Organ sparing potential and inter-fraction robustness of adaptive intensity modulated proton therapy for lung cancer.

Background: The aim of this study was to compare adaptive intensity modulated proton therapy (IMPT) robustness and organ sparing capabilities with that of adaptive volumetric arc photon therapy (VMAT).Material and methods: Eighteen lung cancer patients underwent a planning 4DCT (p4DCT) and 5 weekly repeated 4DCT (r4DCT) scans. Target volumes and organs at risk were manually delineated on the three-dimensional (3D) average scans of the p4DCT (av_p4DCT) and of the r4DCT scans (av_r4DCT). Planning target volume (PTV)-based VMAT plans and internal clinical target volume (ICTV)-based robust IMPT plans were optimized in 3D on the av_p4DCT and re-calculated on the av_r4DCTs. Re-planning on av_r4DCTs was performed when indicated and accumulated doses were evaluated on the av_p4DCT.Results: Adaptive VMAT and IMPT resulted in adequate ICTV coverage on av_r4DCT in all patients and adequate accumulated-dose ICTV coverage on av_p4DCT in 17/18 patients (due to a shrinking target in one patient). More frequent re-planning was needed for IMPT than for VMAT. The average mean heart dose reduction with IMPT compared with VMAT was 4.6 Gy (p = .001) and it was >5 Gy for five patients (6, 7, 8, 15, and 22 Gy). The average mean lung dose reduction was 3.2 Gy (p < .001). Significant reductions in heart and lung V5 Gy were observed with IMPT.Conclusion: Robust-planned IMPT required re-planning more often than VMAT but resulted in similar accumulated ICTV coverage. With IMPT, heart and lung mean dose values and low dose regions were significantly reduced. Substantial cardiac sparing was obtained in a subgroup of five patients (28%).

Estimating how contouring differences affect normal tissue complication probability modelling.

Background and purpose: Normal tissue complication probability (NTCP) models are developed from large retrospective datasets where automatic contouring is often used to contour the organs at risk. This study proposes a methodology to estimate how discrepancies between two sets of contours are reflected on NTCP model performance. We apply this methodology to heart contours within a dataset of non-small cell lung cancer (NSCLC) patients. Materials and methods: One of the contour sets is designated the ground truth and a dosimetric parameter derived from it is used to simulate outcomes via a predefined NTCP relationship. For each simulated outcome, the selected dosimetric parameters associated with each contour set are individually used to fit a toxicity model and their performance is compared. Our dataset comprised 605 stage IIA-IIIB NSCLC patients. Manual, deep learning, and atlas-based heart contours were available. Results: How contour differences were reflected in NTCP model performance depended on the slope of the predefined model, the dosimetric parameter utilized, and the size of the cohort. The impact of contour differences on NTCP model performance increased with steeper NTCP curves. In our dataset, parameters on the low range of the dose-volume histogram were more robust to contour differences. Conclusions: Our methodology can be used to estimate whether a given contouring model is fit for NTCP model development. For the heart in comparable datasets, average Dice should be at least as high as between our manual and deep learning contours for shallow NTCP relationships (88.5 ± 4.5 %) and higher for steep relationships.

A planning study of proton therapy dose escalation for non-small cell lung cancer.

In non-small-cell lung cancer (NSCLC), improving local control through radiotherapy dose escalation might improve survival. However, a photon-based RCT showed increased organ at risk dose exposure and worse overall survival in the dose escalation arm. In this study, intensity-modulated proton therapy plans with dose escalation to the primary tumour were created for 20 NSCLC patients. The mediastinal envelope was delineated to spare structures around the heart. It was possible to increase primary tumour dose up to 74.0 Gy without a significant increase in organ at risk doses and predicted toxicity.

Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.

BACKGROUND: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours is one of the causes of the lack of generalizability and the suboptimal performance of deep learning (DL) based tumor auto-segmentation models. Therefore, a DL-based method was developed that outputs predicted tumor probabilities for each PET-CT voxel in the form of a probability map instead of one fixed contour. The aim of this study was to show that DL-generated probability maps for tumor segmentation are clinically relevant, intuitive, and a more suitable solution to assist radiation oncologists in gross tumor volume segmentation on PET-CT images of head and neck cancer patients. METHOD: A graphical user interface (GUI) was designed, and a prototype was developed to allow the user to interact with tumor probability maps. Furthermore, a user study was conducted where nine experts in tumor delineation interacted with the interface prototype and its functionality. The participants' experience was assessed qualitatively and quantitatively. RESULTS: The interviews with radiation oncologists revealed their preference for using a rainbow colormap to visualize tumor probability maps during contouring, which they found intuitive. They also appreciated the slider feature, which facilitated interaction by allowing the selection of threshold values to create single contours for editing and use as a starting point. Feedback on the prototype highlighted its excellent usability and positive integration into clinical workflows. CONCLUSIONS: This study shows that DL-generated tumor probability maps are explainable, transparent, intuitive and a better alternative to the single output of tumor segmentation models.

Proton and photon radiotherapy in stage III NSCLC: Effects on hematological toxicity and adjuvant immune therapy.

BACKGROUND AND PURPOSE: Concurrent chemo-radiotherapy (CCRT) followed by adjuvant durvalumab is standard-of-care for fit patients with unresectable stage III NSCLC. Intensity modulated proton therapy (IMPT) results in different doses to organs than intensity modulated photon therapy (IMRT). We investigated whether IMPT compared to IMRT reduce hematological toxicity and whether it affects durvalumab treatment. MATERIALS AND METHODS: Prospectively collected series of consecutive patients with stage III NSCLC receiving CCRT between 06.16 and 12.22 (staged with FDG-PET-CT and brain imaging) were retrospectively analyzed. The primary endpoint was the incidence of lymphopenia grade ≥ 3 in IMPT vs IMRT treated patients. RESULTS: 271 patients were enrolled (IMPT: n = 71, IMRT: n = 200) in four centers. All patients received platinum-based chemotherapy. Median age: 66 years, 58 % were male, 36 % had squamous NSCLC. The incidence of lymphopenia grade ≥ 3 during CCRT was 67 % and 47 % in the IMRT and IMPT group, respectively (OR 2.2, 95 % CI: 1.0-4.9, P = 0.03). The incidence of anemia grade ≥ 3 during CCRT was 26 % and 9 % in the IMRT and IMPT group respectively (OR = 4.9, 95 % CI: 1.9-12.6, P = 0.001). IMPT was associated with a lower rate of Performance Status (PS) ≥ 2 at day 21 and 42 after CCRT (13 % vs. 26 %, P = 0.04, and 24 % vs. 39 %, P = 0.02). Patients treated with IMPT had a higher probability of receiving adjuvant durvalumab (74 % vs. 52 %, OR 0.35, 95 % CI: 0.16-0.79, P = 0.01). CONCLUSION: IMPT was associated with a lower incidence of severe lymphopenia and anemia, better PS after CCRT and a higher probability of receiving adjuvant durvalumab.

Baseline Blood CD8+ T Cell Activation Potency Discriminates Responders from Non-Responders to Immune Checkpoint Inhibition Combined with Stereotactic Radiotherapy in Non-Small-Cell Lung Cancer.

BACKGROUND: Tumor-infiltrating immune cells have been correlated with prognosis for patients treated with immune checkpoint inhibitor (ICI) treatment of various cancers. However, no robust biomarker has been described to predict treatment response yet. We hypothesized that the activation potency of circulating T cells may predict response to ICI treatment. METHODS: An exploratory analysis was conducted to investigate the association between the response to immune checkpoint inhibition (ICI) combined with stereotactic radiotherapy (SBRT) and the potency of circulating T cells to be activated. Blood-derived lymphocytes from 14 patients were stimulated ex vivo with, among others, Staphylococcal enterotoxin B (SEB) and compared to healthy controls (HCs). Patients were grouped into responders (>median progression free survival (PFS)) and non-responders (

Real-World Acute Toxicity and 90-Day Mortality in Patients With Stage I NSCLC Treated With Stereotactic Body Radiotherapy.

INTRODUCTION: Stereotactic body radiotherapy (SBRT) has firmly established its role in stage I NSCLC. Clinical trial results may not fully apply to real-world scenarios. This study aimed to uncover the real-world incidence of acute toxicity and 90-day mortality in patients with SBRT-treated stage I NSCLC and develop prediction models for these outcomes. METHODS: Prospective data from the Dutch Lung Cancer Audit for Radiotherapy (DLCA-R) were collected nationally. Patients with stage I NSCLC (cT1-2aN0M0) treated with SBRT in 2017 to 2021 were included. Acute toxicity was assessed, defined as grade greater than or equal to 2 radiation pneumonitis or grade greater than or equal to 3 non-hematologic toxicity less than or equal to 90 days after SBRT. Prediction models for acute toxicity and 90-day mortality were developed and internally validated. RESULTS: Among 7279 patients, the mean age was 72.5 years, with 21.6% being above 80 years. Most were male (50.7%), had WHO scores 0 to 1 (73.3%), and had cT1a-b tumors (64.6%), predominantly in the upper lobes (65.2%). Acute toxicity was observed in 280 (3.8%) of patients and 90-day mortality in 122 (1.7%). Predictors for acute toxicity included WHO greater than or equal to 2, lower forced expiratory volume in 1 second and diffusion capacity for carbon monoxide, no pathology confirmation, middle or lower lobe tumor location, cT1c-cT2a stage, and higher mean lung dose (c-statistic 0.68). Male sex, WHO greater than or equal to 2, and acute toxicity predicted higher 90-day mortality (c-statistic 0.73). CONCLUSIONS: This nationwide study revealed a low rate of acute toxicity and an acceptable 90-day mortality rate in patients with SBRT-treated stage I NSCLC. Notably, advanced age did not increase acute toxicity or mortality risk. Our predictive models, with satisfactory performance, offer valuable tools for identifying high-risk patients.

Inter- and intrafractional 4D dose accumulation for evaluating ΔNTCP robustness in lung cancer.

BACKGROUND AND PURPOSE: Model-based selection of proton therapy patients relies on a predefined reduction in normal tissue complication probability (NTCP) with respect to photon therapy. The decision is necessarily made based on the treatment plan, but NTCP can be affected when the delivered treatment deviates from the plan due to delivery inaccuracies. Especially for proton therapy of lung cancer, this can be important because of tissue density changes and, with pencil beam scanning, the interplay effect between the proton beam and breathing motion. MATERIALS AND METHODS: In this work, we verified whether the expected benefit of proton therapy is retained despite delivery inaccuracies by reconstructing the delivered treatment using log-file based dose reconstruction and inter- and intrafractional accumulation. Additionally, the importance of two uncertain parameters for treatment reconstruction, namely deformable image registration (DIR) algorithm and α/ß ratio, was assessed. RESULTS: The expected benefit or proton therapy was confirmed in 97% of all studied cases, despite regular differences up to 2 percent point (p.p.) NTCP between the delivered and planned treatments. The choice of DIR algorithm affected NTCP up to 1.6 p.p., an order of magnitude higher than the effect of α/ß ratio. CONCLUSION: For the patient population and treatment technique employed, the predicted clinical benefit for patients selected for proton therapy was confirmed for 97.0% percent of all cases, although the NTCP based proton selection was subject to 2 p.p. variations due to delivery inaccuracies.

Clinical 3D/4D cumulative proton dose assessment methods for thoracic tumours with large motion.

PURPOSE: Despite the anticipated clinical benefits of intensity-modulated proton therapy (IMPT), plan robustness may be compromised due to its sensitivity to patient treatment uncertainties, especially for tumours with large motion. In this study, we investigated treatment course-wise plan robustness for intra-thoracic tumours with large motion comparing a 4D pre-clinical evaluation method (4DREM) to our clinical 3D/4D dose reconstruction and accumulation methods. MATERIALS AND METHODS: Twenty patients with large target motion (>10 mm) were treated with five times layered rescanned IMPT. The 3D-robust optimised plans were generated on the averaged planning 4DCT. Using multiple 4DCTs, treatment plan robustness was assessed on a weekly and treatment course-wise basis through the 3D robustness evaluation method (3DREM, based on averaged 4DCTs), the 4D robustness evaluation method (4DREM, including the time structure of treatment delivery and 4DCT phases) and 4D dose reconstruction and accumulation (4DREAL, based on fraction-wise information). RESULTS: Baseline target motion for all patients ranged from 11-17 mm. For the offline adapted course-wise dose assessment, adequate target dose coverage was found for all patients. The target volume receiving 95% of the prescription dose was consistent between methods with 16/20 patients showing differences < 1%. 4DREAL showed the highest target coverage (99.8 ± 0.6%, p < 0.001), while no differences were observed between 3DREM and 4DREM (99.3 ± 1.3% and 99.4 ± 1.1%, respectively). CONCLUSION: Our results show that intra-thoracic tumours can be adequately treated with IMPT in free breathing for target motion amplitudes up to 17 mm employing any of the accumulation methods. Anatomical changes, setup and range errors demonstrated a more severe impact on target coverage than motion in these patients treated with fractionated proton radiotherapy.

Adjuvant durvalumab after concurrent chemoradiotherapy for patients with unresectable stage III NSCLC harbouring uncommon genomic alterations.

Adjuvant durvalumab is the standard of care for patients with stage III unresectable non-small cell lung cancer (NSCLC), without progression after concurrent chemo-radiation (CCRT). Patients with stage III NSCLC harbouring epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase rearrangements do not seem to benefit from durvalumab. Data are lacking about patients harbouring other driver genomic alterations (dGA). We performed a multicentre (N = 4, Netherlands and Italy) retrospective study including consecutive patients with unresectable stage III NSCLC and treated with CCRT-with or without adjuvant durvalumab-between 2016 and 2022. We enrolled 271 patients; 130 of which received adjuvant durvalumab. Sixty-six patients had dGA (41 KRAS mutations, 4 EGFR common mutations and 21 uncommon dGA). In the entire population, the median PFS was 24.9 months (95% CI 17.5-32.4) and 12.6 months (95% CI 9.0-16.1) with and without durvalumab (p = 0.001). In the dGA group (excluding common EGFR), mPFS was 12.3 months (95% CI 7.8-16.8) with and 7.6 (95% CI 3.4-11.9) without durvalumab (p = 0.038). For patients with KRAS mutations, mPFS was 12.3 months (95% CI 3.6-20.9) with and 7.2 months (95% CI 1.8-12.6) without durvalumab (p = 0.12). Among patients with uncommon dGA, mPFS was 12.9 months (95% CI 8.4-17.4) with and 7.6 months (95% CI 1.4-14) without durvalumab (p = 0.23). We have shown a meaningful survival benefit of adjuvant durvalumab in patients harbouring KRAS mutations and uncommon dGA. This is the largest stage III NSCLC cohort showing the efficacy of durvalumab in patients with uncommon dGA. Further prospective studies are needed to confirm our results.

Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning.

BACKGROUND AND PURPOSE: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-small cell lung cancer (NSCLC) patients who received definitive radiotherapy by considering clinical variables and image features from pre-treatment CT-scans. MATERIALS AND METHODS: NSCLC patients who received stereotactic radiotherapy were prospectively collected at the UMCG and split into a training and a hold out test set including 189 and 81 patients, respectively. External validation was performed on 228 NSCLC patients who were treated with radiation or concurrent chemoradiation at the Maastro clinic (Lung1 dataset). A hybrid model that integrated both image and clinical features was implemented using deep learning. Image features were learned from cubic patches containing lung tumours extracted from pre-treatment CT scans. Relevant clinical variables were selected by univariable and multivariable analyses. RESULTS: Multivariable analysis showed that age and clinical stage were significant prognostic clinical factors for 2-year OS. Using these two clinical variables in combination with image features from pre-treatment CT scans, the hybrid model achieved a median AUC of 0.76 [95 % CI: 0.65-0.86] and 0.64 [95 % CI: 0.58-0.70] on the complete UMCG and Maastro test sets, respectively. The Kaplan-Meier survival curves showed significant separation between low and high mortality risk groups on these two test sets (log-rank test: p-value < 0.001, p-value = 0.012, respectively) CONCLUSION: We demonstrated that a hybrid model could achieve reasonable performance by utilizing both clinical and image features for 2-year OS prediction. Such a model has the potential to identify patients with high mortality risk and guide clinical decision making.

The Dutch Lung Cancer Audit-Radiotherapy (DLCA-R): Real-World Data on Stage III Non-Small Cell Lung Cancer Patients Treated With Curative Chemoradiation.

INTRODUCTION: Chemoradiotherapy (CRT) is the standard of care in inoperable non-small-cell lung cancer (NSCLC) patients, favoring concurrent (cCRT) over sequential CRT (seqCRT), with adjuvant immunotherapy in responders. Elderly and frail NSCLC patients have generally been excluded from trials in the past. In elderly patients however, the higher treatment related morbidity of cCRT, may outweigh the possible lower tumor control of seqCRT. For elderly patients with locally advanced NSCLC real-world data is essential to be able to balance treatment toxicity and treatment outcome. The aim of this study is to analyze acute toxicity and 3-month mortality of curative chemoradiation (CRT) in patients with stage III NSCLC and to analyze whether cCRT for elderly stage III NSCLC patients is safe. METHODS: The Dutch Lung Cancer Audit-Radiotherapy (DLCA-R) is a national lung cancer audit that started in 2013 for patients treated with curative intent radiotherapy. All Dutch patients treated for stage III NSCLC between 2015 and 2018 with seqCRT or cCRT for (primary or recurrent) stage III lung cancer are included in this population-based study. Information was collected on patient, tumor- and treatment characteristics and the incidence and severity of acute non-hematological toxicity (CTCAE-4 version 4.03) and mortality within 3 months after the end of radiotherapy. To evaluate the association between prognostic factors and outcome (acute toxicity and mortality within 3 months), an univariable and multivariable analysis was performed. The definition of cCRT was:radiotherapy started within 30 days after the start of chemotherapy. RESULTS: Out of all 20 Dutch departments of radiation oncology, 19 centers participated in the registry. A total of 2942 NSCLC stage III patients were treated with CRT. Of these 67.2% (n = 1977) were treated with cCRT (median age 66 years) and 32.8% (n = 965) were treated with seqCRT (median age 69 years). Good performance status (WHO 0-1) was scored in 88.6% for patients treated with cCRT and in 71.0% in the patients treated with seqCRT. Acute nonhematological 3-month toxicity (CTCAE grade ≥3 or radiation pneumonitis grade ≥2) was scored in 21.9% of the patients treated with cCRT and in 17.7% of the patients treated with seqCRT. The univariable analysis for acute toxicity showed significantly increased toxicity for cCRT (P = .008), WHO ≥2 (P = .006), and TNM IIIC (P = .031). The multivariable analysis for acute toxicity was significant for cCRT (P = .015), WHO ≥2 (P = .001) and TNM IIIC (P = .016). The univariable analysis for 3-month mortality showed significance for seqCRT (P = .025), WHO ≥2 (P < .001), higher cumulative radiotherapy dose (P < .001), higher gross tumor volume total (P = .020) and male patients (p < .001). None of these variables reached significance in the multivariable analysis for 3-month mortality. CONCLUSION: In this national lung cancer audit of inoperable NSCLC patients, 3-month toxicity was significantly higher in patients treated with cCRT (21.9% vs. 17.7% for seqCRT) higher TNM stage IIIC, and poor performance (WHO≥2) patients.The 3-months mortality was not significantly different for tested parameters. Age was not a risk factor for acute toxicity, nor 3 months mortality.

External validation of NTCP-models for radiation pneumonitis in lung cancer patients treated with chemoradiotherapy.

PURPOSE: Normal tissue complication probability (NTCP) models can be used to estimate the risk of radiation pneumonitis (RP). The aim of this study was to externally validate the most frequently used prediction models for RP, i.e., the QUANTEC and APPELT models, in a large cohort of lung cancer patients treated with IMRT or VMAT. [1-2] METHODS AND MATERIALS: This prospective cohort study, included lung cancer patients treated between 2013 and 2018. A closed testing procedure was performed to test the need for model updating. To improve model performance, modification or removal of variables was considered. Performance measures included tests for goodness of fit, discrimination, and calibration. RESULTS: In this cohort of 612 patients, the incidence of RP ≥ grade 2 was 14.5%. For the QUANTEC-model, recalibration was recommended which resulted in a revised intercept and adjusted regression coefficient (from 0.126 to 0.224) of the mean lung dose (MLD),. The APPELT-model needed revision including model updating with modification and elimination of variables. After revision, the New RP-model included the following predictors (and regression coefficients): MLD (B = 0.250), age (B = 0.049, and smoking status (B = 0.902). The discrimination of the updated APPELT-model was higher compared to the recalibrated QUANTEC-model (AUC: 0.79 vs. 0.73). CONCLUSIONS: This study demonstrated that both the QUANTEC- and APPELT-model needed revision. Next to changes of the intercept and regression coefficients, the APPELT model improved further by model updating and performed better than the recalibrated QUANTEC model. This New RP-model is widely applicable containing non-tumour site specific variables, which can easily be collected.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

BACKGROUND: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations. PURPOSE: In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible. METHODS: A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals. RESULTS: 4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D98 between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues. CONCLUSION: In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing.

BACKGROUND AND PURPOSE: Large radiotherapy (RT) planning imaging datasets with consistently contoured cardiovascular structures are essential for robust cardiac radiotoxicity research in thoracic cancers. This study aims to develop and validate a highly accurate automatic contouring model for the heart, cardiac chambers, and great vessels for RT planning computed tomography (CT) images that can be used for dose-volume parameter estimation. MATERIALS AND METHODS: A neural network model was trained using a dataset of 127 expertly contoured planning CT images from RT treatment of locally advanced non-small-cell lung cancer (NSCLC) patients. Evaluation of geometric accuracy and quality of dosimetric parameter estimation was performed on 50 independent scans with contrast and without contrast enhancement. The model was further evaluated regarding the clinical acceptability of the contours in 99 scans randomly sampled from the RTOG-0617 dataset by three experienced radiation oncologists. RESULTS: Median surface dice at 3 mm tolerance for all dedicated thoracic structures was 90% in the test set. Median absolute difference between mean dose computed with model contours and expert contours was 0.45 Gy averaged over all structures. The mean clinical acceptability rate by majority vote in the RTOG-0617 scans was 91%. CONCLUSION: This model can be used to contour the heart, cardiac chambers, and great vessels in large datasets of RT planning thoracic CT images accurately, quickly, and consistently. Additionally, the model can be used as a time-saving tool for contouring in clinic practice.

Trends and variations in the treatment of stage I-III small cell lung cancer from 2008 to 2019: A nationwide population-based study from the Netherlands.

OBJECTIVES: Recent treatment patterns for small cell lung cancer (SCLC) in the Netherlands were unknown. This nationwide population-based study describes trends and variations in the treatment of stage I-III SCLC in the Netherlands over the period 2008-2019. MATERIALS AND METHODS: Patients were selected from the population-based Netherlands Cancer Registry. Treatments were studied stratified for clinical stage. In stage II-III, factors associated with the use of concurrent (cCRT) versus sequential chemoradiation (sCRT) and accelerated versus conventionally fractionated radiotherapy in the context of cCRT were identified. RESULTS: In stage I (N = 535), 29% of the patients underwent surgery in 2008-2009 which increased to 44% in 2018-2019. Combined use of chemotherapy and radiotherapy decreased in stage I from 47% to 15%, remained constant (64%) in stage II (N = 472), and increased from 57% (2008) to 70% (2019) in stage III (N = 5,571). Use of cCRT versus sCRT in stage II-III increased over time (odds ratio (OR) 2008-2011 vs 2016-2019: 0.53 (95%-confidence interval (95%CI): 0.41-0.69)) and was strongly associated with lower age, WHO performance status 0, and diagnosis in a hospital with in-house radiotherapy. Forty-six percent of patients with stage III received cCRT in 2019. Until 2012, concurrent radiotherapy was mainly conventionally fractionated, thereafter a hyperfractionated accelerated scheme was administered more frequently (57%). Accelerated radiotherapy was strongly associated with geographic region (ORsouth vs north: 4.13 (95%CI: 3.00-5.70)), WHO performance (OR1 vs 0: 0.50 (95%CI: 0.35-0.71)), and radiotherapy facilities treating ≥ 16 vs < 16 SCLC patients annually (OR: 3.01 (95%CI: 2.38-3.79)). CONCLUSIONS: The use of surgery increased in stage I. In stages II and III, the use of cCRT versus sCRT increased over time, and since 2012 most radiotherapy in cCRT was accelerated. Treatment regimens and radiotherapy fractionation schemes varied between patient groups, regions and hospitals. Possible unwarranted treatment variation should be countered.

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer.

PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients. METHODS: A dataset of 33 thoracic cancer patients, containing CBCTs, same-day repeat CTs (rCT), planning-CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT-based correction method. Mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU-accuracy of sCTs in terms of range errors. RESULTS: On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%). CONCLUSION: CBCT-based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.

Towards the clinical implementation of intensity-modulated proton therapy for thoracic indications with moderate motion: Robust optimised plan evaluation by means of patient and machine specific information.

PURPOSE: Compared to volumetric modulated arc therapy (VMAT), clinical benefits are anticipated when treating thoracic tumours with intensity-modulated proton therapy (IMPT). However, the current concern of plan robustness as a result of motion hampers its wide clinical implementation. To define an optimal protocol to treat lung and oesophageal cancers, we present a comprehensive evaluation of IMPT planning strategies, based on patient 4DCTs and machine log files. MATERIALS AND METHODS: For ten lung and ten oesophageal cancer patients, a planning 4DCT and weekly repeated 4DCTs were collected. For these twenty patients, the CTV volume and motion were assessed based on the 4DCTs. In addition to clinical VMAT plans, layered rescanned 3D and 4D robust optimised IMPT plans (IMPT_3D and IMPT_4D respectively) were generated, and approved clinically, for all patients. The IMPT plans were then delivered in dry runs at our proton facility to obtain log files, and subsequently evaluated through our 4D robustness evaluation method (4DREM). With this method, for each evaluated plan, fourteen 4D accumulated scenario doses were obtained, representing 14 possible fractionated treatment courses. RESULTS: From VMAT to IMPT_3D, nominal Dmean(lungs-GTV) decreased 2.75 ± 0.56 GyRBE and 3.76 ± 0.92 GyRBE over all lung and oesophageal cancer patients, respectively. A more pronounced reduction was verified for Dmean(heart): 5.38 ± 7.36 GyRBE (lung cases) and 9.51 ± 2.25 GyRBE (oesophagus cases). Target coverage robustness of IMPT_3D was sufficient for 18/20 patients. Averaged dose in critical structures over all 4DREM scenarios changed only slightly for both IMPT_3D and IMPT_4D. Relative to IMPT_3D, no gain in IMPT_4D was observed. CONCLUSION: The dosimetric superiority of IMPT over VMAT has been established. For most thoracic tumours, our IMPT_3D planning protocol showed to be robust and clinically suitable. Nevertheless, accurate patient positioning and adapting to anatomical variations over the course of treatment remain compulsory.

Evaluation of continuous beam rescanning versus pulsed beam in pencil beam scanned proton therapy for lung tumours.

The treatment of moving targets with pencil beam scanned proton therapy (PBS-PT) may rely on rescanning strategies to smooth out motion induced dosimetric disturbances. PBS-PT machines, such as Proteus®Plus (PPlus) and Proteus®One (POne), deliver a continuous or a pulsed beam, respectively. In PPlus, scaled (or no) rescanning can be applied, while POne implies intrinsic 'rescanning' due to its pulsed delivery. We investigated the efficacy of these PBS-PT delivery types for the treatment of lung tumours. In general, clinically acceptable plans were achieved, and PPlus and POne showed similar effectiveness.

Evaluation of interplay and organ motion effects by means of 4D dose reconstruction and accumulation.

PURPOSE: Pencil beam scanned proton therapy (PBS-PT) treatment quality might be compromised by interplay and motion effects. Via fraction-wise reconstruction of 4D dose distributions and dose accumulation, we assess the clinical relevance of motion related target dose degradation in thoracic cancer patients. METHODS AND MATERIALS: For the ten thoracic patients (Hodgkin lymphoma and non-small cell lung cancer) treated at our proton therapy facility, daily breathing pattern records, treatment delivery log-files and weekly repeated 4DCTs were collected. Patients exhibited point-max target motion of up to 20 mm. They received robustly optimized treatment plans, delivered with five-times rescanning in fractionated regimen. Treatment delivery records were used to reconstruct 4D dose distributions and the accumulated treatment course dose per patient. Fraction-wise target dose degradations were analyzed and the accumulated treatment course dose, representing an estimation of the delivered dose, was compared with the prescribed dose. RESULTS: No clinically relevant loss of target dose homogeneity was found in the fraction-wise reconstructed 4D dose distributions. Overall, in 97% of all reconstructed fraction doses, D98 remained within 5% from the prescription dose. The V95 of accumulated treatment course doses was higher than 99.7% for all ten patients. CONCLUSIONS: 4D dose reconstruction and accumulation enables the clinical estimation of actual exhibited interplay and motion effects. In the patients considered here, the loss of homogeneity caused by interplay and organ motion did not show systematic pattern and smeared out throughout the course of fractionated PBS-PT treatment. Dose degradation due to anatomical changes showed to be more severe and triggered treatment adaptations for five patients.