應用 DCE-MRI 分析乳癌生物標記

DCE-MRI Analysis for Breast Tumor Biomarker

陳政揚(Cheng-Yang Chen)

指導教授 ： 張瑞峰

國立臺灣大學/電機資訊學院/資訊網路與多媒體研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.10207

摘要

由於近年來乳癌已經被證明是全球女性十大死因之一，所以在醫生治療方式、預後、以及復發率與存活率對於病患都是非常重要的資訊。在近年的臨床上，有許多生物標記被用來預測上述資訊，例如動情激素受體、黃體素受體以及第二型表皮生長因子受體。本篇論文的目標是運用動態對比增強核磁共振影像上的特徵預測腫瘤的動情激素受體以及預測腫瘤是否具有三陰性特性(動情激素受體、黃體素受體以及第二型表皮生長因子受體皆為陰性反應)。動態對比增強的核磁共振影像是一種能記錄顯影劑在腫瘤中隨著時間變化的影像，動態對比影像中的腫瘤是由使用者標記後採用類區域生長演算法切割，在切割完後會對切割出的腫瘤擷取四大類的特徵，其中包含區域、材質、形狀特徵以及腫瘤動力曲線分析。區域特徵主要是用來量化腫瘤中的異質性與亂度，三維的形狀分析包含腫瘤的緊實度、邊緣的變化，以及所對應橢圓模型比對的結果，而三維的材質特徵是使用灰階值共生矩陣對腫瘤進行紋理分析，最後我們利用fuzzy C-means方法來找出能夠代表腫瘤的動力曲線，接著對該曲線進行腫瘤動力取曲線的分析。在腫瘤的動情激素受體分類實驗中，總共有71個病理檢驗過的腫瘤做為實驗資料，其中包含48個有動情激素受體陽性反應的腫瘤以及23個有動情激素受體陰性反應的腫瘤。最後的結果能達到準確性83.10% (59/71)、敏感性83.33% (40/48)、專一性82.61% (19/23)以及Az值0.7651。在對於腫瘤的三陰性分類實驗中，總共有65個病理檢驗過的腫瘤作為實驗資料，其中包含55個有三陰性反應的腫瘤以及10個無三陰性反應的腫瘤。最後的結果能達到83.08% (54/65)、敏感性80.00% (8/10)、專一性83.64% (46/55)以及Az值0.8565。

關鍵字

核磁共振影像 ； 乳房 ； 動情激素受體 ； 三陰性 ； 異質性 ； 灰階共生矩陣 ； 橢圓 ； 動力曲線

並列摘要

Breast cancer has become the women leading causes of the death in recent years. Recently, different therapies are used to cure the breast cancer and the doctor will decide the treatment plan according to molecular biomarkers. There are several biomarkers which can be used in prognosis or predictive, such as estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (Her2). In this paper, the main purpose is to use the features extracted from the dynamic contrast-enhance MRI (DCE-MRI) to predict the biomarkers. There are two types of biomarkers using in this paper as targets to classify the tumor which is ER and triple negative (ER-/PR-/Her2-). DCE-MRI is a method which records the signal intensity of the tumor by using the contrast agent. In the proposed DCE-MRI computer-aided classification system, the tumor is indicated by user and the tumor is segmented by a region growing based algorithm. After segmenting the tumor, four categories of features are used to improve the classification performance, including region features, texture features, shape features, and kinetic curve analysis. The region features are used to quantify the heterogeneity and randomness of the tumor. The shape features including compactness, margin, and ellipsoid fitting model are used to quantify the 3 dimensions (3-D) shape information of the tumor, and the texture features based on the grey level co-occurrence matrix are also used to quantify 3-D texture information of the tumor. At last, after using fuzzy c-means clustering to find the representative kinetic curve of the tumor, the representative kinetic curve is used in the kinetic curve analysis to quantify temporal features. In the experiment of classification of ER tumors, 71 biopsy-proved tumors with 48 ER positive tumors and 23 ER negative tumors are used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value are up to 83.10% (59/71), 83.33% (40/48), 82.61% (19/23), and 0.7651. In the second experiment of classification of triple negative tumors, 65 biopsy-proved tumors with 55 triple negative tumors and 10 non-triple negative tumors are used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value are up to 83.08% (54/65), 80.00 (8/10), 83.64 (46/55), and 0.8565.

並列關鍵字

DCE-MRI ； breast ； strogen receptor ； Triple negative ； heterogeneity ； GLCM ； ellipsoid ； kinetic curve

參考文獻

[1] R. Siegel, J. Ma, Z. Zou, and A. Jemal, "Cancer statistics, 2014," CA: a cancer journal for clinicians, vol. 64, pp. 9-29, 2014.

[2] R. I. CTSU, "Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials," Lancet, vol. 365, pp. 1687-1717, 2005.

[3] M. C. Cheang, D. Voduc, C. Bajdik, S. Leung, S. McKinney, S. K. Chia, et al., "Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype," Clinical Cancer Research, vol. 14, pp. 1368-1376, 2008.

[4] J. H. Chen, H. M. Baek, O. Nalcioglu, and M. Y. Su, "Estrogen receptor and breast MR imaging features: a correlation study," Journal of Magnetic Resonance Imaging, vol. 27, pp. 825-833, 2008.

[5] F. Montemurro, L. Martincich, I. Sarotto, I. Bertotto, R. Ponzone, L. Cellini, et al., "Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer," European radiology, vol. 17, pp. 1490-1497, 2007.