These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 33610852)

  • 1. Radiomic Machine Learning Classifiers in Spine Bone Tumors: A Multi-Software, Multi-Scanner Study.
    Chianca V; Cuocolo R; Gitto S; Albano D; Merli I; Badalyan J; Cortese MC; Messina C; Luzzati A; Parafioriti A; Galbusera F; Brunetti A; Sconfienza LM
    Eur J Radiol; 2021 Apr; 137():109586. PubMed ID: 33610852
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.
    Gitto S; Bologna M; Corino VDA; Emili I; Albano D; Messina C; Armiraglio E; Parafioriti A; Luzzati A; Mainardi L; Sconfienza LM
    Radiol Med; 2022 May; 127(5):518-525. PubMed ID: 35320464
    [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. 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]  

  • 5. Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions.
    Stanzione A; Cuocolo R; Verde F; Galatola R; Romeo V; Mainenti PP; Aprea G; Guadagno E; Del Basso De Caro M; Maurea S
    Magn Reson Imaging; 2021 Jun; 79():52-58. PubMed ID: 33727148
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
    Bernatz S; Ackermann J; Mandel P; Kaltenbach B; Zhdanovich Y; Harter PN; Döring C; Hammerstingl R; Bodelle B; Smith K; Bucher A; Albrecht M; Rosbach N; Basten L; Yel I; Wenzel M; Bankov K; Koch I; Chun FK; Köllermann J; Wild PJ; Vogl TJ
    Eur Radiol; 2020 Dec; 30(12):6757-6769. PubMed ID: 32676784
    [TBL] [Abstract][Full Text] [Related]  

  • 7. X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.
    Gitto S; Annovazzi A; Nulle K; Interlenghi M; Salvatore C; Anelli V; Baldi J; Messina C; Albano D; Di Luca F; Armiraglio E; Parafioriti A; Luzzati A; Biagini R; Castiglioni I; Sconfienza LM
    EBioMedicine; 2024 Mar; 101():105018. PubMed ID: 38377797
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.
    Qian Z; Li Y; Wang Y; Li L; Li R; Wang K; Li S; Tang K; Zhang C; Fan X; Chen B; Li W
    Cancer Lett; 2019 Jun; 451():128-135. PubMed ID: 30878526
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones.
    Gitto S; Cuocolo R; van Langevelde K; van de Sande MAJ; Parafioriti A; Luzzati A; Imbriaco M; Sconfienza LM; Bloem JL
    EBioMedicine; 2022 Jan; 75():103757. PubMed ID: 34933178
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
    Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
    Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors.
    Chiappa V; Interlenghi M; Salvatore C; Bertolina F; Bogani G; Ditto A; Martinelli F; Castiglioni I; Raspagliesi F
    Gynecol Oncol; 2021 Jun; 161(3):838-844. PubMed ID: 33867144
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.
    Song D; Zhai Y; Tao X; Zhao C; Wang M; Wei X
    Sci Rep; 2021 Sep; 11(1):18872. PubMed ID: 34556732
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas.
    Gitto S; Cuocolo R; Annovazzi A; Anelli V; Acquasanta M; Cincotta A; Albano D; Chianca V; Ferraresi V; Messina C; Zoccali C; Armiraglio E; Parafioriti A; Sciuto R; Luzzati A; Biagini R; Imbriaco M; Sconfienza LM
    EBioMedicine; 2021 Jun; 68():103407. PubMed ID: 34051442
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features.
    Zhang H; Yuan G; Wang C; Zhao H; Zhu K; Guo J; Chen M; Liu H; Yang G; Wang Y; Ma X
    Eur Radiol; 2023 Jul; 33(7):5069-5076. PubMed ID: 37099176
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions.
    Romeo V; Cuocolo R; Apolito R; Stanzione A; Ventimiglia A; Vitale A; Verde F; Accurso A; Amitrano M; Insabato L; Gencarelli A; Buonocore R; Argenzio MR; Cascone AM; Imbriaco M; Maurea S; Brunetti A
    Eur Radiol; 2021 Dec; 31(12):9511-9519. PubMed ID: 34018057
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.
    Dong F; Li Q; Jiang B; Zhu X; Zeng Q; Huang P; Chen S; Zhang M
    Eur Radiol; 2020 May; 30(5):3015-3022. PubMed ID: 32006166
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients.
    Mainenti PP; Stanzione A; Cuocolo R; Del Grosso R; Danzi R; Romeo V; Raffone A; Di Spiezio Sardo A; Giordano E; Travaglino A; Insabato L; Scaglione M; Maurea S; Brunetti A
    Eur J Radiol; 2022 Apr; 149():110226. PubMed ID: 35231806
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
    Braman NM; Etesami M; Prasanna P; Dubchuk C; Gilmore H; Tiwari P; Plecha D; Madabhushi A
    Breast Cancer Res; 2017 May; 19(1):57. PubMed ID: 28521821
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.