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Gilmer Valdes, PhD

Title(s)Assistant Professor, Radiation Oncology
SchoolSchool of Medicine
Address1600 Divisadero
San Francisco CA 94115
Phone415-353-8659
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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
    AHRQ R01HS027369Sep 30, 2019 - Sep 29, 2022
    Role: Co-Investigator
    Development of Accurate and Interpretable Machine Learning Algorithms for their application in Medicine
    NIH/NIBIB K08EB026500Aug 7, 2019 - Jun 30, 2022
    Role: Principal Investigator

    Collapse ORNG Applications 
    Collapse Featured Publications
    Collapse Collaboration Interests
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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. Targeted transfer learning to improve performance in small medical physics datasets. Med Phys. 2020 Oct 02. Romero M, Interian Y, Solberg T, Valdes G. PMID: 33007112.
      View in: PubMed   Mentions:    Fields:    
    2. 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:    Fields:    
    3. 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.
      View in: PubMed   Mentions:    Fields:    
    4. 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.
      View in: PubMed   Mentions: 1     Fields:    
    5. 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.
      View in: PubMed   Mentions: 4     Fields:    
    6. 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.
      View in: PubMed   Mentions:
    7. 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:    Fields:    
    8. 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: 9     Fields:    Translation:Humans
    9. 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: 1     Fields:    
    10. 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:    
    11. 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: 3     Fields:    Translation:HumansPHPublic Health
    12. 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: 4     Fields:    Translation:Humans
    13. 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.
      View in: PubMed   Mentions: 1     Fields:    Translation:Humans
    14. 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: 3     Fields:    Translation:Humans
    15. 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: 10     Fields:    Translation:Humans
    16. 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.
      View in: PubMed   Mentions: 3     Fields:    
    17. 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.
      View in: PubMed   Mentions: 4     Fields:    Translation:Humans
    18. 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.
      View in: PubMed   Mentions: 22     Fields:    Translation:Humans
    19. 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.
      View in: PubMed   Mentions: 17     Fields:    Translation:Humans
    20. 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: 1     Fields:    Translation:Humans
    21. 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: 1     Fields:    
    22. 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: 5     Fields:    Translation:Humans
    23. Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs. Front Oncol. 2018; 8:110. Feng M, Valdes G, Dixit N, Solberg TD. PMID: 29719815.
      View in: PubMed   Mentions:
    24. 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: 1     Fields:    Translation:Humans
    25. 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:    Fields:    Translation:Humans
    26. 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: 1     Fields:    Translation:Humans
    27. 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: 8     Fields:    Translation:Humans
    28. 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.
      View in: PubMed   Mentions: 9     Fields:    Translation:Humans
    29. 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:    Fields:    Translation:Humans
    30. 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.
      View in: PubMed   Mentions: 15     Fields:    Translation:Humans
    31. 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.
      View in: PubMed   Mentions: 15     Fields:    Translation:Humans
    32. 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: 17     Fields:    Translation:Humans
    33. 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.
      View in: PubMed   Mentions: 6     Fields:    Translation:Humans
    34. 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
    35. 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.
      View in: PubMed   Mentions: 11     Fields:    Translation:HumansAnimalsCells
    36. The high-affinity maltose switch MBP317-347 has low affinity for glucose: implications for targeting tumors with metabolically directed enzyme prodrug therapy. Chem Biol Drug Des. 2014 Mar; 83(3):266-71. Valdes G, Schulte RW, Ostermeier M, Iwamoto KS. PMID: 24131788.
      View in: PubMed   Mentions: 1     Fields:    Translation:HumansCells
    37. Re-evaluation of cellular radiosensitization by 5-fluorouracil: high-dose, pulsed administration is effective and preferable to conventional low-dose, chronic administration. Int J Radiat Biol. 2013 Oct; 89(10):851-62. Valdes G, Iwamoto KS. PMID: 23607451.
      View in: PubMed   Mentions: 2     Fields:    Translation:HumansCells
    38. Effects of gamma radiation on phase behaviour and critical micelle concentration of Triton X-100 aqueous solutions. Journal of Colloid and Interface Science. 2007; 311:253:261. Valdés G, S. Rodríguez-Calvo, M. Rapado-Paneque, A. Pérez-Gramatges, F. A. Fernández, E. Frota, C. Ribeiro.
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