Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound-Based Deep Learning.

OBJECTIVES: The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)-based models for predicting the occurrence of stent underexpansion. BACKGROUND: Although post-stenting IVUS has been used to optimize percutaneous coronary intervention, there are no pre-procedural guidelines to estimate the degree of stent expansion and provide preemptive management before stent deployment. METHODS: A total of 618 coronary lesions in 618 patients undergoing percutaneous coronary intervention were randomized into training and test sets in a 5:1 ratio. Following the coregistration of pre- and post-stenting IVUS images, the pre-procedural images and clinical information (stent diameter, length, and inflation pressure; balloon diameter; and maximal balloon pressure) were used to develop a regression model using a convolutional neural network to predict post-stenting stent area. To separate the frames with from those without the occurrence of underexpansion (stent area <5.5 mm2), binary classification models (XGBoost) were developed. RESULTS: Overall, the frequency of stent underexpansion was 15% (5,209 of 34,736 frames). At the frame level, stent areas predicted by the pre-procedural IVUS-based regression model significantly correlated with those measured on post-stenting IVUS (r = 0.802). To predict stent underexpansion, maximal accuracy of 94% (area under the curve = 0.94) was achieved when the convolutional neural network- and mask image-derived features were used for the classification model. At the lesion level, there were significant correlations between predicted and measured minimal stent area (r = 0.832) and between predicted and measured total stent volume (r = 0.958). CONCLUSIONS: Deep-learning algorithms accurately predicted incomplete stent expansion. A data-driven approach may assist clinicians in making treatment decisions to avoid stent underexpansion as a preventable cause of stent failure.

Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning.

AIMS: The aim of this study was to develop a deep learning model for classifying frames with versus without optical coherence tomography (OCT)-derived thin-cap fibroatheroma (TCFA). METHODS AND RESULTS: A total of 602 coronary lesions from 602 angina patients were randomised into training and test sets in a 4:1 ratio. A DenseNet model was developed to classify OCT frames with or without OCT-derived TCFA. Gradient-weighted class activation mapping was used to visualise the area of attention. In the training sample (35,678 frames of 480 lesions), the model with fivefold cross-validation had an overall accuracy of 91.6±1.7%, sensitivity of 88.7±3.4%, and specificity of 91.8±2.0% (averaged AUC=0.96±0.01) in predicting the presence of TCFA. In the test samples (9,722 frames of 122 lesions), the overall accuracy at the frame level was 92.8% within the lesion (AUC=0.96) and 91.3% in the entire OCT pullback. The correlation between the %TCFA burden per vessel predicted by the model compared with that identified by experts was significant (r=0.87, p<0.001). The region of attention was localised at the site of the thin cap in 93.4% of TCFA-containing frames. Total computational time per pullback was 2.1±0.3 seconds. CONCLUSIONS: A deep learning algorithm can accurately detect an OCT-TCFA with high reproducibility. The time-saving computerised process may assist clinicians to recognise high-risk lesions easily and to make decisions in the catheterisation laboratory.

Cytokeratin 20/p53 dual immunocytochemistry for improving the diagnostic accuracy of urine liquid-based cytology in the detection of urothelial neoplasm: A retrospective study.

BACKGROUNDS: Dual immunocytochemistry (DIC) with cytokeratin (CK) 20 and p53 in liquid-based cytology is a tool for improving the accuracy of urine cytology (UC). This study was conducted to compare the diagnostic accuracy of UC alone with that of UC combined with CK20/p53 DIC. METHODS: We retrieved urine samples collected between January 2015 and March 2016 stored in PreservCyt®solution that were from cases categorized as malignant, highly suspicious, suspicious, and atypical and that were matched with a subsequent biopsy. We re-prepared 63 samples of 28 patients for DIC and blindly evaluated 63 pairs of original Papanicolaou smears and DIC. RESULTS: Of the 63 samples, 11 could not be analyzed because of the low number of atypical urothelial cells, and the results of the remaining 52 samples were as follows: 34 positive and 18 negative. The positive predictive value of DIC was 100%, and the negative predictive value was 78%. Fifteen DIC-positive cases, histologically proven as malignant were originally diagnosed as highly suspicious (4), suspicious (8), and atypical (3), which were strongly suggestive of "urothelial carcinoma". Four negative cases, histologically confirmed as non-neoplastic cases, were filtered from false positivity. CONCLUSIONS: Despite the small sample size, this study demonstrated the diagnostic utility, high sensitivity, and positive predictive value of CK20/p53 DIC, especially in cases with a small number of single malignant cells or cellular clusters of reactive atypical urothelial cells. Thus, CK20/p53 DIC can be used for improving diagnostic accuracy of UC, either as an ancillary method to cytology or as a part of a potential future diagnostic panel to improve patient diagnosis and management.