Research area · MEDomics platform

Clinical Data Science

Most hospitals are not ready for data science research. Can longitudinal records, imaging and clinical text be organized so that models are built, checked and updated as a patient's illness unfolds?

Approach

The MEDomics profile

MEDomics organizes multimodal health data into a profile for each patient and measures its quality, so that artificial intelligence can be applied to unmet clinical needs.2

  • Identified the Framingham risk score as robustly associated with mortality in early and advanced stage cancer.2
  • Showed that natural language processing of clinical notes can update prognosis over the course of illness.2

The open-source platform built on it is described below.

MEDomics profile: the patient timeline in the health system feeds a per-patient profile and AI applications
Fig. 2 From the health system timeline to a MEDomics profile.2

Platform

MEDomicsLab

An open-source computation platform for integrative data modeling in oncology, written in Python. Five modules load, process and explore multimodal data, then build and evaluate predictive models, with levels of abstraction for both new users and developers.

Modules
5
Clinical endpoints
9
Patients
1,184

Demonstrated on lung cancer, low-grade glioma and meningioma. Manuscript submitted to npj Precision Oncology in 2020 [status to confirm].

The five MEDomicsLab modules: input, text and image processing, discovery, learning, and application and analysis

Video

Watch the overview

Consortium

The MEDomics Consortium

Led by Olivier Morin (UCSF) and Martin Vallières (Université de Sherbrooke), see Team. Investigators at partner institutions:

Jan Seuntjens, PhDMcGill University
Jean-François Éthier, MD, PhDUniversité de Sherbrooke
Philippe Lambin, MD, PhDD-Lab, Maastricht University
Henry Woodruff, PhDD-Lab, Maastricht University
Avishek Chatterjee, PhDD-Lab, Maastricht University
Alexander Zwanenburg, PhDOncoRay, Dresden
Steffen Löck, PhDOncoRay, Dresden
Timothy Solberg, PhDUniversity of California, San Francisco
UCSF
UCSF
McGill University · Université de Sherbrooke
McGill University · Université de Sherbrooke
D-Lab, Maastricht University
D-Lab, Maastricht University
OncoRay, Dresden
OncoRay, Dresden

Membership as of 2019, to be confirmed.

Code

Open source

repoPublicmedomics_NatCancerReproduce the Nature Cancer 2021 experiments with sample data.repoPublictheibsi.github.ioImage Biomarker Standardisation Initiative documentation.

Publications

Papers in this area (16)

  1. 2025
    Zhu M, Lin H, Jiang J, Jinia AJ, Jee J, Pichotta K, Waters M, Rose D, Schultz N, Chalise S, Valleru L, Morin O, Moran J, Deasy JO, Pilai S, Nichols C, Riely G, Braunstein LZ, Li A. Large language model trained on clinical oncology data predicts cancer progression. NPJ Digit Med. 2025 Jul 02; 8(1):397. PMID: 40604229. PMCID: PMC12223279PMID 40604229
  2. 2024
    Whybra P, Zwanenburg A, Andrearczyk V, Schaer R, Apte AP, Ayotte A, Baheti B, Bakas S, Bettinelli A, Boellaard R, Boldrini L, Buvat I, Cook GJR, Dietsche F, Dinapoli N, Gabrys HS, Goh V, Guckenberger M, Hatt M, Hosseinzadeh M, Iyer A, Lenkowicz J, Loutfi MAL, Löck S, Marturano F, Morin O, Nioche C, Orlhac F, Pati S, Rahmim A, Rezaeijo SM, Rookyard CG, Salmanpour MR, Schindele A, Shiri I, Spezi E, Tanadini-Lang S, Tixier F, Upadhaya T, Valentini V, van Griethuysen JJM, Yousefirizi F, Zaidi H, Müller H, Vallières M, Depeursinge A. The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights. Radiology. 2024 Feb; 310(2):e231319. PMID: 38319168. PMCID: PMC10902595PMID 38319168
  3. 2023
    Bitterman DS, Gensheimer MF, Jaffray D, Pryma DA, Jiang SB, Morin O, Ginart JB, Upadhaya T, Vallis KA, Buatti JM, Deasy J, Hsiao HT, Chung C, Fuller CD, Greenspan E, Cloyd-Warwick K, Courdy S, Mao A, Barnholtz-Sloan J, Topaloglu U, Hands I, Maurer I, Terry M, Curran WJ, Le QT, Nadaf S, Kibbe W. Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology. JCO Clin Cancer Inform. 2023 Sep; 7:e2300136. PMID: 38055914. PMCID: PMC10703125PMID 38055914
  4. 2022
    Keek SA, Beuque M, Primakov S, Woodruff HC, Chatterjee A, van Timmeren JE, Valli?res M, Hendriks LEL, Kraft J, Andratschke N, Braunstein SE, Morin O, Lambin P. Predicting Adverse Radiation Effects in Brain Tumors After Stereotactic Radiotherapy With Deep Learning and Handcrafted Radiomics. Front Oncol. 2022; 12:920393. PMID: 35912214. PMCID: PMC9326101PMID 35912214
  5. 2022
    Chan JW, Hohenstein N, Carpenter C, Pattison AJ, Morin O, Valdes G, Thompson M, Perkins J, Solberg TD, Yom SS. 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. PMID: 35387423. PMCID: PMC8977910PMID 35387423
  6. 2022
    Simon A Keek, Manon Beuque, Sergey Primakov, Henry C Woodruff, Avishek Chatterjee, Janita E van Timmeren, Martin Vallières, Lizza EL Hendriks, Johannes Kraft, Nicolaus Andratschke, Steve E Braunstein, Olivier Morin, Philippe Lambin, Predicting adverse radiation effects in brain tumors after stereotactic radiotherapy with deep learning and radiomics, Frontiers in Oncology, 2022/7/13, Pages 3341, 2022.
  7. 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, Lambin P. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer. 2021 Jul; 2(7):709-722. PMID: 35121948PMID 35121948
  8. 2021
    Chen WC, Boreta L, Braunstein SE, Rabow MW, Kaplan LE, Tenenbaum JD, Morin O, Park CC, Hong JC. Association of mental health diagnosis with race and all-cause mortality after a cancer diagnosis: Large-scale analysis of electronic health record data. Cancer. 2021 Sep 22. PMID: 34550601PMID 34550601
  9. 2020
    Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Löck S. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 Mar 10; 191145. PMID: 32154773PMID 32154773
  10. 2020
    Kang J, Morin O, Hong JC. Closing the Gap Between Machine Learning and Clinical Cancer Care-First Steps Into a Larger World. JAMA Oncol. 2020 Sep 24. PMID: 32970129PMID 32970129
  11. 2020
    Hasse K, Scholey J, Ziemer BP, Natsuaki Y, Morin O, Solberg TD, Hirata E, Valdes G, Witztum A. Use of ROC analysis and machine learning with an independent dose calculation system reduces number of physical dose measurements required for patient-specific quality assurance. Int J Radiat Oncol Biol Phys. 2020 Nov 13. PMID: 33197530PMID 33197530
  12. 2020
    Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys. 2020 Jun; 47(5):e185-e202. PMID: 32418336PMID 32418336
  13. 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. The Future of Artificial Intelligence in Radiation Oncology. Int J Radiat Oncol Biol Phys. 2018 10 01; 102(2):247-248. PMID: 30191856PMID 30191856
  14. 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. Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation? Radiother Oncol. 2018 12; 129(3):421-426. PMID: 29907338PMID 29907338
  15. 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 T, Monshouwer R, Bussink J, Dekker A, Lambin P. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys. 2018 Jul; 45(7):3449-3459. PMID: 29763967.PMID 29763967
  16. 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. Artificial Intelligence in Radiation Oncology Imaging. Int J Radiat Oncol Biol Phys. 2018 11 15; 102(4):1159-1161. PMID: 30353870PMID 30353870

Updates

News from this area

  1. 2025-08Clinical Data ScienceGlioblastoma recurrence prediction for target definition published in npj Digital Medicine.
  2. 2025-07Clinical Data ScienceOncology language model predicting cancer progression published in npj Digital Medicine.
  3. 2021-07Clinical Data ScienceMEDomics framework published in Nature Cancer.
  4. 2019-07Clinical Data ScienceMEDomics concept featured in ASTRO News, Summer 2019.
  5. 2019-06Clinical Data ScienceMEDomics and MEDomicsLab presented at ICCR 2019, Montréal.
  6. 2019-06Clinical Data ScienceMEDomics and MEDomicsLab introduced at the Practical Big Data Workshop, Ann Arbor.

References

  1. Barrios J, Porter E, Capaldi DPI, et al. Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy. Nat Commun. 2025;16:4536. doi:10.1038/s41467-025-59584-7
  2. Morin O, Vallières M, Braunstein S, et al. An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication. Nat Cancer. 2021;2(7):709-722. PMID 35121948