[1]
|
Doi, K. (2007) Computer-Aided Diagnosis in Medical Imaging: Historical Review, Current Status and Future Potential. Computerized Medical Imaging and Graphics, 31, 198-211. https://doi.org/10.1016/j.compmedimag.2007.02.002
|
[2]
|
Giger, M.L. (2004) Computerized Analysis of Images in the Detection and Diagnosis of Breast Cancer. Seminars in Ultrasound, CT and MRI, 25, 411-418. https://doi.org/10.1053/j.sult.2004.07.003
|
[3]
|
Li, Q. and Nishikawa, R.M. (2015) Computer-Aided Detection and Diagnosis in Medical Imaging. CRC Press, Boca Raton. https://doi.org/10.1201/b18191
|
[4]
|
Rabinowits, G., Gercel-Taylor, C.G., Day, J.M., Taylor, D.D. and Kloecker, G.H. (2009) Exosomal microRNA: A Diagnostic Marker for Lung Cancer. Clinical Lung Cancer, 10, 42-46. https://doi.org/10.3816/CLC.2009.n.006
|
[5]
|
Eichelser, C., Stückrath, I., Müller, V., Milde-Langosch, K., Wikman, H., Pantel, K. and Schwarzenbach, H. (2014) Increased Serum Levels of Circulating Exosomal microRNA-373 in Receptor-Negative Breast Cancer Patients. Oncotarget, 5, 9650-9663. https://doi.org/10.18632/oncotarget.2520
|
[6]
|
Que, R., Ding, G., Chen, J. and Cao, L. (2013) Analysis of Serum Exosomal microRNAs and Clinicopathologic Features of Patients with Pancreatic Adenocarcinoma. World Journal of Surgical Oncology, 11, 219. https://doi.org/10.1186/1477-7819-11-219
|
[7]
|
Taylor, D.D. and Gercel-Taylor, C.G. (2008) MicroRNA Signatures of Tumor-Derived Exosomes as Diagnostic Biomarkers of Ovarian Cancer. Gynecologic Oncology, 110, 13-21. https://doi.org/10.1016/j.ygyno.2008.04.033
|
[8]
|
Wang, J., Kato, F., Oyama-Manabe, N.O., Li, R., Cui, Y., Tha, K.K., Yamashita, H., Kudo, K. and Shirato, H. (2015) Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study. PLoS ONE, 10, e0143308. https://doi.org/10.1371/journal.pone.0143308
|
[9]
|
Feng, Q., Hu, Q., Liu, Y., Yang, T. and Yin, Z. (2020) Diagnosis of Triple Negative Breast Cancer Based on Radiomics Signatures Extracted from Preoperative Contrast-Enhanced Chest Computed Tomography. BMC Cancer, 20, 579. https://doi.org/10.1186/s12885-020-07053-3
|
[10]
|
Ma, W., Zhao, Y., Ji, Y., Guo, X., Jian, X., Liu, P. and Wu, S. (2019) Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features. Academic Radiology, 26, 196-201. https://doi.org/10.1016/j.acra.2018.01.023
|
[11]
|
Li, H., Zhu, Y., Burnside, E.S., Huang, E., Drukker, K., Hoadley, K.A., Fan, C., Conzen, S.D., Zuley, M., Net, J.M., Sutton, E., Whitman, G.J., Morris, E., Perou, C.E., Ji, Y. and Giger, M.L. (2016) Quantitative MRI Radiomics in the Prediction of Molecular Classifications of Breast Cancer Subtypes in the TCGA/TCIA Data Set. NPJ Breast Cancer, 2, 16012. https://doi.org/10.1038/npjbcancer.2016.12
|
[12]
|
Xie, T., Wang, Z., Zhao, Q., Bai, Q., Zhou, X., Gu, Y., Peng, W. and Wang, H. (2019) Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer. Frontiers in Oncology, 9, 505. https://doi.org/10.3389/fonc.2019.00505
|
[13]
|
Leithner, D., Horvat, J.V., Marino, M.A., Bernard-Davila, B., Jochelson, M.S., Ochoa-Albiztegui, R.E., Martinez, D.F., Morris, E.A., Thakur, S. and Pinker, K. (2019) Radiomic Signatures with Contrast-Enhanced Magnetic Resonance Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes: Initial Results. Breast Cancer Research, 21, 106. https://doi.org/10.1186/s13058-019-1187-z
|
[14]
|
Leithner, D., Bernard-Davila, B.B., Martinez, D.F., Horvat, J.V., Jochelson, M.S., Marino, M.A., Avendano, D., Ochoa-Albiztegui, R.E., Sutton, E.J., Morris, E.A., Thakur, S.B. and Pinker, K. (2020) Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes. Molecular Imaging and Biology, 22, 453-461. https://doi.org/10.1007/s11307-019-01383-w
|
[15]
|
Leithner, D., Mayerhoefer, M.E., Martinez, D.F., Jochelson, M.S., Morris, E.A., Thakur, S.B. and Pinker, K. (2020) Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics. Journal of Clinical Medicine, 9, 1853. https://doi.org/10.3390/jcm9061853
|
[16]
|
Li, W., Yu, K., Feng, C. and Zhao, D. (2019) Molecular Subtypes Recognition of Breast Cancer in Dynamic Contrast-Enhanced Breast Magnetic Resonance Imaging Phenotypes from Radiomics Data. Computational and Mathematical Methods in Medicine, 2019, Article ID: 6978650. https://doi.org/10.1155/2019/6978650
|
[17]
|
Ni, M., Zhou, X., Liu, J., Yu, H., Gao, Y., Zhang, X. and Li, Z. (2020) Prediction of the Clinicopathological Subtypes of Breast Cancer Using a Fisher Discriminant Analysis Model Based on Radiomic Features of Diffusion-Weighted MRI. BMC Cancer, 20, 1073. https://doi.org/10.1186/s12885-020-07557-y
|
[18]
|
Son, J., Lee, S.E., Kim, E.K. and Kim, S. (2020) Prediction of Breast Cancer Molecular Subtypes Using Radiomics Signatures of Synthetic Mammography from Digital Breast Tomosynthesis. Scientific Reports, 10, Article No. 21566. https://doi.org/10.1038/s41598-020-78681-9
|
[19]
|
Demircioglu, A., Grueneisen, J., Ingenwerth, M., Hoffmann, O., Pinker-Domenig, K., Morris, E., Haubold, J., Forsting, M., Nensa, F. and Umutlu, L. (2020) A Rapid Volume of Interest-Based Approach of Radiomics Analysis of Breast MRI for Tumor Decoding and Phenotyping of Breast Cancer. PLoS ONE, 15, e0234871. https://doi.org/10.1371/journal.pone.0234871
|
[20]
|
Li, H., Zhu, Y., Burnside, E.S., Drukker, K., Hoadley, K.A., Fan, C., Conzen, S.D., Whitman, G.J., Sutton, E.J., Net, J.M., Ganott, M., Huang, E., Morris, E.A., Perou, C.M., Ji, Y. and Giger, M.L. (2016) MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology, 281, 382-391. https://doi.org/10.1148/radiol.2016152110
|
[21]
|
Koh, J., Lee, E., Han, K., Kim, S., Kim, D.K., Kwak, J.Y., Yoon, J.H. and Moon, H.J. (2020) Three-Dimensional Radiomics of Triple-Negative Breast Cancer: Prediction of Systemic Recurrence. Scientific Reports, 10, Article No. 2976. https://doi.org/10.1038/s41598-020-59923-2
|
[22]
|
Jiang, X., Zou, X., Sun, J., Zheng, A. and Su, C. (2020) A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer. Contrast Media & Molecular Imaging, 2020, Article ID: 5418364. https://doi.org/10.1155/2020/5418364
|
[23]
|
TCIA (2021). https://wiki.cancerimagingarchive.net/display/Public/ISPY1
|
[24]
|
Technical University of Lodz (2021). http://eletel.eu/mazda
|
[25]
|
Szczypiński, P.M., Strzelecki, M., Materka, A. and Klepaczko, A. (2009) MaZda—A Software Package for Image Texture Analysis. Computer Methods and Programs in Biomedicine, 94, 66-76. https://doi.org/10.1016/j.cmpb.2008.08.005
|
[26]
|
Strzelecki, M., Szczypinski, P., Materka, A. and Klepaczko, A. (2013) A Software Tool for Automatic Classification and Segmentation of 2D/3D Medical Images. Nuclear Instruments and Methods in Physics Research, 702, 137-140. https://doi.org/10.1016/j.nima.2012.09.006
|
[27]
|
Hastie, T., Tibshirani, R. and Friedman, J. (2009) The Elements of Statistical Learning, Data Mining, Inference and Prediction. 2nd Edition, Springer, New York.
|
[28]
|
Duda, R.O., Hart, P.E. and Stork, D.G. (2001) Pattern Classification. John Wiley & Sons, New York.
|
[29]
|
Metz, C.E. (1989) Some Practical Issues of Experimental Design and Data Analysis in Radiological ROC Studies. Investigative Radiology, 24, 234-245. https://doi.org/10.1097/00004424-198903000-00012
|
[30]
|
Cain, E.H., Saha, A., Harowicz, M.R., Marks, J.R., Marcom, P.K. and Mazurowski, M.A. (2019) Multivariate Machine Learning Models for Prediction of Pathologic Response to Neoadjuvant Therapy in Breast Cancer Using MRI Features: A Study Using an Independent Validation Set. Breast Cancer Research and Treatment, 173, 455-463. https://doi.org/10.1007/s10549-018-4990-9
|
[31]
|
Drukker, K., Edwards, A., Doyle, C., Papaioannou, J., Kulkarni, K. and Giger, M.L. (2019) Breast MRI Radiomics for the Pretreatment Prediction of Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer Patients. Journal of Medical Imaging, 6, Article ID: 034502. https://doi.org/10.1117/1.JMI.6.3.034502
|
[32]
|
Chen, X., Chen, X., Yang, J., Li, Y., Fan, W. and Yang, Z. (2020) Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. Journal of Computer Assisted Tomography, 44, 275-283. https://doi.org/10.1097/RCT.0000000000000978
|
[33]
|
Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., Sun, K., Tang, Z., Jiang, H., Li, H., Xiong, Q., Ding, Y., Zhao, X., Wang, K., Liu, Z. and Tian, J. (2019) Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study. Clinical Cancer Research, 25, 3538-3547. https://doi.org/10.1158/1078-0432.CCR-18-3190
|
[34]
|
Li, P., Wang, X., Xu, C., Liu, C., Zheng, C., Fulham, M.J., Feng, D., Wang, L., Song, S. and Huang, G. (2020) 18F-FDG PET/CT Radiomic Predictors of Pathologic Complete Response (pCR) to Neoadjuvant Chemotherapy in Breast Cancer Patients. European Journal of Nuclear Medicine and Molecular Imaging, 47, 1116-1126. https://doi.org/10.1007/s00259-020-04684-3
|