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Timothy Solberg

Ke Nie, Hania Al-Hallaq, X Allen Li, Stanley H Benedict, Jason W Sohn, Jean M Moran, Yong Fan, Mi Huang, Michael V Knopp, Jeff M Michalski, James Monroe, Ceferino Obcemea, Christina I Tsien, Timothy Solberg, Jackie Wu, Ping Xia, Ying Xiao, Issam El Naqa
Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. It has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing general assessment and early recommendations for incorporation in oncology studies...
January 31, 2019: International Journal of Radiation Oncology, Biology, Physics
Vasant Kearney, Jason W Chan, Gilmer Valdes, Timothy D Solberg, Sue S Yom
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...
December 2018: Oral Oncology
Vasant Kearney, Jason W Chan, Samuel Haaf, Martina Descovich, Timothy D Solberg
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...
December 4, 2018: Physics in Medicine and Biology
Brian C Baumann, Ioannis I Verginadis, Chuan Zeng, Brett Bell, Sravya Koduri, Carolyn Vachani, Kelly M MacArthur, Timothy D Solberg, Constantinos Koumenis, James M Metz
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...
December 1, 2018: JAMA Oncology
Efstathios D Gennatas, Ashley Wu, Steve E Braunstein, Olivier Morin, William C Chen, Stephen T Magill, Chetna Gopinath, Javier E Villaneueva-Meyer, Arie Perry, Michael W McDermott, Timothy D Solberg, Gilmer Valdes, David R Raleigh
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...
2018: PloS One
Olivier Morin, Martin Vallières, Arthur Jochems, Henry C Woodruff, Gilmer Valdes, Steve E Braunstein, Joachim E Wildberger, Javier E Villanueva-Meyer, Vasant Kearney, Sue S Yom, Timothy D Solberg, Philippe Lambin
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...
November 15, 2018: International Journal of Radiation Oncology, Biology, Physics
Vasant P Kearney, Samuel Haaf, Atchar Sudhyadhom, Gilmer Valdes, Timothy D Solberg
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...
August 15, 2018: Physics in Medicine and Biology
Jared H Hara, Ashley Wu, Javier E Villanueva-Meyer, Gilmer Valdes, Vikas Daggubati, Sabine Mueller, Timothy D Solberg, Steve E Braunstein, Olivier Morin, David R Raleigh
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...
November 15, 2018: International Journal of Radiation Oncology, Biology, Physics
Sheng Huang, Minglei Kang, Kevin Souris, Christopher Ainsley, Timothy D Solberg, James E McDonough, Charles B Simone, Liyong Lin
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)...
September 2018: Journal of Applied Clinical Medical Physics
Hann-Hsiang Chao, Gilmer Valdes, Jose M Luna, Marina Heskel, Abigail T Berman, Timothy D Solberg, Charles B Simone
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...
September 2018: Journal of Applied Clinical Medical Physics
Timo M Deist, Frank J W M Dankers, Gilmer Valdes, Robin Wijsman, I-Chow Hsu, Cary Oberije, Tim Lustberg, Johan van Soest, Frank Hoebers, Arthur Jochems, Issam El Naqa, Leonard Wee, Olivier Morin, David R Raleigh, Wouter Bots, Johannes H Kaanders, José Belderbos, Margriet Kwint, Timothy Solberg, René Monshouwer, Johan Bussink, Andre Dekker, Philippe Lambin
PURPOSE: Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction...
July 2018: Medical Physics
Mary Feng, Gilmer Valdes, Nayha Dixit, Timothy D Solberg
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...
2018: Frontiers in Oncology
Gilmer Valdes, Albert J Chang, Yannet Interian, Kenton Owen, Shane T Jensen, Lyle H Ungar, Adam Cunha, Timothy D Solberg, I-Chow Hsu
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...
July 1, 2018: International Journal of Radiation Oncology, Biology, Physics
Yannet Interian, Vincent Rideout, Vasant P Kearney, Efstathios Gennatas, Olivier Morin, Joey Cheung, Timothy Solberg, Gilmer Valdes
PURPOSE: The purpose of this study was to compare the performance of Deep Neural Networks against a technique designed by domain experts in the prediction of gamma passing rates for Intensity Modulated Radiation Therapy Quality Assurance (IMRT QA). METHOD: A total of 498 IMRT plans across all treatment sites were planned in Eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. Measurements were performed using a commercial 2D diode array, and passing rates for 3%/3 mm local dose/distance-to-agreement (DTA) were recorded...
June 2018: Medical Physics
Krista C J Wink, Erik Roelofs, Charles B Simone, David Dechambre, Alina Santiago, Judith van der Stoep, Wim Dries, Julia Smits, Stephen Avery, Filippo Ammazzalorso, Nicolas Jansen, Urszula Jelen, Timothy Solberg, Dirk de Ruysscher, Esther G C Troost
PURPOSE: To compare dose to organs at risk (OARs) and dose-escalation possibility for 24 stage I non-small cell lung cancer (NSCLC) patients in a ROCOCO (Radiation Oncology Collaborative Comparison) trial. METHODS: For each patient, 3 photon plans [Intensity-modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT) and CyberKnife], a double scattered proton (DSP) and an intensity-modulated carbon-ion (IMIT) therapy plan were created. Dose prescription was 60 Gy (equivalent) in 8 fractions...
July 2018: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Michael T Milano, Jimm Grimm, Scott G Soltys, Ellen Yorke, Vitali Moiseenko, Wolfgang A Tomé, Arjun Sahgal, Jinyu Xue, Lijun Ma, Timothy D Solberg, John P Kirkpatrick, Louis S Constine, John C Flickinger, Lawrence B Marks, Issam El Naqa
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...
January 31, 2018: International Journal of Radiation Oncology, Biology, Physics
Yunhe Xie, Christopher Ainsley, Lingshu Yin, Wei Zou, James McDonough, Timothy D Solberg, Alexander Lin, Boon-Keng Kevin Teo
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 eff ), relative electron density ([Formula: see text]), and SPRs of phantom and biological materials...
March 7, 2018: Physics in Medicine and Biology
Vasant Kearney, Timothy Solberg, Shane Jensen, Joey Cheung, Cynthia Chuang, Gilmer Valdes
PURPOSE: Task Group 119 (TG-119) has been adopted for evaluating the adequacy of intensity-modulated radiation therapy (IMRT) commissioning and for establishing patient-specific IMRT quality assurance (QA) passing criteria in clinical practice. TG-119 establishes 95% confidence limits (CLs), which help clinics identify systematic IMRT QA errors and identify outliers. In TG-119, the 95% CLs are established by fitting the Gamma Γ analysis passing rate results to an assumed distribution, then calculating the limit in which 95% of the data fall...
March 2018: Medical Physics
Gilmer Valdes, Charles B Simone, Josephine Chen, Alexander Lin, Sue S Yom, Adam J Pattison, Colin M Carpenter, Timothy D Solberg
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...
December 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Stephen G Chun, Timothy D Solberg, David R Grosshans, Quynh-Nhu Nguyen, Charles B Simone, Radhe Mohan, Zhongxing Liao, Stephen M Hahn, Joseph M Herman, Steven J Frank
No abstract text is available yet for this article.
2017: Frontiers in Oncology
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