Gilmer Valdes, PhD

Title(s)Associate Professor, Radiation Oncology
SchoolSchool of Medicine
Address1825 4th Street, #001
San Francisco CA 94107
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    Collapse Biography 
    Collapse Education and Training
    University of Havana, HavanaBS07/2005Nuclear Sciences
    University of Havana, HavanaMS07/2007Radiochemistry
    UCLA, Los AngelesPhD07/2013Medical Physics
    University of Pennsylvania, PhiladelphiaResidency in Medical Physics06/2017Radiotherapy
    Collapse Awards and Honors
    University of Havana2004  - 2005Suma Cum Laude
    Cuban Academy of Science2007Nomination to the Best Young Researcher
    UCLA2010Eugene V. Cota-Robles Fellowship
    AAPM2013First Place, Best Graduate Student Norm Baily Award
    AAPM2015First Place, Young Investigator Award.
    UCSF2018Jean Pouliot Award for Excellence in Teaching

    Collapse Overview 
    Collapse Overview
    The expanding collection and sharing of health-related data, increases in computational power, and advances in machine learning (ML) are hoped to enable discoveries of better ways to prevent, diagnose, and treat disease. In our field of Radiation Oncology, Machine Learning has been applied to outcome prediction, quality assurance, auto-segmentation and image registration, image classification, treatment planning and it is poised to become an indispensable tool in our daily clinical workflows. Despite new advances, Radiation Oncology has many specific challenges, ranging from unique and complex datasets with multiple source of information (e.g. comorbidities, 4DCT, CBCT, CT, dose, structures, setup and quality assurance or genetic information), limited clinical outcome data, lack of standard of care for many disease sites, interaction of radiation and chemotherapy, limited access to genomics data, and the presence of confounders in many of our clinical datasets. If we pair these challenges with suboptimal algorithms, the indiscriminate deployment of models developed can compromise medicine’s fundamental oath to primum non nocere. For instance, an artificial neural network (a non-interpretable algorithm) that was developed to triage patients with pneumonia for hospital discharge was found to inadvertently label asthmatic patients as low risk. Deploying this neural network could have had detrimental consequences for these patients but if an interpretable algorithm had been used this error could have been easily detected by physicians. Similar problems have been found for image classification tasks using deep learning giving a false sense of accuracy to physicians (e.g a model used the label “portable” on X-ray images to predict an increased risk of cardiomyopathy since patients that cannot move need to have the x-rays done at their beds). Therefore, to make ML part of everyday clinical practice in Radiation Oncology and Medicine at large, a critical challenge is to increase the robustness and transparency of the models developed. Equally important is to create a set of tools, commissioning procedures and a quality assurance program that could let us detect population shifts from the data used to train the algorithms or errors due to the presence of confounders. Towards achieving these goals, I would like to devote my scientific career. In that regard I have already made important contributions, both theoretical and practical, and continue to do so. Theoretical Contributions: In collaboration with Penn Computer Science Department and Stanford Statistics Department I developed MediBoost, an algorithm that improves the accuracy of the most popular decision tree algorithm (CART) while keeping its same topology and as such its interpretability. This algorithm was further extended in one of my hallmark publications to show how it unified two of the most popular frameworks to build ML models: CART and Gradient Boosting. This new framework was called “The Additive Tree” and due to its impact on accuracy and interpretability of decision trees, and the importance of the later in medicine, we belief that it opens a new era of research on Decision Tree algorithms. Additionally, in collaboration with the Berkeley Biostatistics and Statistics Department, I have developed the Conditional Interpretable Super Learner (CiSL), an algorithm that removes the topological constraints that interpretable algorithms have while still building a transparent mode (under preparation for submission). Further, in this work we show for the first time how it is possible to learn in the cross validation space and improve on widely popular techniques like stacking. We believe that CiSL, for its characteristics, is especially important for the analysis of structured clinical trial data and dynamic treatment allocation. Big part of my future intellectual activity will be dedicated to the application of CiSL to Radiation Oncology clinical trial to optimize treatment selection. Finally, I have led a team that have created the framework Expert Augmented Machine Learning (EAML), the first platform that effectively combine physicians and AI knowledge to improve over both. Applied Contributions: I have also been widely interested in the applications of Machine Learning for Quality Assurance (QA). In this sense, I have pioneered the use of predictive models for their application to QA in Radiation Therapy. Specifically, I was one of the first authors to apply Machine Learning to Quality Assurance data in Radiation Oncology with the goal to improve patient safety. I developed ML models that predicted errors on the imaging system on the Linacs, a key factor in the delivery of accurate radiation treatments . Additionally, I developed and validated the concept of Virtual IMRT QA, an application that enables safe pre-treatment radiation therapy plan verification. Virtual IMRT QA will play a key role in the safe introduction of Adaptiative Radiation Therapy, one of the frontiers for Radiation Therapy in the next decade. A good part of my applied research program is intended to the deployment of Virtual IMRT QA into clinical practice and enabling adaptative Radiation Therapy.

    Collapse Research 
    Collapse Research Activities and Funding
    Utility of Predictive Systems to identify Inpatient Diagnostic Errors: The UPSIDE Study
    NIH R01HS027369Sep 30, 2019 - Sep 29, 2022
    Role: Co-Investigator
    Development of Accurate and Interpretable Machine Learning Algorithms for their application in Medicine
    NIH K08EB026500Aug 7, 2019 - Jun 30, 2022
    Role: Principal Investigator

    Collapse ORNG Applications 
    Collapse Featured Publications
    Collapse Collaboration Interests
    Collapse Faculty Mentoring

    Collapse Featured Content 
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    Collapse Bibliographic 
    Collapse Publications
    Publications listed below are automatically derived from MEDLINE/PubMed and other sources, which might result in incorrect or missing publications. Researchers can login to make corrections and additions, or contact us for help. to make corrections and additions.
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    Altmetrics Details PMC Citations indicate the number of times the publication was cited by articles in PubMed Central, and the Altmetric score represents citations in news articles and social media. (Note that publications are often cited in additional ways that are not shown here.) Fields are based on how the National Library of Medicine (NLM) classifies the publication's journal and might not represent the specific topic of the publication. Translation tags are based on the publication type and the MeSH terms NLM assigns to the publication. Some publications (especially newer ones and publications not in PubMed) might not yet be assigned Field or Translation tags.) Click a Field or Translation tag to filter the publications.
    1. Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care. JAMA Intern Med. 2024 Jan 08. Auerbach AD, Lee TM, Hubbard CC, Ranji SR, Raffel K, Valdes G, Boscardin J, Dalal AK, Harris A, Flynn E, Schnipper JL, UPSIDE Research Group. PMID: 38190122; PMCID: PMC10775080.
      View in: PubMed   Mentions:    Fields:    Translation:Humans
    2. Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial. Int J Radiat Oncol Biol Phys. 2023 Nov 23. Valdes G, Scholey J, Nano TF, Gennatas ED, Mohindra P, Mohammed N, Zeng J, Kotecha R, Rosen LR, Chang J, Tsai HK, Urbanic JJ, Vargas CE, Yu NY, Ungar LH, Eaton E, Simone CB. PMID: 38000701.
      View in: PubMed   Mentions: 1     Fields:    
    3. A unified path seeking algorithm for IMRT and IMPT beam orientation optimization. Phys Med Biol. 2023 09 22; 68(19). Ramesh P, Valdes G, O'Connor D, Sheng K. PMID: 37659406.
      View in: PubMed   Mentions:    Fields:    Translation:Humans
    4. Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model. J Appl Clin Med Phys. 2023 Dec; 24(12):e14146. Lafrenière M, Valdes G, Descovich M. PMID: 37696265; PMCID: PMC10691617.
      View in: PubMed   Mentions: 1     Fields:    Translation:Humans
    5. Multi-institutional Development and External Validation of a Machine Learning Model for the Prediction of Distant Metastasis in Patients Treated by Salvage Radiotherapy for Biochemical Failure After Radical Prostatectomy. Eur Urol Focus. 2023 Jul 26. Sabbagh A, Tilki D, Feng J, Huland H, Graefen M, Wiegel T, Böhmer D, Hong JC, Valdes G, Cowan JE, Cooperberg M, Feng FY, Mohammad T, Shelan M, D'Amico AV, Carroll PR, Mohamad O. PMID: 37507248.
      View in: PubMed   Mentions:    Fields:    
    6. Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images. Med Phys. 2023 May; 50(5):2662-2671. Petragallo R, Bertram P, Halvorsen P, Iftimia I, Low DA, Morin O, Narayanasamy G, Saenz DL, Sukumar KN, Valdes G, Weinstein L, Wells MC, Ziemer BP, Lamb JM. PMID: 36908243.
      View in: PubMed   Mentions:    Fields:    Translation:Humans
    7. Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer. Eur Urol Oncol. 2023 Oct; 6(5):501-507. Sabbagh A, Washington SL, Tilki D, Hong JC, Feng J, Valdes G, Chen MH, Wu J, Huland H, Graefen M, Wiegel T, Böhmer D, Cowan JE, Cooperberg M, Feng FY, Roach M, Trock BJ, Partin AW, D'Amico AV, Carroll PR, Mohamad O. PMID: 36868922.
      View in: PubMed   Mentions: 1     Fields:    
    8. The Conditional Super Learner. IEEE Trans Pattern Anal Mach Intell. 2022 12; 44(12):10236-10243. Valdes G, Interian Y, Gennatas E, Van der Laan M. PMID: 34851823.
      View in: PubMed   Mentions: 1     Fields:    
    9. Representational Gradient Boosting: Backpropagation in the Space of Functions. IEEE Trans Pattern Anal Mach Intell. 2022 12; 44(12):10186-10195. Valdes G, Friedman JH, Jiang F, Gennatas ED. PMID: 34941500.
      View in: PubMed   Mentions:    Fields:    
    10. NSMCE2, a novel super-enhancer-regulated gene, is linked to poor prognosis and therapy resistance in breast cancer. BMC Cancer. 2022 Oct 12; 22(1):1056. Di Benedetto C, Oh J, Choudhery Z, Shi W, Valdes G, Betancur P. PMID: 36224576; PMCID: PMC9555101.
      View in: PubMed   Mentions: 2     Fields:    Translation:HumansCells
    11. Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol. 2022 05 27; 67(11). Barragán-Montero A, Bibal A, Dastarac MH, Draguet C, Valdés G, Nguyen D, Willems S, Vandewinckele L, Holmström M, Löfman F, Souris K, Sterpin E, Lee JA. PMID: 35421855; PMCID: PMC9870296.
      View in: PubMed   Mentions: 9     Fields:    
    12. Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans. Int J Radiat Oncol Biol Phys. 2022 08 01; 113(5):1091-1102. Wall PDH, Hirata E, Morin O, Valdes G, Witztum A. PMID: 35533908.
      View in: PubMed   Mentions: 3     Fields:    Translation:Humans
    13. Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation. Adv Radiat Oncol. 2022 Mar-Apr; 7(2):100886. Chan JW, Hohenstein N, Carpenter C, Pattison AJ, Morin O, Valdes G, Sanchez CT, Perkins J, Perkins J, Solberg TD, Yom SS. PMID: 35387423; PMCID: PMC8977910.
      View in: PubMed   Mentions: 2  
    14. Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health. Annu Rev Public Health. 2022 04 05; 43:59-78. Wesson P, Hswen Y, Valdes G, Stojanovski K, Handley MA. PMID: 34871504; PMCID: PMC8983486.
      View in: PubMed   Mentions: 7     Fields:    Translation:HumansPHPublic Health
    15. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer. 2021 07; 2(7):709-722. Morin O, Vallières M, Braunstein S, Ginart JB, Upadhaya T, Woodruff HC, Zwanenburg A, Chatterjee A, Villanueva-Meyer JE, Valdes G, Chen W, Hong JC, Yom SS, Solberg TD, Löck S, Seuntjens J, Park C, Lambin P. PMID: 35121948.
      View in: PubMed   Mentions: 15     Fields:    Translation:Humans
    16. A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients. Phys Med. 2021 Jul; 87:11-23. Luo Y, Jolly S, Palma D, Lawrence TS, Tseng HH, Valdes G, McShan D, Ten Haken RK, Ei Naqa I. PMID: 34091197; PMCID: PMC8284560.
      View in: PubMed   Mentions: 2     Fields:    Translation:Humans
    17. Salvage High-Dose-Rate Brachytherapy for Recurrent Prostate Cancer After Definitive Radiation. Pract Radiat Oncol. 2021 Nov-Dec; 11(6):515-526. Wu SY, Wong AC, Shinohara K, Roach M, Cunha JAM, Valdes G, Hsu IC. PMID: 34077809.
      View in: PubMed   Mentions: 2     Fields:    Translation:Humans
    18. Artificial intelligence for prediction of measurement-based patient-specific quality assurance is ready for prime time. Med Phys. 2021 06; 48(6):2701-2704. Valdes G, Adamson J, Cai J. PMID: 33797761.
      View in: PubMed   Mentions: 3     Fields:    Translation:Humans
    19. Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance. Int J Radiat Oncol Biol Phys. 2021 03 15; 109(4):1086-1095. Hasse K, Scholey J, Ziemer BP, Natsuaki Y, Morin O, Solberg TD, Hirata E, Valdes G, Witztum A. PMID: 33197530.
      View in: PubMed   Mentions: 2     Fields:    Translation:Humans
    20. Targeted transfer learning to improve performance in small medical physics datasets. Med Phys. 2020 Dec; 47(12):6246-6256. Romero M, Interian Y, Solberg T, Valdes G. PMID: 33007112.
      View in: PubMed   Mentions: 11     Fields:    Translation:Humans
    21. Integration of AI and Machine Learning in Radiotherapy QA. Front Artif Intell. 2020; 3:577620. Chan MF, Witztum A, Valdes G. PMID: 33733216; PMCID: PMC7861232.
      View in: PubMed   Mentions: 23  
    22. Machine learning for radiation outcome modeling and prediction. Med Phys. 2020 Jun; 47(5):e178-e184. Luo Y, Chen S, Valdes G. PMID: 32418338.
      View in: PubMed   Mentions: 9     Fields:    Translation:Humans
    23. Reply to Nock and Nielsen: On the work of Nock and Nielsen and its relationship to the additive tree. Proc Natl Acad Sci U S A. 2020 04 21; 117(16):8694-8695. Valdes G, Luna JM, Gennatas ED, Ungar LH, Eaton E, Diffenderfer ES, Jensen ST, Simone CB, Friedman JH, Solberg TD. PMID: 32265277; PMCID: PMC7183189.
      View in: PubMed   Mentions:    Fields:    
    24. Expert-augmented machine learning. Proc Natl Acad Sci U S A. 2020 03 03; 117(9):4571-4577. Gennatas ED, Friedman JH, Ungar LH, Pirracchio R, Eaton E, Reichmann LG, Interian Y, Luna JM, Simone CB, Auerbach A, Delgado E, van der Laan MJ, Solberg TD, Valdes G. PMID: 32071251; PMCID: PMC7060733.
      View in: PubMed   Mentions: 24     Fields:    
    25. Building more accurate decision trees with the additive tree. Proc Natl Acad Sci U S A. 2019 10 01; 116(40):19887-19893. Luna JM, Gennatas ED, Ungar LH, Eaton E, Diffenderfer ES, Jensen ST, Simone CB, Friedman JH, Solberg TD, Valdes G. PMID: 31527280; PMCID: PMC6778203.
      View in: PubMed   Mentions: 12     Fields:    
    26. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neurooncol Adv. 2019 May-Dec; 1(1):vdz011. Morin O, Chen WC, Nassiri F, Susko M, Magill ST, Vasudevan HN, Wu A, Vallières M, Gennatas ED, Valdes G, Pekmezci M, Alcaide-Leon P, Choudhury A, Interian Y, Mortezavi S, Turgutlu K, Bush NAO, Solberg TD, Braunstein SE, Sneed PK, Perry A, Zadeh G, McDermott MW, Villanueva-Meyer JE, Raleigh DR. PMID: 31608329; PMCID: PMC6777505.
      View in: PubMed   Mentions: 44  
    27. Optimizing beam models for dosimetric accuracy over a wide range of treatments. Phys Med. 2019 Feb; 58:47-53. Chen J, Morin O, Weethee B, Perez-Andujar A, Phillips J, Held M, Kearney V, Han DY, Cheung J, Chuang C, Valdes G, Sudhyadhom A, Solberg T. PMID: 30824149.
      View in: PubMed   Mentions: 2     Fields:    
    28. Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning. Radiother Oncol. 2019 04; 133:106-112. Luna JM, Chao HH, Diffenderfer ES, Valdes G, Chinniah C, Ma G, Cengel KA, Solberg TD, Berman AT, Simone CB. PMID: 30935565.
      View in: PubMed   Mentions: 34     Fields:    Translation:Humans
    29. Erratum: "Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers" [Med. Phys. 45 (7), 3449-3459 (2018)]. Med Phys. 2019 Feb; 46(2):1080-1087. Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P. PMID: 30730570.
      View in: PubMed   Mentions: 5     Fields:    
    30. In Reply to Gensheimer and Trister. Int J Radiat Oncol Biol Phys. 2018 12 01; 102(5):1594-1596. Valdes G, Chang AJ, Cunnan A, Solberg TD, Hsu IC, Interian Y, Owen K, Jensen ST, Ungar LH. PMID: 31014789.
      View in: PubMed   Mentions:    Fields:    
    31. The application of artificial intelligence in the IMRT planning process for head and neck cancer. Oral Oncol. 2018 12; 87:111-116. Kearney V, Chan JW, Valdes G, Solberg TD, Yom SS. PMID: 30527225.
      View in: PubMed   Mentions: 16     Fields:    Translation:HumansPHPublic Health
    32. Artificial Intelligence in Radiation Oncology Imaging. Int J Radiat Oncol Biol Phys. 2018 11 15; 102(4):1159-1161. Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. PMID: 30353870.
      View in: PubMed   Mentions: 11     Fields:    Translation:Humans
    33. Preoperative and postoperative prediction of long-term meningioma outcomes. PLoS One. 2018; 13(9):e0204161. Gennatas ED, Wu A, Braunstein SE, Morin O, Chen WC, Magill ST, Gopinath C, Villaneueva-Meyer JE, Perry A, McDermott MW, Solberg TD, Valdes G, Raleigh DR. PMID: 30235308; PMCID: PMC6147484.
      View in: PubMed   Mentions: 11     Fields:    Translation:Humans
    34. An unsupervised convolutional neural network-based algorithm for deformable image registration. Phys Med Biol. 2018 09 17; 63(18):185017. Kearney V, Haaf S, Sudhyadhom A, Valdes G, Solberg TD. PMID: 30109996.
      View in: PubMed   Mentions: 21     Fields:    Translation:Humans
    35. 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 15; 102(4):1074-1082. Morin O, Vallières M, Jochems A, Woodruff HC, Valdes G, Braunstein SE, Wildberger JE, Villanueva-Meyer JE, Kearney V, Yom SS, Solberg TD, Lambin P. PMID: 30170101.
      View in: PubMed   Mentions: 41     Fields:    Translation:Humans
    36. Machine learning and modeling: Data, validation, communication challenges. Med Phys. 2018 Oct; 45(10):e834-e840. El Naqa I, Ruan D, Valdes G, Dekker A, McNutt T, Ge Y, Wu QJ, Oh JH, Thor M, Smith W, Rao A, Fuller C, Xiao Y, Manion F, Schipper M, Mayo C, Moran JM, Ten Haken R. PMID: 30144098; PMCID: PMC6181755.
      View in: PubMed   Mentions: 22     Fields:    
    37. 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; 19(5):539-546. Chao HH, Valdes G, Luna JM, Heskel M, Berman AT, Solberg TD, Simone CB. PMID: 29992732; PMCID: PMC6123157.
      View in: PubMed   Mentions: 4     Fields:    Translation:Humans
    38. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys. 2018 Jul; 45(7):3449-3459. Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu IC, Oberije C, Lustberg T, van Soest J, Hoebers F, Jochems A, El Naqa I, Wee L, Morin O, Raleigh DR, Bots W, Kaanders JH, Belderbos J, Kwint M, Solberg T, Monshouwer R, Bussink J, Dekker A, Lambin P. PMID: 29763967; PMCID: PMC6095141.
      View in: PubMed   Mentions: 111     Fields:    Translation:Humans
    39. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol. 2018 12; 129(3):421-426. Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. PMID: 29907338; PMCID: PMC9620952.
      View in: PubMed   Mentions: 66     Fields:    Translation:Humans
    40. Clinical Applications of Quantitative 3-Dimensional MRI Analysis for Pediatric Embryonal Brain Tumors. Int J Radiat Oncol Biol Phys. 2018 11 15; 102(4):744-756. Hara JH, Wu A, Villanueva-Meyer JE, Valdes G, Daggubati V, Mueller S, Solberg TD, Braunstein SE, Morin O, Raleigh DR. PMID: 30108003.
      View in: PubMed   Mentions: 5     Fields:    Translation:Humans
    41. The Future of Artificial Intelligence in Radiation Oncology. Int J Radiat Oncol Biol Phys. 2018 10 01; 102(2):247-248. Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, Lindsay WD, Aerts HJWL, Agrimson B, Deville C, Rosenthal SA, Yu JB, Thomas CR. PMID: 30191856.
      View in: PubMed   Mentions: 5     Fields:    
    42. Deep nets vs expert designed features in medical physics: An IMRT QA case study. Med Phys. 2018 Jun; 45(6):2672-2680. Interian Y, Rideout V, Kearney VP, Gennatas E, Morin O, Cheung J, Solberg T, Valdes G. PMID: 29603278.
      View in: PubMed   Mentions: 24     Fields:    Translation:Humans
    43. Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs. Front Oncol. 2018; 8:110. Feng M, Valdes G, Dixit N, Solberg TD. PMID: 29719815; PMCID: PMC5913324.
      View in: PubMed   Mentions: 33  
    44. Comment on 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study'. Phys Med Biol. 2018 03 15; 63(6):068001. Valdes G, Interian Y. PMID: 29424369.
      View in: PubMed   Mentions: 6     Fields:    Translation:Humans
    45. Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis. Int J Radiat Oncol Biol Phys. 2018 07 01; 101(3):694-703. Valdes G, Chang AJ, Interian Y, Owen K, Jensen ST, Ungar LH, Cunha A, Solberg TD, Hsu IC. PMID: 29709315.
      View in: PubMed   Mentions: 4     Fields:    Translation:Humans
    46. Correcting TG 119 confidence limits. Med Phys. 2018 Mar; 45(3):1001-1008. Kearney V, Solberg T, Jensen S, Cheung J, Chuang C, Valdes G. PMID: 29360150.
      View in: PubMed   Mentions: 2     Fields:    Translation:Humans
    47. Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making. Radiother Oncol. 2017 12; 125(3):392-397. Valdes G, Simone CB, Chen J, Lin A, Yom SS, Pattison AJ, Carpenter CM, Solberg TD. PMID: 29162279.
      View in: PubMed   Mentions: 33     Fields:    Translation:Humans
    48. IMRT QA using machine learning: A multi-institutional validation. J Appl Clin Med Phys. 2017 Sep; 18(5):279-284. Valdes G, Chan MF, Lim SB, Scheuermann R, Deasy JO, Solberg TD. PMID: 28815994; PMCID: PMC5874948.
      View in: PubMed   Mentions: 37     Fields:    Translation:Humans
    49. The relative accuracy of 4D dose accumulation for lung radiotherapy using rigid dose projection versus dose recalculation on every breathing phase. Med Phys. 2017 Mar; 44(3):1120-1127. Valdes G, Lee C, Tenn S, Lee P, Robinson C, Iwamoto K, Low D, Lamb JM. PMID: 28019649.
      View in: PubMed   Mentions: 4     Fields:    Translation:Humans
    50. MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine. Sci Rep. 2016 11 30; 6:37854. Valdes G, Luna JM, Eaton E, Simone CB, Ungar LH, Solberg TD. PMID: 27901055; PMCID: PMC5129017.
      View in: PubMed   Mentions: 34     Fields:    Translation:Humans
    51. Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy. Phys Med Biol. 2016 08 21; 61(16):6105-20. Valdes G, Solberg TD, Heskel M, Ungar L, Simone CB. PMID: 27461154; PMCID: PMC5491385.
      View in: PubMed   Mentions: 34     Fields:    Translation:Humans
    52. A mathematical framework for virtual IMRT QA using machine learning. Med Phys. 2016 Jul; 43(7):4323. Valdes G, Scheuermann R, Hung CY, Olszanski A, Bellerive M, Solberg TD. PMID: 27370147.
      View in: PubMed   Mentions: 49     Fields:    Translation:Humans
    53. Use of TrueBeam developer mode for imaging QA. J Appl Clin Med Phys. 2015 07 08; 16(4):322–333. Valdes G, Morin O, Valenciaga Y, Kirby N, Pouliot J, Chuang C. PMID: 26219002; PMCID: PMC5690025.
      View in: PubMed   Mentions: 12     Fields:    Translation:Humans
    54. Tumor control probability and the utility of 4D vs 3D dose calculations for stereotactic body radiotherapy for lung cancer. Med Dosim. 2015; 40(1):64-9. Valdes G, Robinson C, Lee P, Morel D, Low D, Iwamoto KS, Lamb JM. PMID: 25542785.
      View in: PubMed   Mentions:    Fields:    Translation:Humans
    55. Radiosensitization of gliomas by intracellular generation of 5-fluorouracil potentiates prodrug activator gene therapy with a retroviral replicating vector. Cancer Gene Ther. 2014 Oct; 21(10):405-410. Takahashi M, Valdes G, Hiraoka K, Inagaki A, Kamijima S, Micewicz E, Gruber HE, Robbins JM, Jolly DJ, McBride WH, Iwamoto KS, Kasahara N. PMID: 25301172; PMCID: PMC4246057.
      View in: PubMed   Mentions: 23     Fields:    Translation:HumansAnimalsCells
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