BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

124 related articles for article (PubMed ID: 35440476)

  • 1. Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma.
    Etchebehere E; Andrade R; Camacho M; Lima M; Brink A; Cerci J; Nadel H; Bal C; Rangarajan V; Pfluger T; Kagna O; Alonso O; Begum FK; Mir KB; Magboo VP; Menezes LJ; Paez D; Pascual TN
    J Nucl Med Technol; 2022 Sep; 50(3):256-262. PubMed ID: 35440476
    [No Abstract]   [Full Text] [Related]  

  • 2. Prognostic significance of neutrophil/lymphocyte ratio (NLR) and correlation with PET-CT metabolic parameters in small cell lung cancer (SCLC).
    Mirili C; Guney IB; Paydas S; Seydaoglu G; Kapukaya TK; Ogul A; Gokcay S; Buyuksimsek M; Yetisir AE; Karaalioglu B; Tohumcuoglu M
    Int J Clin Oncol; 2019 Feb; 24(2):168-178. PubMed ID: 30109543
    [TBL] [Abstract][Full Text] [Related]  

  • 3.
    Sibille L; Seifert R; Avramovic N; Vehren T; Spottiswoode B; Zuehlsdorff S; Schäfers M
    Radiology; 2020 Feb; 294(2):445-452. PubMed ID: 31821122
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [
    Constantino CS; Leocádio S; Oliveira FPM; Silva M; Oliveira C; Castanheira JC; Silva Â; Vaz S; Teixeira R; Neves M; Lúcio P; João C; Costa DC
    J Digit Imaging; 2023 Aug; 36(4):1864-1876. PubMed ID: 37059891
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Prognostic value of whole-body metabolic tumour volume and total lesion glycolysis measured on ¹⁸F-FDG PET/CT in patients with extranodal NK/T-cell lymphoma.
    Kim CY; Hong CM; Kim DH; Son SH; Jeong SY; Lee SW; Lee J; Ahn BC
    Eur J Nucl Med Mol Imaging; 2013 Sep; 40(9):1321-9. PubMed ID: 23674211
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [
    Aoki H; Miyazaki Y; Anzai T; Yokoyama K; Tsuchiya J; Shirai T; Shibata S; Sakakibara R; Mitsumura T; Honda T; Furusawa H; Okamoto T; Tateishi T; Tamaoka M; Yamamoto M; Takahashi K; Tateishi U; Yamaguchi T
    Eur Radiol; 2024 Jan; 34(1):374-383. PubMed ID: 37535157
    [TBL] [Abstract][Full Text] [Related]  

  • 7. The usefulness of (18)F-FDG PET/CT for assessing methotrexate-associated lymphoproliferative disorder (MTX-LPD).
    Watanabe S; Manabe O; Hirata K; Oyama-Manabe N; Hattori N; Kikuchi Y; Kobayashi K; Toyonaga T; Tamaki N
    BMC Cancer; 2016 Aug; 16():635. PubMed ID: 27528380
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Comparison of 11 automated PET segmentation methods in lymphoma.
    Weisman AJ; Kieler MW; Perlman S; Hutchings M; Jeraj R; Kostakoglu L; Bradshaw TJ
    Phys Med Biol; 2020 Nov; 65(23):235019. PubMed ID: 32906088
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The ratio between the whole-body and primary tumor burden, measured on
    Oliveira FRA; Santos AO; de Lima MDCL; Toro IFC; de Souza TF; Amorim BJ; Barbeiro AS; Etchebehere E
    Radiol Bras; 2021; 54(5):289-294. PubMed ID: 34602663
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The prognostic role of 18F-FDG PET/CT baseline quantitative metabolic parameters in peripheral T-cell lymphoma.
    Xia J; Zhu HY; Liang JH; Ding CY; Wang L; Wu W; Cao L; Li TL; Li JY; Xu W
    J Cancer; 2019; 10(23):5805-5811. PubMed ID: 31737117
    [No Abstract]   [Full Text] [Related]  

  • 11. Prognostic value of
    Choi EK; Park M; Im JJ; Chung YA; Oh JK
    J Int Med Res; 2020 Apr; 48(4):300060519892419. PubMed ID: 31880209
    [TBL] [Abstract][Full Text] [Related]  

  • 12. qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using
    Gafita A; Bieth M; Krönke M; Tetteh G; Navarro F; Wang H; Günther E; Menze B; Weber WA; Eiber M
    J Nucl Med; 2019 Sep; 60(9):1277-1283. PubMed ID: 30850484
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
    Blanc-Durand P; Jégou S; Kanoun S; Berriolo-Riedinger A; Bodet-Milin C; Kraeber-Bodéré F; Carlier T; Le Gouill S; Casasnovas RO; Meignan M; Itti E
    Eur J Nucl Med Mol Imaging; 2021 May; 48(5):1362-1370. PubMed ID: 33097974
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prognostic value of (18)F-FDG PET/CT volumetric parameters in recurrent epithelial ovarian cancer.
    Mayoral M; Fernandez-Martinez A; Vidal L; Fuster D; Aya F; Pavia J; Pons F; Lomeña F; Paredes P
    Rev Esp Med Nucl Imagen Mol; 2016; 35(2):88-95. PubMed ID: 26541072
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Quantitative metabolic parameters measured on F-18 FDG PET/CT predict survival after relapse in patients with relapsed epithelial ovarian cancer.
    Kim CY; Jeong SY; Chong GO; Son SH; Jung JH; Kim DH; Lee SW; Ahn BC; Lee J
    Gynecol Oncol; 2015 Mar; 136(3):498-504. PubMed ID: 25557270
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Just another "Clever Hans"? Neural networks and FDG PET-CT to predict the outcome of patients with breast cancer.
    Weber M; Kersting D; Umutlu L; Schäfers M; Rischpler C; Fendler WP; Buvat I; Herrmann K; Seifert R
    Eur J Nucl Med Mol Imaging; 2021 Sep; 48(10):3141-3150. PubMed ID: 33674891
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of a convolution neural network for baseline total tumor metabolic volume on [
    Karimdjee M; Delaby G; Huglo D; Baillet C; Willaume A; Dujardin S; Bailliez A
    Eur Radiol; 2023 May; 33(5):3386-3395. PubMed ID: 36600126
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Associations of whole-body 18F-FDG PET/CT parameters and SCC-Ag level with overall survival in patients with cervical cancer.
    Guan L; Zuo R; Wang Z; Xu L; Liu S; Pang H
    Nucl Med Commun; 2022 Jan; 43(1):49-55. PubMed ID: 34887369
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prognostic predictive value of total lesion glycolysis from 18F-FDG PET/CT in post-surgical patients with epithelial ovarian cancer.
    Liao S; Lan X; Cao G; Yuan H; Zhang Y
    Clin Nucl Med; 2013 Sep; 38(9):715-20. PubMed ID: 23856825
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Utility of baseline, interim and end-of-treatment
    Chang Y; Fu X; Sun Z; Xie X; Wang R; Li Z; Zhang X; Sheng G; Zhang M
    Sci Rep; 2017 Jan; 7():41057. PubMed ID: 28117395
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.