170 related articles for article (PubMed ID: 31997918)
1. Radiomics and Machine Learning Differentiate Soft-Tissue Lipoma and Liposarcoma Better than Musculoskeletal Radiologists.
Malinauskaite I; Hofmeister J; Burgermeister S; Neroladaki A; Hamard M; Montet X; Boudabbous S
Sarcoma; 2020; 2020():7163453. PubMed ID: 31997918
[TBL] [Abstract][Full Text] [Related]
2. Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas.
Sudjai N; Siriwanarangsun P; Lektrakul N; Saiviroonporn P; Maungsomboon S; Phimolsarnti R; Asavamongkolkul A; Chandhanayingyong C
J Orthop Surg Res; 2023 Mar; 18(1):255. PubMed ID: 36978182
[TBL] [Abstract][Full Text] [Related]
3. Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI.
Vos M; Starmans MPA; Timbergen MJM; van der Voort SR; Padmos GA; Kessels W; Niessen WJ; van Leenders GJLH; Grünhagen DJ; Sleijfer S; Verhoef C; Klein S; Visser JJ
Br J Surg; 2019 Dec; 106(13):1800-1809. PubMed ID: 31747074
[TBL] [Abstract][Full Text] [Related]
4. Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning.
Cay N; Mendi BAR; Batur H; Erdogan F
Jpn J Radiol; 2022 Sep; 40(9):951-960. PubMed ID: 35430677
[TBL] [Abstract][Full Text] [Related]
5. MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.
Gitto S; Interlenghi M; Cuocolo R; Salvatore C; Giannetta V; Badalyan J; Gallazzi E; Spinelli MS; Gallazzi M; Serpi F; Messina C; Albano D; Annovazzi A; Anelli V; Baldi J; Aliprandi A; Armiraglio E; Parafioriti A; Daolio PA; Luzzati A; Biagini R; Castiglioni I; Sconfienza LM
Radiol Med; 2023 Aug; 128(8):989-998. PubMed ID: 37335422
[TBL] [Abstract][Full Text] [Related]
6. Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis.
Thornhill RE; Golfam M; Sheikh A; Cron GO; White EA; Werier J; Schweitzer ME; Di Primio G
Acad Radiol; 2014 Sep; 21(9):1185-94. PubMed ID: 25107867
[TBL] [Abstract][Full Text] [Related]
7. Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.
Suh HB; Choi YS; Bae S; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Lee SK
Eur Radiol; 2018 Sep; 28(9):3832-3839. PubMed ID: 29626238
[TBL] [Abstract][Full Text] [Related]
8. Differentiation Between Lipomas and Atypical Lipomatous Tumors of the Extremities Using Radiomics.
Tang Y; Cui J; Zhu J; Fan G
J Magn Reson Imaging; 2022 Dec; 56(6):1746-1754. PubMed ID: 35348280
[TBL] [Abstract][Full Text] [Related]
9. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods.
Yang Y; Zhou Y; Zhou C; Ma X
Orphanet J Rare Dis; 2022 Apr; 17(1):158. PubMed ID: 35392952
[TBL] [Abstract][Full Text] [Related]
10. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.
Wang T; Gong J; Li Q; Chu C; Shen W; Peng W; Gu Y; Li W
Eur Radiol; 2021 Aug; 31(8):6125-6135. PubMed ID: 33486606
[TBL] [Abstract][Full Text] [Related]
11. Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI.
Zhang Y; Zhu Y; Shi X; Tao J; Cui J; Dai Y; Zheng M; Wang S
Acad Radiol; 2019 Sep; 26(9):1262-1268. PubMed ID: 30377057
[TBL] [Abstract][Full Text] [Related]
12. [Subfascial lipomatous tumors: management in a series of 37 consecutive cases].
Gouin F; Bertrand-Vasseur A; Collet T; Moreau A; Leaute F; Rolland F; Cussac A; Passuti N
Rev Chir Orthop Reparatrice Appar Mot; 2001 Oct; 87(6):585-95. PubMed ID: 11685150
[TBL] [Abstract][Full Text] [Related]
13. Squamous Cell Carcinoma and Lymphoma of the Oropharynx: Differentiation Using a Radiomics Approach.
Bae S; Choi YS; Sohn B; Ahn SS; Lee SK; Yang J; Kim J
Yonsei Med J; 2020 Oct; 61(10):895-900. PubMed ID: 32975065
[TBL] [Abstract][Full Text] [Related]
14. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
Juntu J; Sijbers J; De Backer S; Rajan J; Van Dyck D
J Magn Reson Imaging; 2010 Mar; 31(3):680-9. PubMed ID: 20187212
[TBL] [Abstract][Full Text] [Related]
15. Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI.
Wei P; Zhong H; Xie Q; Li J; Luo S; Guan X; Liang Z; Yue D
Front Neurol; 2023; 14():1251025. PubMed ID: 37936913
[TBL] [Abstract][Full Text] [Related]
16. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
[TBL] [Abstract][Full Text] [Related]
17. Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.
von Schacky CE; Wilhelm NJ; Schäfer VS; Leonhardt Y; Jung M; Jungmann PM; Russe MF; Foreman SC; Gassert FG; Gassert FT; Schwaiger BJ; Mogler C; Knebel C; von Eisenhart-Rothe R; Makowski MR; Woertler K; Burgkart R; Gersing AS
Eur Radiol; 2022 Sep; 32(9):6247-6257. PubMed ID: 35396665
[TBL] [Abstract][Full Text] [Related]
18. Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images.
Zhao SS; Feng XL; Hu YC; Han Y; Tian Q; Sun YZ; Zhang J; Ge XW; Cheng SC; Li XL; Mao L; Shen SN; Yan LF; Cui GB; Wang W
BMC Neurol; 2020 Feb; 20(1):48. PubMed ID: 32033580
[TBL] [Abstract][Full Text] [Related]
19. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
[TBL] [Abstract][Full Text] [Related]
20. Radiomics and Machine Learning With Multiparametric Preoperative MRI May Accurately Predict the Histopathological Grades of Soft Tissue Sarcomas.
Wang H; Chen H; Duan S; Hao D; Liu J
J Magn Reson Imaging; 2020 Mar; 51(3):791-797. PubMed ID: 31486565
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
