NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.

Feature selection based on unsupervised clustering evaluation for predicting neoadjuvant chemoradiation response for patients with locally advanced rectal cancer

Accurate response prediction allows for personalized cancer treatment of locally advanced rectal cancer (LARC) with neoadjuvant chemoradiation. In this work, we designed a convolutional neural network (CNN) feature extractor with switchable 3D and 2D convolutional kernels to extract deep learning features for response prediction. Compared with radiomics features, convolutional kernels may adaptively extract local or global image features from multi-modal MR sequences without the need of feature predefinition. We then developed an unsupervised clustering based evaluation method to improve the feature selection operation in the feature space formed by the combination of CNN features and radiomics features. While normal process of feature selection generally includes the operations of classifier training and classification execution, the process needs to be repeated many times after new feature combinations were found to evaluate the model performance, which incurs a significant time cost. To address this issue, we proposed a cost effective process to use a constructed unsupervised clustering analysis indicator to replace the classifier training process by indirectly evaluating the quality of new found feature combinations in feature selection process. We evaluated the proposed method using 43 LARC patients underwent neoadjuvant chemoradiation. Our prediction model achieved accuracy, area-under-curve (AUC), sensitivity and specificity of 0.852, 0.871, 0.868, and 0.735 respectively. Compared with traditional radiomics methods, the prediction models (AUC = 0.846) based on deep learning-based feature sets are significantly better than traditional radiomics methods (AUC = 0.714). The experiments also showed following findings: (1) the features with higher predictive power are mainly from high-order abstract features extracted by CNN on ADC images and T2 images; (2) both ADC_Radiomics and ADC_CNN features are more advantageous for predicting treatment responses than the radiomics and CNN features extracted from T2 images; (3) 3D CNN features are more effective than 2D CNN features in the treatment response prediction. The proposed unsupervised clustering indicator is feasible with low computational cost, which facilitates the discovery of valuable solutions by highlighting the correlation and complementarity between different types of features.

Prediction of Radiation Treatment Response for Locally Advanced Rectal Cancer via a Longitudinal Trend Analysis Framework on Cone-Beam CT

Locally advanced rectal cancer (LARC) presents a significant challenge in terms of treatment management, particularly with regards to identifying patients who are likely to respond to radiation therapy (RT) at an individualized level. Patients respond to the same radiation treatment course differently due to inter- and intra-patient variability in radiosensitivity. In-room volumetric cone-beam computed tomography (CBCT) is widely used to ensure proper alignment, but also allows us to assess tumor response during the treatment course. In this work, we proposed a longitudinal radiomic trend (LRT) framework for accurate and robust treatment response assessment using daily CBCT scans for early detection of patient response. The LRT framework consists of four modules: (1) Automated registration and evaluation of CBCT scans to planning CT; (2) Feature extraction and normalization; (3) Longitudinal trending analyses; and (4) Feature reduction and model creation. The effectiveness of the framework was validated via leave-one-out cross-validation (LOOCV), using a total of 840 CBCT scans for a retrospective cohort of LARC patients. The trending model demonstrates significant differences between the responder vs. non-responder groups with an Area Under the Curve (AUC) of 0.98, which allows for systematic monitoring and early prediction of patient response during the RT treatment course for potential adaptive management.

MRI-Guided Radiation Therapy for Prostate Cancer: The Next Frontier in Ultrahypofractionation

Technological advances in MRI-guided radiation therapy (MRIgRT) have improved real-time visualization of the prostate and its surrounding structures over CT-guided radiation therapy. Seminal studies have demonstrated safe dose escalation achieved through ultrahypofractionation with MRIgRT due to planning target volume (PTV) margin reduction and treatment gating. On-table adaptation with MRI-based technologies can also incorporate real-time changes in target shape and volume and can reduce high doses of radiation to sensitive surrounding structures that may move into the treatment field. Ongoing clinical trials seek to refine ultrahypofractionated radiotherapy treatments for prostate cancer using MRIgRT. Though these studies have the potential to demonstrate improved biochemical control and reduced side effects, limitations concerning patient treatment times and operational workflows may preclude wide adoption of this technology outside of centers of excellence. In this review, we discuss the advantages and limitations of MRIgRT for prostate cancer, as well as clinical trials testing the efficacy and toxicity of ultrafractionation in patients with localized or post-prostatectomy recurrent prostate cancer.

Predicting Overall Survival for Patients with Malignant Mesothelioma Following Radiotherapy via Interpretable Machine Learning

PURPOSE/OBJECTIVES

Malignant pleural mesothelioma (MPM) is a rare but aggressive cancer arising from the cells of the thoracic pleura with a poor prognosis. We aimed to develop a model, via interpretable machine learning (ML) methods, predicting overall survival for MPM following radiotherapy based on dosimetric metrics as well as patient characteristics.

MATERIALS/METHODS

Sixty MPM (37 right, 23 left) patients treated on a Tomotherapy unit between 2013 and 2018 were retrospectively analyzed. All patients received 45 Gy (25 fractions). The multivariable Cox regression (Cox PH) model and Survival Support Vector Machine (sSVM) were applied to build predictive models of overall survival (OS) based on clinical, dosimetric, and combined variables.

RESULTS

Significant differences in dosimetric endpoints for critical structures, i.e., the lung, heart, liver, kidney, and stomach, were observed according to target laterality. The OS was found to be insignificantly different (p = 0.18) between MPM patients who tested left- and right-sided, with 1-year OS of 77.3% and 75.0%, respectively. With Cox PH regression, considering dosimetric variables for right-sided patients alone, an increase in PTV_Min, Total_Lung_PTV_Mean, Contra_Lung_Volume, Contra_Lung_V20, Esophagus_Mean, and Heart_Volume had a greater hazard to all-cause death, while an increase in Total_Lung_PTV_V20, Contra_Lung_V5, and Esophagus_Max had a lower hazard to all-cause death. Considering clinical variables alone, males and increases in N stage had greater hazard to all-cause death; considering both clinical and dosimetric variables, increases in N stage, PTV_Mean, PTV_Min, and esophagus_Mean had greater hazard to all-cause death, while increases in T stage and Heart_V30 had lower hazard to all-cause-death. In terms of C-index, the Cox PH model and sSVM performed similarly and fairly well when considering clinical and dosimetric variables independently or jointly.

CONCLUSIONS

Clinical and dosimetric variables may predict the overall survival of mesothelioma patients, which could guide personalized treatment planning towards a better treatment response. The identified predictors and their impact on survival offered additional value for translational application in clinical practice.

A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy

PURPOSE

To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC).

METHODS

A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm.

RESULTS

The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival.

CONCLUSION

We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment.

AAPM Task Group Report 306: Quality control and assurance for tomotherapy: An update to Task Group Report 148

Since the publication of AAPM Task Group (TG) 148 on quality assurance (QA) for helical tomotherapy, there have been many new developments on the tomotherapy platform involving treatment delivery, on-board imaging options, motion management, and treatment planning systems (TPSs). In response to a need for guidance on quality control (QC) and QA for these technologies, the AAPM Therapy Physics Committee commissioned TG 306 to review these changes and make recommendations related to these technology updates. The specific objectives of this TG were (1) to update, as needed, recommendations on tolerance limits, frequencies and QC/QA testing methodology in TG 148, (2) address the commissioning and necessary QA checks, as a supplement to Medical Physics Practice Guidelines (MPPG) with respect to tomotherapy TPS and (3) to provide risk-based recommendations on the new technology implemented clinically and treatment delivery workflow. Detailed recommendations on QA tests and their tolerance levels are provided for dynamic jaws, binary multileaf collimators, and Synchrony motion management. A subset of TPS commissioning and QA checks in MPPG 5.a. applicable to tomotherapy are recommended. In addition, failure mode and effects analysis has been conducted among TG members to obtain multi-institutional analysis on tomotherapy-related failure modes and their effect ranking.

A multi-objective based radiomics feature selection method for response prediction following radiotherapy

Objective.Radiomics contains a large amount of mineable information extracted from medical images, which has important significance in treatment response prediction for personalized treatment. Radiomics analyses generally involve high dimensions and redundant features, feature selection is essential for construction of prediction models.Approach.We proposed a novel multi-objective based radiomics feature selection method (MRMOPSO), where the number of features, sensitivity, and specificity are jointly considered as optimization objectives in feature selection. The MRMOPSO innovated in the following three aspects: (1) Fisher score to initialize the population to speed up the convergence; (2) Min-redundancy particle generation operations to reduce the redundancy between radiomics features, a truncation strategy was introduced to further reduce the number of features effectively; (3) Particle selection operations guided by elitism strategies to improve local search ability of the algorithm. We evaluated the effectiveness of the MRMOPSO by using a multi-institution oropharyngeal cancer dataset from The Cancer Imaging Archive. 357 patients were used for model training and cross validation, an additional 64 patients were used for evaluation.Main results.The area under the curve (AUC) of our method achieved AUCs of 0.82 and 0.84 for cross validation and independent dataset, respectively. Compared with classical feature selection methods, the AUC of MRMOPSO is significantly higher than the Lasso (AUC = 0.74,p-value = 0.02), minimal-redundancy-maximal-relevance criterion (mRMR) (AUC = 0.73,p-value = 0.05), F-score (AUC = 0.48,p-value < 0.01), and mutual information (AUC = 0.69,p-value < 0.01) methods. Compared to single-objective methods, the AUC of MRMOPSO is 12% higher than those of the genetic algorithm (GA) (AUC = 0.68,p-value = 0.02) and particle swarm optimization algorithm (AUC = 0.72,p-value = 0.05) methods. Compared to other multi-objective feature selection methods, the AUC of MRMOPSO is 14% higher than those of multiple objective particle swarm optimization (MOPSO) (AUC = 0.68,p-value = 0.02) and nondominated sorting genetic algorithm II (NSGA2) (AUC = 0.70,p-value = 0.03).Significance.We proposed a multi-objective based radiomics feature selection method. Compared to conventional feature reduction algorithms, the proposed algorithm effectively reduced feature dimension, and achieved superior performance, with improved sensitivity and specificity, for response prediction in radiotherapy.

Radiation therapy practice changes in the COVID-19 pandemic era: A pilot study in California

Purpose

This study aims to investigate practice changes among Southern and Northern California's radiation oncology centers during the COVID‐19 pandemic.

Methods

On the online survey platform SurveyMonkey, we designed 10 survey questions to measure changes in various aspects of medical physics practice. The questions covered patient load and travel rules; scopes to work from home; new protocols to reduce corona virus disease‐2019 (COVID‐19) infection risk; availability of telemedicine; and changes in fractionation schedules and/or type of treatment plans. We emailed the survey to radiation oncology centers throughout Northern and Southern California, requesting one completed survey per center. All responses were anonymized, and data were analyzed using both qualitative and quantitative research methods.

Results

At the end of a 4‐month collection period (July 2, 2021 to October 11, 2021), we received a total of 61 responses throughout Southern and Northern California. On average, 4111 patients were treated per day across the 61 centers. New COVID‐19‐related department and hospital policies, along with hybrid workflow changes, infectious control policies, and changes in patient load have been reported. Results also showed changes in treatment methods during the pandemic, such as increased use of telemedicine, hypofractionation for palliative, breast cancer, and prostate cancer cases; and simultaneous boosts, compared to sequential boosts.

Conclusion

Our California radiation oncology center population study shows changes in various aspects of radiation oncology practices during the COVID‐19 pandemic. This study serves as a pilot study to identify possible correlations and new strategies that allow radiation oncology centers to continue providing quality patient care while ensuring the safety of both staff and patients.

Bridging Feature Gaps to Improve Multi-Organ Segmentation on Abdominal Magnetic Resonance Image

Accurate segmentation of abdominal organs on MRI is crucial for computer-aided surgery and computer-aided diagnosis. Most state-of-the-art methods for MRI segmentation employ an encoder-decoder structure, with skip connections concatenating shallow features from the encoder and deep features from the decoder. In this work, we noticed that simply concatenating shallow and deep features was insufficient for segmentation due to the feature gap between shallow features and deep features. To mitigate this problem, we quantified the feature gap from spatial and semantic aspects and proposed a spatial loss and a semantic loss to bridge the feature gap. The spatial loss enhanced spatial details in deep features, and the semantic loss introduced semantic information into shallow features. The proposed method successfully aggregated the complementary information between shallow and deep features by formulating and bridging the feature gap. Experiments on two abdominal MRI datasets demonstrated the effectiveness of the proposed method, which improved the segmentation performance over a baseline with nearly zero additional parameters. Particularly, the proposed method has advantages for segmenting organs with blurred boundaries or in a small scale, achieving superior performance than state-of-the-art methods.

A Hybrid Feature Selection based Brain Tumor Detection and Segmentation in Multiparametric Magnetic Resonance Imaging

PURPOSE

To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs.

METHODS

We proposed an evolutionary feature selection-based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2-FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416-dimensions radiomics features extracted from 4 sequences of MRIs and 128-dimensions high-order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non-enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using 5-fold cross-validation, the model was independently validated using the rest 35 patients from the same database.

RESULTS

The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852±0.057, 0.844±0.046, and 0.799±0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873±0.074, 0.863±0.072, and 0.852±0.082; the specificities were 0.994±0.005, 0.994±0.005, and 0.995±0.004 for the WT, tumor core and ET, respectively. The performances and calculation times were compared with the state-of-the-art approaches, our approach yielded a better overall performance with average processing time of 139.5 sec per set of four sequence MRIs.

CONCLUSIONS

We demonstrated a robust and computational cost-effective hybrid segmentation approach for glioma and its subregions on multi-sequence MR images. The proposed approach can be used for automated target delineation for glioma patients.

Three-dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre-operative multimodal MR images

PURPOSE

Convolutional neural networks have achieved excellent results in automatic medical image segmentation. In this study, we proposed a novel three-dimensional (3D) multipath DenseNet for generating the accurate glioblastoma (GBM) tumor contour from four multimodal pre-operative MR images. We hypothesized that the multipath architecture could achieve more accurate segmentation than a singlepath architecture.

METHODS

Two hundred and fifty-eight GBM patients were included in this study. Each patient had four MR images (T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR) and the manually segmented tumor contour. We built a 3D multipath DenseNet that could be trained to achieve an end-to-end mapping from four MR images to the corresponding GBM tumor contour. A 3D singlepath DenseNet was also built for comparison. Both DenseNets were based on the encoder-decoder architecture. All four images were concatenated and fed into a single encoder path in the singlepath DenseNet, while each input image had its own encoder path in the multipath DenseNet. The patient cohort was randomly split into a training set of 180 patients, a validation set of 39 patients, and a testing set of 39 patients. Model performance was evaluated using the Dice similarity coefficient (DSC), average surface distance (ASD), and 95% Hausdorff distance (HD_95% ). Wilcoxon signed-rank tests were conducted to assess statistical significances.

RESULTS

The singlepath DenseNet achieved the DSC of 0.911 ± 0.060, ASD of 1.3 ± 0.7 mm, and HD_95% of 5.2 ± 7.1 mm, while the multipath DenseNet achieved the DSC of 0.922 ± 0.041, ASD of 1.1 ± 0.5 mm, and HD_95% of 3.9 ± 3.3 mm. The P-values of all Wilcoxon signed-rank tests were less than 0.05.

CONCLUSIONS

Both DenseNets generated GBM tumor contours in good agreement with the manually segmented contours from multimodal MR images. The multipath DenseNet achieved more accurate tumor segmentation than the singlepath DenseNet. Here presented the 3D multipath DenseNet that demonstrated an improved accuracy over comparable algorithms in the clinical task of GBM tumor segmentation.

Clinical Development and Evaluation of Megavoltage Topogram for Fast Patient Alignment on Helical Tomotherapy

Purpose

To develop and evaluate a fast patient localization tool using megavoltage (MV)-topogram on helical tomotherapy.

Methods and Materials

Eighty-one MV-topogram pairs for 18 pelvis patients undergoing radiation were acquired weekly under an institutional review board–approved clinical trial. The MV-topogram imaging protocol requires 2 orthogonal acquisitions at static gantry angles of 0 degrees and 90 degrees for a programed scan length. A MATLAB based in-house software was developed to reconstruct the MV-topograms offline. Reference images (digitally reconstructed topograms, digitally reconstructed topograms) were generated using the planning computed tomography and tomotherapy geometry. The MV-topogram based alignment was determined by registering the MV-topograms to the digitally reconstructed topogram using bony landmark on commercial MIM software. The daily shifts in 3 translational directions determined from MV-topograms were compared with the megavoltage computed tomography (MVCT) based patient shifts. Linear-regression and two one-sided tests equivalence tests were performed to investigate the relation and equivalence between the 2 techniques. Seventy-eight MV-topogram pairs for 19 head and neck patients were included to validate the finding.

Results

The magnitudes of alignment differences of (MVCT − MV-topogram) (and standard deviations) were −0.3 ± 2.1, −0.8 ± 2.4, and 1.6 ± 1.7 mm for pelvis and 0.6 ± 1.2, 0.8 ± 4.2, 1.6 ± 2.6 mm for head and neck; the linear-regression coefficients between 2 imaging techniques were 1.18, 1.10, 0.94, and 0.86, 0.63, 0.38 in the lateral, longitudinal, vertical directions for pelvis and head and neck, respectively. The acquisition time for a pair of MV-topograms was up to 12.7 times less than MVCT scans (coarse scan mode) while covering longer longitudinal length.

Conclusions

MV-topograms showed equivalent clinical performance to the standard MVCT with significantly less acquisition time for pelvis and H&N patients. The MV-topogram can be used as an alternative or complimentary tool for bony landmark-based patient alignment on tomotherapy.

[Clinical features and outcome of treatment for novel coronavirus pneumonia: a meta-analysis]

Objective: To investigate the clinical features and outcome of treatment for novel coronavirus pneumonia. Methods: Literature on novel coronavirus pneumonia was retrieved from PubMed and EMBASE databases. The relevant data was extracted and a meta-analysis was performed using StatsDirect statistical software V.2.8.0 to calculate the combined odds ratio. Results: Seven studies were included, consisting of 1594 cases. The meta-analysis result showed that the most common clinical symptoms of the novel coronavirus pneumonia were fever (91.6%) and cough (64.5%), followed by dyspnea (32.8%) and sputum (28.1%). Headache (10.5%), sore throat (11.2%), hemoptysis (3.2%), diarrhea (6.6%) and the other symptoms were relatively rare. Aspartate aminotransferase (29%), alanine transaminase (22.7%), and total bilirubin (11.7%) levels were elevated, except for serum albumin levels (80.4%). The common therapeutic agents used were antibiotics (87.7%), antiviral drugs (75.5%), and glucocorticoids (26.6%), while antifungal agents (7.7%) were used in few. Mechanical ventilation (13.4%), extracorporeal membrane oxygenation (1.9%), and continuous renal replacement therapy (3.8%) were used in severe cases. The rate of mortality in hospital was 7.7%, respectively. Heterogeneity between studies was significant; however, subgroup and sensitivity analysis had failed to identify clear sources of heterogeneity. Conclusion: Fever, cough and liver dysfunction are the main clinical manifestations of this disease and the mortality rate is low.

Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models

OBJECTIVES

High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients.

METHODS

Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation.

RESULTS

Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 ± 21.3 months, the predicted survival times were 47.3 ± 38.8, 38.5 ± 13.5 and 29.9 ± 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 ± 15.2, 32.3 ± 13.1 and 31.6 ± 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively.

CONCLUSION

The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival.

ADVANCES IN KNOWLEDGE

The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients.

Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy

Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials.

Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network.

Results: Median follow-up time was 32.3 months (range 3–98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses.

Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.

Dosimetric predictors of patient-reported toxicity after prostate stereotactic body radiotherapy: Analysis of full range of the dose-volume histogram using ensemble machine learning

BACKGROUND AND PURPOSE

This study aims to evaluate the associations between dosimetric parameters and patient-reported outcomes, and to identify latent dosimetric parameters that most correlate with acute and subacute patient-reported urinary and rectal toxicity after prostate stereotactic body radiotherapy (SBRT) using machine learning methods.

MATERIALS AND METHODS

Eighty-six patients who underwent prostate SBRT (40 Gy in 5 fractions) were included. Patient-reported health-related quality of life (HRQOL) outcomes were derived from bowel and bladder symptom scores on the Expanded Prostate Cancer Index Composite (EPIC-26) at 3 and 12 months post-SBRT. We utilized ensemble machine learning (ML) to interrogate the entire dose-volume histogram (DVH) to evaluate relationships between dose-volume parameters and HRQOL changes. The latent predictive dosimetric parameters that were most associated with HRQOL changes in urinary and rectal function were thus identified. An external cohort of 26 prostate SBRT patients was acquired to further test the predictive models.

RESULTS

Bladder dose-volume metrics strongly predicted patient-reported urinary irritative and incontinence symptoms (area under the curves [AUCs] of 0.79 and 0.87, respectively) at 12 months. Maximum bladder dose, bladder V102.5%, bladder volume, and conformity indices (V50/VPTV and V100/VPTV) were most predictive of HRQOL changes in both urinary domains. No strong rectal toxicity dosimetric association was identified (AUC = 0.64).

CONCLUSION

We demonstrated the application of advanced ML methods to identify a set of dosimetric variables that most highly correlated with patient-reported urinary HRQOL. DVH quantities identified with these methods may be used to achieve outcome-driven planning objectives to further reduce patient-reported toxicity with prostate SBRT.

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the handcrafted (explicitly designed) and deep learning (DL)-based radiomic features extracted from pre-treatment diffusion-weighted magnetic resonance images (DWIs) for predicting neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC). 43 Patients receiving nCRT were included. All patients underwent DWIs before nCRT and total mesorectal excision surgery 6-12 weeks after completion of nCRT. Gross tumor volume (GTV) contours were drawn by an experienced radiation oncologist on DWIs. The patient-cohort was split into the responder group (n = 22) and the non-responder group (n = 21) based on the post-nCRT response assessed by postoperative pathology, MRI or colonoscopy. Handcrafted and DL-based features were extracted from the apparent diffusion coefficient (ADC) map of the DWI using conventional computer-aided diagnosis methods and a pre-trained convolution neural network, respectively. Least absolute shrinkage and selection operator (LASSO)-logistic regression models were constructed using extracted features for predicting treatment response. The model performance was evaluated with repeated 20 times stratified 4-fold cross-validation using receiver operating characteristic (ROC) curves and compared using the corrected paired t-test. The model built with handcrafted features achieved the mean area under the ROC curve (AUC) of 0.64, while the one built with DL-based features yielded the mean AUC of 0.73. The corrected paired t-test on AUC showed P-value < 0.05. DL-based features extracted from pre-treatment DWIs achieved significantly better classification performance compared with handcrafted features for predicting nCRT response in patients with LARC.

Fast, Low-Dose Megavoltage-Topogram Localization on TomoTherapy: Initial Clinical Experience With Mesothelioma Patients

PURPOSE

This study aimed to evaluate the potential of megavoltage-topogram (MV-topogram)-based alignment as an alternative to megavoltage computed tomography (MVCT) in reducing setup time and imaging dose for patients with malignant pleural mesothelioma who are receiving TomoTherapy.

METHODS AND MATERIALS

Twelve patients were enrolled in an ongoing institutional review board approved clinical trial at our institute. Patients were set up with a clinical protocol using red lasers. Anteroposterior (AP) and lateral (LAT) MV-topograms were acquired using gantry angles of 0°/90° with a 1 mm collimator opening, all multileaf collimator leaves open, a couch speed of 4 cm/s, and a 12.5-second scanning time. Routine MVCT scans were performed immediately afterward. The MV-topograms were reconstructed and enhanced using contrast-limited adaptive histogram equalization. Anteroposterior and LAT kilovoltage digital reconstructed topogram images were reconstructed based on TomoTherapy geometry from computed tomography simulation scans. Registrations between MV-topograms and kilovoltage-digital reconstructed topogram images were performed manually, and patients' daily shifts were recorded. Results were compared against the corresponding daily MVCT shifts. MV-topogram and MVCT doses were measured and recorded using an ion chamber on a cheese phantom with depths between 1 and 14 cm, as well as the times required to acquire the 2 image modalities.

RESULTS

The mean and standard deviation of shift discrepancies between MV-topogram and MVCT were 0.74 ± 2.08, -0.09 ± 4.46, and 0.45 ± 3.57 mm in the LAT, longitudinal, and vertical directions, respectively. The MVCT imaging doses measured were 14.74 to 26.92 times higher than the MV-topogram doses, depending on depth. On average, MV-topograms with a mean scan length of 50 cm achieved a 5-fold image acquisition time savings over MVCT, with a mean scan length of 38 cm.

CONCLUSIONS

MV-topograms has the potential to provide alignment performance equivalent to that of MVCT for patients with mesothelioma, with a significant reduction in imaging dose and acquisition time.

[An evidence-based terlipressin therapy for gastroesophageal variceal hemorrhage]

Gastroesophageal variceal bleeding is one of the major complications of cirrhosis and also the leading causes of death in patients with decompensated cirrhosis. Terlipressin is a triglycyl-lysine vasopressin, a synthetic vasopressin analogue that is mainly used for the treatment of acute variceal hemorrhage. This article aims to review the current status of treatment of gastroesophageal variceal bleeding with terlipressin from the perspective of evidence-based medicine.

Systematic review with meta-analysis: the gastrointestinal benefits of COX-2 selective inhibitors with concomitant use of low-dose aspirin

BACKGROUND

It is uncertain whether concurrent use of low-dose aspirin removes the gastrointestinal benefit displayed by COX-2 selective inhibitors (coxibs) when compared to traditional nonsteroidal anti-inflammatory drugs (NSAIDs).

AIM

To evaluate the gastrointestinal risks associated with coxibs and traditional NSAIDs and the interaction with concurrent use of low-dose aspirin.

METHODS

We searched MEDLINE, EMBASE and the Cochrane Library through April 2016 to identify randomised trials comparing the gastrointestinal risk between coxibs and traditional NSAIDs in patients taking or not taking low-dose aspirin. Results were combined using random effects meta-analysis. Subgroup analyses by concurrent use of aspirin were undertaken.

RESULTS

Eleven trials (84 150 participants) were included. The overall relative risk (RR) of coxibs vs. traditional NSAIDs for complicated gastrointestinal events was 0.54 (95% CI, confidence interval 0.32-0.92), with a significant subgroup difference (P = 0.04) according to concurrent use of aspirin (used: RR = 0.96, 95% CI 0.66-1.24; not used: RR = 0.33, 95% CI 0.14-0.83). The overall RR for clinical gastrointestinal events was 0.59 (95% CI 0.47-0.75), with a significant subgroup difference according to aspirin usage (P = 0.008; used: RR = 0.77, 95% CI 0.62-0.95; not used: RR = 0.50, 95% CI 0.39-0.64). Overall coxibs were associated with significantly lower risk of symptomatic ulcers (RR = 0.60, 95% CI 0.50-0.72) and endoscopic ulcers (RR = 0.29, 95% CI 0.16-0.53) than traditional NSAIDs; a significant subgroup difference was shown for endoscopic ulcers (P = 0.05) but not for symptomatic ulcers (P = 0.27).

CONCLUSION

Concomitant use of low-dose aspirin reduces but does not completely eliminate the gastrointestinal benefit of coxibs over traditional NSAIDs.

Plan quality and dosimetric association of patient-reported rectal and urinary toxicities for prostate stereotactic body radiotherapy

BACKGROUND AND PURPOSE

To study the association between dosimetric parameters with patient-reported quality-of-life (QOL) in urinary irritative/incontinency and bowel functions for prostate stereotactic body radiotherapy (SBRT).

MATERIAL AND METHODS

The patient-reported QOL was evaluated using the Expanded Prostate Cancer Index Composite (EPIC-26). According to the progression in QOL score over 12months, patients were assigned to one of three subgroups: score decrement, no change, or increment. The dosimetric parameters were cross-compared among subgroups in urinary and bowel domains using univariate Analysis of Variance (ANOVA). The evaluated dosimetric metrics included target volume, V100 (volume receiving 100% prescription dose); rectal volume/dose-volume endpoints, maximum/mean doses; bladder volume/dose-volume endpoints, and maximum/mean doses.

RESULTS

Patients with consistent QOL reduction in urinary irritation function were significantly associated with greater mean bladder dose, greater V85/V90/V95/V100 and D_2cc/D_10cc. Patients with QOL reduction in urinary incontinence were marginally associated with greater mean bladder dose (p=0.06). None of the evaluated dosimetric parameters showed a significant correlation with QOL score change in bowel function.

CONCLUSIONS

Patients with large prostate size were more susceptible to QOL decrements for urinary irritative and incontinency functions. Large bladder V85/V90/V95/V100 was associated with QOL decrements in the urinary irritative domain at 1-year after prostate SBRT.

Near Real-Time Assessment of Anatomic and Dosimetric Variations for Head and Neck Radiation Therapy via Graphics Processing Unit-based Dose Deformation Framework

PURPOSE

The purpose of this study was to systematically monitor anatomic variations and their dosimetric consequences during intensity modulated radiation therapy (IMRT) for head and neck (H&N) cancer by using a graphics processing unit (GPU)-based deformable image registration (DIR) framework.

METHODS AND MATERIALS

Eleven IMRT H&N patients undergoing IMRT with daily megavoltage computed tomography (CT) and weekly kilovoltage CT (kVCT) scans were included in this analysis. Pretreatment kVCTs were automatically registered with their corresponding planning CTs through a GPU-based DIR framework. The deformation of each contoured structure in the H&N region was computed to account for nonrigid change in the patient setup. The Jacobian determinant of the planning target volumes and the surrounding critical structures were used to quantify anatomical volume changes. The actual delivered dose was calculated accounting for the organ deformation. The dose distribution uncertainties due to registration errors were estimated using a landmark-based gamma evaluation.

RESULTS

Dramatic interfractional anatomic changes were observed. During the treatment course of 6 to 7 weeks, the parotid gland volumes changed up to 34.7%, and the center-of-mass displacement of the 2 parotid glands varied in the range of 0.9 to 8.8 mm. For the primary treatment volume, the cumulative minimum and mean and equivalent uniform doses assessed by the weekly kVCTs were lower than the planned doses by up to 14.9% (P=.14), 2% (P=.39), and 7.3% (P=.05), respectively. The cumulative mean doses were significantly higher than the planned dose for the left parotid (P=.03) and right parotid glands (P=.006). The computation including DIR and dose accumulation was ultrafast (∼45 seconds) with registration accuracy at the subvoxel level.

CONCLUSIONS

A systematic analysis of anatomic variations in the H&N region and their dosimetric consequences is critical in improving treatment efficacy. Nearly real-time assessment of anatomic and dosimetric variations is feasible using the GPU-based DIR framework. Clinical implementation of this technology may enable timely plan adaptation and improved outcome.

Large-Scale and Selective Synthesis of Carbon Nanofiber Bundles, Curved Carbon Nanofibers and Helical Carbon Nanofibers

Through the pyrolysis of acetylene at 250 °C, large quantities of carbon nanofiber bundles (CNFBs), curved carbon nanofibers (CCNFs) and helical carbon nanofibers (HCNFs) can be synthesized selectively by controlling the Fe:Cu molar ratio of Fe-Cu nanoparticles. In this study, the systematic experimental results indicated that the Cu content in the Fe-Cu nanoparticles and pyrolysis temperature had great impact on the yield and structure of the final samples. Moreover, the transmission electron microscopic observation indicated that the catalyst nanoparticles were enwrapped tightly by graphite layers, and the obtained HCNFs show good magnetic property. Compared to the methods reported in the literature, the approach described herein has the advantages of being simple, low-cost, and environment-friendly. It is suitable for the controllable and mass production of CNFBs, CCNFs and HCNFs.

Megavoltage CT imaging quality improvement on TomoTherapy via tensor framelet

PURPOSE

This work is to investigate the feasibility of improving megavoltage imaging quality for TomoTherapy using a novel reconstruction technique based on tensor framelet, with either full-view or partial-view data.

METHODS

The reconstruction problem is formulated as a least-square L1-type optimization problem, with the tensor framelet for the image regularization, which is a generalization of L1, total variation, and wavelet. The high-order derivatives of the image are simultaneously regularized in L1 norm at multilevel along the x, y, and z directions. This convex formulation is efficiently solved using the Split Bregman method. In addition, a GPU-based parallel algorithm was developed to accelerate image reconstruction. The new method was compared with the filtered backprojection and the total variation based method in both phantom and patient studies with full or partial projection views.

RESULTS

The tensor framelet based method improved the image quality from the filtered backprojection and the total variation based method. The new method was robust when only 25% of the projection views were used. It required ∼2 min for the GPU-based solver to reconstruct a 40-slice 1 mm-resolution 350×350 3D image with 200 projection views per slice and 528 detection pixels per view.

CONCLUSIONS

The authors have developed a GPU-based tensor framelet reconstruction method with improved image quality for the megavoltage CT imaging on TomoTherapy with full or undersampled projection views. In particular, the phantom and patient studies suggest that the imaging quality enhancement via tensor framelet method is prominent for the low-dose imaging on TomoTherapy with up to a 75% projection view reduction.

An Estimation of Radiobiological Parameters for Head-and-Neck Cancer Cells and the Clinical Implications

In vitro survival measurements using two human head-and-neck cancer (HNC) cell lines were performed. The specially designed split-dose surviving fraction was obtained and fitted to the linear-quadratic formalism. The repair halftime (Tr), the potential doubling time (Td), a/β and radiosensitivity a, were estimated. Other radiobiological models: EUD, BED, TCP, etc., were used to examine the potential treatment effectiveness of different IMRT techniques. Our data indicated the repair halftime of ~17 min based on two HNC cell lines. The combined a/β, a and Td are a/β = 8.1 ± 4.1 Gy, a = 0.22 ± 0.08 Gy-1, Td = 4.0 ± 1.8 day, respectively. The prolonged IMRT dose delivery for entire HNC treatment course could possibly result in the loss of biological effectiveness, i.e., the target EUDs decreased by 11% with fraction dose delivery time varying from 5 to 30 min. We determined the sublethal damage repair halftime and other radiobiological parameters for HNC cells, and to evaluate treatment effectiveness of the prolonged dose delivery times associated with different IMRT techniques. The estimated repair halftime for HNC is relatively short and may be comparable to the step-and-shoot IMRT fraction dose delivery time. The effectiveness of IMRT treatment may be improved by reducing the fraction delivery time for HNC treatment.

Potential for improved intelligence quotient using volumetric modulated arc therapy compared with conventional 3-dimensional conformal radiation for whole-ventricular radiation in children

PURPOSE

To compare volumetric modulated arc therapy (VMAT) with 3-dimensional conformal radiation therapy (3D-CRT) in the treatment of localized intracranial germinoma. We modeled the effect of the dosimetric differences on intelligence quotient (IQ).

METHOD AND MATERIALS

Ten children with intracranial germinomas were used for planning. The prescription doses were 23.4 Gy to the ventricles followed by 21.6 Gy to the tumor located in the pineal region. For each child, a 3D-CRT and full arc VMAT was generated. Coverage of the target was assessed by computing a conformity index and heterogeneity index. We also generated VMAT plans with explicit temporal lobe sparing and with smaller ventricular margin expansions. Mean dose to the temporal lobe was used to estimate IQ 5 years after completion of radiation, using a patient age of 10 years.

RESULTS

Compared with the 3D-CRT plan, VMAT improved conformality (conformity index 1.10 vs 1.85), with slightly higher heterogeneity (heterogeneity index 1.09 vs 1.06). The averaged mean doses for left and right temporal lobes were 31.3 and 31.7 Gy, respectively, for VMAT plans and 37.7 and 37.6 Gy for 3D-CRT plans. This difference in mean temporal lobe dose resulted in an estimated IQ difference of 3.1 points at 5 years after radiation therapy. When the temporal lobes were explicitly included in the VMAT optimization, the mean temporal lobe dose was reduced 5.6-5.7 Gy, resulting in an estimated IQ difference of an additional 3 points. Reducing the ventricular margin from 1.5 cm to 0.5 cm decreased mean temporal lobe dose 11.4-13.1 Gy, corresponding to an estimated increase in IQ of 7 points.

CONCLUSION

For treatment of children with intracranial pure germinomas, VMAT compared with 3D-CRT provides increased conformality and reduces doses to normal tissue. This may result in improvements in IQ in these children.

A planning and delivery study of a rotational IMRT technique with burst delivery

PURPOSE

A novel rotational IMRT (rIMRT) technique using burst delivery (continuous gantry rotation with beam off during MLC repositioning) is investigated. The authors evaluate the plan quality and delivery efficiency and accuracy of this dynamic technique with a conventional flat 6 MV photon beam.

METHODS

Burst-delivery rIMRT was implemented in a planning system and delivered with a 160-MLC linac. Ten rIMRT plans were generated for five anonymized patient cases encompassing head and neck, brain, prostate, and prone breast. All plans were analyzed retrospectively and not used for treatment. Among the varied plan parameters were the number of optimization points, number of arcs, gantry speed, and gantry angle range (alpha) over which the beam is turned on at each optimization point. Combined rotational/step-and-shoot rIMRT plans were also created by superimposing multiple-segment static fields at several optimization points. The rIMRT trial plans were compared with each other and with plans generated using helical tomotherapy and VMAT. Burst-mode rotational IMRT plans were delivered and verified using a diode array, ionization chambers, thermoluminescent dosimeters, and film.

RESULTS

Burst-mode rIMRT can achieve plan quality comparable to helical tomotherapy, while the former may lead to slightly better OAR sparing for certain cases and the latter generally achieves slightly lower hot spots. Few instances were found in which increasing the number of optimization points above 36, or superimposing step-and-shoot IMRT segments, led to statistically significant improvements in OAR sparing. Using an additional rIMRT partial arc yielded substantial OAR dose improvements for the brain case. Measured doses from the rIMRT plan delivery were within 4% of the plan calculation in low dose gradient regions. Delivery time range was 228-375 s for single-arc rIMRT 200-cGy prescription with a 300 MU/min dose rate, comparable to tomotherapy and VMAT.

CONCLUSIONS

Rotational IMRT with burst delivery, whether combined with static fields or not, yields clinically acceptable and deliverable treatment plans.

Is α/β for breast cancer really low?

PURPOSE

Low α/β ratio for breast cancer has drawn a growing interest for exploring hypofractionation for breast irradiation. This work is to confirm the low α/β ratio based on large randomized clinical trials of breast irradiation.

METHODS AND MATERIALS

A model based on the generalized linear-quadratic (LQ) model and Poisson statistical model was developed to calculate disease-free survival with consideration of clonogen proliferation during the course of radiation treatment and exponential behavior of survival rate with follow-up time. Outcome data from a series of randomized clinical trials of early-stage breast radiotherapy were fitted to estimate the model parameters. Other clinical outcomes, including treatments with surgery alone or radiotherapy alone were used to validate the model and the estimated parameters. Hypofractionation regimens were proposed based on the newly estimated LQ parameters.

RESULTS

Plausible population averaged radiobiologic parameters for breast cancer (95% confidence level) are α/β=2.88 (0.75-5.01) Gy; α=0.08±0.02Gy(-1); potential doubling time T(d)=14.4±7.8day. The analysis of the radiation-alone data suggested an α/β ratio of 3.89±6.25Gy, verifying the low α/β ratio based on the post-lumpectomy irradiation data. The hypofractionation regimens that are equivalent to the conventional regimen of 2.0Gy×25 in 5weeks include 2.26Gy×20, 3.34Gy×10, 4.93Gy×5 or 3.39Gy×10 (BID).

CONCLUSIONS

The analysis of the available clinical data from multiple institutions support that breast cancer has a low ratio of α/β, encouraging hypofractionated radiotherapy regimens for breast cancer.

Respiratory organ motion and dosimetric impact on breast and nodal irradiation

PURPOSE

To examine the respiratory motion for target and normal structures during whole breast and nodal irradiation and the resulting dosimetric impact.

METHODS AND MATERIALS

Four-dimensional CT data sets of 18 patients with early-stage breast cancer were analyzed retrospectively. A three-dimensional conformal dosimetric plan designed to irradiate the breast was generated on the basis of CT images at 20% respiratory phase (reference phase). The reference plans were copied to other respiratory phases at 0% (end of inspiration) and 50% (end of expiration) to simulate the effects of breathing motion on whole breast irradiation. Dose-volume histograms, equivalent uniform dose, and normal tissue complication probability were evaluated and compared.

RESULTS

Organ motion of up to 8.8mm was observed during free breathing. A large lung centroid movement was typically associated with a large shift of other organs. The variation of planning target volume coverage during a free breathing cycle is generally within 1%-5% (17 of 18 patients) compared with the reference plan. However, up to 28% of V(45) variation for the internal mammary nodes was observed. Interphase mean dose variations of 2.2%, 1.2%, and 1.4% were observed for planning target volume, ipsilateral lung, and heart, respectively. Dose variations for the axillary nodes and brachial plexus were minimal.

CONCLUSIONS

The doses delivered to the target and normal structures are different from the planned dose based on the reference phase. During normal breathing, the dosimetric impact of respiratory motion is clinically insignificant with the exception of internal mammary nodes. However, noticeable degradation in dosimetric plan quality may be expected for the patients with large respiratory motion.

Possible fractionated regimens for image-guided intensity-modulated radiation therapy of large arteriovenous malformations

The aim of this study was to estimate a plausible alpha/beta ratio for arteriovenous malformations (AVMs) based on reported clinical data, and to design possible fractionation regimens suitable for image-guided intensity-modulated radiation therapy (IG-IMRT) for large AVMs based on the newly obtained alpha/beta ratio. The commonly used obliteration rate (OR) for AVMs with a three year angiographic follow-up from many institutes was fitted to linear-quadratic (LQ) formalism and the Poisson OR model. The determined parameters were then used to calculate possible fractionation regimens for IG-IMRT based on the concept of a biologically effective dose (BED) and an equivalent uniform dose (EUD). The radiobiological analysis yields a alpha/beta ratio of 2.2 +/- 1.6 Gy for AVMs. Three sets of possible fractionated schemes were designed to achieve equal or better biological effectiveness than the single-fraction treatments while maintaining the same probability of normal brain complications. A plausible alpha/beta ratio was derived for AVMs and possible fractionation regimens that may be suitable for IG-IMRT for large AVM treatment are proposed. The sensitivity of parameters on the calculation was also studied. The information may be useful to design new clinical trials that use IG-IMRT for the treatment of large AVMs.

Comparison of three accelerated partial breast irradiation techniques: Treatment effectiveness based upon biological models

BACKGROUND AND PURPOSE

Accelerated partial breast irradiation (APBI) is being studied in a phase III randomized trial as an alternative to whole breast irradiation (WBI) for early stage breast cancer patients. There are three methods for APBI: multi-catheter brachytherapy (MCT), MammoSite brachytherapy (MST), or 3D conformal (3DCRT). There is a paucity of data comparing among methods. Using a linear-quadratic (LQ) model, we evaluated the anticipated efficacy among the APBI methods for equivalent uniform dose (EUD), Tumor Control Probability (TCP), and Normal Tissue Complication Probability (NTCP).

MATERIALS AND METHODS

Treatment plans from five patients treated by each APBI modality were retrospectively selected. Dose-volume-histograms (DVH) for planning target volume (PTV), breast, and lung were generated. The LQ parameters alpha=0.3Gy(-1) and alpha/beta=10Gy were used for calculations. The values of EUD, TCP, and NTCP were calculated based on DVHs.

RESULTS

The average EUD (normalized to 3.4Gy BID) for the MCT, MST, and 3DCRT APBI was 35, 37.2, and 37.6Gy. When normalized to 2Gy fractionation these become, 42.2, 46.4, and 46.9Gy. Average TCP for MCT, MST, and 3DCRT PBI was 94.8%, 99.1%, and 99.2%. The NTCP values for breast and lung were low for all three methods.

CONCLUSIONS

The EUD for PTV and TCP were most similar in MST and 3DCRT APBI and were lower in MCT APBI. This questions the equivalence of the three APBI modalities that are currently being evaluated in the NSABP-B39/RTOG 0413 protocol.

Interfractional Variations in Patient Setup and Anatomic Change Assessed by Daily Computed Tomography

PURPOSE

To analyze the interfractional variations in patient setup and anatomic changes at seven anatomic sites observed in image-guided radiotherapy.

METHODS AND MATERIALS

A total of 152 patients treated at seven anatomic sites using a Hi-Art helical tomotherapy system were analyzed. Daily tomotherapy megavoltage computed tomography images acquired before each treatment were fused to the planning kilovoltage computed tomography images to determine the daily setup errors and organ motions and deformations. The setup errors were corrected before treatment and were used, along with the organ motions, to determine the clinical target volume/planning target volume margins. The organ motions and deformations for 3 representative patient cases (pancreas, uterus, and soft-tissue sarcoma) and for 14 kidneys of 7 patients are presented.

RESULTS

Interfractional setup errors in the skull, brain, and head and neck are significantly smaller than those in the chest, abdomen, pelvis, and extremities. These site-specific relationships are statistically significant. The margins required to account for these setup errors range from 3 to 8 mm for the seven sites. The margin to account for both setup errors and organ motions for kidney is 16 mm. Substantial interfractional anatomic changes were observed. For example, the pancreas moved up to +/-20 mm and volumes of the uterus and sarcoma varied <or=30% and 100%, respectively.

CONCLUSION

The interfractional variations in patient setup and in shapes, sizes, and positions of both targets and normal structures are site specific and may be used to determine the site-specific margins. The data presented in this work dealing with seven anatomic sites may be useful in developing adaptive radiotherapy.

An estimation of radiobiologic parameters from clinical outcomes for radiation treatment planning of brain tumor

PURPOSE

To estimate a plausible set of radiobiologic parameters such as alpha, alpha/beta values, from clinical outcomes for biologically based radiation treatment planning of brain tumors.

METHODS AND MATERIALS

Linear-quadratic (LQ) formalism and the concept of equivalent uniform dose were used to analyze a series of published clinical data for malignant gliomas involving different forms of radiation therapy.

RESULTS

A plausible set of LQ parameters was obtained for gliomas: alpha = 0.06 +/- 0.05 Gy(-1), alpha/beta = 10.0 +/- 15.1 Gy, the tumor cell doubling time T(d) = 50 +/- 30 days, with the repair half-time of 0.5 h. The present estimated biologic parameters can reasonably predict the effectiveness of most of the recently reported clinical results employing either single or combined radiation therapy modalities. Different LQ parameters between Grade 3 and Grade 4 astrocytomas were found, implying the radiosensitivity for different grade tumors may be different. Smaller alpha, beta from in vivo was observed, indicating lower radiosensitivity occurred in vivo as compared with in vitro.

CONCLUSIONS

A plausible set of radiobiologic parameters for gliomas was estimated based on clinical data. These parameters can reasonably predict most of the clinical results. They may be used to design new treatment fractionation schemes and to evaluate and optimize treatment plans.

Effect of water content in perchloric acid on the non-aqueous potentiometric titration of nitrogen-containing compounds

In the United States Pharmacopeia (USP), 0.1 N perchloric acid in acetic acid volumetric solution (hereafter HClO4 VS) used for non-aqueous titration has specified a water content between 0.02 and 0.05%. Preparing this titrant with such a narrow range of water content is very time consuming, precludes the use of commercially available titrants, and, consequently, prompted an investigation to try and expand the range up to 0.5%. In this study, the titrimetric results obtained using HClO4 VS containing more water were very close to those obtained using the USP specified titrants. A maximum assay difference of 0.7% in the titrations of three selected nitrogen-containing compounds, clonidine hydrochloride, dipyridamole, and adenosine were observed. The titrimetric results obtained using these titrants were also precise with RSDs of not more than 0.4%. Therefore, a wider range of water content in HClO4 VS between 0.02 and 0.5% is suggested for the USP potentiometric titration of nitrogen-containing compounds.