Cong Wei

Lung metastases (LMs) are common and clinically significant in kidney cancer, contributing to poor prognosis and limited treatment response. Early detection of patients at high risk for LMs is crucial for developing personalized treatment strategies. This retrospective study included 2652 patients with kidney cancer, pathologically confirmed, from two medical centers between 2000 and 2020. A training cohort (n = 1500) was used to develop a predictive model for LMs based on clinical, pathological, and laboratory variables. Multivariate logistic regression was applied to identify independent predictors, and a nomogram was constructed. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, probability density functions (PDFs), and decision curve analysis (DCA). An external validation cohort (n = 1152) was used to assess generalizability. Family cancer history, advanced T stage, and N nodal stage were identified as independent predictors of LMs. The nomogram demonstrated excellent discrimination in the training cohort (AUC = 0.938) and moderate performance in the validation cohort (AUC = 0.883), outperforming individual variables. Calibration analysis showed good agreement between predicted and observed probabilities. Patients with LMs had significantly worse overall survival than those without (p < 0.001). DCA validated the nomogram's greater clinical value over treat-all or treat-none approaches. This nomogram-based model offers an efficient and accessible tool for the early detection of kidney cancer patients at risk for LMs. It enables individualized risk stratification and may assist in optimizing clinical decision-making and improving patient outcomes. Further prospective validation is warranted.

Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature. Ultrasound localization microscopy (ULM) can provide detailed microvascular architecture at super-resolution. The objective of this trial is to explore the role of ULM in distinguishing benign from malignant breast tumors and to explore the correlations between ULM qualitative and quantitative parameters and histologic biomarkers in malignant breast tumors. This prospective and multicenter study will include 83 patients with breast tumors that will undergo ULM. 55 patients will be assigned to the malignant group, and 28 patients will be assigned to the benign group. The primary outcome is the differences in the qualitative parameters (microvasculature morphology, distribution, and flow direction) between benign and malignant breast tumors on ULM. Secondary outcomes include (1) differences in the quantitative parameters (microvasculature density, tortuosity, diameter, and flow velocity) between benign and malignant breast tumors based on ULM; (2) diagnostic performance of the qualitative parameters in distinguishing benign and malignant breast tumors; (3) diagnostic performance of the quantitative parameters in distinguishing benign and malignant breast tumors; (4) relationships between the qualitative parameters and histologic biomarkers in malignant breast tumors; (5) relationships between the quantitative parameters and histologic biomarkers in malignant breast tumors; and (6) the evaluation of inter-reader and intra-reader reproducibility. Detecting vascularity in breast tumors is of great significance to differentiate benign from malignant tumors and to predict histologic biomarkers. These histologic biomarkers, such as ER, PR, HER2 and Ki67, are closely related to prognosis evaluation. This trial will provide maximum information about the microvasculature of breast tumors and thereby will help with the formulation of subsequent differential diagnosis and the prediction of histologic biomarkers. Chinese Clinical Trial Registry ChiCTR2100048361/6th/July/2021. This study is a part of that clinical trial.

Objectives: To assess the combined diagnostic strategy of contrast-enhanced ultrasound (CEUS) and acoustic radiation force impulse (ARFI) in the precise differential diagnosis of clear cell renal cell carcinoma (CCRCC) and urothelium carcinoma of the renal pelvis (UCRP) with other small renal tumors (SRTs) ＜3 cm in size. Methods: The elastography self-corrected CEUS (ESC) mode was established to perform the quantitative differential diagnosis of SRTs (<3 cm). The kidney shear wave velocity (SWV) value recorded by ARFI showed substantial variability in patients with CCRCC (high elasticity value) and UCRP (low elasticity value) compared with other renal masses, thus providing critical self-correction information for the ultrasound differential diagnosis of SRTs. Results: In this work, the ESC observations and the corresponding ESC criteria show a remarkable 94.6% accuracy in reference to the gold standards, thus allowing the quantitative, early triple distinction of CCRCC with UCRP and other SRTs in patients with suspicious SRTs. Conclusions: This ARFI self-corrected CEUS diagnostic strategy is far beyond a screening method and may have the potential to identify a window of therapeutic opportunity in which emerging therapies might be applied to patients with CCRCC and UCRP, reducing overtreatment and medical costs. Advances in knowledge: In our study, a new rapid and non-invasive elastography self-corrected CEUS (ESC) ultrasound imaging mode was developed, which was useful in the triple distinction of CCRCC, UCRP, and other SRTs with 94.6% accuracy. ESC is a promising method in the differential diagnosis of SRTs with accuracy and practicability far beyond a single screening model.