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 *

215 related articles for article (PubMed ID: 34817635)

  • 1. Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.
    Yang Y; Wang M; Qiu K; Wang Y; Ma X
    Strahlenther Onkol; 2022 Feb; 198(2):183-193. PubMed ID: 34817635
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma.
    Yang Y; Zhou Y; Zhou C; Ma X
    Eur J Surg Oncol; 2022 May; 48(5):1068-1077. PubMed ID: 34862094
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.
    Peng H; Dong D; Fang MJ; Li L; Tang LL; Chen L; Li WF; Mao YP; Fan W; Liu LZ; Tian L; Lin AH; Sun Y; Tian J; Ma J
    Clin Cancer Res; 2019 Jul; 25(14):4271-4279. PubMed ID: 30975664
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma.
    Hu Y; Xie C; Yang H; Ho JWK; Wen J; Han L; Lam KO; Wong IYH; Law SYK; Chiu KWH; Vardhanabhuti V; Fu J
    Radiother Oncol; 2021 Jan; 154():6-13. PubMed ID: 32941954
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods.
    Yang Y; Zhou Y; Zhou C; Ma X
    Orphanet J Rare Dis; 2022 Apr; 17(1):158. PubMed ID: 35392952
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma.
    Wang G; He L; Yuan C; Huang Y; Liu Z; Liang C
    Eur J Radiol; 2018 Jan; 98():100-106. PubMed ID: 29279146
    [TBL] [Abstract][Full Text] [Related]  

  • 7. CT-based peritumoral radiomics nomogram on prediction of response and survival to induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.
    Zeng F; Ye Z; Zhou Q
    J Cancer Res Clin Oncol; 2024 Jan; 150(2):50. PubMed ID: 38286865
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma.
    Liao H; Chen X; Lu S; Jin G; Pei W; Li Y; Wei Y; Huang X; Wang C; Liang X; Bao H; Liu L; Su D
    J Magn Reson Imaging; 2022 Aug; 56(2):547-559. PubMed ID: 34970824
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.
    Du D; Feng H; Lv W; Ashrafinia S; Yuan Q; Wang Q; Yang W; Feng Q; Chen W; Rahmim A; Lu L
    Mol Imaging Biol; 2020 Jun; 22(3):730-738. PubMed ID: 31338709
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma.
    Zhao L; Gong J; Xi Y; Xu M; Li C; Kang X; Yin Y; Qin W; Yin H; Shi M
    Eur Radiol; 2020 Jan; 30(1):537-546. PubMed ID: 31372781
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography-Based Radiomics.
    Yang Y; Wu J; Mai W; Li H
    Contrast Media Mol Imaging; 2022; 2022():3417480. PubMed ID: 36226269
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Pretreatment MRI-Derived Radiomics May Evaluate the Response of Different Induction Chemotherapy Regimens in Locally advanced Nasopharyngeal Carcinoma.
    Zhang L; Ye Z; Ruan L; Jiang M
    Acad Radiol; 2020 Dec; 27(12):1655-1664. PubMed ID: 33004261
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The comparison of prognostic value of tumour volumetric regression ratio and RECIST 1.1 criteria after induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.
    Zeng YY; Xiang ZZ; He T; Liu F; Shao BF; Yan RN; Ma JC; Wang XR; Liu L
    Oral Oncol; 2020 Dec; 111():104924. PubMed ID: 32736209
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Nomograms based on multiparametric MRI radiomics integrated with clinical-radiological features for predicting the response to induction chemotherapy in nasopharyngeal carcinoma.
    Chen Z; Wang Z; Liu S; Zhang S; Zhou Y; Zhang R; Yang W
    Eur J Radiol; 2024 Jun; 175():111438. PubMed ID: 38613869
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.
    Gu B; Meng M; Xu M; Feng DD; Bi L; Kim J; Song S
    Eur J Nucl Med Mol Imaging; 2023 Nov; 50(13):3996-4009. PubMed ID: 37596343
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer.
    Zhang AQ; Zhao HP; Li F; Liang P; Gao JB; Cheng M
    Front Oncol; 2022; 12():969707. PubMed ID: 36212443
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Pretreatment dual-energy CT for predicting early response to induction chemotherapy and survival in nasopharyngeal carcinoma.
    Zhan Y; Wang Y; Wang P; Wang Y; Ni X; Wang J; Tang Z
    Eur Radiol; 2023 Dec; 33(12):9052-9062. PubMed ID: 37405505
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A Rulefit-based prognostic analysis using structured MRI report to select potential beneficiaries from induction chemotherapy in advanced nasopharyngeal carcinoma: A dual-centre study.
    Li S; Zhang W; Liang B; Huang W; Luo C; Zhu Y; Kou KI; Ruan G; Liu L; Zhang G; Li H
    Radiother Oncol; 2023 Dec; 189():109943. PubMed ID: 37813309
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Radiomic analysis of MRI for prediction of response to induction chemotherapy in nasopharyngeal carcinoma patients.
    Wang A; Xu H; Zhang C; Ren J; Liu J; Zhou P
    Clin Radiol; 2023 Sep; 78(9):e644-e653. PubMed ID: 37331848
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Prognostic Predictive System Based on Deep Learning for Locoregionally Advanced Nasopharyngeal Carcinoma.
    Qiang M; Li C; Sun Y; Sun Y; Ke L; Xie C; Zhang T; Zou Y; Qiu W; Gao M; Li Y; Li X; Zhan Z; Liu K; Chen X; Liang C; Chen Q; Mai H; Xie G; Guo X; Lv X
    J Natl Cancer Inst; 2021 May; 113(5):606-615. PubMed ID: 32970812
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.