Mr. Yuanzhe Li Profile

Yuanzhe Li

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Proceedings Article | 13 March 2019 Presentation + Paper

DCE-MRI based analysis of intratumor heterogeneity by decomposing method for prediction of HER2 status in breast cancer

Peng Zhang, Ming Fan, Yuanzhe Li, Maosheng Xu, Lihua Li

Proceedings Volume 10950, 109500H (2019) https://doi.org/10.1117/12.2513102

KEYWORDS: Tumors, Breast cancer, Magnetic resonance imaging, Breast, Feature extraction, Image segmentation, Content addressable memory, Lithium, Medicine, Fluctuations and noise

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Human epidermal growth factor receptor-2 (HER2) plays an important role in treatment strategy and prognosis determination in breast cancers. However, breast cancers are characterized by considerable heterogeneity both between and within tumors, which is a key impediment to accurately determine HER2 status for radiomic analysis. To this end, tumor heterogeneity was evaluated by unsupervised decomposition method on breast magnetic resonance imaging (MRI), in which three tumor subregions were generated terms as Input, Fast and Slow. This tumor decomposition was performed by a convex analysis of mixtures (CAM) method, which was designed according to analysis of contrast-enhancement patterns. The study retrospectively investigated 181 patients who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Among them, 124 were HER2-negative and 57 were HER2-positive status. Imaging features of texture and histogram were computed in each subregion. Multivariate logistic regression classifiers were trained and validated with leave-one-out cross-validation (LOOCV) method. An area under a receiver operating characteristic curve (AUC) was calculated to assess performance of the classifier. The classifier based on features from Fast subregion obtained an AUC of 0.802 ± 0.067 and was significantly (P = 0.0113) outperformed the classifier based on features from the whole tumors. When the predicted values from the respective classifiers were fused by weighted average, the AUC significantly increased to 0.820 ± 0.063 (P = 0.0011). The results indicate that analysis of intratumor heterogeneity through decomposing method of DCE-MRI has the potential to serve as a marker for predicting HER2 status.

Proceedings Article | 6 March 2018 Presentation + Paper

Analysis of DCE-MRI features in tumor and the surrounding stroma for prediction of Ki-67 proliferation status in breast cancer

Hui Li, Ming Fan, Peng Zhang, Yuanzhe Li, Hu Cheng, Juan Zhang, Guoliang Shao, Lihua Li

Proceedings Volume 10579, 1057907 (2018) https://doi.org/10.1117/12.2293047

KEYWORDS: Tumors, Breast cancer, Breast, Tissues, Magnetic resonance imaging, Feature extraction, Image segmentation, Lithium, Statistical analysis, Performance modeling

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Breast cancer, with its high heterogeneity, is the most common malignancies in women. In addition to the entire tumor itself, tumor microenvironment could also play a fundamental role on the occurrence and development of tumors. The aim of this study is to investigate the role of heterogeneity within a tumor and the surrounding stromal tissue in predicting the Ki-67 proliferation status of oestrogen receptor (ER)-positive breast cancer patients. To this end, we collected 62 patients imaged with preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for analysis. The tumor and the peritumoral stromal tissue were segmented into 8 shells with 5 mm width outside of tumor. The mean enhancement rate in the stromal shells showed a decreasing order if their distances to the tumor increase. Statistical and texture features were extracted from the tumor and the surrounding stromal bands, and multivariate logistic regression classifiers were trained and tested based on these features. An area under the receiver operating characteristic curve (AUC) were calculated to evaluate performance of the classifiers. Furthermore, the statistical model using features extracted from boundary shell next to the tumor produced AUC of 0.796±0.076, which is better than that using features from the other subregions. Furthermore, the prediction model using 7 features from the entire tumor produced an AUC value of 0.855±0.065. The classifier based on 9 selected features extracted from peritumoral stromal region showed an AUC value of 0.870±0.050. Finally, after fusion of the predictive model obtained from entire tumor and the peritumoral stromal regions, the classifier performance was significantly improved with AUC of 0.920. The results indicated that heterogeneity in tumor boundary and peritumoral stromal region could be valuable in predicting the indicator associated with prognosis.

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