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 *

176 related articles for article (PubMed ID: 38649795)

  • 21. Utility of Normalized Body Composition Areas, Derived From Outpatient Abdominal CT Using a Fully Automated Deep Learning Method, for Predicting Subsequent Cardiovascular Events.
    Magudia K; Bridge CP; Bay CP; Farah S; Babic A; Fintelmann FJ; Brais LK; Andriole KP; Wolpin BM; Rosenthal MH
    AJR Am J Roentgenol; 2023 Feb; 220(2):236-244. PubMed ID: 36043607
    [No Abstract]   [Full Text] [Related]  

  • 22. Beyond sarcopenia: Characterization and integration of skeletal muscle quantity and radiodensity in a curable breast cancer population.
    Weinberg MS; Shachar SS; Muss HB; Deal AM; Popuri K; Yu H; Nyrop KA; Alston SM; Williams GR
    Breast J; 2018 May; 24(3):278-284. PubMed ID: 29139618
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Application of machine learning model to predict osteoporosis based on abdominal computed tomography images of the psoas muscle: a retrospective study.
    Huang CB; Hu JS; Tan K; Zhang W; Xu TH; Yang L
    BMC Geriatr; 2022 Oct; 22(1):796. PubMed ID: 36229793
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Prognostic Value of Fat Mass and Skeletal Muscle Mass Determined by Computed Tomography in Patients Who Underwent Transcatheter Aortic Valve Implantation.
    Mok M; Allende R; Leipsic J; Altisent OA; Del Trigo M; Campelo-Parada F; DeLarochellière R; Dumont E; Doyle D; Côté M; Freeman M; Webb J; Rodés-Cabau J
    Am J Cardiol; 2016 Mar; 117(5):828-33. PubMed ID: 26754122
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening.
    Peng T; Zeng X; Li Y; Li M; Pu B; Zhi B; Wang Y; Qu H
    Osteoporos Int; 2024 Jan; 35(1):117-128. PubMed ID: 37670164
    [TBL] [Abstract][Full Text] [Related]  

  • 26. The use of computed tomography images as a prognostic marker in critically ill cancer patients.
    Toledo DO; Carvalho AM; Oliveira AMRR; Toloi JM; Silva AC; Francisco de Mattos Farah J; Prado CM; Silva JM
    Clin Nutr ESPEN; 2018 Jun; 25():114-120. PubMed ID: 29779805
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Pre-operative Sarcopenia Identifies Patients at Risk for Poor Survival After Resection of Biliary Tract Cancers.
    Chakedis J; Spolverato G; Beal EW; Woelfel I; Bagante F; Merath K; Sun SH; Chafitz A; Galo J; Dillhoff M; Cloyd J; Pawlik TM
    J Gastrointest Surg; 2018 Oct; 22(10):1697-1708. PubMed ID: 29855867
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment.
    Nowak S; Kloth C; Theis M; Marinova M; Attenberger UI; Sprinkart AM; Luetkens JA
    Eur Radiol; 2024 Jan; 34(1):279-286. PubMed ID: 37572195
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.
    Kim J; Han SH; Kim HI
    J Surg Oncol; 2021 Dec; 124(8):1347-1355. PubMed ID: 34490899
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients.
    Wang X; Zhang C; Cao F; Wang CB; Dong JN; Wang ZH
    Acad Radiol; 2022 Sep; 29(9):1394-1403. PubMed ID: 34955366
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Preoperative computed tomography-determined sarcopenia is a reliable prognostic factor in patients with gastric cancer after radical gastrectomy: A sex-specific analysis.
    Liu T; Yi X; Ge J; Zhang J; Tan F; Song K; Liu H; Tang M
    Front Nutr; 2022; 9():884586. PubMed ID: 36352903
    [TBL] [Abstract][Full Text] [Related]  

  • 32. [Association of abdominal fat distribution by computed tomography with body mass index and metabolic syndrome in Chinese elders].
    Wang C; Wang X; Tian H; Fang F; Han X
    Zhonghua Yi Xue Za Zhi; 2014 Apr; 94(12):908-12. PubMed ID: 24854910
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Feasibility of using chest computed tomography (CT) imaging at the first lumbar vertebra (L1) level to assess skeletal muscle mass: a retrospective study.
    Liu S; Han X; Li J; Xie X; Yang Y; Jiang W; Liu L; Liu Z
    PeerJ; 2023; 11():e16652. PubMed ID: 38099314
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Prognostic value of computed tomography associated body composition measurement changes in metastatic colorectal cancer patients.
    Sevgilioglu ZE; Evrimler S; Iscan G; Kayikcioglu E; Sengul SS; Cetin B
    Acta Radiol; 2023 Nov; 64(11):2849-2857. PubMed ID: 37661639
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms.
    Pickhardt PJ; Graffy PM; Zea R; Lee SJ; Liu J; Sandfort V; Summers RM
    AJR Am J Roentgenol; 2021 Jan; 216(1):85-92. PubMed ID: 32603223
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves.
    Magudia K; Bridge CP; Bay CP; Babic A; Fintelmann FJ; Troschel FM; Miskin N; Wrobel WC; Brais LK; Andriole KP; Wolpin BM; Rosenthal MH
    Radiology; 2021 Feb; 298(2):319-329. PubMed ID: 33231527
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Derivation and validation of thoracic sarcopenia assessment in patients undergoing thoracic endovascular aortic repair.
    Panthofer AM; Olson SL; Harris DG; Matsumura JS
    J Vasc Surg; 2019 May; 69(5):1379-1386. PubMed ID: 30598352
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Relationship between sarcopenia classification and thigh muscle mass, fat area, muscle CT value and osteoporosis in middle-aged and older Japanese adults.
    Kuriyama K; Matsui Y; Suzuki Y; Mizuno T; Watanabe T; Takemura M; Ishizuka S; Yamashita S; Imagama S; Arai H
    Bone; 2022 Oct; 163():116487. PubMed ID: 35843483
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Importance of combined assessment of skeletal muscle mass and density by computed tomography in predicting clinical outcomes after transcatheter aortic valve replacement.
    Tokuda T; Yamamoto M; Kagase A; Koyama Y; Otsuka T; Tada N; Naganuma T; Araki M; Yamanaka F; Shirai S; Mizutani K; Tabata M; Ueno H; Takagi K; Higashimori A; Watanabe Y; Hayashida K;
    Int J Cardiovasc Imaging; 2020 May; 36(5):929-938. PubMed ID: 32040683
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Utility of multidetector computed tomography quantitative measurements in identifying sarcopenia: a propensity score matched study.
    Zuo YQ; Gao ZH; Wang Z; Liu Q; Yang X; Yin YL; Feng PY
    Skeletal Radiol; 2022 Jun; 51(6):1303-1312. PubMed ID: 34757481
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.