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Jean-Philippe Foy, C. Durdux, P. Giraud, J. Bibault (2018)
RE: The Rise of Radiomics and Implications for Oncologic Management.Journal of the National Cancer Institute, 110 11
G. Eskiler, G. Cecener, U. Egeli, B. Tunca (2018)
Triple negative breast cancer: new therapeutic approaches and BRCA statusAPMIS, 126
Dominika Piasecka, Marcin Braun, R. Kordek, R. Sądej, H. Romanska (2018)
MicroRNAs in regulation of triple-negative breast cancer progressionJournal of Cancer Research and Clinical Oncology, 144
T. Zuo, Parker Wilson, A. Çiçek, M. Harigopal (2018)
Androgen receptor expression is a favorable prognostic factor in triple-negative breast cancers.Human pathology, 80
S. Agner, M. Rosen, S. Englander, J. Tomaszewski, M. Feldman, Paul Zhang, C. Mies, M. Schnall, A. Madabhushi (2014)
Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.Radiology, 272 1
G. Langs, S. Röhrich, J. Hofmanninger, F. Prayer, J. Pan, Christian Herold, H. Prosch (2018)
Machine learning: from radiomics to discovery and routineDer Radiologe, 58
Wenjuan Ma, Yumei Zhao, Y. Ji, Xinpeng Guo, X. Jian, Peifang Liu, Shandong Wu (2021)
Breast Cancer Molecular Subtypes Prediction by Mammographic Radiomics Features
G. Bianchini, J. Balko, I. Mayer, M. Sanders, L. Gianni (2016)
Triple-negative breast cancer: challenges and opportunities of a heterogeneous diseaseNature Reviews Clinical Oncology, 13
M. Avanzo, J. Stancanello, I. Naqa (2017)
Beyond imaging: The promise of radiomics.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics, 38
A. Ocaña, A. Pandiella (2017)
Targeting oncogenic vulnerabilities in triple negative breast cancer: biological bases and ongoing clinical studiesOncotarget, 8
J. Youk, E. Son, Jin Chung, Jeong-Ah Kim, Eun-Kyung Kim (2012)
Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypesEuropean Radiology, 22
Bohao Zheng, Long-Zi Liu, Zhizhi Zhang, Jieyi Shi, Liangqing Dong, Lingyu Tian, Zhen–Bin Ding, Yuan Ji, Sheng-xiang Rao, Jian Zhou, Jia Fan, Xiao-Ying Wang, Q. Gao (2018)
Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patientsBMC Cancer, 18
Cuishan Liang, Zixuan Cheng, Yanqi Huang, Lan He, Xin Chen, Zelan Ma, Xiaomei Huang, C. Liang, Zaiyi Liu (2018)
An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer.Academic radiology, 25 9
Shaokun Shu, Charles Lin, H. He, R. Witwicki, D. Tabassum, Justin Roberts, M. Janiszewska, S. Huh, Yi Liang, J. Ryan, Ernest Doherty, Hisham Mohammed, Hao Guo, D. Stover, Muhammad Ekram, Guillermo Peluffo, Jonathan Brown, C. D’Santos, I. Krop, D. Dillon, M. Mckeown, Christopher Ott, J. Qi, M. Ni, P. Rao, Melissa Duarte, Shwu‐Yuan Wu, C. Chiang, L. Anders, R. Young, E. Winer, A. Letai, W. Barry, J. Carroll, Henry Long, Myles Brown, X. Liu, Clifford Meyer, J. Bradner, K. Polyak (2015)
Response and resistance to BET bromodomain inhibitors in triple negative breast cancerNature, 529
R. Thawani, Michael Mclane, Niha Beig, S. Ghose, P. Prasanna, V. Velcheti, A. Madabhushi (2018)
Radiomics and radiogenomics in lung cancer: A review for the clinician.Lung cancer, 115
Aifu Lin, Chunlai Li, Zhen Xing, Qingsong Hu, K. Liang, Leng Han, Cheng Wang, D. Hawke, Shouyu Wang, Yanyan Zhang, Yongkun Wei, G. Ma, P. Park, Jianwei Zhou, Yan Zhou, Zhibin Hu, Yubin Zhou, Jeffery Marks, Han Liang, M. Hung, Chunru Lin, Liuqing Yang (2016)
The LINK-A lncRNA Activates Normoxic HIF1α Signaling in Triple-negative Breast CancerNature cell biology, 18
A. Luck, A. Evans, J. James, E. Rakha, E. Paish, A. Green, I. Ellis (2008)
Breast carcinoma with basal phenotype: mammographic findings.AJR. American journal of roentgenology, 191 2
Si Lee, Kyunghwa Han, J. Kwak, Eunjung Lee, Eun-Kyung Kim (2018)
Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenomaScientific Reports, 8
Y. Park, Jong-Kap Oh, S. You, Kyunghwa Han, S. Ahn, Y. Choi, Jong-Hee Chang, S. Kim, Seung-Koo Lee (2018)
Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imagingEuropean Radiology
M. Telli, K. Timms, J. Reid, B. Hennessy, G. Mills, K. Jensen, Z. Szallasi, W. Barry, E. Winer, N. Tung, S. Isakoff, P. Ryan, April Greene-Colozzi, A. Gutin, Zaina Sangale, D. Iliev, C. Neff, V. Abkevich, Joshua Jones, J. Lanchbury, A. Hartman, J. Garber, J. Ford, D. Silver, A. Richardson (2016)
Homologous Recombination Deficiency (HRD) Score Predicts Response to Platinum-Containing Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast CancerClinical Cancer Research, 22
Z. Sporikova, V. Koudelakova, R. Trojanec, M. Hajdůch (2018)
Genetic Markers in Triple‐Negative Breast CancerClinical Breast Cancer, 18
(2018)
Repeatability and reproducibility of radiomic features: A systematic review, International Journal of Radiation Oncology
PURPOSE:To explore the radiomics features of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC) based on X-ray mammography, and to differentiate the two groups of cases.MATERIALS AND METHODS:Preoperative mammograms of 120 patients with breast ductal carcinoma confirmed by surgical pathology were retrospectively analyzed, which include 30 TNBC and 90 non-TNBC patients. The manual segmentation of breast lesions was performed by ITK-SNAP software and 12 radiomics features were extracted by Omni-Kinetics software. The differences of these radiomics features between TNBC and non-TNBC groups were compared, and the receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value of each radiomics parameter for differentiating TNBC from non-TNBC, and the corresponding area under the curve (AUC), sensitivity and specificity were obtained.RESULTS:There were statistically significant differences for 4 radiomics features between TNBC and non-TNBC datasets (P < 0.05). They were the roundness, concavity, gray average and skewness of breast lesions. Compared with non-TNBC, TNBC cases have following characteristics of (1) more round with the roundness of 0.621 vs. 0.413 (P < 0.001), (2) more regular with the concavity of 0.087 vs. 0.141 (P < 0.01), (3) higher density or gray average (67.261 vs. 56.842, P < 0.05), and (4) lower skewness (– 0.837 vs.– 0.671, P = 0.034). AUCs of ROC curves computed using features of the roundness and concavity were both larger than 0.70.CONCLUSION:Radiomics features based on X-ray mammography may be helpful to distinguish between TNBC and non-TNBC, which were associated with breast tumor histology.
Journal of X-Ray Science and Technology – IOS Press
Published: Jan 1, 2019
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