Treatment planning comparison for head and neck cancer between photon, proton, and combined proton-photon therapy - From a fixed beam line to an arc.

BACKGROUND AND PURPOSE: This study investigates whether combined proton-photon therapy (CPPT) improves treatment plan quality compared to single-modality intensity-modulated radiation therapy (IMRT) or intensity-modulated proton therapy (IMPT) for head and neck cancer (HNC) patients. Different proton beam arrangements for CPPT and IMPT are compared, which could be of specific interest concerning potential future upright-positioned treatments. Furthermore, it is evaluated if CPPT benefits remain under inter-fractional anatomical changes for HNC treatments. MATERIAL AND METHODS: Five HNC patients with a planning CT and multiple (4-7) repeated CTs were studied. CPPT with simultaneously optimized photon and proton fluence, single-modality IMPT, and IMRT treatment plans were optimized on the planning CT and then recalculated and reoptimized on each repeated CT. For CPPT and IMPT, plans with different degrees of freedom for the proton beams were optimized. Fixed horizontal proton beam line (FHB), gantry-like, and arc-like plans were compared. RESULTS: The target coverage for CPPT without adaptation is insufficient (average V95%=88.4 %), while adapted plans can recover the initial treatment plan quality for target (average V95%=95.5 %) and organs-at-risk. CPPT with increased proton beam flexibility increases plan quality and reduces normal tissue complication probability of Xerostomia and Dysphagia. On average, Xerostomia NTCP reductions compared to IMRT are -2.7 %/-3.4 %/-5.0 % for CPPT FHB/CPPT Gantry/CPPT Arc. The differences for IMPT FHB/IMPT Gantry/IMPT Arc are + 0.8 %/-0.9 %/-4.3 %. CONCLUSION: CPPT for HNC needs adaptive treatments. Increasing proton beam flexibility in CPPT, either by using a gantry or an upright-positioned patient, improves treatment plan quality. However, the photon component is substantially reduced, therefore, the balance between improved plan quality and costs must be further determined.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications.

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Patient-specific quality assurance for deformable IMRT/IMPT dose accumulation: Proposition and validation of energy conservation based validation criterion.

BACKGROUND: Deformable image registration (DIR)-based dose accumulation (DDA) is regularly used in adaptive radiotherapy research. However, the applicability and reliability of DDA for direct clinical usage are still being debated. One primary concern is the validity of DDA, particularly for scenarios with substantial anatomical changes, for which energy-conservation problems were observed in conceptual studies. PURPOSE: We present and validate an energy-conservation (EC)-based DDA validation workflow and further investigate its usefulness for actual patient data, specifically for lung cancer cases. METHODS: For five non-small cell lung cancer (NSCLC) patients, DDA based on five selective DIR methods were calculated for five different treatment plans, which include one intensity-modulated photon therapy (IMRT), two intensity-modulated proton therapy (IMPT), and two combined proton-photon therapy (CPPT) plans. All plans were optimized on the planning CT (planCT) acquired in deep inspiration breath-hold (DIBH) and were re-optimized on the repeated DIBH CTs of three later fractions. The resulting fractional doses were warped back to the planCT using each DIR. An EC-based validation of the accumulation process was implemented and applied to all DDA results. Correlations between relative organ mass/volume variations and the extent of EC violation were then studied using Bayesian linear regression (BLR). RESULTS: For most OARs, EC violation within 10% is observed. However, for the PTVs and GTVs with substantial regression, severe overestimation of the fractional energy was found regardless of treatment type and applied DIR method. BLR results show that EC violation is linearly correlated to the relative mass variation (R^2 > 0.95) and volume variation (R^2 > 0.60). CONCLUSION: DDA results should be used with caution in regions with high mass/volume variation for intensity-based DIRs. EC-based validation is a useful approach to provide patient-specific quality assurance of the validity of DDA in radiotherapy.

Combined proton-photon therapy for non-small cell lung cancer.

PURPOSE: Advanced non-small cell lung cancer (NSCLC) is still a challenging indication for conventional photon radiotherapy. Proton therapy has the potential to improve outcomes, but proton treatment slots remain a limited resource despite an increasing number of proton therapy facilities. This work investigates the potential benefits of optimally combined proton-photon therapy delivered using a fixed horizontal proton beam line in combination with a photon Linac, which could increase accessibility to proton therapy for such a patient cohort. MATERIALS AND METHODS: A treatment planning study has been conducted on a patient cohort of seven advanced NSCLC patients. Each patient had a planning computed tomography scan (CT) and multiple repeated CTs from three different days and for different breath-holds on each day. Treatment plans for combined proton-photon therapy (CPPT) were calculated for individual patients by optimizing the combined cumulative dose on the initial planning CT only (non-adapted) as well as on each daily CT respectively (adapted). The impact of inter-fractional changes and/or breath-hold variability was then assessed on the repeat breath-hold CTs. Results were compared to plans for IMRT or IMPT alone, as well as against combined treatments assuming a proton gantry. Plan quality was assessed in terms of dosimetric, robustness and NTCP metrics. RESULTS: Combined treatment plans improved plan quality compared to IMRT treatments, especially in regard to reductions of low and medium doses to organs at risk (OARs), which translated into lower NTCP estimates for three side effects. For most patients, combined treatments achieved results close to IMPT-only plans. Inter-fractional changes impact mainly the target coverage of combined and IMPT treatments, while OARs doses were less affected by these changes. With plan adaptation however, target coverage of combined treatments remained high even when taking variability between breath-holds into account. CONCLUSIONS: Optimally combined proton-photon plans improve treatment plan quality compared to IMRT only, potentially reducing the risk of toxicity while also allowing to potentially increase accessibility to proton therapy for NSCLC patients.

Preselection of robust radiomic features does not improve outcome modelling in non-small cell lung cancer based on clinical routine FDG-PET imaging.

BACKGROUND: Radiomics is a promising tool for identifying imaging-based biomarkers. Radiomics-based models are often trained on single-institution datasets; however, multi-centre imaging datasets are preferred for external generalizability owing to the influence of inter-institutional scanning differences and acquisition settings. The study aim was to determine the value of preselection of robust radiomic features in routine clinical positron emission tomography (PET) images to predict clinical outcomes in locally advanced non-small cell lung cancer (NSCLC). METHODS: A total of 1404 primary tumour radiomic features were extracted from pre-treatment [18F]fluorodeoxyglucose (FDG)-PET scans of stage IIIA/N2 or IIIB NSCLC patients using a training cohort (n = 79; prospective Swiss multi-centre randomized phase III trial SAKK 16/00; 16 centres) and an internal validation cohort (n = 31; single centre). Robustness studies investigating delineation variation, attenuation correction and motion were performed (intraclass correlation coefficient threshold > 0.9). Two 12-/24-month event-free survival (EFS) and overall survival (OS) logistic regression models were trained using standardized imaging: (1) with robust features alone and (2) with all available features. Models were then validated using fivefold cross-validation, and validation on a separate single-centre dataset. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS: Robustness studies identified 179 stable features (13%), with 25% stable features for 3D versus 4D acquisition, 31% for attenuation correction and 78% for delineation. Univariable analysis found no significant robust features predicting 12-/24-month EFS and 12-month OS (p value > 0.076). Prognostic models without robust preselection performed well for 12-month EFS in training (AUC = 0.73) and validation (AUC = 0.74). Patient stratification into two risk groups based on 12-month EFS was significant for training (p value = 0.02) and validation cohorts (p value = 0.03). CONCLUSIONS: A PET-based radiomics model using a standardized, multi-centre dataset to predict EFS in locally advanced NSCLC was successfully established and validated with good performance. Prediction models with robust feature preselection were unsuccessful, indicating the need for a standardized imaging protocol.

An approach for estimating dosimetric uncertainties in deformable dose accumulation in pencil beam scanning proton therapy for lung cancer.

Deformable image registration (DIR) is an important component for dose accumulation and associated clinical outcome evaluation in radiotherapy. However, the resulting deformation vector field (DVF) is subject to unavoidable discrepancies when different algorithms are applied, leading to dosimetric uncertainties of the accumulated dose. We propose here an approach for proton therapy to estimate dosimetric uncertainties as a consequence of modeled or estimated DVF uncertainties. A patient-specific DVF uncertainty model was built on the first treatment fraction, by correlating the magnitude differences of five DIR results at each voxel to the magnitude of any single reference DIR. In the following fractions, only the reference DIR needs to be applied, and DVF geometric uncertainties were estimated by this model. The associated dosimetric uncertainties were then derived by considering the estimated geometric DVF uncertainty, the dose gradient of fractional recalculated dose distribution and the direction factor from the applied reference DIR of this fraction. This estimated dose uncertainty was respectively compared to the reference dose uncertainty when different DIRs were applied individually for each dose warping. This approach was validated on seven NSCLC patients, each with nine repeated CTs. The proposed model-based method is able to achieve dose uncertainty distribution on a conservative voxel-to-voxel comparison within ±5% of the prescribed dose to the 'reference' dosimetric uncertainty, for 77% of the voxels in the body and 66%-98% of voxels in investigated structures. We propose a method to estimate DIR induced uncertainties in dose accumulation for proton therapy of lung tumor treatments.

Comparison of robust to standardized CT radiomics models to predict overall survival for non-small cell lung cancer patients.

BACKGROUND: Radiomics is a promising tool for the identification of new prognostic biomarkers. Radiomic features can be affected by different scanning protocols, often present in retrospective and prospective clinical data. We compared a computed tomography (CT) radiomics model based on a large but highly heterogeneous multicentric image dataset with robust feature pre-selection to a model based on a smaller but standardized image dataset without pre-selection. MATERIALS AND METHODS: Primary tumor radiomics was extracted from pre-treatment CTs of IIIA/N2/IIIB NSCLC patients from a prospective Swiss multicentric randomized trial (npatient  = 124, ninstitution  = 14, SAKK 16/00) and a validation dataset (npatient  = 31, ninstitution  = 1). Four robustness studies investigating inter-observer delineation variation, motion, convolution kernel, and contrast were conducted to identify robust features using an intraclass correlation coefficient threshold >0.9. Two 12-months overall survival (OS) logistic regression models were trained: (a) on the entire multicentric heterogeneous dataset but with robust feature pre-selection (MCR) and (b) on a smaller standardized subset using all features (STD). Both models were validated on the validation dataset acquired with similar reconstruction parameters as the STD dataset. The model performances were compared using the DeLong test. RESULTS: In total, 113 stable features were identified (nshape  = 8, nintensity  = 0, ntexture  = 7, nwavelet  = 98). The convolution kernel had the strongest influence on the feature robustness (<20% stable features). The final models of MCR and STD consisted of one and two features respectively. Both features of the STD model were identified as non-robust. MCR did not show performance significantly different from STD on the validation cohort (AUC [95%CI] = 0.72 [0.48-0.95] and 0.79 [0.63-0.95], p = 0.59). CONCLUSION: Prognostic OS CT radiomics model for NSCLC based on a heterogeneous multicentric imaging dataset with robust feature pre-selection performed equally well as a model on a standardized dataset.