140 related articles for article (PubMed ID: 33791381)
1. Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.
Pan D; Liu R; Zheng B; Yuan J; Zeng H; He Z; Luo Z; Qin G; Chen W
Biomed Res Int; 2021; 2021():8811056. PubMed ID: 33791381
[TBL] [Abstract][Full Text] [Related]
2. A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.
Liu R; Pan D; Xu Y; Zeng H; He Z; Lin J; Zeng W; Wu Z; Luo Z; Qin G; Chen W
Eur Radiol; 2022 Feb; 32(2):1371-1383. PubMed ID: 34432121
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.
Hong JH; Jung JY; Jo A; Nam Y; Pak S; Lee SY; Park H; Lee SE; Kim S
Radiology; 2021 Jun; 299(3):626-632. PubMed ID: 33787335
[TBL] [Abstract][Full Text] [Related]
5. Automated classification of benign and malignant lesions in
Perk T; Bradshaw T; Chen S; Im HJ; Cho S; Perlman S; Liu G; Jeraj R
Phys Med Biol; 2018 Nov; 63(22):225019. PubMed ID: 30457118
[TBL] [Abstract][Full Text] [Related]
6. Deep learning-based classification of primary bone tumors on radiographs: A preliminary study.
He Y; Pan I; Bao B; Halsey K; Chang M; Liu H; Peng S; Sebro RA; Guan J; Yi T; Delworth AT; Eweje F; States LJ; Zhang PJ; Zhang Z; Wu J; Peng X; Bai HX
EBioMedicine; 2020 Dec; 62():103121. PubMed ID: 33232868
[TBL] [Abstract][Full Text] [Related]
7. Machine and Deep Learning Based Radiomics Models for Preoperative Prediction of Benign and Malignant Sacral Tumors.
Yin P; Mao N; Chen H; Sun C; Wang S; Liu X; Hong N
Front Oncol; 2020; 10():564725. PubMed ID: 33178593
[TBL] [Abstract][Full Text] [Related]
8. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
[TBL] [Abstract][Full Text] [Related]
9. Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors.
Shao S; Mao N; Liu W; Cui J; Xue X; Cheng J; Zheng N; Wang B
J Xray Sci Technol; 2020; 28(4):799-808. PubMed ID: 32538891
[TBL] [Abstract][Full Text] [Related]
10. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. MRI radiomics-based machine-learning classification of bone chondrosarcoma.
Gitto S; Cuocolo R; Albano D; Chianca V; Messina C; Gambino A; Ugga L; Cortese MC; Lazzara A; Ricci D; Spairani R; Zanchetta E; Luzzati A; Brunetti A; Parafioriti A; Sconfienza LM
Eur J Radiol; 2020 Jul; 128():109043. PubMed ID: 32438261
[TBL] [Abstract][Full Text] [Related]
13. Osseous Tumor Reporting and Data System-Multireader Validation Study.
Chhabra A; Gupta A; Thakur U; Pezeshk P; Dettori N; Callan A; Xi Y; Weatherall P
J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):571-585. PubMed ID: 34270485
[TBL] [Abstract][Full Text] [Related]
14. Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data.
Lindsay WD; Sachs N; Gee JC; Mortani Barbosa EJ
Acad Radiol; 2022 Feb; 29 Suppl 2():S156-S164. PubMed ID: 34373194
[TBL] [Abstract][Full Text] [Related]
15. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
16. Clear cell chondrosarcoma: radiographic, computed tomographic, and magnetic resonance findings in 34 patients with pathologic correlation.
Collins MS; Koyama T; Swee RG; Inwards CY
Skeletal Radiol; 2003 Dec; 32(12):687-94. PubMed ID: 14530882
[TBL] [Abstract][Full Text] [Related]
17. Malignant transformation in monostotic fibrous dysplasia: clinical features, imaging features, outcomes in 10 patients, and review.
Qu N; Yao W; Cui X; Zhang H
Medicine (Baltimore); 2015 Jan; 94(3):e369. PubMed ID: 25621678
[TBL] [Abstract][Full Text] [Related]
18. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
19. Performance of machine learning software to classify breast lesions using BI-RADS radiomic features on ultrasound images.
Fleury E; Marcomini K
Eur Radiol Exp; 2019 Aug; 3(1):34. PubMed ID: 31385114
[TBL] [Abstract][Full Text] [Related]
20. Magnetic Resonance Imaging-Based Grading of Cartilaginous Bone Tumors: Added Value of Quantitative Texture Analysis.
Fritz B; Müller DA; Sutter R; Wurnig MC; Wagner MW; Pfirrmann CWA; Fischer MA
Invest Radiol; 2018 Nov; 53(11):663-672. PubMed ID: 29863601
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
