In Ophthalmology science
PURPOSE : No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability.
DESIGN : Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy.
PARTICIPANTS : Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution).
METHODS : A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response.
MAIN OUTCOME MEASURES : The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance.
RESULTS : The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained.
CONCLUSIONS : Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.
Kar Sudeshna Sil, Cetin Hasan, Lunasco Leina, Le Thuy K, Zahid Robert, Meng Xiangyi, Srivastava Sunil K, Madabhushi Anant, Ehlers Justis P
2022-Dec
3D, 3-dimensional, AMD, age-related macular degeneration, AUC, area under the receiver operating characteristic curve, AUC-PRC, area under the precision recall curve, IAI, intravitreal aflibercept injection, ILM, internal limiting membrane, IRF, intraretinal fluid, ML, machine learning, OCT, QDA, quadratic discriminant analysis, RFI, retinal fluid index, RPE, retinal pigment epithelium, Radiomics, SHRM, subretinal hyperreflective material, SRF, subretinal fluid, SRFI, subretinal fluid index, TRFI, total retinal fluid index, Wet age-related macular degeneration, mRmR, minimum redundancy maximum relevance, nAMD, neovascular age-related macular degeneration
