Dataset Information

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

ABSTRACT:

Purpose

To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.

Design

Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).

Participants

Patients with type 1 DM and controls included in the progenitor study.

Methods

Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.

Main outcome measures

Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.

Results

A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.

Conclusions

Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.

Financial disclosures

Proprietary or commercial disclosure may be found after the references.

SUBMITTER: Carrera-Escale L

PROVIDER: S-EPMC9791596 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Carrera-Escalé Laura L Benali Anass A Rathert Ann-Christin AC Martín-Pinardel Ruben R Bernal-Morales Carolina C Alé-Chilet Anibal A Barraso Marina M Marín-Martinez Sara S Feu-Basilio Silvia S Rosinés-Fonoll Josep J Hernandez Teresa T Vilá Irene I Castro-Dominguez Rafael R Oliva Cristian C Vinagre Irene I Ortega Emilio E Gimenez Marga M Vellido Alfredo A Romero Enrique E Zarranz-Ventura Javier J

Ophthalmology science 20221121 2

<h4>Purpose</h4>To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.<h4>Design</h4>Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).<h4>Participants</h4>Patients with type 1 DM and controls included in the progenitor study.<h4>Methods< ...[more]

PMID: 36578904

Similar Datasets

Project description:To investigate the distribution of clinically significant nonperfusion areas (NPAs) on widefield OCT angiography (OCTA) images in patients with diabetes. Prospective, cross-sectional, observational study. One hundred and forty-four eyes of 114 patients with diabetes. Nominal 20 × 23 mm OCTA images were obtained using a swept-source OCTA device (Xephilio OCT-S1), followed by the creation of en face images 20-mm (1614 pixels) in diameter centering on the fovea. The nonperfusion squares (NPSs) were defined as the 10 × 10 pixel squares without retinal vessels, and the ratio of eyes with the NPSs to all eyes in each square was referred to as the NPS ratio. The areas with probabilistic differences (APD) for proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) (APD[PDR] and APD[NPDR]) were defined as sets of squares with higher NPS ratios in eyes with PDR and NPDR, respectively. The P ratio (NPSs within APD[PDR] but not APD[NPDR]/all NPSs) was also calculated. The probabilistic distribution of the NPSs and the association with diabetic retinopathy (DR) severity. The NPSs developed randomly in eyes with mild and moderate NPDR and were more prevalent in the extramacular areas and the temporal quadrant in eyes with severe NPDR and PDR. The APD(PDR) was distributed mainly in the extramacular areas, sparing the areas around the vascular arcades and radially peripapillary capillaries. The APD(PDR) contained retinal neovascularization more frequently than the non-APD(PDR) (P = 0.023). The P ratio was higher in eyes with PDR than in those with NPDR (P < 0.001). The multivariate analysis designated the P ratio (odds ratio, 8.293 × 107; 95% confidence interval, 6.529 × 102-1.053 × 1013; P = 0.002) and the total NPSs (odds ratio, 1.002; 95% confidence interval, 1.001-1.003; P < 0.001) as independent risk factors of PDR. Most eyes with NPDR and 4-2-1 rule findings of DR severity had higher P ratios but not necessarily greater NPS numbers. The APD(PDR) is uniquely distributed on widefield OCTA images, and the NPA location patterns are associated with DR severity, independent of the entire area of NPAs. Proprietary or commercial disclosure may be found after the references.

Project description:IntroductionThere is a high and ever-increasing global prevalence of diabetic retinopathy (DR) and invasive imaging techniques are often required to confirm the presence of proliferative disease. The aim of this study was to explore the images of a rapid and non-invasive technique, widefield optical coherence tomography angiography (OCT-A), to study differences between patients with severe non-proliferative and proliferative DR (PDR).MethodsWe conducted an observational longitudinal study from November 2022 to March 2023. We recruited 75 patients who were classified into a proliferative group (28 patients) and severe non-proliferative group (47 patients). Classification was done by specialist clinicians who had full access to any multimodal imaging they required to be confident of their diagnosis, including fluorescein angiography. For all patients, we performed single-shot 4 × 4 and 10 × 10 mm (widefield) OCT-A imaging and when possible, the multiple images required for mosaic 17.5 × 17.5 mm (ultra widefield) OCT-A imaging. We assessed the frequency with which proliferative disease was identifiable solely from these OCT-A images and used custom-built MATLAB software to analyze the images and determine computerized metrics such as density and intensity of vessels, foveal avascular zone, and ischemic areas.ResultsOn clinically assessing the OCT-A 10 × 10 fields, we were only able to detect new vessels in 25% of known proliferative images. Using ultra-widefield mosaic images, however, we were able to detect new vessels in 100% of PDR patients. The image analysis metrics of 4 × 4 and 10 × 10 mm images did not show any significant differences between the two clinical groups. For mosaics, however, there were significant differences in the capillary density in patients with PDR compared to severe non-PDR (9.1% ± 1.9 in the PDR group versus 11.0% ± 1.9 for severe group). We also found with mosaics a significant difference in the metrics of ischemic areas; average area of ischemic zones (253,930.1 ± 108,636 for the proliferative group versus 149,104.2 ± 55,101.8 for the severe group.ConclusionsOur study showed a high sensitivity for detecting PDR using only ultra-widefield mosaic OCT-A imaging, compared to multimodal including fluorescein angiography imaging. It also suggests that image analysis of aspects such as ischemia levels may be useful in identifying higher risk groups as a warning sign for future conversion to neovascularization.

Project description:PurposeTo assess whether the combination of en face OCT and OCT angiography (OCTA) can capture observable, but subtle, structural changes that precede clinically evident retinal neovascularization (RNV) in eyes with diabetic retinopathy (DR).DesignRetrospective, longitudinal study.ParticipantsPatients with DR that had at least 2 visits.MethodsWe obtained wide-field OCTA scans of 1 eye from each participant and generated en face OCT, en face OCTA, and cross-sectional OCTA. We identified eyes with RNV sprouts, defined as epiretinal hyperreflective materials on en face OCT with flow signals breaching the internal limiting membrane on the cross-sectional OCTA without recognizable RNV on en face OCTA and RNV fronds, defined as recognizable abnormal vascular structures on the en face OCTA. We examined the corresponding location from follow-up or previous visits for the presence or progression of the RNV.Main outcome measuresThe characteristics and longitudinal observation of early signs of RNV.ResultsFrom 71 eyes, we identified RNV in 20 eyes with the combination of OCT and OCTA, of which 13 (65%) were photographically graded as proliferative DR, 6 (30%) severe nonproliferative DR, and 1 (5%) moderate nonproliferative diabetic retinopathy. From these eyes, we identified 38 RNV sprouts and 26 RNV fronds at the baseline. Thirty-four RNVs (53%) originated from veins, 24 (38%) were from intraretinal microabnormalities, and 6 (9%) were from a nondilated capillary bed. At the final visit, 53 RNV sprouts and 30 RNV fronds were detected. Ten eyes (50%) showed progression, defined as having a new RNV lesion or the development of an RNV frond from an RNV sprout. Four (11%) RNV sprouts developed into RNV fronds with a mean interval of 7.0 months. Nineteen new RNV sprouts developed during the follow-up, whereas no new RNV frond was observed outside an identified RNV sprout. The eyes with progression were of younger age (P = 0.014) and tended to be treatment naive (P = 0.07) compared with eyes without progression.ConclusionsLongitudinal observation demonstrated that a combination of en face OCT and cross-sectional OCTA can identify an earlier form of RNV before it can be recognized on en face OCTA.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Purpose

Design

Participants

Methods

Main outcome measures

Results

Conclusions

Financial disclosures

Publications

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets