计算机科学与人工智能 |
|
|
|
|
采用影像组学的肾肿瘤组织学亚型分类 |
杨熠1,2(),钱旭升2,周志勇2,朱建兵3,4,沈钧康5,戴亚康2,*() |
1. 中国科学技术大学,安徽 合肥 230000 2. 中国科学院苏州生物医学工程技术研究所,江苏 苏州 215163 3. 苏州科技城医院,江苏 苏州 215163 4. 南京医科大学附属苏州科技城医院,江苏 苏州 215163 5. 苏州大学附属第二医院,江苏 苏州 215163 |
|
Classification of renal tumor histology subtypes using radiomics |
Yi YANG1,2(),Xu-sheng QIAN2,Zhi-yong ZHOU2,Jian-bing ZHU3,4,Jun-kang SHEN5,Ya-kang DAI2,*() |
1. University of Science and Technology of China, Hefei 230000, China 2. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China 3. Suzhou Science and Technology Town Hospital, Suzhou 215163, China 4. Suzhou Science and Technology Hospital Affiliated to Nanjing Medical University, Suzhou 215163, China 5. Second Affiliated Hospital of Suzhou University, Suzhou 215163, China |
引用本文:
杨熠,钱旭升,周志勇,朱建兵,沈钧康,戴亚康. 采用影像组学的肾肿瘤组织学亚型分类[J]. 浙江大学学报(工学版), 2019, 53(12): 2381-2388.
Yi YANG,Xu-sheng QIAN,Zhi-yong ZHOU,Jian-bing ZHU,Jun-kang SHEN,Ya-kang DAI. Classification of renal tumor histology subtypes using radiomics. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2381-2388.
链接本文:
http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.12.016
或
http://www.zjujournals.com/eng/CN/Y2019/V53/I12/2381
|
1 |
HSIEH J J, PURDUE P M, SIGNORETTI S, et al Renal cell carcinoma[J]. Nature Reviews Disease Primers, 2017, 3: 17009
doi: 10.1038/nrdp.2017.9
|
2 |
WORLD Cancer Research Fund International. Kidney cancer statistics [J/OL]. Available at https://www.wcrf.org/dietandcancer/cancer-trends/kidney-cancer-statistics, January 2018.
|
3 |
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
doi: 10.1371/journal.pone.0195699
|
4 |
JINZAKI M, SIVERMAN S G, and TANIMOTO A Angiomyolipoma that do not contain fat attenuation at unenhanced CT[J]. Radiology, 2005, 234 (1): 311
doi: 10.1148/radiol.2341041128
|
5 |
PRASAD S R, SURABHI V R, MENIAS C O, et al Benign renal neoplasms in adults: cross-sectional imaging findings[J]. American Journal of Roentgenology, 2008, 190 (1): 158- 164
doi: 10.2214/AJR.07.2724
|
6 |
HALPENNY D, SNOW A, MCNEILL G, et al The radiological diagnosis and treatment of renal angiomyolipoma-current status[J]. Clinical Radiology, 2010, 65 (2): 99- 108
doi: 10.1016/j.crad.2009.09.014
|
7 |
LANE B R, AYDIN H, DANFORTH T L, 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, 180 (3): 836- 843
doi: 10.1016/j.juro.2008.05.041
|
8 |
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
doi: 10.1007/s00330-017-5118-z
|
9 |
LEE H S, HONG H, JUNG D C, 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
doi: 10.1002/mp.2017.44.issue-7
|
10 |
MOLINA D, JULIáN P, BELéN L, et al Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival[J]. The British Journal of Radiology, 2016, 89 (1064): 20160242
doi: 10.1259/bjr.20160242
|
11 |
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al Radiomics: the bridge between medical imaging and personalized medicine[J]. Nature Reviews Clinical Oncology, 2017, 14: 749
doi: 10.1038/nrclinonc.2017.141
|
12 |
ELISABETTA D B, 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
doi: 10.1186/s13550-018-0441-1
|
13 |
FERREIRA JUNIOR J R, KOENIGKAM-SANTOS M, CIPRIANO F E G, 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
doi: 10.1016/j.cmpb.2018.02.015
|
14 |
SAPATE S G, MAHAJAN A, TALBAR S N, 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
doi: 10.1016/j.cmpb.2018.05.017
|
15 |
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, 2018, 1- 10
|
16 |
QIAN X, HUANG H, CHEN X, et al Efficient construction of sparse radial basis function neural networks using L1-regularization[J]. Neural Networks, 2017, 94: 239- 254
doi: 10.1016/j.neunet.2017.07.004
|
17 |
LUBNER M G, SMITH A D, SANDRASEGARAN K, et al CT texture analysis: definitions, applications, biologic correlates, and challenges[J]. RadioGraphics, 2017, 37 (5): 1483- 1503
doi: 10.1148/rg.2017170056
|
18 |
ZWANENBURG A, LEGER S, VALLIèRES M, et al. Image biomarker standardisation initiative-feature definitions [J/OL]. arXiv eprints, 2016. https://ui.adsabs.harvard.edu/abs/2016arXiv161207003Z
|
19 |
AERTS H J, VELAZQUEZ E R, LEIJENAAR R T, et al Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach[J]. Nature Communications, 2014, 5: 4006
doi: 10.1038/ncomms5006
|
20 |
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
doi: 10.1002/ima.v25.1
|
21 |
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
doi: 10.1098/rspl.1895.0041
|
22 |
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
doi: 10.20982/tqmp.12.1.p030
|
23 |
AHA D W, BANKERT R L. A Comparative Evaluation of Sequential Feature Selection Algorithms [M]. New York: Springer, 1996: 199-206.
|
24 |
FALLAHPOUR S, LAKVAN E N, and ZADEH M H 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
doi: 10.1016/j.jretconser.2016.10.002
|
25 |
BUGDOL M D, BUGDOL M N, LIPOWICZ A M, et al Prediction of menarcheal status of girls using voice features[J]. Computers in Biology and Medicine, 2018, 100: 296- 304
doi: 10.1016/j.compbiomed.2017.11.005
|
26 |
PARMAR C, GROSSMANN P, BUSSINK J, et al Machine learning methods for quantitative radiomic biomarkers[J]. Scientific Reports, 2015, 5: 13087
doi: 10.1038/srep13087
|
27 |
JOSHI A, METHA A Analysis of K-nearest neighbor technique for breast cancer disease classification[J]. International Journal of Recent Scientific Research, 2018, 9 (4): 26126- 26130
|
28 |
JOSé LUIS R, MANEL M, JORDI M, et al. Support vector machine and kernel classification algorithms [C] // Digital Signal Processing with Kernel Methods. New Jersey: Wiley-IEEE Press, 2018: 672.
|
29 |
CHENG J Z, NI D, CHOU Y H, et al Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans[J]. Scientific Reports, 2016, 6: 24454
doi: 10.1038/srep24454
|
30 |
LEE H, HONG H, KIM J, et al Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation[J]. Medical Physics, 2018, 45 (4): 1550
doi: 10.1002/mp.2018.45.issue-4
|
31 |
CHAPELLE O, VAPNIK V, BOUSQUET O, et al Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 46 (1): 131- 159
|
32 |
LUKAS L, DEVOS A, SUYKENS J A K, et al Brain tumor classification based on long echo proton MRS signals[J]. Artificial Intelligence in Medicine, 2004, 31 (1): 73- 89
doi: 10.1016/j.artmed.2004.01.001
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|