Publicidad

Análisis de textura de la imagen de ganglio centinela axilar para predecir la histopatología ganglionar en pacientes con cáncer de mama

Referencias

  1. Lyman GH, Giuliano AE, Somerfield MR, et al. American Society of Clinical Oncology guideline recommendations for sentinel lymph node biopsy in early-stage breast cancer. J Clin Oncol 2005; 23:7703-20.
  2. Morton DL, Wen DR, Cochran AJ. Management of early-stage melanoma by intraoperative lymphatic mapping and selective lymphadenectomy or "watch and wait". Surg Oncol Clin North Am 1992; 1:247-59.
  3. Krag DN. Surgical resection and radiolocalization of the SLN in breast cancer using a gamma probe. Surg Oncol 1993; 2:335-40.
  4. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol 2004; 59: 1061-9.
  5. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278:563-77.
  6. Zhou H, Jin Y, Dai L, et al. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. Eur J Radiol 2020;Jun;127:108992.
  7. Manth N, Virmani J, Kumar V, et al. Application of texture features for classification of primary benign and primary malignant focal liver lesions. Image Feature Detectors and Descriptors, pp. 385–409, Berlin, Germany, Springer, 2016.
  8. Gibbs P, Turnbull LW. Textural analysis of contrast enhanced MR images of the breast. Magn Reson Med 2003; 50:92-8.
  9. Wang H, Zhou Y, Li L, et al. Current status and quality of radiomics studies in lymphoma: a systematic review. Eur Radiol 2020; 30:6228-40.
  10. Rui Xu , Shoji Kido, Kazuyoshi Suga, Yasushi Hirano, et al. Texture analysis on 18F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions. Ann Nucl Med 2014; 28:926-35.
  11. Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med 2020; 61:488-95.
  12. Strzelecki M, Szczypinski P, Materka A, Klepaczko A. A software tool for automatic classification and segmentation of 2D/3D medical images. Nucl Instrum Methods Phys Res 2013; Sect A, 702:137-40.
  13. Szczypiński PM, Strzelecki M, Materka A, Klepaczko A. MaZda—a software package for image texture analysis. Comput Methods Programs Biomed 2009; 94:66-76.
  14. Haralick RM, Shanmugam K. Textural features for image classification. IEEE Trans Syst Man Cybern 2009; 3:610-21.
  15. Hall M, Frank E, Holmes G, et al. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsl 2009; 11:10-8.
  16. Fawceet T. An introduction to ROC analysis. Pattern Recogn Lett 2006; 27:861-74.
  17. Zhong Y, Yuan M, Zhang T, et al. Radiomics approach to prediction of occult mediastinal lymph node metastasis of lung adenocarcinoma. Am J Roentgenol 2018; 211:109-13.
  18. Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 2018; 28:582-91.