Left atrial pressure (LAP) is a major determinant of pulmonary congestion and a key hemodynamic parameter in patients with heart failure and atrial fibrillation. LAP has traditionally been regarded as a surrogate of left ventricular (LV) filling pressure. However, growing evidence indicates that LAP reflects integrated interactions between left atrial (LA) mechanics, LV diastolic and systolic function, and extrinsic mechanical restraints throughout the cardiac cycle. Understanding the physiological basis of the LAP waveform provides important insights into the LA booster pump, reservoir, and conduit functions, as well as LA-LV interactions. Recent advances in echocardiography have expanded the noninvasive assessment of LAP beyond conventional Doppler indices. Contemporary approaches integrate the values of transmitral flow, mitral annular velocity, pulmonary venous flow, LA volume, and LA reservoir strain to provide a more comprehensive noninvasive assessment of LAP. In particular, LA reservoir strain has emerged as a promising marker of LAP and atrial mechanics. Additionally, exercise stress echocardiography and preload stress testing can reveal occult abnormalities in LAP responses that are not apparent at rest, particularly in patients with heart failure with preserved ejection fraction. Abnormal LAP also plays a central role in the pathophysiology of atrial fibrillation, atrial remodeling, and stiff LA physiology. This review summarizes the physiological determinants of LAP, the hemodynamic significance of the LAP waveform, and contemporary echocardiographic approaches for the assessment of LAP, and the impact of extrinsic mechanical restraints on LAP, with particular emphasis on the clinical relevance of LA-LV interactions in heart failure with preserved ejection fraction and atrial fibrillation.
The sonographic glandular tissue component (GTC) is a potential imaging biomarker for stratifying breast cancer risk in women with dense breasts. This study evaluated the reproducibility, reliability, and clinical applicability of GTC classification and GTC-based sonographic breast density assessment in a multicenter observer study. Fifteen readers (nine physicians and six sonographers) from 12 institutions independently assessed 100 paired ultrasound images twice, 1 month apart, with the dataset rearranged for the second reading. GTC was qualitatively classified into four categories according to its estimated proportion within the fibroglandular tissue (FGT): minimal, mild, moderate, and marked; minimal/mild were grouped as low GTC and moderate/marked as high GTC. Sonographic breast density was categorized as sonographic dense breast (high GTC), sonographic non-dense breast (visible fat lobules), or intermediate, with intermediate cases further divided into high or low categories using an FGT-to-FAT (subcutaneous and retromammary fat) ratio cutoff of 1. Interobserver and intraobserver agreement was evaluated using κ statistics. Substantial interobserver agreement was observed for the four-class GTC classification, the two-class GTC classification, and sonographic breast density assessment. Intraobserver agreement was moderate for the four-class GTC classification and substantial for the two-class GTC classification and sonographic breast density assessment. The majority-vote sonographic breast density assessment showed substantial agreement with the mammographic ground truth, whereas individual-reader assessments showed moderate concordance with mammographic breast density both overall and in intermediate-density breasts. No significant differences in agreement were observed between physicians and sonographers. Substantial interobserver agreement and moderate-to-substantial intraobserver agreement support the reproducibility and reliability of GTC classification and GTC-based sonographic breast density assessment.
Carotid plaque screening using ultrasound is challenged by class imbalance and ambiguous lesion boundaries. However, how different anatomical auxiliary tasks influence carotid plaque detection performance and clinical operating characteristics remains insufficiently investigated. We aimed to externally evaluate multitask deep learning models with different anatomical auxiliary tasks and investigate their effects on clinical performance profiles. We developed a multitask network incorporating three anatomical auxiliary segmentation targets (vessel lumen, intima-media thickness (IMT), and plaque), together with a classification-only baseline. The model was developed on 1297 images from 262 patients and evaluated on an external cohort of 1091 images from 263 patients. To ensure fair comparison, we froze the encoder of each variant and retrained an identical classification head on the internal training set, and then applied a fixed threshold determined via Youden index maximization for each model on the internal validation set to the external cohort. In external validation (263 patients, 94 plaque-positive), all multitask models outperformed the classification-only baseline (AUC 0.615, p < 0.0001), while showing comparable AUCs among themselves (lumen-guided 0.852, IMT-guided 0.853, plaque-guided 0.858; all p > 0.76). Despite comparable discrimination, they exhibited distinct clinical profiles: the IMT-guided model achieved the highest sensitivity (SEN 0.830), the lumen-guided model achieved the highest specificity (SPE 0.864), and the plaque-guided model offered more balanced performance (SEN 0.723, SPE 0.822). Radiofrequency balloon ablation significantly reduced external AUC for the lumen-guided (0.852 vs 0.790; p=0.0145) and plaque-guided (0.858 vs 0.796; p=0.0091) models, but not for the IMT-guided model (p=0.2171), indicating task-dependent effects. The choice of auxiliary task does not confer universal superiority in discrimination but instead determines the model’s clinical orientation, allowing task-specific adaptation to different screening or confirmatory scenarios.
