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.

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.

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.

Feasibility of patient specific quality assurance for proton therapy based on independent dose calculation and predicted outcomes.

PURPOSE: Patient specific quality assurance (PSQA) is required to verify the treatment delivery and the dose calculation by the treatment planning system (TPS). The objective of this work is to demonstrate the feasibility to substitute resource consuming measurement based PSQA (PSQAM) by independent dose recalculations (PSQAIDC), and that PSQAIDC results may be interpreted in a clinically relevant manner using normal tissue complication probability (NTCP) and tumor control probability (TCP) models. METHODS AND MATERIALS: A platform for the automatic execution of the two following PSQAIDC workflows was implemented: (i) using the TPS generated plan and (ii) using treatment delivery log files (log-plan). 30 head and neck cancer (HNC) patients were retrospectively investigated. PSQAM results were compared with those from the two PSQAIDC workflows. TCP/NTCP variations between PSQAIDC and the initial TPS dose distributions were investigated. Additionally, for two example patients that showed low passing PSQAM results, eight error scenarios were simulated and verified via measurements and log-plan based calculations. For all error scenarios ΔTCP/NTCP values between the nominal and the log-plan dose were assessed. RESULTS: Results of PSQAM and PSQAIDC from both implemented workflows agree within 2.7% in terms of gamma pass ratios. The verification of simulated error scenarios shows comparable trends between PSQAM and PSQAIDC. Based on the 30 investigated HNC patients, PSQAIDC observed dose deviations translate into a minor variation in NTCP values. As expected, TCP is critically related to observed dose deviations. CONCLUSIONS: We demonstrated a feasibility to substitute PSQAM with PSQAIDC. In addition, we showed that PSQAIDC results can be interpreted in clinically more relevant manner, for instance using TCP/NTCP.