Milea Timbergen
144 21 . Klein S, Staring M, Murphy K, Viergever MA, Pluim JP. elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2010;29(1):196-205. 22 . Starmans MPA, van der Voort SR, Vos M, Incekara F, Visser JJ, Smits M, et al. Fully automatic construction of optimal radiomics workflows. European Conference of Radiology (ECR)2019. 23 . Starmans MPA. Workflow for Optimal Radiomics Classification (WORC) 2018 [Available from: https:// github.com/MStarmans91/WORC. 24 . Vos M, Starmans MPA, Timbergen MJM, et al. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. Br J Surg 2019;106(13):1800-9. 25 . Hand DJ, Till RJ. A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine learning. 2001;45(2):171-186. 26 . Marinescu RV, Oxtoby NP, Young AL, Bron EE, Toga AW, Weiner MW, et al. TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer’s Disease. arXiv preprint arXiv:180503909. 2018. 27 . Nadeau C, Bengio Y, editors. Inference for the generalization error, 2000. 28 . Macskassy SA, Provost F, Rosset S, editors. ROC confidence bands: An empirical evaluation. Proceedings of the 22nd international conference on Machine learning; 2005: ACM. 29 . M.P.A. Starmans, DMRadiomics, 2020. http://doi.org/10.5281/zenodo.4017191. [accessed September 7, 2020] 30 . Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med. 2016;15(2):155-163. 31 . DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837-845. 32 . Penel N, Le Cesne A, Bonvalot S, Giraud A, Bompas E, Rios M, et al. Surgical versus non-surgical approach in primary desmoid-type fibromatosis patients: A nationwide prospective cohort from the French Sarcoma Group. Eur J Cancer. 2017;83:125-131. 33 . Gondim Teixeira PA, Chanson A, Verhaeghe JL, Lecocq S, Louis M, Hossu G, et al. Correlation between tumor growth and hormonal therapy with MR signal characteristics of desmoid-type fibromatosis: A preliminary study. Diagn Interv Imaging. 2019;100(1):47-55. 34 . Castellazzi G, Vanel D, Le Cesne A, Le Pechoux C, Caillet H, Perona F, et al. Can the MRI signal of aggressive fibromatosis be used to predict its behavior? Eur J Radiol. 2009;69(2):222-229. 35 . Sheth PJ, Del Moral S, Wilky BA, Trent JC, Cohen J, Rosenberg AE, et al. Desmoid fibromatosis: MRI features of response to systemic therapy. Skeletal Radiol. 2016;45(10):1365-1373. 36 . Cassidy MR, Lefkowitz RA, Long N, Qin LX, Kirane A, Sbaity E, et al. Association of MRI T2 Signal Intensity With Desmoid Tumor Progression During Active Observation: A Retrospective Cohort Study. Ann Surg. 2018;271(4):748-755. 37 . Tuncbilek N, Karakas HM, Okten OO. Dynamic contrast enhanced MRI in the differential diagnosis of soft tissue tumors. Eur J Radiol. 2005;53(3):500-505. 38 . Oka K, Yakushiji T, Sato H, Fujimoto T, Hirai T, Yamashita Y, et al. Usefulness of diffusion-weighted imaging for differentiating between desmoid tumors and malignant soft tissue tumors. Journal of Magnetic Resonance Imaging. 2011;33(1):189-193. 39 . Khanna M, Ramanathan S, Kambal AS, Al-Berawi M, Yadav S, Kumar D, et al. Multi-parametric (mp) MRI for the diagnosis of abdominal wall desmoid tumors. Eur J Radiol. 2017;92:103-110. 40 . Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. 2017;42:60-88. 5
Made with FlippingBook
RkJQdWJsaXNoZXIy ODAyMDc0