應用動態增強核磁共振與擴散磁振分析乳癌生物標記

DCE-MRI and DWI Analysis for Breast Tumor Biomarker

鄭琮錡(Tsung-Chi Cheng)

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(2016年)

https://doi.org/10.6342/NTU201610359

摘要

近年來，已經有許多乳癌的療法。醫師會根據病人乳癌的致因和生物標記的結果來設計療程。已經有許多生物標記用於估計預後的效果，包括動情激素受體、黃體素受體以及第二型表皮生長因子受體。本篇論文的目標是用動態對比增強核磁共振影像與擴散核磁共振影像上的特徵來預測情激素受體、黃體素受體、第二型表皮生長因子受體以及三陰性特性(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)。動態對比增強核磁共振影像是一種利用顯影劑來增強特定器官、組織或腫瘤的對比。擴散核磁共振影像是一種紀錄水分子在組織間布朗運動的影像。本篇提出的電腦輔助分類系統中，動態對比增強核磁共振影像上的腫瘤區塊會以使用者標記區塊及信心連接切割演算法切割，擴散核磁共振影像上的腫瘤運用套合演算法來得到，並在這兩種影像上擷取七大類特徵，其中含有顯著擴散係數區域、形狀、材質、ranklet材質、血管特徵及腫瘤動力曲線。顯著擴散特徵是量化水分子在組織間擴散程度。三維區域特徵是量化腫瘤的異質性與亂度。形狀特徵量化包含腫瘤的緊實度、邊緣的變化以及所對應橢圓模型的比對結果。三維材質特徵是用灰階共生矩陣對腫瘤作紋理分析。Ranklet材質特徵會用灰階共生矩陣計算經由ranklet轉換後的腫瘤紋理。血管特徵是擷取與血管有管的形態學特徵。而動力曲線是利用fuzzy C-means方法來找出能代表腫瘤的動力曲線，並對該曲線作分析。在腫瘤的激情素受體分類實驗中，有78個病理檢驗過的腫瘤作為實驗資料，其中包含47個動情激素受體陽性反應的腫瘤以及31個動情激素受體陰性反應的腫瘤,並可以達到準確性80.76% (63/78)、敏感性82.98% (39/47)、專一性77.42% (24/31)以及Az值0.8006的結果。在腫瘤的黃體素受體分類實驗中，有78個病理檢驗過的腫瘤作為實驗資料，其中包含27個黃體素受體陽性反應的腫瘤以及51個黃體素受體陰性反應的腫瘤，並可以達到準確性79.49% (62/78)、敏感性70.37% (19/27)、專一性84.31% (43/51)以及Az值0.7911的結果。在腫瘤的第二型表皮生長因子受體分類實驗中，有78個病理檢驗過的腫瘤作為實驗資料，其中包含36個第二型表皮生長因子受體陽性反應的腫瘤以及42個第二型表皮生長因子受體陰性反應的腫瘤，並可以達到準確性80.77% (63/78)、敏感性77.78% (28/36)、專一性80.95% (34/42)以及Az值0.8501的結果。在腫瘤的三陰性分類實驗中，有78個病理檢驗過的腫瘤作為實驗資料，其中包含14個三陰性反應的腫瘤以及64個無三陰性反應的腫瘤，並可以達到準確性80.77% (63/78)、敏感性71.43% (10/14)、專一性82.81% (53/64)以及Az值0.7043的結果。

關鍵字

動態對比增強核磁共振影像 ； 擴散核磁共振影像 ； 乳房 ； 動情激素受體 ； 黃體素受體 ； 第二型表皮生長因子受體 ； 三陰性 ； 顯著擴散係數 ； 異質性 ； 灰階共生矩陣 ； 橢圓 ； ranklet 轉換 ； 血管 ； 動力曲線

並列摘要

There are several breast cancer therapies at this day. Doctors will design different treatments for patient according to the cause of breast cancer and molecular biomarkers. Several biomarkers are used in estimate the prognosis, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). The main purpose of this study is to predict the biomarkers with features extracted from dynamic contrast-enhance MRI (DCE-MRI) and diffusion-weighted MRI (DWI). There were four types of biomarker, ER, PR, HER2, and triple negative (TN, ER-/PR-/HER2-), using in this paper as classification targets. The DCE-MRI uses contrast material to enhance particular organs, tissues, or tumors, and uses fast and continuous imaging to trace the variation of contrast enhancement. The DWI represents the different mobility of water molecules between normal tissue and diseased tissue. In the proposed computer-aided classification system, the tumor was indicated by a user-specified volume of interest (VOI) and segmented by the confident connected method. Then we applied a registration method to obtain the DWI tumor. After the tumor images of DCE-MRI and DWI were obtained, seven categories of features were extracted to improve the classification performance, including ADC features, region features, shape features, texture features, ranklet texture features, vascular features, and kinetic curve analysis. The ADC features used the apparent diffusion coefficient (ADC) to quantify the water diffusion within tissue. The region features were used to quantify the heterogeneity and randomness of the tumor. The shape features including compactness, margin, and ellipsoid fitting model were used to quantify the three dimensions (3-D) shape information of the tumor, and the texture features based on the grey level co-occurrence matrix were also used to quantify 3-D texture information of the tumor. The ranklet texture features were extracted after applying the ranklet transformation. The vascular features were used to extract the morphology features of vessel. At last, after using the fuzzy c-means clustering to find the representative kinetic curve of the tumor in DCE-MRI, the representative kinetic curve was used in the kinetic curve analysis to quantify temporal features. In the experiment of classification of ER tumors, 78 biopsy-proved tumors with 47 ER positive tumors and 31 ER negative tumors were used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value were up to 80.76% (63/78), 82.98% (39/47), 77.42% (24/31), and 0.8006. In the second experiment of classification of PR, 78 biopsy-proved tumors with 27 PR positive tumors and 51 PR negative tumors were used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value were up to 79.49% (62/78), 70.37% (19/27), 84.31% (43/51), and 0.7911. In the experiment of classification of HER2 tumors, 78 biopsy-proved tumors with 36 HER2 positive tumors and 42 HER2 negative tumors were used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value were up to 80.77% (63/78), 77.78% (28/36), 80.95% (34/42), and 0.8501. In the experiment of classification of TN tumors, 78 biopsy-proved tumors with 14 TN positive tumors and 64 non-TN tumors were used to evaluate the performance of the proposed classification system. Its accuracy, sensitivity, specificity, and Az value were up to 80.77% (63/78), 71.43% (10/14), 82.81% (53/64), and 0.7043.

並列關鍵字

DCE-MRI ； DWI ； breast ； estrogen receptor ； progesterone receptor ； human epidermal growth factor receptor 2 ； triple negative ； apparent diffusion coefficient ； heterogeneity ； GLCM ； ellipsoid ； ranklet transformation ； vessel ； kinetic curve

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