Gilmer Valdes, PhD
Associate Professor
Epidemiology & Biostatistics
School of Medicine
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.
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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 medicines 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.
Awards
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- Jean Pouliot Award for Excellence in Teaching, UCSF, 2018
- First Place, Young Investigator Award., AAPM, 2015
- First Place, Best Graduate Student Norm Baily Award, AAPM, 2013
- Eugene V. Cota-Robles Fellowship, UCLA, 2010
- Nomination to the Best Young Researcher, Cuban Academy of Science, 2007
- Suma Cum Laude, University of Havana, 2004-2005
Education & Training
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- Residency in Medical Physics Radiotherapy University of Pennsylvania 06/2017
- PhD Medical Physics UCLA 07/2013
- MS Radiochemistry University of Havana 07/2007
- BS Nuclear Sciences University of Havana 07/2005
Interests
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- clinical informatics
- imaging informatics
- big data
- artificial intelligence
- precision medicine
- data analysis
- natural language processing
- data visualization
- data science
- usability
- clinical decision support
- machine learning
Websites
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- @@GilmerValdes on Twitter (twitter.com)
- Statistical Learning in Medicine (SLM) Lab (statisticallearningmedicine.ucsf.edu)
Grants and Projects
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Publications (58)
Top publication keywords:
Radiotherapy Planning, Computer-AssistedDecision TreesAlgorithmsRadiotherapy, Intensity-ModulatedFour-Dimensional Computed TomographyLung NeoplasmsParticle AcceleratorsArtificial IntelligenceBrachytherapyMedical InformaticsMachine LearningRadiation PneumonitisRadiotherapy DosageQuality Assurance, Health CareExpert Systems
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Representational Gradient Boosting: Backpropagation in the Space of Functions.
IEEE transactions on pattern analysis and machine intelligence 2022 Valdes G, Friedman JH, Jiang F, Gennatas ED -
The Conditional Super Learner.
IEEE transactions on pattern analysis and machine intelligence 2022 Valdes G, Interian Y, Gennatas E, Van der Laan M -
Expert-augmented machine learning.
Proceedings of the National Academy of Sciences of the United States of America 2020 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 -
Building more accurate decision trees with the additive tree.
Proceedings of the National Academy of Sciences of the United States of America 2019 Luna JM, Gennatas ED, Ungar LH, Eaton E, Diffenderfer ES, Jensen ST, Simone CB, Friedman JH, Solberg TD, Valdes G -
MediBoost: a Patient Stratification Tool for Interpretable Decision Making in the Era of Precision Medicine.
Scientific reports 2016 Valdes G, Luna JM, Eaton E, Simone CB, Ungar LH, Solberg TD
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Diagnostic Errors in Hospitalized Adults Who Died or Were Transferred to Intensive Care.
JAMA internal medicine 2024 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 -
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.
International journal of radiation oncology, biology, physics 2023 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 -
A unified path seeking algorithm for IMRT and IMPT beam orientation optimization.
Physics in medicine and biology 2023 Ramesh P, Valdes G, O'Connor D, Sheng K -
Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model.
Journal of applied clinical medical physics 2023 Lafrenière M, Valdes G, Descovich M -
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.
European urology focus 2023 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 -
Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images.
Medical physics 2023 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 -
Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.
European urology oncology 2023 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… -
NSMCE2, a novel super-enhancer-regulated gene, is linked to poor prognosis and therapy resistance in breast cancer.
BMC cancer 2022 Di Benedetto C, Oh J, Choudhery Z, Shi W, Valdes G, Betancur P -
Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.
Physics in medicine and biology 2022 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 -
Prospective Clinical Validation of Virtual Patient-Specific Quality Assurance of Volumetric Modulated Arc Therapy Radiation Therapy Plans.
International journal of radiation oncology, biology, physics 2022 Wall PDH, Hirata E, Morin O, Valdes G, Witztum A -
Artificial Intelligence-Guided Prediction of Dental Doses Before Planning of Radiation Therapy for Oropharyngeal Cancer: Technical Development and Initial Feasibility of Implementation.
Advances in radiation oncology 2021 Chan JW, Hohenstein N, Carpenter C, Pattison AJ, Morin O, Valdes G, Sanchez CT, Perkins J, Solberg TD, Yom SS -
Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.
Annual review of public health 2021 Wesson P, Hswen Y, Valdes G, Stojanovski K, Handley MA -
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication.
Nature cancer 2021 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, … -
A situational awareness Bayesian network approach for accurate and credible personalized adaptive radiotherapy outcomes prediction in lung cancer patients.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) 2021 Luo Y, Jolly S, Palma D, Lawrence TS, Tseng HH, Valdes G, McShan D, Ten Haken RK, Ei Naqa I -
Salvage High-Dose-Rate Brachytherapy for Recurrent Prostate Cancer After Definitive Radiation.
Practical radiation oncology 2021 Wu SY, Wong AC, Shinohara K, Roach M, Cunha JAM, Valdes G, Hsu IC -
Artificial intelligence for prediction of measurement-based patient-specific quality assurance is ready for prime time.
Medical physics 2021 Valdes G, Adamson J, Cai J -
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.
International journal of radiation oncology, biology, physics 2020 Hasse K, Scholey J, Ziemer BP, Natsuaki Y, Morin O, Solberg TD, Hirata E, Valdes G, Witztum A -
Targeted transfer learning to improve performance in small medical physics datasets.
Medical physics 2020 Romero M, Interian Y, Solberg T, Valdes G -
Integration of AI and Machine Learning in Radiotherapy QA.
Frontiers in artificial intelligence 2020 Chan MF, Witztum A, Valdes G -
Machine learning for radiation outcome modeling and prediction.
Medical physics 2020 Luo Y, Chen S, Valdes G -
Reply to Nock and Nielsen: On the work of Nock and Nielsen and its relationship to the additive tree.
Proceedings of the National Academy of Sciences of the United States of America 2020 Valdes G, Luna JM, Gennatas ED, Ungar LH, Eaton E, Diffenderfer ES, Jensen ST, Simone CB, Friedman JH, Solberg TD -
Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival.
Neuro-oncology advances 2019 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… -
Optimizing beam models for dosimetric accuracy over a wide range of treatments.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) 2019 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 -
Predicting radiation pneumonitis in locally advanced stage II-III non-small cell lung cancer using machine learning.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2019 Luna JM, Chao HH, Diffenderfer ES, Valdes G, Chinniah C, Ma G, Cengel KA, Solberg TD, Berman AT, Simone CB -
Erratum: "Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers" [Med. Phys. 45 (7), 3449-3459 (2018)].
Medical physics 2019 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… -
In Reply to Gensheimer and Trister.
International journal of radiation oncology, biology, physics 2018 Valdes G, Chang AJ, Cunnan A, Solberg TD, Hsu IC, Interian Y, Owen K, Jensen ST, Ungar LH -
The application of artificial intelligence in the IMRT planning process for head and neck cancer.
Oral oncology 2018 Kearney V, Chan JW, Valdes G, Solberg TD, Yom SS -
Artificial Intelligence in Radiation Oncology Imaging.
International journal of radiation oncology, biology, physics 2018 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 -
Preoperative and postoperative prediction of long-term meningioma outcomes.
PloS one 2018 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 -
An unsupervised convolutional neural network-based algorithm for deformable image registration.
Physics in medicine and biology 2018 Kearney V, Haaf S, Sudhyadhom A, Valdes G, Solberg TD -
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change.
International journal of radiation oncology, biology, physics 2018 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 -
Machine learning and modeling: Data, validation, communication challenges.
Medical physics 2018 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 -
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.
Journal of applied clinical medical physics 2018 Chao HH, Valdes G, Luna JM, Heskel M, Berman AT, Solberg TD, Simone CB -
Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers.
Medical physics 2018 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… -
Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2018 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 -
Clinical Applications of Quantitative 3-Dimensional MRI Analysis for Pediatric Embryonal Brain Tumors.
International journal of radiation oncology, biology, physics 2018 Hara JH, Wu A, Villanueva-Meyer JE, Valdes G, Daggubati V, Mueller S, Solberg TD, Braunstein SE, Morin O, Raleigh DR -
The Future of Artificial Intelligence in Radiation Oncology.
International journal of radiation oncology, biology, physics 2018 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 -
Deep nets vs expert designed features in medical physics: An IMRT QA case study.
Medical physics 2018 Interian Y, Rideout V, Kearney VP, Gennatas E, Morin O, Cheung J, Solberg T, Valdes G -
Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.
Frontiers in oncology 2018 Feng M, Valdes G, Dixit N, Solberg TD -
Comment on 'Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study'.
Physics in medicine and biology 2018 Valdes G, Interian Y -
Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis.
International journal of radiation oncology, biology, physics 2018 Valdes G, Chang AJ, Interian Y, Owen K, Jensen ST, Ungar LH, Cunha A, Solberg TD, Hsu IC -
Correcting TG 119 confidence limits.
Medical physics 2018 Kearney V, Solberg T, Jensen S, Cheung J, Chuang C, Valdes G -
Clinical decision support of radiotherapy treatment planning: A data-driven machine learning strategy for patient-specific dosimetric decision making.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 2017 Valdes G, Simone CB, Chen J, Lin A, Yom SS, Pattison AJ, Carpenter CM, Solberg TD -
IMRT QA using machine learning: A multi-institutional validation.
Journal of applied clinical medical physics 2017 Valdes G, Chan MF, Lim SB, Scheuermann R, Deasy JO, Solberg TD -
The relative accuracy of 4D dose accumulation for lung radiotherapy using rigid dose projection versus dose recalculation on every breathing phase.
Medical physics 2017 Valdes G, Lee C, Tenn S, Lee P, Robinson C, Iwamoto K, Low D, Lamb JM -
Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy.
Physics in medicine and biology 2016 Valdes G, Solberg TD, Heskel M, Ungar L, Simone CB -
A mathematical framework for virtual IMRT QA using machine learning.
Medical physics 2016 Valdes G, Scheuermann R, Hung CY, Olszanski A, Bellerive M, Solberg TD -
Use of TrueBeam developer mode for imaging QA.
Journal of applied clinical medical physics 2015 Valdes G, Morin O, Valenciaga Y, Kirby N, Pouliot J, Chuang C -
Tumor control probability and the utility of 4D vs 3D dose calculations for stereotactic body radiotherapy for lung cancer.
Medical dosimetry : official journal of the American Association of Medical Dosimetrists 2014 Valdes G, Robinson C, Lee P, Morel D, Low D, Iwamoto KS, Lamb JM -
Radiosensitization of gliomas by intracellular generation of 5-fluorouracil potentiates prodrug activator gene therapy with a retroviral replicating vector.
Cancer gene therapy 2014 Takahashi M, Valdes G, Hiraoka K, Inagaki A, Kamijima S, Micewicz E, Gruber HE, Robbins JM, Jolly DJ, McBride WH, Iwamoto KS, Kasahara N -
The high-affinity maltose switch MBP317-347 has low affinity for glucose: implications for targeting tumors with metabolically directed enzyme prodrug therapy.
Chemical biology & drug design 2013 Valdes G, Schulte RW, Ostermeier M, Iwamoto KS -
Re-evaluation of cellular radiosensitization by 5-fluorouracil: high-dose, pulsed administration is effective and preferable to conventional low-dose, chronic administration.
International journal of radiation biology 2013 Valdes G, Iwamoto KS -
Effects of gamma radiation on phase behaviour and critical micelle concentration of Triton X-100 aqueous solutions
Journal of Colloid and Interface Science 2007 Valdés G, S. Rodríguez-Calvo, M. Rapado-Paneque, A. Pérez-Gramatges, F. A. Fernández, E. Frota, C. Ribeiro