June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Machine learning to predict anti-VEGF treatment response in a Treat-and-Extend regimen (TER).
Author Affiliations & Notes
  • Mathias Gallardo
    ARTORG, Aimi, University of Bern, Bern, Switzerland
  • Marion Munk
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
  • Thomas Kurmann
    ARTORG, Aimi, University of Bern, Bern, Switzerland
  • Sandro De Zanet
    RetinAI Medical AG, Bern, Switzerland
  • Agata Mosinska
    RetinAI Medical AG, Bern, Switzerland
  • Mark van Grinsven
    Thirona, Nijmegen, Netherlands
  • Clara I Sanchez
    Department of Radiology and Nuclear Medicine, Radboud UMC, Nijmegen, Netherlands
  • Martin Sebastian Zinkernagel
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
  • Sebastian Wolf
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland
  • Raphael Sznitman
    ARTORG, Aimi, University of Bern, Bern, Switzerland
  • Footnotes
    Commercial Relationships   Mathias Gallardo, None; Marion Munk, Bayer (C), Gensight (C), Isarna Therapeutics (E), Lumithera (C), Novartis (C), Zeiss (C); Thomas Kurmann, None; Sandro De Zanet, RetinAI Medical AG (E); Agata Mosinska, RetinAI Medical AG (E); Mark van Grinsven, Thirona (E); Clara Sanchez, None; Martin Zinkernagel, Allergan (C), Bayer (C), Bayer (F), Boehringer Ingelheim (F), Heidelberg Engineering (R), Novartis (C), Novartis (I), Zeiss (R); Sebastian Wolf, Allergan (F), Bayer Healthcare Pharmaceuticals (F), Bayer Healthcare Pharmaceuticals (C), Carl Zeiss Meditec (F), Carl Zeiss Meditec (C), Chengdu Kanghong Biotechnology (C), Heidelberg Engineering (F), Heidelberg Engineering (C), Novartis Pharmaceuticals Corporation (F), Novartis Pharmaceuticals Corporation (C), RetinAI Medical AG (C), Roche (F), Roche (C); Raphael Sznitman, Bayer AG (R), Haag-Streit AG (C), medupdate (R), RetinAI Medical AG (I)
  • Footnotes
    Support  EU EUROSTAR E!12712
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1629. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Mathias Gallardo, Marion Munk, Thomas Kurmann, Sandro De Zanet, Agata Mosinska, Mark van Grinsven, Clara I Sanchez, Martin Sebastian Zinkernagel, Sebastian Wolf, Raphael Sznitman; Machine learning to predict anti-VEGF treatment response in a Treat-and-Extend regimen (TER).. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1629.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : To explore the capabilities of a machine learning approach to predict treatment response of patients with wAMD, DME and RVO treated according to a TER.

Methods : We performed a retrospective study including 532 eyes (464 patients) with wAMD, 206 eyes (200 patients) with RVO and 262 eyes (194 patients) with DME, treated with anti-VEGF according to a predefined TER during 2014-2018. Eyes were grouped for each disease into good, mediocre and poor responders, using the average treatment interval (good≥8 weeks, poor ≤5 weeks, mediocre: remaining eyes). We trained two Random Forest models to infer the probability of a patient being a good or poor long-term responder. Both models use as inputs morphological information automatically extracted from the OCT B-scans of the three first visits, the three first prescribed treatment intervals and patient-specific information. The training and evaluation of the models were performed using a 10-fold cross-validation on the retrospective cohort while ensuring that, in every split, no patient was present in both training (wAMD:~478, RVO:~185 and DME:~236) and test sets (wAMD: ~54, RVO:~21 and DME:~26).

Results : We observed the capability of predicting, after the third visit, good and poor treatment response for patients with wAMD, RVO and DME with similar performance. In the cohort of wAMD patients, we identified 137 good-, 78 poor- and 317 mediocre responders. For both models and over the 10 folds, a mean AuC of 0.68 (best model over the folds: 0.77) and 0.75 (0.85) was achieved respectively. Models on RVO and DME showed similar results with a mean AuC of 0.7 (0.97) and 0.82 (0.95) and respectively a mean AuC of 0.74 (1) and 0.63 (0.79).

Conclusions : We showed that machine learning classifiers are able to predict the long-term treatment response. This approach may be useful to tailor individual treatment plans for patients.

This is a 2020 ARVO Annual Meeting abstract.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×