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To analyse the longitudinal changes in visual acuity and risk factors for recurrence or development of choroidal neovascularisation (CNV) in eyes with acute or chronic central serous chorioretinopathy (CSCR).This was a retrospective, multicentric, longitudinal, observational study done in patients with a diagnosis of unilateral or bilateral CSCR and having at least 4 years of follow-up between the years 1999 and 2020. Kaplan-Meier curves were used for assessing cumulative risks. Multivariate logistic, linear and cox regression models were used for risk factor analyses. The trend in visual acuity, cumulative risks of recurrence and CNV formation was analysed.A total of 117 out of 175 eyes (66.8%) had stable or improvement in vision at last follow-up, while 24 eyes had more than/equal to 3 line loss of vision. Four eyes (7.7%) with acute CSCR at initial presentation developed features of chronic CSCR at the final presentation. Thirty-seven eyes had recurrence during the follow-up with a 10-year cumulative recurrence rate of around 30%. On Cox proportional hazard regression analysis, history of previous treatment and male gender (p = 0.03) were associated with a lower risk of recurrence. Twenty-four developed de novo CNV by the end of follow-up and higher age (p = 0.001) and a higher number of recurrences (p = 0.05) were associated with a higher risk of early de novo CNV formation. The cumulative 10-year CNV development rate was 17.4%.A non-temporal relationship between acute and chronic CSCR was seen. Previous treatment, smoking and baseline RPE abnormality affected recurrence of SRF or CNV formation.

Various vision-threatening eye diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy(CSCR) are caused due to the dysfunctions manifested in the highly vascular choroid layer of the posterior segment of the eye.In the current clinical practice, screening choroidal structural changes is widely based on optical coherence tomography (OCT)images. Accordingly, to assist clinicians, several automated choroidal biomarker detection methods using OCT images aredeveloped. However, the performance of these algorithms is largely constrained by the quality of the OCT scan. Consequently,determining the quality of choroidal features in OCT scans is significant in building standardized quantification tools and henceconstitutes our main objective. This study includes a dataset of 1593 good and 2581 bad quality Spectralis OCT images gradedby an expert. Noting the efficacy of deep-learning (DL) in medical image analysis, we propose to train three state-of-the-artDL models: ResNet18, EfficientNet-B0 and EfficientNet-B3 to detect the quality of OCT images. The choice of these modelswas inspired by their ability to preserve the salient features across all the layers without information loss. To evaluate theattention of DL models on the choroid, we introduced color transparency maps (CTMs) based on GradCAM explanations.Further, we proposed two subjective grading scores: overall choroid coverage (OCC) and choroid coverage in the visibleregion(CCVR) based on CTMs to objectively correlate visual explanations vis-à-vis DL model attentions. We observed that theaverage accuracy and F-scores for the three DL models are greater than 96%. Further, the OCC and CCVR scores achievedfor the three DL models under consideration substantiate that they mostly focus on the choroid layer in making the decision. Inparticular, of the three DL models, EfficientNet-B3 is in close agreement with the clinician’s inference. The proposed DL-basedframework demonstrated high detection accuracy as well as attention on the choroid layer, where EfficientNet-B3 reportedsuperior performance. Our work assumes significance in bench-marking the automated choroid biomarker detection tools andfacilitating high-throughput screening. Further, the methods proposed in this work can be adopted for evaluating the attentionof DL-based approaches developed for other region-specific quality assessment tasks.

We provide an automated analysis of the pigment epithelial detachments (PEDs) in neovascular age-related macular degeneration (nAMD) and estimate areas of serous, neovascular, and fibrous tissues within PEDs. A retrospective analysis of high-definition spectral-domain OCT B-scans from 43 eyes of 37 patients with nAMD with presence of fibrovascular PED was done. PEDs were manually segmented and then filtered using 2D kernels to classify pixels within the PED as serous, neovascular, or fibrous. A set of PED composition indices were calculated on a per-image basis using relative PED area of serous (PEDCI-S), neovascular (PEDCI-N), and fibrous (PEDCI-F) tissue. Accuracy of segmentation and classification within the PED were graded in masked fashion. Mean overall intra-observer repeatability and inter-observer reproducibility were 0.86 ± 0.07 and 0.86 ± 0.03 respectively using intraclass correlations. The mean graded scores were 96.99 ± 8.18, 92.12 ± 7.97, 91.48 ± 8.93, and 92.29 ± 8.97 for segmentation, serous, neovascular, and fibrous respectively. Mean (range) PEDCI-S, PEDCI-N, and PEDCI-F were 0.253 (0–0.952), 0.554 (0–1), and 0.193 (0–0.693). A kernel-based image processing approach demonstrates potential for approximating PED composition. Evaluating follow up changes during nAMD treatment with respect to PEDCI would be useful for further clinical applications.

To study the regression patterns of subretinal fluid (SRF) in central serous chorioretinopathy (CSCR) on sequential en face optical coherence tomography (OCT) and its relationship to leak locations.Retrospective study on patients with acute CSCR. Inclusion criteria were (i) availability of data, sequential OCT and OCT angiography (B scan and en face OCT) every 2 weeks until resolution of SRF or 6 months, whichever is earlier; (ii) single active leak. Exclusion criteria were (i) presence of macular neovascularization or atypical CSCR, (ii) diffuse pigment epitheliopathy, (iii) multiple leaks. Serial en face OCT scans were evaluated and the area of SRF was measured using ImageJ software. Correlation coefficient was calculated for the regression rate of SRF area and central retinal thickness (CRT) over the first month of follow-up and the time of complete SRF resolution.Out of the 25 eyes, 20 eyes demonstrated a centripetal regression, and 5 eyes demonstrated a centrifugal regression. In eyes with a leakage point1000μ from the fovea, 86% resolved in a centripetal fashion, and in eyes with leak site ≥1000μ away from fovea, 70% eyes resolved centripetally. There was a correlation (r=-0.47, p=0.018) of the rate regression of SRF area during the first month and timing of resolution. In contrast, this correlation was absent (r=-0.16, p=0.44) for CRT regression.Our en face-based analysis of sequential OCTs of regressing CSCR demonstrated a tendency for the subfoveal SRF to resolve towards the end or a centripetal pattern of regression. Prediction of resolution of SRF at 1 month is better with en face area of SRF in comparison to CRT.

Tumor downstaging after neoadjuvant chemoradiation (CRT) in rectal cancer patients is typically assessed via Magnetic Resonance Imaging (MRI) in order to determine follow-up surgical interventions, but is associated with marked inter-reader variability and limited performance. While radiomic features have shown promise for evaluating chemoradiation response and tumor stage in rectal cancers, there is a need to determine how reproducible these features are across different MRI scanners and acquisitions. In this study, we evaluated radiomic feature reproducibility in terms of feature instability within a uniquely curated true healthy" rectum cohort in order to construct a stability-informed radiomic classifier for differentiating poorly from markedly down-staged rectal tumors after chemoradiation in a multi-site setting. We utilized a cohort of 156 patients, with (a) 74 MRIs visualizing the healthy rectum, (b) 52 post-CRT MRI scans in the discovery cohort, and (c) 30 post-CRT MRI scans in a second-site validation cohort; the latter 2 being from rectal cancer patients. 764 radiomic features were extracted from within the entire rectal wall on each MRI scan. Feature instability was used to quantify how reproducible each radiomic feature was between the discovery cohort and the healthy rectum cohort, using locations along the rectum that were spatially distinct from the treated tumor region. From the resulting stability-informed" feature set, the most relevant features were identified to distinguish pathologic tumor stage groups in the discovery cohort via a QDA classifier with cross-validation to ensure robustness. The top 4 radiomic features were then evaluated in hold-out fashion on scans from the validation cohort. We found that utilizing a stability-informed radiomic model (which comprised features that were reproducible in 100% of all comparisons) was significantly more accurate in identifying pathological tumor stage regression in both discovery (AUC=0:66 ± 0:09) and validation (AUC=0.73) cohorts, compared to a basic radiomic model that used all extracted features (AUC=0:60 ± 0:07 in discovery, AUC=0.62 in validation). Evaluating feature instability with respect to healthy rectal tissue may thus enhance the performance of radiomic models in characterizing pathologic downstaging in rectal cancers, via MRI.

A major clinical challenge in rectal cancer currently is non-invasive identification of tumor regression to standard- of-care neoadjuvant chemoradiation (CRT). Multi-parametric MRI is routinely acquired after CRT, but expert radiologists find it highly challenging to assess the degree of tumor regression on both T2-weighted (T2w) and Gadolinium contrast-enhanced (CE) MRI; resulting in poor agreement with gold-standard pathologic evaluation. In this study, we present initial results for integrating quantitative image appearance (radiomic) features from post-CRT T2w and CE MRI towards in vivo assessment of pathologic rectal tumor response to chemoradiation. 29 rectal cancer patients with post-CRT multi-parametric 3 T MRI (with T2w, initial and delayed CE phases) were included in this study. Through spatial co-registration, the treated region of the rectal wall was identified and annotated on T2w and all CE phases (as well as correcting for motion artifacts in CE MRI). 165 radiomic features (including Haralick, Gabor, Laws, Sobel/Kirsch) were separately extracted from each of T2w and 2 CE phases; within the entire rectal wall. The top 2 response-associated radiomic features for each of (a) T2w, (b) 2 CE phases, (c) combined T2w+CE phases were identified via feature selection and evaluated in a leave- one-patient-out cross validation setting. Integrating T2w and CE radiomic features was found to be markedly more accurate (AUC=0.93) for assessing post-CRT pathologic tumor stage, compared to T2w radiomic features (AUC=0.80) and CE radiomic features (AUC=0.63) individually. Top-ranked features captured heterogeneity of gradient responses on T2w MRI and macro-scale Gabor wavelet responses of contrast enhancement on CE MRI. Combining radiomic features from post-CRT T2w and CE MRI may hence enable more comprehensive evaluation of response to neoadjuvant therapy in rectal cancers, which can be used to better guide follow-up interventions.