Publications by authors named "Timothy D Solberg"

117 Publications

NRG Oncology Survey on Practice and Technology Use in SRT and SBRT Delivery.

Front Oncol 2020 27;10:602607. Epub 2020 Nov 27.

Cancer Institute, Allegheny Health Network, Pittsburgh, PA, United States.

Purpose: To assess stereotactic radiotherapy (SRT)/stereotactic body radiotherapy (SBRT) practices by polling clinics participating in multi-institutional clinical trials.

Methods: The NRG Oncology Medical Physics Subcommittee distributed a survey consisting of 23 questions, which covered general technologies, policies, and procedures used in the Radiation Oncology field for the delivery of SRT/SBRT (9 questions), and site-specific questions for brain SRT, lung SBRT, and prostate SBRT (14 questions). Surveys were distributed to 1,996 radiotherapy institutions included on the membership rosters of the five National Clinical Trials Network (NCTN) groups. Patient setup, motion management, target localization, prescriptions, and treatment delivery technique data were reported back by 568 institutions (28%).

Results: 97.5% of respondents treat lung SBRT patients, 77.0% perform brain SRT, and 29.1% deliver prostate SBRT. 48.8% of clinics require a physicist present for every fraction of SBRT, 18.5% require a physicist present for the initial SBRT fraction only, and 14.9% require a physicist present for the entire first fraction, including set-up approval for all subsequent fractions. 55.3% require physician approval for all fractions, and 86.7% do not reposition without x-ray imaging. For brain SRT, most institutions (83.9%) use a planning target volume (PTV) margin of 2 mm or less. Lung SBRT PTV margins of 3 mm or more are used in 80.6% of clinics. Volumetric modulated arc therapy (VMAT) is the dominant delivery method in 62.8% of SRT treatments, 70.9% of lung SBRT, and 68.3% of prostate SBRT.

Conclusion: This report characterizes SRT/SBRT practices in radiotherapy clinics participating in clinical trials. Data made available here allows the radiotherapy community to compare their practice with that of other clinics, determine what is achievable, and assess areas for improvement.
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http://dx.doi.org/10.3389/fonc.2020.602607DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729187PMC
November 2020

DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.

Sci Rep 2020 07 6;10(1):11073. Epub 2020 Jul 6.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the-art techniques rely on convolutional neural networks (CNNs) that use pixel-to-pixel loss to update network parameters. However, stereotactic body radiotherapy (SBRT) dose is often heterogeneous, making it difficult to model using pixel-level loss. Generative adversarial networks (GANs) utilize adversarial learning that incorporates image-level loss and is better suited to learn from heterogeneous labels. However, GANs are difficult to train and rely on compromised architectures to facilitate convergence. This study suggests an attention-gated generative adversarial network (DoseGAN) to improve learning, increase model complexity, and reduce network redundancy by focusing on relevant anatomy. DoseGAN was compared to alternative state-of-the-art dose prediction algorithms using heterogeneity index, conformity index, and various dosimetric parameters. All algorithms were trained, validated, and tested using 141 prostate SBRT patients. DoseGAN was able to predict more realistic volumetric dosimetry compared to all other algorithms and achieved statistically significant improvement compared to all alternative algorithms for the V and V of the PTV, V of the rectum, and heterogeneity index.
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http://dx.doi.org/10.1038/s41598-020-68062-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338467PMC
July 2020

Attention-Aware Discrimination for MR-to-CT Image Translation Using Cycle-Consistent Generative Adversarial Networks.

Radiol Artif Intell 2020 Mar 25;2(2):e190027. Epub 2020 Mar 25.

Department of Radiation Oncology, University of California, 1600 Divisidero St, San Francisco, CA 94115.

Purpose: To suggest an attention-aware, cycle-consistent generative adversarial network (A-CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to current state-of-the-art MR-to-CT image translation methods.

Materials And Methods: An attention-gating mechanism is incorporated into a discriminator network to encourage a more parsimonious use of network parameters, whereas VAE enhancement enables deeper discrimination architectures without inhibiting model convergence. Findings from 60 patients with head, neck, and brain cancer were used to train and validate A-CycleGAN, and findings from 30 patients were used for the holdout test set and were used to report final evaluation metric results using mean absolute error (MAE) and peak signal-to-noise ratio (PSNR).

Results: A-CycleGAN achieved superior results compared with U-Net, a generative adversarial network (GAN), and a cycle-consistent GAN. The A-CycleGAN averages, 95% confidence intervals (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83, 20.39], = .0104), structure similarity index metric (0.778 [95% CI: 0.758, 0.798], = .0495), and PSNR (62.35 [95% CI: 61.80, 62.90], = .0571).

Conclusion: A-CycleGANs were a superior alternative to state-of-the-art MR-to-CT image translation methods.© RSNA, 2020.
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http://dx.doi.org/10.1148/ryai.2020190027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017410PMC
March 2020

Expert-augmented machine learning.

Proc Natl Acad Sci U S A 2020 03 18;117(9):4571-4577. Epub 2020 Feb 18.

Department of Radiation Oncology, University of California, San Francisco, CA 94143.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications.
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http://dx.doi.org/10.1073/pnas.1906831117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060733PMC
March 2020

Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival.

Neurooncol Adv 2019 May-Dec;1(1):vdz011. Epub 2019 Aug 28.

Department of Radiation Oncology, University of California San Francisco, California.

Background: We investigated prognostic models based on clinical, radiologic, and radiomic feature to preoperatively identify meningiomas at risk for poor outcomes.

Methods: Retrospective review was performed for 303 patients who underwent resection of 314 meningiomas (57% World Health Organization grade I, 35% grade II, and 8% grade III) at two independent institutions, which comprised primary and external datasets. For each patient in the primary dataset, 16 radiologic and 172 radiomic features were extracted from preoperative magnetic resonance images, and prognostic features for grade, local failure (LF) or overall survival (OS) were identified using the Kaplan-Meier method, log-rank tests and recursive partitioning analysis. Regressions and random forests were used to generate and test prognostic models, which were validated using the external dataset.

Results: Multivariate analysis revealed that apparent diffusion coefficient hypointensity (HR 5.56, 95% CI 2.01-16.7, = .002) was associated with high grade meningioma, and low sphericity was associated both with increased LF (HR 2.0, 95% CI 1.1-3.5, = .02) and worse OS (HR 2.94, 95% CI 1.47-5.56, = .002). Both radiologic and radiomic predictors of adverse meningioma outcomes were significantly associated with molecular markers of aggressive meningioma biology, such as somatic mutation burden, DNA methylation status, and expression. Integrated prognostic models combining clinical, radiologic, and radiomic features demonstrated improved accuracy for meningioma grade, LF, and OS (area under the curve 0.78, 0.75, and 0.78, respectively) compared to models based on clinical features alone.

Conclusions: Preoperative radiologic and radiomic features such as apparent diffusion coefficient and sphericity can predict tumor grade, LF, and OS in patients with meningioma.
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http://dx.doi.org/10.1093/noajnl/vdz011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777505PMC
August 2019

Building more accurate decision trees with the additive tree.

Proc Natl Acad Sci U S A 2019 10 16;116(40):19887-19893. Epub 2019 Sep 16.

Department of Radiation Oncology, University of California, San Francisco, CA 94115;

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches.
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http://dx.doi.org/10.1073/pnas.1816748116DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778203PMC
October 2019

Attention-enabled 3D boosted convolutional neural networks for semantic CT segmentation using deep supervision.

Phys Med Biol 2019 07 2;64(13):135001. Epub 2019 Jul 2.

These two authors contributed equally. Author to whom correspondence should be addressed.

A deeply supervised attention-enabled boosted convolutional neural network (DAB-CNN) is presented as a superior alternative to current state-of-the-art convolutional neural networks (CNNs) for semantic CT segmentation. Spatial attention gates (AGs) were incorporated into a novel 3D cascaded CNN framework to prioritize relevant anatomy and suppress redundancies within the network. Due to the complexity and size of the network, incremental channel boosting was used to decrease memory usage and facilitate model convergence. Deep supervision was used to encourage semantically meaningful deep features and mitigate local minima traps during training. The accuracy of DAB-CNN is compared to seven architectures: a variation of U-Net (UNet), attention-enabled U-Net (A-UNet), boosted U-Net (B-UNet), deeply-supervised U-Net (D-UNet), U-Net with ResNeXt blocks (ResNeXt), life-long learning segmentation CNN (LL-CNN), and deeply supervised attention-enabled U-Net (DA-UNet). The accuracy of each method was assessed based on Dice score compared to manually delineated contours as the gold standard. One hundred and twenty patients who had definitive prostate radiotherapy were used in this study. Training, validation, and testing followed Kaggle competition rules, with 80 patients used for training, 20 patients used for internal validation, and 20 test patients used to report final accuracies. Comparator p -values indicate that DAB-CNN achieved significantly superior Dice scores than all alternative algorithms for the prostate, rectum, and penile bulb. This study demonstrated that attention-enabled boosted convolutional neural networks (CNNs) using deep supervision are capable of achieving superior prediction accuracy compared to current state-of-the-art automatic segmentation methods.
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http://dx.doi.org/10.1088/1361-6560/ab2818DOI Listing
July 2019

In Reply to Gensheimer and Trister.

Int J Radiat Oncol Biol Phys 2018 12;102(5):1594-1596

Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania.

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http://dx.doi.org/10.1016/j.ijrobp.2018.08.009DOI Listing
December 2018

Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.

Radiother Oncol 2019 04 23;133:106-112. Epub 2019 Jan 23.

Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, United States.

Background And Purpose: Radiation pneumonitis (RP) is a radiotherapy dose-limiting toxicity for locally advanced non-small cell lung cancer (LA-NSCLC). Prior studies have proposed relevant dosimetric constraints to limit this toxicity. Using machine learning algorithms, we performed analyses of contributing factors in the development of RP to uncover previously unidentified criteria and elucidate the relative importance of individual factors.

Materials And Methods: We evaluated 32 clinical features per patient in a cohort of 203 stage II-III LA-NSCLC patients treated with definitive chemoradiation to a median dose of 66.6 Gy in 1.8 Gy daily fractions at our institution from 2008 to 2016. Of this cohort, 17.7% of patients developed grade ≥2 RP. Univariate analysis was performed using trained decision stumps to individually analyze statistically significant predictors of RP and perform feature selection. Applying Random Forest, we performed multivariate analysis to assess the combined performance of important predictors of RP.

Results: On univariate analysis, lung V20, lung mean, lung V10 and lung V5 were found to be significant RP predictors with the greatest balance of specificity and sensitivity. On multivariate analysis, Random Forest (AUC = 0.66, p = 0.0005) identified esophagus max (20.5%), lung V20 (16.4%), lung mean (15.7%) and pack-year (14.9%) as the most common primary differentiators of RP.

Conclusions: We highlight Random Forest as an accurate machine learning method to identify known and new predictors of symptomatic RP. Furthermore, this analysis confirms the importance of lung V20, lung mean and pack-year as predictors of RP while also introducing esophagus max as an important RP predictor.
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http://dx.doi.org/10.1016/j.radonc.2019.01.003DOI Listing
April 2019

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

Med Phys 2019 May 4;46(5):2204-2213. Epub 2019 Apr 4.

Department of Radiation Oncology, University of California, San Francisco, CA, 94115, USA.

Purpose: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk.

Methods And Materials: Lifelong learning-based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single-task convolutional layer. The single-task transfer learning network was trained on each OAR separately with early stoppage. The accuracy of LL-CNN was assessed based on Dice score and root-mean-square error (RMSE) compared to manually delineated contours set as the gold standard. LL-CNN was compared with 2D-UNet, 3D-UNet, a single-task CNN (ST-CNN), and a pure multitask CNN (MT-CNN). Training, validation, and testing followed Kaggle competition rules, where 160 patients were used for training, 20 were used for internal validation, and 20 in a separate test set were used to report final prediction accuracies.

Results: On average contours generated with LL-CNN had higher Dice coefficients and lower RMSE than 2D-UNet, 3D-Unet, ST- CNN, and MT-CNN. LL-CNN required ~72 hrs to train using a distributed learning framework on 2 Nvidia 1080Ti graphics processing units. LL-CNN required 20 s to predict all 12 OARs, which was approximately as fast as the fastest alternative methods with the exception of MT-CNN.

Conclusions: This study demonstrated that for head and neck organs at risk, LL-CNN achieves a prediction accuracy superior to all alternative algorithms.
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http://dx.doi.org/10.1002/mp.13495DOI Listing
May 2019

The application of artificial intelligence in the IMRT planning process for head and neck cancer.

Oral Oncol 2018 12 31;87:111-116. Epub 2018 Oct 31.

Department of Radiation Oncology, University of California, San Francisco, CA 94115, USA. Electronic address:

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and neck patients. However, the complexity and novelty of AI algorithms make them susceptible to misuse by researchers and clinicians. Understanding nuances of new technologies could serve to mitigate potential clinical implementation pitfalls. This article is intended to facilitate integration of AI into the radiotherapy clinic by providing an overview of AI algorithms, including support vector machines (SVMs), random forests (RF), gradient boosting (GB), and several variations of deep learning. This document describes current AI algorithms that have been applied to head and neck IMRT planning and identifies rapidly growing branches of AI in industry that have potential applications to head and neck cancer patients receiving IMRT. AI algorithms have great clinical potential if used correctly but can also cause harm if misused, so it is important to raise the level of AI competence within radiation oncology so that the benefits can be realized in a controlled and safe manner.
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http://dx.doi.org/10.1016/j.oraloncology.2018.10.026DOI Listing
December 2018

DoseNet: a volumetric dose prediction algorithm using 3D fully-convolutional neural networks.

Phys Med Biol 2018 12 4;63(23):235022. Epub 2018 Dec 4.

These two authors contributed equally.

The goal of this study is to demonstrate the feasibility of a novel fully-convolutional volumetric dose prediction neural network (DoseNet) and test its performance on a cohort of prostate stereotactic body radiotherapy (SBRT) patients. DoseNet is suggested as a superior alternative to U-Net and fully connected distance map-based neural networks for non-coplanar SBRT prostate dose prediction. DoseNet utilizes 3D convolutional downsampling with corresponding 3D deconvolutional upsampling to preserve memory while simultaneously increasing the receptive field of the network. DoseNet was implemented on 2 Nvidia 1080 Ti graphics processing units and utilizes a 3 phase learning protocol to help achieve convergence and improve generalization. DoseNet was trained, validated, and tested with 151 patients following Kaggle completion rules. The dosimetric quality of DoseNet was evaluated by comparing the predicted dose distribution with the clinically approved delivered dose distribution in terms of conformity index, heterogeneity index, and various clinically relevant dosimetric parameters. The results indicate that the DoseNet algorithm is a superior alternative to U-Net and fully connected methods for prostate SBRT patients. DoseNet required ~50.1 h to train, and ~0.83 s to make a prediction on a 128  ×  128  ×  64 voxel image. In conclusion, DoseNet is capable of making accurate volumetric dose predictions for non-coplanar SBRT prostate patients, while simultaneously preserving computational efficiency.
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http://dx.doi.org/10.1088/1361-6560/aaef74DOI Listing
December 2018

Assessing the Validity of Clinician Advice That Patients Avoid Use of Topical Agents Before Daily Radiotherapy Treatments.

JAMA Oncol 2018 12;4(12):1742-1748

Department of Radiation Oncology, University of Pennsylvania, Philadelphia.

Importance: Radiation dermatitis is common and often treated with topical therapy. Patients are typically advised to avoid topical agents for several hours before daily radiotherapy (RT) out of concern that topical agents might increase the radiation dose to the skin. With modern RT's improved skin-sparing properties, this recommendation may be irrelevant.

Objective: To assess whether applying either metallic or nonmetallic topical agents before radiation treatment alters the skin dose.

Design, Setting, And Participants: A 24-question online survey of patients and clinicians was conducted from January 15, 2015, to March 15, 2017, to determine current practices regarding topical therapy use. In preclinical studies, dosimetric effect of the topical agents was evaluated by delivering 200 monitor units and measuring the dose at the surface and at 2-cm depth in a tissue-equivalent phantom with or without 2 common topical agents: a petroleum-based ointment (Aquaphor, petrolatum 41%) and silver sulfadiazine cream, 1%. Skin doses associated with various photon and electron energies, topical agent thicknesses, and beam incidence were assessed. Whether topical agents altered the skin dose was also evaluated in 24 C57BL/6 mice by using phosphorylated histone (γ-H2AX) immunofluorescent staining and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Preclinical studies took place at the University of Pennsylvania.

Main Outcomes And Measures: Patient and clinician survey responses; surface radiation dose readings in tissue-equivalent phantom; and γ-H2AX and TUNEL intensity measured in mice.

Results: The 133 patients surveyed received RT for cancer and had a median (range) age of 60 (18-86) years; 117 (87.9%) were women. One hundred eight clinicians completed the survey with 105 reporting that they were involved in managing patient skin care during RT. One hundred eleven (83.4%) of the patients and 96 (91.4%) of the 105 clinicians received or gave the advice to avoid applying topical agents before RT treatments. Dosimetric measurements showed no difference in the delivered dose at either the surface or a 2-cm depth with or without a 1- to 2-mm application of either topical agent when using en face 6- or 15-megavoltage (MV) photons. The same application of topicals did not alter the surface dose as a function of beam incident angle from 15° to 60°, except for a 6% increase at 60° with the silver sulfadiazine cream. Surface dose for 6- and 15-MV beams were significantly increased with a thicker (≥3-mm) topical application. For 6 MV, the surface dose was 1.05 Gy with a thick layer of petroleum-based ointment and 1.02 Gy for silver sulfadiazine cream vs 0.88 Gy without topical agents. For 15 MV, the doses were 0.70 Gy for a thick layer of petroleum-based ointment and 0.60 Gy for silver sulfadiazine cream vs 0.52 Gy for the controls. With 6- and 9-MeV electrons, there was a 2% to 5% increase in surface dose with the use of the topical agents. There were no dose differences at 2-cm depth. Irradiated skin in mice showed no differences in γ-H2AX-positive foci or in TUNEL staining with or without topical agents of varying thickness.

Conclusions And Relevance: Thin or moderately applied topical agents, even if applied just before RT, may have minimal influence on skin dose regardless of beam energy or beam incidence. The findings of this study suggest that applying very thick amounts of a topical agent before RT may increase the surface dose and should be avoided.
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http://dx.doi.org/10.1001/jamaoncol.2018.4292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440719PMC
December 2018

Preoperative and postoperative prediction of long-term meningioma outcomes.

PLoS One 2018 20;13(9):e0204161. Epub 2018 Sep 20.

Department of Radiation Oncology, University of California San Francisco, San Francisco, United States of America.

Background: Meningiomas are stratified according to tumor grade and extent of resection, often in isolation of other clinical variables. Here, we use machine learning (ML) to integrate demographic, clinical, radiographic and pathologic data to develop predictive models for meningioma outcomes.

Methods And Findings: We developed a comprehensive database containing information from 235 patients who underwent surgery for 257 meningiomas at a single institution from 1990 to 2015. The median follow-up was 4.3 years, and resection specimens were re-evaluated according to current diagnostic criteria, revealing 128 WHO grade I, 104 grade II and 25 grade III meningiomas. A series of ML algorithms were trained and tuned by nested resampling to create models based on preoperative features, conventional postoperative features, or both. We compared different algorithms' accuracy as well as the unique insights they offered into the data. Machine learning models restricted to preoperative information, such as patient demographics and radiographic features, had similar accuracy for predicting local failure (AUC = 0.74) or overall survival (AUC = 0.68) as models based on meningioma grade and extent of resection (AUC = 0.73 and AUC = 0.72, respectively). Integrated models incorporating all available demographic, clinical, radiographic and pathologic data provided the most accurate estimates (AUC = 0.78 and AUC = 0.74, respectively). From these models, we developed decision trees and nomograms to estimate the risks of local failure or overall survival for meningioma patients.

Conclusions: Clinical information has been historically underutilized in the prediction of meningioma outcomes. Predictive models trained on preoperative clinical data perform comparably to conventional models trained on meningioma grade and extent of resection. Combination of all available information can help stratify meningioma patients more accurately.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204161PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6147484PMC
March 2019

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change.

Int J Radiat Oncol Biol Phys 2018 11 28;102(4):1074-1082. Epub 2018 Aug 28.

The D-Lab, Grow Research Institute for Oncology, Maastricht University, Maastricht, The Netherlands.

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.
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http://dx.doi.org/10.1016/j.ijrobp.2018.08.032DOI Listing
November 2018

An unsupervised convolutional neural network-based algorithm for deformable image registration.

Phys Med Biol 2018 09 17;63(18):185017. Epub 2018 Sep 17.

Department of Radiation Oncology, University of California, San Francisco, CA, United States of America.

The purpose of the work is to develop a deep unsupervised learning strategy for cone-beam CT (CBCT) to CT deformable image registration (DIR). This technique uses a deep convolutional inverse graphics network (DCIGN) based DIR algorithm implemented on 2 Nvidia 1080 Ti graphics processing units. The model is comprised of an encoding and decoding stage. The fully-convolutional encoding stage learns hierarchical features and simultaneously forms an information bottleneck, while the decoding stage restores the original dimensionality of the input image. Activations from the encoding stage are used as the input channels to a sparse DIR algorithm. DCIGN was trained using a distributive learning-based convolutional neural network architecture and used 285 head and neck patients to train, validate, and test the algorithm. The accuracy of the DCIGN algorithm was evaluated on 100 synthetic cases and 12 hold out test patient cases. The results indicate that DCIGN performed better than rigid registration, intensity corrected Demons, and landmark-guided deformable image registration for all evaluation metrics. DCIGN required ~14 h to train, and ~3.5 s to make a prediction on a 512  ×  512  ×  120 voxel image. In conclusion, DCIGN is able to maintain high accuracy in the presence of CBCT noise contamination, while simultaneously preserving high computational efficiency.
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http://dx.doi.org/10.1088/1361-6560/aada66DOI Listing
September 2018

Clinical Applications of Quantitative 3-Dimensional MRI Analysis for Pediatric Embryonal Brain Tumors.

Int J Radiat Oncol Biol Phys 2018 11 8;102(4):744-756. Epub 2018 Jun 8.

Department of Radiation Oncology, University of California, San Francisco, San Francisco, California; Department of Neurological Surgery, University of California, San Francisco, San Francisco, California. Electronic address:

Purpose: To investigate the prognostic utility of quantitative 3-dimensional magnetic resonance imaging radiomic analysis for primary pediatric embryonal brain tumors.

Methods And Materials: Thirty-four pediatric patients with embryonal brain tumor with concurrent preoperative T1-weighted postcontrast (T1PG) and T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance images were identified from an institutional database. The median follow-up period was 5.2 years. Radiomic features were extracted from axial T1PG and FLAIR contours using MATLAB, and 15 features were selected for analysis based on qualitative radiographic features with prognostic significance for pediatric embryonal brain tumors. Logistic regression, linear regression, receiver operating characteristic curves, the Harrell C index, and the Somer D index were used to test the relationships between radiomic features and demographic variables, as well as clinical outcomes.

Results: Pediatric embryonal brain tumors in older patients had an increased normalized mean tumor intensity (P = .05, T1PG), decreased tumor volume (P = .02, T1PG), and increased markers of heterogeneity (P ≤ .01, T1PG and FLAIR) relative to those in younger patients. We identified 10 quantitative radiomic features that delineated medulloblastoma, pineoblastoma, and supratentorial primitive neuroectodermal tumor, including size and heterogeneity (P ≤ .05, T1PG and FLAIR). Decreased markers of tumor heterogeneity were predictive of neuraxis metastases and trended toward significance (P = .1, FLAIR). Tumors with an increased size (area under the curve = 0.7, FLAIR) and decreased heterogeneity (area under the curve = 0.7, FLAIR) at diagnosis were more likely to recur.

Conclusions: Quantitative radiomic features are associated with pediatric embryonal brain tumor patient age, histology, neuraxis metastases, and recurrence. These data suggest that quantitative 3-dimensional magnetic resonance imaging radiomic analysis has the potential to identify radiomic risk features for pediatric patients with embryonal brain tumors.
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http://dx.doi.org/10.1016/j.ijrobp.2018.05.077DOI Listing
November 2018

Validation and clinical implementation of an accurate Monte Carlo code for pencil beam scanning proton therapy.

J Appl Clin Med Phys 2018 Sep 30;19(5):558-572. Epub 2018 Jul 30.

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.

Monte Carlo (MC)-based dose calculations are generally superior to analytical dose calculations (ADC) in modeling the dose distribution for proton pencil beam scanning (PBS) treatments. The purpose of this paper is to present a methodology for commissioning and validating an accurate MC code for PBS utilizing a parameterized source model, including an implementation of a range shifter, that can independently check the ADC in commercial treatment planning system (TPS) and fast Monte Carlo dose calculation in opensource platform (MCsquare). The source model parameters (including beam size, angular divergence and energy spread) and protons per MU were extracted and tuned at the nozzle exit by comparing Tool for Particle Simulation (TOPAS) simulations with a series of commissioning measurements using scintillation screen/CCD camera detector and ionization chambers. The range shifter was simulated as an independent object with geometric and material information. The MC calculation platform was validated through comprehensive measurements of single spots, field size factors (FSF) and three-dimensional dose distributions of spread-out Bragg peaks (SOBPs), both without and with the range shifter. Differences in field size factors and absolute output at various depths of SOBPs between measurement and simulation were within 2.2%, with and without a range shifter, indicating an accurate source model. TOPAS was also validated against anthropomorphic lung phantom measurements. Comparison of dose distributions and DVHs for representative liver and lung cases between independent MC and analytical dose calculations from a commercial TPS further highlights the limitations of the ADC in situations of highly heterogeneous geometries. The fast MC platform has been implemented within our clinical practice to provide additional independent dose validation/QA of the commercial ADC for patient plans. Using the independent MC, we can more efficiently commission ADC by reducing the amount of measured data required for low dose "halo" modeling, especially when a range shifter is employed.
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http://dx.doi.org/10.1002/acm2.12420DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123159PMC
September 2018

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

J Appl Clin Med Phys 2018 Sep 10;19(5):539-546. Epub 2018 Jul 10.

Department of Radiation Oncology, University of Maryland, School of Medicine, Baltimore, MD, USA.

Background And Purpose: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.

Materials And Methods: Twenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.

Results: Univariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.

Conclusions: Using machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis.
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http://dx.doi.org/10.1002/acm2.12415DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123157PMC
September 2018

A continuous arc delivery optimization algorithm for CyberKnife m6.

Med Phys 2018 Jun 1. Epub 2018 Jun 1.

Department of Radiation Oncology, University of California, San Francisco, CA, USA.

Purpose: This study aims to reduce the delivery time of CyberKnife m6 treatments by allowing for noncoplanar continuous arc delivery. To achieve this, a novel noncoplanar continuous arc delivery optimization algorithm was developed for the CyberKnife m6 treatment system (CyberArc-m6).

Methods And Materials: CyberArc-m6 uses a five-step overarching strategy, in which an initial set of beam geometries is determined, the robotic delivery path is calculated, direct aperture optimization is conducted, intermediate MLC configurations are extracted, and the final beam weights are computed for the continuous arc radiation source model. This algorithm was implemented on five prostate and three brain patients, previously planned using a conventional step-and-shoot CyberKnife m6 delivery technique. The dosimetric quality of the CyberArc-m6 plans was assessed using locally confined mutual information (LCMI), conformity index (CI), heterogeneity index (HI), and a variety of common clinical dosimetric objectives.

Results: Using conservative optimization tuning parameters, CyberArc-m6 plans were able to achieve an average CI difference of 0.036 ± 0.025, an average HI difference of 0.046 ± 0.038, and an average LCMI of 0.920 ± 0.030 compared with the original CyberKnife m6 plans. Including a 5 s per minute image alignment time and a 5-min setup time, conservative CyberArc-m6 plans achieved an average treatment delivery speed up of 1.545x ± 0.305x compared with step-and-shoot plans.

Conclusions: The CyberArc-m6 algorithm was able to achieve dosimetrically similar plans compared to their step-and-shoot CyberKnife m6 counterparts, while simultaneously reducing treatment delivery times.
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http://dx.doi.org/10.1002/mp.13022DOI Listing
June 2018

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.

Front Oncol 2018 17;8:110. Epub 2018 Apr 17.

Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.

Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.
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http://dx.doi.org/10.3389/fonc.2018.00110DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5913324PMC
April 2018

Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis.

Int J Radiat Oncol Biol Phys 2018 07 13;101(3):694-703. Epub 2018 Mar 13.

Radiation Oncology Department, University of California San Francisco, San Francisco, California.

Purpose: Salvage high-dose-rate brachytherapy (sHDRB) is a treatment option for recurrences after prior radiation therapy. However, only approximately 50% of patients benefit, with the majority of second recurrences after salvage brachytherapy occurring distantly. Therefore, identification of characteristics that can help select patients who may benefit most from sHDRB is critical. Machine learning may be used to identify characteristics that predict outcome following sHDRB. We aimed to use machine learning to identify patient characteristics associated with biochemical failure (BF) following prostate sHDRB.

Methods And Materials: We analyzed data for 52 patients treated with sHDRB for locally recurrent prostate cancer after previous definitive radiation therapy between 1998 and 2009. Following pathologic confirmation of locally recurrent disease without evidence of metastatic disease, 36 Gy in 6 fractions was administered to the prostate and seminal vesicles. BF following sHDRB was defined using the Phoenix definition. Sixteen different clinical risk features were collected, and machine learning analysis was executed to identify subpopulations at higher risk of BF. Decision tree-based algorithms including classification and regression trees, MediBoost, and random forests were constructed.

Results: Patients were followed up for a minimum of 5 years after sHDRB. Those with a fraction of positive cores ≥0.35 and a disease-free interval <4.12 years after their initial radiation treatment experienced a higher failure rate after sHDRB of 0.75 versus 0.38 for the rest of the population.

Conclusions: Using machine learning, we have identified that patients with a fraction of positive cores ≥0.35 and a disease-free interval <4.1 years might be associated with a high risk of BF following sHDRB.
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http://dx.doi.org/10.1016/j.ijrobp.2018.03.001DOI Listing
July 2018

Single- and Multi-Fraction Stereotactic Radiosurgery Dose Tolerances of the Optic Pathways.

Int J Radiat Oncol Biol Phys 2021 May 31;110(1):87-99. Epub 2018 Jan 31.

Department of Radiation Oncology, Lineberger Cancer Center, University of North Carolina, Chapel Hill, North Carolina.

Purpose: Dosimetric and clinical predictors of radiation-induced optic nerve/chiasm neuropathy (RION) after single-fraction stereotactic radiosurgery (SRS) or hypofractionated (2-5 fractions) radiosurgery (fSRS) were analyzed from pooled data that were extracted from published reports (PubMed indexed from 1990 to June 2015). This study was undertaken as part of the American Association of Physicists in Medicine Working Group on Stereotactic Body Radiotherapy, investigating normal tissue complication probability (NTCP) after hypofractionated radiation.

Methods And Materials: Eligible studies described dose delivered to optic nerve/chiasm and provided crude or actuarial toxicity risks, with visual endpoints (ie, loss of visual acuity, alterations in visual fields, and/or blindness/complete vision loss). Studies of patients with optic nerve sheath tumors, optic nerve gliomas, or ocular/uveal melanoma were excluded to obviate direct tumor effects on visual outcomes, as were studies not specifying causes of vision loss (ie, tumor progression vs RION).

Results: Thirty-four studies (1578 patients) were analyzed. Histologies included pituitary adenoma, cavernous sinus meningioma, craniopharyngioma, and malignant skull base tumors. Prior resection (76% of patients) did not correlate with RION risk (P = .66). Prior irradiation (6% of patients) was associated with a crude 10-fold increased RION risk versus no prior radiation therapy. In patients with no prior radiation therapy receiving SRS/fSRS in 1-5 fractions, optic apparatus maximum point doses resulting in <1% RION risks include 12 Gy in 1 fraction (which is greater than our recommendation of 10 Gy in 1 fraction), 20 Gy in 3 fractions, and 25 Gy in 5 fractions. Omitting multi-fraction data (and thereby eliminating uncertainties associated with dose conversions), a single-fraction dose of 10 Gy was associated with a 1% RION risk. Insufficient details precluded modeling of NTCP risks after prior radiation therapy.

Conclusions: Optic apparatus NTCP and tolerance doses after single- and multi-fraction stereotactic radiosurgery are presented. Additional standardized dosimetric and toxicity reporting is needed to facilitate future pooled analyses and better define RION NTCP after SRS/fSRS.
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http://dx.doi.org/10.1016/j.ijrobp.2018.01.053DOI Listing
May 2021

Ex vivo validation of a stoichiometric dual energy CT proton stopping power ratio calibration.

Phys Med Biol 2018 03 7;63(5):055016. Epub 2018 Mar 7.

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA 19104, United States of America.

A major source of uncertainty in proton therapy is the conversion of Hounsfield unit (HU) to proton stopping power ratio relative to water (SPR). In this study, we measured and quantified the accuracy of a stoichiometric dual energy CT (DECT) SPR calibration. We applied a stoichiometric DECT calibration method to derive the SPR using CT images acquired sequentially at [Formula: see text] and [Formula: see text]. The dual energy index was derived based on the HUs of the paired spectral images and used to calculate the effective atomic number (Z ), relative electron density ([Formula: see text]), and SPRs of phantom and biological materials. Two methods were used to verify the derived SPRs. The first method measured the sample's water equivalent thicknesses to deduce the SPRs using a multi-layer ion chamber (MLIC) device. The second method utilized Gafchromic EBT3 film to directly compare relative ranges between sample and water after proton pencil beam irradiation. Ex vivo validation was performed using five different types of frozen animal tissues with the MLIC and three types of fresh animal tissues using film. In addition, the residual ranges recorded on the film were used to compare with those from the treatment planning system using both DECT and SECT derived SPRs. Bland-Altman analysis indicates that the differences between DECT and SPR measurement of tissue surrogates, frozen and fresh animal tissues has a mean of 0.07% and standard deviation of 0.58% compared to 0.55% and 1.94% respectively for single energy CT (SECT) and SPR measurement. Our ex vivo study indicates that the stoichiometric DECT SPR calibration method has the potential to be more accurate than SECT calibration under ideal conditions although beam hardening effects and other image artifacts may increase this uncertainty.
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http://dx.doi.org/10.1088/1361-6560/aaae91DOI Listing
March 2018

Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.

Radiother Oncol 2017 12 20;125(3):392-397. Epub 2017 Nov 20.

Department of Radiation Oncology, University of California, San Francisco, United States.

Background And Purpose: Clinical decision support systems are a growing class of tools with the potential to impact healthcare. This study investigates the construction of a decision support system through which clinicians can efficiently identify which previously approved historical treatment plans are achievable for a new patient to aid in selection of therapy.

Material And Methods: Treatment data were collected for early-stage lung and postoperative oropharyngeal cancers treated using photon (lung and head and neck) and proton (head and neck) radiotherapy. Machine-learning classifiers were constructed using patient-specific feature-sets and a library of historical plans. Model accuracy was analyzed using learning curves, and historical treatment plan matching was investigated.

Results: Learning curves demonstrate that for these datasets, approximately 45, 60, and 30 patients are needed for a sufficiently accurate classification model for radiotherapy for early-stage lung, postoperative oropharyngeal photon, and postoperative oropharyngeal proton, respectively. The resulting classification model provides a database of previously approved treatment plans that are achievable for a new patient. An exemplary case, highlighting tradeoffs between the heart and chest wall dose while holding target dose constant in two historical plans is provided.

Conclusions: We report on the first artificial-intelligence based clinical decision support system that connects patients to past discrete treatment plans in radiation oncology and demonstrate for the first time how this tool can enable clinicians to use past decisions to help inform current assessments. Clinicians can be informed of dose tradeoffs between critical structures early in the treatment process, enabling more time spent on finding the optimal course of treatment for individual patients.
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http://dx.doi.org/10.1016/j.radonc.2017.10.014DOI Listing
December 2017

The Potential of Heavy-Ion Therapy to Improve Outcomes for Locally Advanced Non-Small Cell Lung Cancer.

Front Oncol 2017 5;7:201. Epub 2017 Sep 5.

Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

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http://dx.doi.org/10.3389/fonc.2017.00201DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5591826PMC
September 2017

The Dark Side of the MedPhys Match.

J Appl Clin Med Phys 2017 Sep 30;18(5):4-5. Epub 2017 Aug 30.

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http://dx.doi.org/10.1002/acm2.12169DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874951PMC
September 2017

Prompt Gamma Imaging for In Vivo Range Verification of Pencil Beam Scanning Proton Therapy.

Int J Radiat Oncol Biol Phys 2017 09 3;99(1):210-218. Epub 2017 May 3.

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address:

Purpose: To report the first clinical results and value assessment of prompt gamma imaging for in vivo proton range verification in pencil beam scanning mode.

Methods And Materials: A stand-alone, trolley-mounted, prototype prompt gamma camera utilizing a knife-edge slit collimator design was used to record the prompt gamma signal emitted along the proton tracks during delivery of proton therapy for a brain cancer patient. The recorded prompt gamma depth detection profiles of individual pencil beam spots were compared with the expected profiles simulated from the treatment plan.

Results: In 6 treatment fractions recorded over 3 weeks, the mean (± standard deviation) range shifts aggregated over all spots in 9 energy layers were -0.8 ± 1.3 mm for the lateral field, 1.7 ± 0.7 mm for the right-superior-oblique field, and -0.4 ± 0.9 mm for the vertex field.

Conclusions: This study demonstrates the feasibility and illustrates the distinctive benefits of prompt gamma imaging in pencil beam scanning treatment mode. Accuracy in range verification was found in this first clinical case to be better than the range uncertainty margin applied in the treatment plan. These first results lay the foundation for additional work toward tighter integration of the system for in vivo proton range verification and quantification of range uncertainties.
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http://dx.doi.org/10.1016/j.ijrobp.2017.04.027DOI Listing
September 2017

IMRT QA using machine learning: A multi-institutional validation.

J Appl Clin Med Phys 2017 Sep 17;18(5):279-284. Epub 2017 Aug 17.

Department of Radiation Oncology, University of California San Francisco Medical Center, San Francisco, CA, USA.

Purpose: To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.

Methods: A Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.

Results: The methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.

Conclusions: We have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process.
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http://dx.doi.org/10.1002/acm2.12161DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874948PMC
September 2017