July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Use of machine learning for prediction of ocular conservation and visual outcomes after proton beam radiotherapy for choroidal melanoma
Author Affiliations & Notes
  • Stylianos Serghiou
    Stanford University School of Medicine, Stanford, California, United States
    Ocular Oncology Service, Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States
  • Bertil E Damato
    Ocular Oncology Service, Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States
    Oxford Eye Hospital and the Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
  • Armin R Afshar
    Ocular Oncology Service, Department of Ophthalmology, University of California, San Francisco, San Francisco, California, United States
    Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California, United States
  • Footnotes
    Commercial Relationships   Stylianos Serghiou, None; Bertil Damato, None; Armin Afshar, None
  • Footnotes
    Support  Supported in part by the National Eye Institute (Core Grant for Vision Research, EY002162, and 1K23EY027466 to A.R.A.), Research to Prevent Blindness, Inc., New York, NY (an unrestricted grant and a Career Development Award to A.R.A.) and That Man May See, Inc., San Francisco, CA.
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 962. doi:
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    • Get Citation

      Stylianos Serghiou, Bertil E Damato, Armin R Afshar; Use of machine learning for prediction of ocular conservation and visual outcomes after proton beam radiotherapy for choroidal melanoma. Invest. Ophthalmol. Vis. Sci. 2019;60(9):962.

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

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Abstract

Purpose : While local control rates for proton beam radiotherapy (PBRT) treatment of choroidal malignant melanoma (CMM) are well known, visual acuity outcomes can vary, and are currently roughly predicted by ophthalmologists based on tumor size and location. The purpose of this study is to apply machine learning methods on a large CMM patient cohort, to predict visual acuity and ocular salvage after PBRT.

Methods : This was a prospective cohort study of 1022 adult CMM patients. Our database of 169 features included patient demographics, medical history, ophthalmic history, tumor dimensions, tumor-node-metastasis (TNM) stage, histology, genetics, radiation dosimetry and visual acuity (VA). We predicted final VA and enucleation using cross-validation (CV) to tune and fit the following machine learning methods: Elastic Net, Bayesian Generative Learning, Gradient Tree Boosting and Deep Neural Networks. We finally evaluated our models and a stacked ensemble of our models using cross-validated R2 for VA and cross-validated area under the curve (CV-AUC) for enucleation.

Results : Our cohort consisted of otherwise predominantly healthy adults (834/1022, 82%) with a median age of 59 years (Interquartile Range (IQR), 48-68 years) followed for a median of 8 years (IQR, 3-13 years). Most patients presented with TNM Stage I disease (500/1022, 49%) and VA between 6/5-6/12 (700/1022, 69%). Final vision varied substantially from 6/5-6/12 (384/1022, 38%) to hand-motions or less (114/1022, 11%); 76 (7%) received secondary enucleation. VA was best predicted by Gradient Tree Boosting with a CV-R2 of 28% (95% Confidence Interval (CI), 19-38%) with dosimetry values and CV-R2 of 25% (95% CI, 18-32%) without. The top three most important variables were tumor thickness, radiation received by the macula and radiation received by the globe volume. Enucleation was best predicted by the Elastic Net with a CV-AUC of 80% (95% CI, 72-87%); using dosimetry values did not improve predictive ability. The top three most important variables were tumor thickness, smallest tumor diameter and TNM stage.

Conclusions : Our approach identified models with high predictive ability for enucleation and moderate predictive ability for final visual acuity. Variables related to physical extent of tumor and radiation received appeared most important in predicting enucleation and VA, respectively.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

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