DISCUSSION: As surgery and radiotherapy alter the appearance of the breasts, distinguishing between recurrence and benign post-surgical changes can be challenging radiologically due to overlapping features. Despite this, differentiation between these two entities is usually possible by recognizing characteristic features of post-treatment sequelae and the evolution of the appearance of the conservatively treated breast by comparing interval findings on serial studies.

DESIGN: Prospective cross-sectional study.

METHODS: Consecutive CSC patients were recruited from retina clinic. The reference standard for CNV was determined by interpretation of multimodal imaging with OCTA, structural OCT line scan, fluorescein angiography (FA), indocyanine green angiography (ICGA), ultra-widefield fundus photography and fundus autofluorescence (FAF). Two independent masked graders examined OCTA without FA and ICGA to diagnose CNV. Univariate and multivariate analyses were performed to evaluate factors associated with CNV.

RESULTS: CNV was detected in 69 eyes in 64 out of 277 CSC patients according to reference standard. The two masked graders who examined OCTA had sensitivity of 81.2% (95% Confidence Interval [CI], 71.9%-90.4%) and 78.3% (95% CI, 68.5%-88.0%), specificity of 97.3% (95% CI, 95.9%-98.8%) and 96.2% (95% CI, 94.5%-98.0%), positive predictive values of 82.4% (95% CI, 73.3%-91.4%) and 76.1% (95% CI, 66.1%-86.0%), and negative predictive values of 97.1% (95% CI, 95.6%-98.7%) and 96.7% (95% CI, 95.0%-98.3%). Their mean area under the receiver operating characteristic curve (AUC) was 0.88 with good agreement (Kappa coefficient 0.80 [95% CI, 0.72-0.89]). Flat irregular pigment epithelial detachment on structural OCT, neovascular network on OCTA and ill-defined late leakage on FA significantly correlated with CNV in CSC from multiple regression (P < 0.001, P < 0.001 and P = 0.005, respectively).

METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.

RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.

COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.