Objectives

To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling.

Methods

A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10^-3 mm²/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10^-3 mm²/s were obtained and compared by the McNemar test.

Results

The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015).

Conclusion

Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models.

Key points

• When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

Objective- A large number of genetic loci have been associated with risk of coronary artery disease (CAD) through genome-wide association studies, however, for most loci the underlying biological mechanism is unknown. Determining the molecular pathways and cellular processes affected by these loci will provide new insights into CAD pathophysiology and may lead to new therapies. The CAD-associated variants at 10p11.23 fall in JCAD, which encodes an endothelial junction protein, however, its molecular function in endothelial cells is not known. In this study, we characterize the molecular role of JCAD (junctional cadherin 5 associated) in endothelial cells. Approach and Results- We show that JCAD knockdown in endothelial cells affects key phenotypes related to atherosclerosis including proliferation, migration, apoptosis, tube formation, and monocyte binding. We demonstrate that JCAD interacts with LATS2 (large tumor suppressor kinase 2) and negatively regulates Hippo signaling leading to increased activity of YAP (yes-associated protein), the transcriptional effector of the pathway. We also show by double siRNA knockdown that the phenotypes caused by JCAD knockdown require LATS2 and that JCAD is involved in transmission of RhoA-mediated signals into the Hippo pathway. In human tissues, we find that the CAD-associated lead variant, rs2487928, is associated with expression of JCAD in arteries, including atherosclerotic arteries. Gene co-expression analyses across disease-relevant tissues corroborate our phenotypic findings and support the link between JCAD and Hippo signaling. Conclusions- Our results show that JCAD negatively regulates Hippo signaling in endothelial cells and we suggest that JCAD contributes to atherosclerosis by mediating YAP activity and contributing to endothelial dysfunction.

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.