北京生物醫(yī)學(xué)工程

基于CT影像組學(xué)特征的腎腫瘤組織學(xué)亞型分類

Classification of renal tumor histology subtypes based on radiomicsfeatures of CT images

作者：楊熠錢(qián)旭升周志勇沈鈞康朱建兵戴亞康

單位：1中國(guó)科學(xué)技術(shù)大學(xué)（合肥 230027） 2中國(guó)科學(xué)院蘇州生物醫(yī)學(xué)工程技術(shù)研究所（蘇州 215163） 3蘇州大學(xué)附屬第二醫(yī)院（蘇州 215163） 4南京醫(yī)科大學(xué)附屬蘇州醫(yī)院（蘇州 215163） 5蘇州科技城醫(yī)院（蘇州 215163）

關(guān)鍵詞：乏脂肪血管平滑肌脂肪瘤；腎透明細(xì)胞癌；影像組學(xué)；機(jī)器學(xué)習(xí)

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2020·39·1（15-20）

摘要：

目的在術(shù)前準(zhǔn)確鑒別乏脂肪血管平滑肌脂肪瘤（fat-poor angiomyolipoma, fp-AML）和腎透明細(xì)胞癌（clear cell renal cell carcinoma, ccRCC）對(duì)制定正確的治療方案是至關(guān)重要的。為了提高fp-AML和ccRCC的分類準(zhǔn)確率，本文提出一種基于影像組學(xué)技術(shù)的分類模型。方法回顧性地收集蘇州大學(xué)附屬第二醫(yī)院放射科18例fp-AML患者和42例ccRCC患者的CT圖像。首先，從CT圖像中提取430個(gè)影像組學(xué)特征。然后，分三步進(jìn)行特征選擇：計(jì)算皮爾森相關(guān)矩陣剔除冗余特征；使用Welch’s t檢驗(yàn)確定具有顯著差異的特征；利用序列浮動(dòng)前向選擇算法選擇具有鑒別能力的特征。最后，建立k最近鄰（k-nearest neighborhood, kNN）、隨機(jī)森林（random forest, RF）、支持向量機(jī)（support vector machine, SVM）和AdaBoost四種分類器進(jìn)行分類。結(jié)果 SVM分類器所構(gòu)建的模型獲得了最佳分類性能，正確率、敏感度、特異性、陽(yáng)性預(yù)測(cè)值、陰性預(yù)測(cè)值和 ROC曲線下面積分別為91.67%、88.89%、92.86%、84.21%、95.12%和0.9418。結(jié)論本研究所構(gòu)建的模型能提高fp-AML和ccRCC的分類準(zhǔn)確率，能輔助醫(yī)生進(jìn)行fp-AML和ccRCC的鑒別診斷。

Objective Accurate preoperative differential diagnosis of fat-poor angiomyolipoma (fp-AML) and clear cell renal cell carcinoma (ccRCC) is essential for proper treatment planning. In order to increase the accuracy of discrimination of fp-AML from ccRCC, we develop a classification model based on radiomics technology. Methods The study retrospectively collected CT images of 18 cases with fp-AML and 42 cases with ccRCC from department of radiology, the Second Affiliated Hospital of Suzhou University. Firstly, 430 radiomics features were extracted from CT images. Then, the feature selection was carried by three steps: Pearson’s correlation matrices were calculated to remove redundant features, Welch’s t-test was utilized to determine the statistically significant features, and sequential forward floating selection method was used to select the discriminative features. Finally, k-nearest neighborhood, random forest, support vector machine and AdaBoost classifiers were built for classification. Results The model built by SVM classifier achieved the best classification performance, with accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curves of 91.67%, 88.89%, 92.86%, 84.21%, 95.12%, and 0.9418. Conclusions The proposed model can increase the classification accuracy of discrimination of fp-AML from ccRCC, and has great potential in helping radiologists to discriminate fp-AML from ccRCC.

參考文獻(xiàn)：

[1] Hsieh JJ, Purdue MP, Signoretti S, et al. Renal cell carcinoma [J]. Nature Reviews Disease Primers, 2017, 3: 17009.

[2] Wei J, Zhao J, Zhang X, et al. Analysis of dual energy spectral CT and pathological grading of clear cell renal cell carcinoma (ccRCC) [J]. Plos One, 2018, 13(5): e0195699.

[3] Prasad SR, Surabhi VR, C. O. Menias, et al. Chintapalli. Benign Renal Neoplasms in Adults: Cross-Sectional Imaging Findings [J]. American Journal of Roentgenology, 2008, 190(1): 158-164.

[4] Lane BR, Aydin H, Danforth TL, et al. Clinical Correlates of Renal Angiomyolipoma Subtypes in 209 Patients: Classic, Fat Poor, Tuberous Sclerosis Associated and Epithelioid [J]. The Journal of Urology, 2008, 80(3): 836-843.

[5] Yan L, Liu Z, Wang G, et al. Angiomyolipoma with Minimal Fat : Differentiation From Clear Cell Renal Cell Carcinoma and Papillary Renal Cell Carcinoma by Texture Analysis on CT Images [J]. Academic Radiology, 2015, 22(9): 1115.

[6] 許玥. 基于CT圖像紋理分析鑒別乏脂肪腎血管平滑肌脂肪瘤與腎透明細(xì)胞癌的初步研究 [D]. 沈陽(yáng)：中國(guó)醫(yī)科大學(xué), 2017.

Xu Y. Preliminary study on differential diagnosis of renal angiomyolipoma and renal clear cell carcinoma based on CT image texture analysis [D]. Shenyang: China Medical University, 2017.

[7] Feng Z, Rong P, Cao P, et al. Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma [J]. European Radiology, 2018, 28(4): 1625-1633.

[8] Lee HS, Hong H, Jung DC, et al. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification [J]. Medical Physics, 2017, 44(7): 3604-3614.

[9] Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine [J]. Nature Reviews Clinical Oncology, 2017, 14: 749.

[10] Bernardi ED, Buda A, Guerra L, et al. Radiomics of the primary tumour as a tool to improve 18F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer [J]. EJNMMI Research, 2018, 8(1): 86.

[11] Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, et al. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases [J]. Computer Methods and Programs in Biomedicine, 2018, 159: 23-30.

[12] Sapate SG, Mahajan A, Talbar SN, et al. Radiomics based detection and characterization of suspicious lesions on full field digital mammograms [J]. Computer Methods and Programs in Biomedicine, 2018, 163: 1-20.

[13] Meng X, Xia W, Xie P, et al. Preoperative radiomic signature based on multiparametric magnetic resonance imaging for noninvasive evaluation of biological characteristics in rectal cancer [J]. European Radiology, 2019, 29(6): 3200-3209.

[14] Inza I, Larra?aga P, and Saeys Y. A review of feature selection techniques in bioinformatics [J]. Bioinformatics, 2007, 23(19): 2507-2517.

[15] Zwanenburg A, Leger S, Vallières M, et al. Image biomarker standardisation initiative - feature definitions [J]. arXiv preprint arXiv: 1612.07003, 2016.

[16] Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges [J]. RadioGraphics, 2017, 37(5): 1483-1503.

[17] Harikumar R and Vinoth kumar B. Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor [J]. International Journal of Imaging Systems and Technology, 2015, 25(1): 33-40.

[18] Pearson K. Note on regression and inheritance in the case of two parents [J]. Proceedings of the Royal Society of London, 1895, 58: 240-242.

[19] Derrick B and White P. Why Welch's test is Type I error robust [J]. The Quantitative Methods in Psychology, 2016, 12(1): 30-38.

[20] Aha DW and Bankert RL. A comparative evaluation of sequential feature selection algorithms [M]// Learning from Data: Artificial Intelligence and Statistics V. D. Fisher and H.-J. Lenz, Eds. New York: Springer New York, 1996: 199-206.

[21] Bugdol MD, Bugdol MN, Lipowicz AM, et al. Prediction of menarcheal status of girls using voice features [J]. Computers in Biology and Medicine, 2018, 100: 296-304.

[22] Fallahpour S, Lakvan EN, and Zadeh MH. Using an ensemble classifier based on sequential floating forward selection for financial distress prediction problem [J]. Journal of Retailing and Consumer Services, 2017, 34: 159-167.

[23] Chapelle O, Vapnik V, Bousquet O, et al. Choosing Multiple Parameters for Support Vector Machines [J]. Machine Learning, 2002, 46(1): 131-159.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請(qǐng)登錄！點(diǎn)此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频