of tumor volume (24%), review of match (18%), shift of the mediastinum (15%), shift of tumor (15%) and other (4%). The majority of requests (474, 87%) did not require further action. In 6% an adjustment of the match criteria sufficed; in 7% treatment plan adaptation was required. Plan adaptation was frequently seen in the categories changed anatomy of lung, change of tumor volume and shift of tumor outside the PTV. Shift of mediastinum outside PRV and shift of GTV outside CTV (but inside PTV) never required plan adaptation and were omitted to optimize the TLP, which reduced the CBCT review requests by 23%. CONCLUSIONS: The original TLP selected patients that required a treatment adaptation, but with a high false positive rate. The optimized TLP reduced the amount of CBCT review requests, while still correctly identifying patients requiring adaptation.

Real-world analysis of manual editing of deep learning contouring in the thorax region.

Background and purpose: User-adjustments after deep-learning (DL) contouring in radiotherapy were evaluated to get insight in real-world editing during clinical practice. This study assessed the amount, type and spatial regions of editing of auto-contouring for organs-at-risk (OARs) in routine clinical workflow for patients in the thorax region. Materials and methods: A total of 350 lung cancer and 362 breast cancer patients, contoured between March 2020 and March 2021 using a commercial DL-contouring method followed by manual adjustments were retrospectively analyzed. Subsampling was performed for some OARs, using an inter-slice gap of 1-3 slices. Commonly-used whole-organ contouring assessment measures were calculated, and all cases were registered to a common reference shape per OAR to identify regions of manual adjustment. Results were expressed as the median, 10th-90th percentile of adjustment and visualized using 3D renderings. Results: Per OAR, the median amount of editing was below 1 mm. However, large adjustments were found in some locations for most OARs. In general, enlarging of the auto-contours was needed. Subsampling DL-contours showed less adjustments were made in the interpolated slices compared to simulated no-subsampling for these OARs. Conclusion: The real-world performance of automatic DL-contouring software was evaluated and proven useful in clinical practice. Specific regions-of-adjustment were identified per OAR in the thorax region, and separate models were found to be necessary for specific clinical indications different from training data. This analysis showed the need to perform routine clinical analysis especially when procedures or acquisition protocols change to have the best configuration of the workflow.

Treatment planning and 4D robust evaluation strategy for proton therapy of lung tumors with large motion amplitude.

PURPOSE: Intensity-modulated proton therapy (IMPT) for lung tumors with a large tumor movement is challenging due to loss of robustness in the target coverage. Often an upper cut-off at 5-mm tumor movement is used for proton patient selection. In this study, we propose (1) a robust and easily implementable treatment planning strategy for lung tumors with a movement larger than 5 mm, and (2) a four-dimensional computed tomography (4DCT) robust evaluation strategy for evaluating the dose distribution on the breathing phases. MATERIALS AND METHODS: We created a treatment planning strategy based on the internal target volume (ITV) concept (aim 1). The ITV was created as a union of the clinical target volumes (CTVs) on the eight 4DCT phases. The ITV expanded by 2 mm was the target during robust optimization on the average CT (avgCT). The clinical plan acceptability was judged based on a robust evaluation, computing the voxel-wise min and max (VWmin/max) doses over 28 error scenarios (range and setup errors) on the avgCT. The plans were created in RayStation (RaySearch Laboratories, Stockholm, Sweden) using a Monte Carlo dose engine, commissioned for our Mevion S250i Hyperscan system (Mevion Medical Systems, Littleton, MA, USA). We developed a new 4D robust evaluation approach (4DRobAvg; aim 2). The 28 scenario doses were computed on each individual 4DCT phase. For each scenario, the dose distributions on the individual phases were deformed to the reference phase and combined to a weighted sum, resulting in 28 weighted sum scenario dose distributions. From these 28 scenario doses, VWmin/max doses were computed. This new 4D robust evaluation was compared to two simpler 4D evaluation strategies: re-computing the nominal plan on each individual 4DCT phase (4DNom) and computing the robust VWmin/max doses on each individual phase (4DRobInd). The treatment planning and dose evaluation strategies were evaluated for 16 lung cancer patients with tumor movement of 4-26 mm. RESULTS: The ratio of the ITV and CTV volumes increased linearly with the tumor amplitude, with an average ratio of 1.4. Despite large ITV volumes, a clinically acceptable plan fulfilling all target and organ at risk (OAR) constraints was feasible for all patients. The 4DNom and 4DRobInd evaluation strategies were found to under- or overestimate the dosimetric effect of the tumor movement, respectively. 4DRobInd showed target underdosage for five patients, not observed in the robust evaluation on the avgCT or in 4DRobAvg. The accuracy of dose deformation used in 4DRobAvg was quantified and found acceptable, with differences for the dose-volume parameters below 1 Gy in most cases. CONCLUSION: The proposed ITV-based planning strategy on the avgCT was found to be a clinically feasible approach with adequate tumor coverage and no OAR overdosage even for large tumor movement. The new proposed 4D robust evaluation, 4DRobAvg, was shown to give an easily interpretable understanding of the effect of respiratory motion dose distribution, and to give an accurate estimate of the dose delivered in the different breathing phases.