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 *

219 related articles for article (PubMed ID: 37114635)

  • 21. An
    Feng L; Lu X; Yang X; Kan Y; Sun D; Wang W; Yang J
    Eur J Radiol; 2022 Sep; 154():110444. PubMed ID: 35917754
    [TBL] [Abstract][Full Text] [Related]  

  • 22.
    Xu X; Sun X; Ma L; Zhang H; Ji W; Xia X; Lan X
    Front Oncol; 2023; 13():1149791. PubMed ID: 36969043
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer.
    Wu J; Zhang Q; Zhao Y; Liu Y; Chen A; Li X; Wu T; Li J; Guo Y; Liu A
    Front Oncol; 2019; 9():1250. PubMed ID: 31824843
    [No Abstract]   [Full Text] [Related]  

  • 24. Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.
    Bian X; Sun Q; Wang M; Dong H; Dai X; Zhang L; Fan G; Chen G
    BMC Med Imaging; 2024 Apr; 24(1):77. PubMed ID: 38566000
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Radiomic analysis for predicting prognosis of colorectal cancer from preoperative
    Lv L; Xin B; Hao Y; Yang Z; Xu J; Wang L; Wang X; Song S; Guo X
    J Transl Med; 2022 Feb; 20(1):66. PubMed ID: 35109864
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Preoperative prediction of microsatellite instability status: development and validation of a pan-cancer PET/CT-based radiomics model.
    Wang M; Peng M; Yang X; Zhang Y; Wu T; Wang Z; Wang K
    Nucl Med Commun; 2024 May; 45(5):372-380. PubMed ID: 38312051
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer.
    Chen X; Zhuang Z; Pen L; Xue J; Zhu H; Zhang L; Wang D
    Abdom Radiol (NY); 2024 May; 49(5):1363-1375. PubMed ID: 38305796
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based
    Tian D; Shiiya H; Takahashi M; Terasaki Y; Urushiyama H; Shinozaki-Ushiku A; Yan HJ; Sato M; Nakajima J
    J Heart Lung Transplant; 2022 Jun; 41(6):722-731. PubMed ID: 35430149
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer.
    Xue XQ; Yu WJ; Shao XL; Wang YT
    Abdom Radiol (NY); 2023 Feb; 48(2):510-518. PubMed ID: 36418614
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Clinical parameters combined with radiomics features of PET/CT can predict recurrence in patients with high-risk pediatric neuroblastoma.
    Feng L; Qian L; Yang S; Ren Q; Zhang S; Qin H; Wang W; Wang C; Zhang H; Yang J
    BMC Med Imaging; 2022 May; 22(1):102. PubMed ID: 35643445
    [TBL] [Abstract][Full Text] [Related]  

  • 31. A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer.
    Lee H; Moon SH; Hong JY; Lee J; Hyun SH
    Cancers (Basel); 2023 Jul; 15(15):. PubMed ID: 37568657
    [TBL] [Abstract][Full Text] [Related]  

  • 32. The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [
    Nakajo M; Kawaji K; Nagano H; Jinguji M; Mukai A; Kawabata H; Tani A; Hirahara D; Yamashita M; Yoshiura T
    Mol Imaging Biol; 2023 Apr; 25(2):303-313. PubMed ID: 35864282
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Machine learning based evaluation of clinical and pretreatment
    Nakajo M; Jinguji M; Tani A; Yano E; Hoo CK; Hirahara D; Togami S; Kobayashi H; Yoshiura T
    Abdom Radiol (NY); 2022 Feb; 47(2):838-847. PubMed ID: 34821963
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.
    Grahovac M; Spielvogel CP; Krajnc D; Ecsedi B; Traub-Weidinger T; Rasul S; Kluge K; Zhao M; Li X; Hacker M; Haug A; Papp L
    Eur J Nucl Med Mol Imaging; 2023 May; 50(6):1607-1620. PubMed ID: 36738311
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Combination of
    Li S; Li Y; Zhao M; Wang P; Xin J
    Korean J Radiol; 2022 Sep; 23(9):921-930. PubMed ID: 36047542
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Radiomics Features of
    Kang J; Lee JH; Lee HS; Cho ES; Park EJ; Baik SH; Lee KY; Park C; Yeu Y; Clemenceau JR; Park S; Xu H; Hong C; Hwang TH
    Cancers (Basel); 2021 Jan; 13(3):. PubMed ID: 33494345
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Use of radiomics based on
    Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
    Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Integrated CT Radiomics Features Could Enhance the Efficacy of
    Zhou W; Huang Q; Wen J; Li M; Zhu Y; Liu Y; Dai Y; Guan Y; Zhou Z; Hua T
    Front Oncol; 2021; 11():772703. PubMed ID: 34869011
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Development and validation of an [
    Takahashi N; Tanaka S; Umezawa R; Takanami K; Takeda K; Yamamoto T; Suzuki Y; Katsuta Y; Kadoya N; Jingu K
    Acta Oncol; 2023 Feb; 62(2):159-165. PubMed ID: 36794365
    [TBL] [Abstract][Full Text] [Related]  

  • 40. PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images.
    Lou J; Xu J; Zhang Y; Sun Y; Fang A; Liu J; Mur LAJ; Ji B
    Comput Methods Programs Biomed; 2022 Oct; 225():107095. PubMed ID: 36057226
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 11.