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17 results on '"Hou, Hongjun"'

9. Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging

Author

Xu Dong, Zhang Xuexi, Hou Hongjun, and Yu Chunrong

Subjects
medicine.medical_specialty, medicine.diagnostic_test, business.industry, Magnetic resonance imaging, General Medicine, Lymph node metastasis, medicine.disease, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Dynamic contrast, 0302 clinical medicine, Breast cancer, Text mining, medicine.anatomical_structure, 030220 oncology & carcinogenesis, medicine, Radiology, business, Lymph node, Original Research
Abstract

Objective: Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI. Methods and materials: A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values. Results: Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780). Conclusion: In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific. Advances in knowledge: This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.

Published
2019

17. Prediction of malignancy for solitary pulmonary nodules based on imaging, clinical characteristics and tumor marker levels.

Author

Hou H, Yu S, Xu Z, Zhang H, Liu J, and Zhang W

Subjects
Carcinoembryonic Antigen, Female, Humans, Infant, Male, ROC Curve, Retrospective Studies, Lung Neoplasms diagnostic imaging, Lung Neoplasms pathology, Solitary Pulmonary Nodule diagnostic imaging, Solitary Pulmonary Nodule pathology
Abstract

Objective: To establish a prediction model of malignancy for solitary pulmonary nodules (SPNs) on the basis of imaging, clinical characteristics and tumor marker levels., Methods: Totally, 341 cases of SPNs were enrolled in this retrospective study, in which 70% were selected as the training group (n = 238) and the rest 30% as the verification group (n = 103). The imaging, clinical characteristics and tumor marker levels of patients with benign and malignant SPNs were compared. Influencing factors were identified using multivariate logistic regression analysis. The model was assessed by the area under the curve (AUC) of the receiver operating characteristic curve., Results: Differences were evident between patients with benign and malignant SPNs in age, gender, smoking history, carcinoembryonic antigen (CEA), neuron-specific enolase, nodule location, edge smoothing, spiculation, lobulation, vascular convergence sign, air bronchogram, ground-glass opacity, vacuole sign and calcification (all P < 0.05). Influencing factors for malignancy included age, gender, nodule location, spiculation, vacuole sign and CEA (all P < 0.05). The established model was as follows: Y = -5.368 + 0.055 × age + 1.012 × gender (female = 1, male = 0) + 1.302 × nodule location (right upper lobe = 1, others = 0) + 1.208 × spiculation (yes = 1, no = 0) + 2.164 × vacuole sign (yes = 1, no = 0) -0.054 × CEA. The AUC of the model with CEA was 0.818 (95% confidence interval, 0.763-0.865), with a sensitivity of 64.80% and a specificity of 84.96%, and the stability was better through internal verification., Conclusions: The prediction model established in our study exhibits better accuracy and internal stability in predicting the probability of malignancy for SPNs., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc.)

Published
2021
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