Similar articles for PMID: 34771660

Year	Number of Results
2005	1
2011	3
2012	1
2015	6
2016	5
2017	6
2018	15
2019	20
2020	14
2021	21
2022	13
2023	10
2024	7

1

Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation.

Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Conant EF, Scott CG, Winham SJ, Kerlikowske K, Vachon C, Maidment ADA, Kontos D. Acciavatti RJ, et al. Cancers (Basel). 2021 Nov 1;13(21):5497. doi: 10.3390/cancers13215497. Cancers (Basel). 2021. PMID: 34771660 Free PMC article.

2

Robust Radiomic Feature Selection in Digital Mammography: Understanding the Effect of Imaging Acquisition Physics Using Phantom and Clinical Data Analysis.

Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Conant EF, Scott CG, Winham SJ, Kerlikowske K, Vachon C, Maidment ADA, Kontos D. Acciavatti RJ, et al. Proc SPIE Int Soc Opt Eng. 2020 Feb;11314:113140W. doi: 10.1117/12.2549163. Epub 2020 Mar 16. Proc SPIE Int Soc Opt Eng. 2020. PMID: 37982014 Free PMC article.

3

Radiomics robustness assessment and classification evaluation: A two-stage method demonstrated on multivendor FFDM.

Robinson K, Li H, Lan L, Schacht D, Giger M. Robinson K, et al. Med Phys. 2019 May;46(5):2145-2156. doi: 10.1002/mp.13455. Epub 2019 Mar 12. Med Phys. 2019. PMID: 30802972 Free PMC article.

4

Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma.

Li H, Mendel KR, Lan L, Sheth D, Giger ML. Li H, et al. Radiology. 2019 Apr;291(1):15-20. doi: 10.1148/radiol.2019181113. Epub 2019 Feb 12. Radiology. 2019. PMID: 30747591 Free PMC article.

5

Improving radiomic model reliability using robust features from perturbations for head-and-neck carcinoma.

Teng X, Zhang J, Ma Z, Zhang Y, Lam S, Li W, Xiao H, Li T, Li B, Zhou T, Ren G, Lee FK, Au KH, Lee VH, Chang ATY, Cai J. Teng X, et al. Front Oncol. 2022 Oct 14;12:974467. doi: 10.3389/fonc.2022.974467. eCollection 2022. Front Oncol. 2022. PMID: 36313629 Free PMC article.

6

Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment.

Hao W, Gong J, Wang S, Zhu H, Zhao B, Peng W. Hao W, et al. Front Oncol. 2020 Oct 29;10:531476. doi: 10.3389/fonc.2020.531476. eCollection 2020. Front Oncol. 2020. PMID: 33194589 Free PMC article.

7

Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses.

Verburg E, van Gils CH, Bakker MF, Viergever MA, Pijnappel RM, Veldhuis WB, Gilhuijs KGA. Verburg E, et al. Invest Radiol. 2020 Jul;55(7):438-444. doi: 10.1097/RLI.0000000000000656. Invest Radiol. 2020. PMID: 32149858 Clinical Trial.

8

Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths.

Whitney HM, Drukker K, Edwards A, Papaioannou J, Medved M, Karczmar G, Giger ML. Whitney HM, et al. Magn Reson Imaging. 2021 Oct;82:111-121. doi: 10.1016/j.mri.2021.06.021. Epub 2021 Jun 24. Magn Reson Imaging. 2021. PMID: 34174331 Free PMC article.

9

Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.

Wang G, Shi D, Guo Q, Zhang H, Wang S, Ren K. Wang G, et al. Front Oncol. 2022 Apr 1;12:843436. doi: 10.3389/fonc.2022.843436. eCollection 2022. Front Oncol. 2022. PMID: 35433437 Free PMC article.

10

Calculation of Radiomic Features to Validate the Textural Realism of Physical Anthropomorphic Phantoms for Digital Mammography.

Acciavatti RJ, Cohen EA, Maghsoudi OH, Gastounioti A, Pantalone L, Hsieh MK, Barufaldi B, Bakic PR, Chen J, Conant EF, Kontos D, Maidment ADA. Acciavatti RJ, et al. Proc SPIE Int Soc Opt Eng. 2020 May;11513:1151309. doi: 10.1117/12.2564363. Epub 2020 May 22. Proc SPIE Int Soc Opt Eng. 2020. PMID: 37818096 Free PMC article.

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