Simple estimation of clinically relevant lesion volumes using spectral domain-optical coherence tomography in neovascular age-related macular degeneration.

PURPOSE

To evaluate simple methods of estimating the volume of clinically relevant features in neovascular age-related macular degeneration (NVAMD) using spectral domain-optical coherence tomography (SD-OCT).

METHODS

Using a database of NVAMD cases imaged with macular cube (512 A-scans × 128 B-scans) SD-OCT scans, the authors retrospectively selected visits where cystoid macular edema (CME), subretinal fluid (SRF), or pigment epithelial detachments (PEDs) were evident. Patients with single visits were analyzed in the cross-sectional analysis (CSA) and those with a baseline visit and three or more follow-up visits in the longitudinal analysis (LA). The volume of each feature was measured by manual grading using validated grading software. Simplified measurements for each feature included: number of B-scans or A-scans involved and maximum height. Automated measurements of total macular volume and foveal central subfield were also collected from each machine. Correlations were performed between the volumes measured with 3D-OCTOR, automated measurements, and the simplified measures.

RESULTS

Forty-five visits for 25 patients were included in this study: 26 cube scans from 26 eyes of 25 patients in the CSA and 24 scans from 5 eyes of 5 patients in the LA. The simplified measures that correlated best with manual grading in the CSA group were maximum lesion height for CME (r² value = 0.96) and B-scan count for SRF and PED volume (r² values of 0.88 and 0.70). In the LA group, intervisit differences were correlated. Change in B-scan count correlated well with change in SRF volume (r² = 0.97), whereas change in maximum height correlated with change in CME and PED volume (r² = 0.98 and 0.43, respectively).

CONCLUSIONS

These data suggest that simplified estimators of some NVAMD lesion volumes exist and are accessible by clinicians without the need for specialized software or time-consuming manual segmentation. These simple approaches could enhance quantitative disease monitoring strategies in clinical trials and clinical practice.