A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography

Schwartz, Roy; Khalid, Hagar; Liakopoulos, Sandra; Ouyang, Yanling; Vente, Coen de; González-Gonzalo, Cristina; Lee, Aaron; Guymer, Robyn H.; Chew, Emily Y.; Egan, Catherine; Wu, Zhichao; Kumar, Himeesh; Farrington, Joseph; Müller, Philipp L.; Sánchez, Clara I.; Tufail, Adnan

doi:10.1167/tvst.11.12.3

Cited by 18 publications

(5 citation statements)

References 41 publications

(31 reference statements)

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“…To calculate both of the intra- and inter-reader reproducibility for VOI measurements for the present study, the reproducibility error was assessed by calculating the root mean square average of the single coefficients of variation on a percentage basis, as previously reported [ 20 ]. The inter-reader intraclass correlation coefficients (ICCs) were calculated using standard guidelines [ 21 ]. ICC values below 0.50 were considered poor, values 0.50–0.75 moderate, values 0.75–0.90 good, and values above 0.90 excellent [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study

Gersing,

Kimm,

Bollwein

et al. 2024

Eur Radiol Exp

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Background Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment. Methods We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation. Results We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002). Conclusions Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible. Relevance statement 3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics. Key points • Matrix calcifications are a relevant diagnostic feature of bone tumors. • Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. • Micro-CT has the potential to be an integrative part of clinical diagnostics. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study

Gersing,

Kimm,

Bollwein

et al. 2024

Eur Radiol Exp

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“…Therefore, determining the presence of RPD assessed by multimodal imaging in patients with AMD is important if we are to fully understand the factors that drive disease progression. Further research that assesses the extent of RPD (e.g., by volume and/or area) could assist in establishing a more quantitative relationship between RPD and progression from iAMD to late AMD, and algorithms are being developed to help with this task [ 49 ].…”

Section: Structural Biomarkersmentioning

confidence: 99%

“…The OptinNet deep learning model has been trained to identify “points of interest” in SD-OCT scans of patients with AMD; it classified drusen, RPE, retinal nerve fiber, and choroidal layers as of interest in 337 scans of 62 eyes with AMD [ 169 ]. Deep learning has also been employed to detect RPD from OCT scans; agreement with human graders was 0.6, versus 0.68 agreement between two human graders [ 49 ].…”

Section: Structural Biomarkersmentioning

confidence: 99%

Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration

Lad,

Finger,

Guymer

2023

Ophthalmol Ther

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Age-related macular degeneration (AMD) is a leading cause of severe vision loss worldwide, with a global prevalence that is predicted to substantially increase. Identifying early biomarkers indicative of progression risk will improve our ability to assess which patients are at greatest risk of progressing from intermediate AMD (iAMD) to vision-threatening late-stage AMD. This is key to ensuring individualized management and timely intervention before substantial structural damage. Some structural biomarkers suggestive of AMD progression risk are well established, such as changes seen on color fundus photography and more recently optical coherence tomography (drusen volume, pigmentary abnormalities). Emerging biomarkers identified through multimodal imaging, including reticular pseudodrusen, hyperreflective foci, and drusen sub-phenotypes, are being intensively explored as risk factors for progression towards late-stage disease. Other structural biomarkers merit further research, such as ellipsoid zone reflectivity and choriocapillaris flow features. The measures of visual function that best detect change in iAMD and correlate with risk of progression remain under intense investigation, with tests such as dark adaptometry and cone-specific contrast tests being explored. Evidence on blood and plasma markers is preliminary, but there are indications that changes in levels of C-reactive protein and high-density lipoprotein cholesterol may be used to stratify patients and predict risk. With further research, some of these biomarkers may be used to monitor progression. Emerging artificial intelligence methods may help evaluate and validate these biomarkers; however, until we have large and well-curated longitudinal data sets, using artificial intelligence effectively to inform clinical trial design and detect outcomes will remain challenging. This is an exciting area of intense research, and further work is needed to establish the most promising biomarkers for disease progression and their use in clinical care and future trials. Ultimately, a multimodal approach may yield the most accurate means of monitoring and predicting future progression towards vision-threatening, late-stage AMD.

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“…Their proposed network slightly enhanced the performance obtained using FAF images only with AUC of 0.933. 7 To our knowledge, only Schwartz et al 8 have attempted to detect both RPD and conventional drusen from OCT scans, where their model achieved AUC of 0.99. However, they detected the presence of RPD or drusen without distinguishing between them.…”

Section: Introductionmentioning

confidence: 99%

Detection of reticular pseudodrusen on optical coherence tomography images

Elsawy,

Keenan,

Agron

et al. 2024

Medical Imaging 2024: Image Processing

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Reticular pseudodrusen (RPD) are subretinal drusenoid deposits that represent an important disease feature in age-related macular degeneration (AMD). RPD are of particular interest because their presence is a strong predictor of progression to advanced AMD. RPD features can be characterized using volumetric spectral-domain optical coherence tomography (SD-OCT). In this work, we curated a dataset from the Age-Related Eye Diseases Study 2 (AREDS2) ancillary OCT study. The dataset included 826 SD-OCT scans, with RPD present in 222 SD-OCT scans. Binary RPD labels were transferred from fundus autofluorescence (FAF) images taken at the same visits as the SD-OCT scans. The dataset was split at the participant level into training (70%), validation (10%), and test sets (20%). We proposed a 3D classification network to detect RPD from SD-OCT scans. We compared it to a baseline 2D network with average bagging and a 3D network with multi-tasking. The proposed network achieved the highest accuracy of 0.7784, area under receiver characteristic operating curve of 0.8689, and mean average precision of 0.7706 for detecting RPD from SD-OCT scans.

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A Deep Learning Framework for the Detection and Quantification of Reticular Pseudodrusen and Drusen on Optical Coherence Tomography

Cited by 18 publications

References 41 publications

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study

Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration

Detection of reticular pseudodrusen on optical coherence tomography images

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