RaySearch Americas Inc. (NY) has introduced a commercial Monte Carlo dose algorithm (RS-MC) for routine clinical use in proton spot scanning. In this report, we provide a validation of this algorithm ...against phantom measurements and simulations in the GATE software package. We also compared the performance of the RayStation analytical algorithm (RS-PBA) against the RS-MC algorithm. A beam model (G-MC) for a spot scanning gantry at our proton center was implemented in the GATE software package. The model was validated against measurements in a water phantom and was used for benchmarking the RS-MC. Validation of the RS-MC was performed in a water phantom by measuring depth doses and profiles for three spread-out Bragg peak (SOBP) beams with normal incidence, an SOBP with oblique incidence, and an SOBP with a range shifter and large air gap. The RS-MC was also validated against measurements and simulations in heterogeneous phantoms created by placing lung or bone slabs in a water phantom. Lateral dose profiles near the distal end of the beam were measured with a microDiamond detector and compared to the G-MC simulations, RS-MC and RS-PBA. Finally, the RS-MC and RS-PBA were validated against measured dose distributions in an Alderson-Rando (AR) phantom. Measurements were made using Gafchromic film in the AR phantom and compared to doses using the RS-PBA and RS-MC algorithms. For SOBP depth doses in a water phantom, all three algorithms matched the measurements to within ±3% at all points and a range within 1 mm. The RS-PBA algorithm showed up to a 10% difference in dose at the entrance for the beam with a range shifter and >30 cm air gap, while the RS-MC and G-MC were always within 3% of the measurement. For an oblique beam incident at 45°, the RS-PBA algorithm showed up to 6% local dose differences and broadening of distal fall-off by 5 mm. Both the RS-MC and G-MC accurately predicted the depth dose to within ±3% and distal fall-off to within 2 mm. In an anthropomorphic phantom, the gamma index (dose tolerance = 3%, distance-to-agreement = 3 mm) was greater than 90% for six out of seven planes using the RS-MC, and three out seven for the RS-PBA. The RS-MC algorithm demonstrated improved dosimetric accuracy over the RS-PBA in the presence of homogenous, heterogeneous and anthropomorphic phantoms. The computation performance of the RS-MC was similar to the RS-PBA algorithm. For complex disease sites like breast, head and neck, and lung cancer, the RS-MC algorithm will provide significantly more accurate treatment planning.
The best survival for patients with unresectable, locally advanced NSCLC is currently achieved through concurrent chemoradiation followed by durvalumab for a year. Despite the best standard of care ...treatment, the majority of patients still develop disease recurrence, which could be distant and/or local. Trials continue to try and improve outcomes for patients with unresectable NSCLC, typically through treatment intensification, with the addition of more systemic agents, or more radiation dose to the tumor. Although RTOG 0617 showed that uniform dose escalation across an unselected population of patients undergoing chemoradiation is not beneficial, efforts continue to select patients and tumor subsets that are likely to benefit from dose escalation. This review describes some of the ongoing therapeutic trials in unresectable NSCLC, with an emphasis on quantitative imaging and precision radiation dose escalation.
Background
Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy.
Purpose
To conduct a technical exploration of ...longitudinal changes in tumor heterogeneity patterns on dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI), diffusion‐weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer.
Study Type
Prospective observational study with longitudinal MRI and PET/CT pre‐RT, early‐RT (2 weeks), and mid‐RT (5 weeks).
Population
Twenty‐one FIGO IB2‐IVA cervical cancer patients receiving definitive external beam RT and brachytherapy.
Field Strength/Sequence
1.5T, precontrast axial T1‐weighted, axial and sagittal T2‐weighted, sagittal DWI (multi‐b values), sagittal DCE MRI (<10 sec temporal resolution), postcontrast axial T1‐weighted.
Assessment
Response assessment 1 month after completion of treatment by a board‐certified radiation oncologist from manually delineated tumor volume changes.
Statistical Tests
Intensity histogram (IH) quantiles (DCE SI10% and DWI ADC10%, FDG‐PET SUVmax) and distribution moments (mean, variance, skewness, kurtosis) were extracted. Differences in IH features between timepoints and modalities were evaluated by Skillings–Mack tests with Holm's correction. Area under receiver‐operating characteristic curve (AUC) and Mann–Whitney testing was performed to discriminate treatment response using IH features.
Results
Tumor IH means and quantiles varied significantly during RT (SUVmean: ↓28–47%, SUVmax: ↓30–59%, SImean: ↑8–30%, SI10%: ↑8–19%, ADCmean: ↑16%, P < 0.02 for each). Among IH heterogeneity features, FDG‐PET SUVCoV (↓16–30%, P = 0.011) and DW‐MRI ADCskewness decreased (P = 0.001). FDG‐PET SUVCoV was higher than DCE‐MRI SICoV and DW‐MRI ADCCoV at baseline (P < 0.001) and 2 weeks (P = 0.010). FDG‐PET SUVkurtosis was lower than DCE‐MRI SIkurtosis and DW‐MRI ADCkurtosis at baseline (P = 0.001). Some IH features appeared to associate with favorable tumor response, including large early RT changes in DW‐MRI ADCskewness (AUC = 0.86).
Data Conclusion
Preliminary findings show tumor heterogeneity was variable between patients, modalities, and timepoints. Radiomic assessment of changing tumor heterogeneity has the potential to personalize treatment and power outcome prediction.
Level of Evidence: 2
Technical Efficacy: Stage 3
J. Magn. Reson. Imaging 2018;47:1388–1396.
Charged particle therapy with proton beam therapy (PBT) and carbon ion radiotherapy (CIRT) has emerged as a promising radiation modality to minimize radiation hepatotoxicity while maintaining high ...rates of tumor local control. Both PBT and CIRT deposit the majority of their dose at the Bragg peak with little to no exit dose, resulting in superior sparing of normal liver tissue. CIRT has an additional biological advantage of increased relative biological effectiveness, which may allow for increased hypofractionation regimens. Retrospective and prospective studies have demonstrated encouragingly high rates of local control and overall survival and low rates of hepatotoxicity with PBT and CIRT. Ongoing randomized trials will evaluate the value of PBT over photons and other standard liver-directed therapies and future randomized trials are needed to assess the value of CIRT over PBT.
The purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS).
A 3-dimensional (3D) U-Net model has been ...implemented in a commercial treatment planning system (TPS) that uses contoured regions of interest (ROI) binary masks as model inputs with a predicted dose distribution as the model output. Predicted dose distributions were converted to deliverable PBS treatment plans using a voxel-wise robust dose mimicking optimization algorithm. This model was leveraged to generate machine learning (ML) optimized plans for patients receiving proton PBS irradiation of the chest wall. Model training was carried out on a retrospective set of 48 previously-treated chest wall patient treatment plans. Model evaluation was carried out by generating ML-optimized plans on a hold-out set of 12 contoured chest wall patient CT datasets from previously treated patients. Clinical goal criteria and gamma analysis were used to compare dose distributions of the ML-optimized plans against the clinically approved plans across the test patients.
Statistical analysis of mean clinical goal criteria indicates that compared to the clinical plans, the ML optimization workflow generated robust plans with similar dose to the heart, lungs, and esophagus while achieving superior dosimetric coverage to the PTV chest wall (clinical mean V95 = 97.6% vs. ML mean V95 = 99.1%, p < 0.001) across the 12 test patients.
ML-based automated treatment plan optimization using the 3D U-Net model can generate treatment plans of similar clinical quality compared to human-driven optimization.
Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of ...radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 0.67, 0.75) than CT radiomics (c-index 0.64 0.60, 0.71) or perfusion SPECT radiomics (c-index 0.60 0.57, 0.63). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 0.67, 0.75) outperformed benchmark clinical imaging (c-index 0.65 0.59, 0.71) and FDG-PET delta radiomics (c-index 0.52 0.48, 0.58) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.
Abstract
BACKGROUND
Spinal cord dose limits are critically important for the safe practice of spine stereotactic body radiotherapy (SBRT). However, the effect of inherent spinal cord motion on cord ...dose in SBRT is unknown.
OBJECTIVE
To assess the effects of cord motion on spinal cord dose in SBRT.
METHODS
Dynamic balanced fast field echo (BFFE) magnetic resonance imaging (MRI) was obtained in 21 spine metastasis patients treated with SBRT. Planning computed tomography (CT), conventional static T2-weighted MRI, BFFE MRI, and dose planning data were coregistered. Spinal cord from the dynamic BFFE images (corddyn) was compared with the T2-weighted MRI (cordstat) to analyze motion of corddyn beyond the cordstat (Dice coefficient, Jaccard index), and beyond cordstat with added planning organ at risk volume (PRV) margins. Cord dose was compared between cordstat, and corddyn (Wilcoxon signed-rank test).
RESULTS
Dice coefficient (0.70-0.95, median 0.87) and Jaccard index (0.54-0.90, median 0.77) demonstrated motion of corddyn beyond cordstat. In 62% of the patients (13/21), the dose to corddyn exceeded that of cordstat by 0.6% to 13.8% (median 4.3%). The corddyn spatially excursed outside the 1-mm PRV margin of cordstat in 9 patients (43%); among these dose to corddyn exceeded dose to cordstat >+ 1-mm PRV margin in 78% of the patients (7/9). Corddyn did not excurse outside the 1.5-mm or 2-mm PRV cord cordstat margin.
CONCLUSION
Spinal cord motion may contribute to increases in radiation dose to the cord from SBRT for spine metastasis. A PRV margin of at least 1.5 to 2 mm surrounding the cord should be strongly considered to account for inherent spinal cord motion.
Abstract Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. ...Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.
Image heterogeneity metrics such as textural features are an active area of research for evaluating clinical outcomes with positron emission tomography (PET) imaging and other modalities. However, ...the effects of stochastic image acquisition noise on these metrics are poorly understood. We performed a simulation study by generating 50 statistically independent PET images of the NEMA IQ phantom with realistic noise and resolution properties. Heterogeneity metrics based on gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, and zone size matrices were evaluated within regions of interest surrounding the lesions. The impact of stochastic variability was evaluated with percent difference from the mean of the 50 realizations, coefficient of variation and estimated sample size for clinical trials. Additionally, sensitivity studies were performed to simulate the effects of patient size and image reconstruction method on the quantitative performance of these metrics. Complex trends in variability were revealed as a function of textural feature, lesion size, patient size, and reconstruction parameters. In conclusion, the sensitivity of PET textural features to normal stochastic image variation and imaging parameters can be large and is feature-dependent. Standards are needed to ensure that prospective studies that incorporate textural features are properly designed to measure true effects that may impact clinical outcomes.
PDF
