BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

497 related articles for article (PubMed ID: 31010618)

  • 1. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.
    Bur AM; Holcomb A; Goodwin S; Woodroof J; Karadaghy O; Shnayder Y; Kakarala K; Brant J; Shew M
    Oral Oncol; 2019 May; 92():20-25. PubMed ID: 31010618
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma.
    Farrokhian N; Holcomb AJ; Dimon E; Karadaghy O; Ward C; Whiteford E; Tolan C; Hanly EK; Buchakjian MR; Harding B; Dooley L; Shinn J; Wood CB; Rohde SL; Khaja S; Parikh A; Bulbul MG; Penn J; Goodwin S; Bur AM
    JAMA Netw Open; 2022 Apr; 5(4):e227226. PubMed ID: 35416990
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning Predicts Lymph Node Metastasis in Early-Stage Oral Tongue Squamous Cell Carcinoma.
    Shan J; Jiang R; Chen X; Zhong Y; Zhang W; Xie L; Cheng J; Jiang H
    J Oral Maxillofac Surg; 2020 Dec; 78(12):2208-2218. PubMed ID: 32649894
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.
    Yuan Y; Ren J; Tao X
    Eur Radiol; 2021 Sep; 31(9):6429-6437. PubMed ID: 33569617
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Depth of invasion in early stage oral cavity squamous cell carcinoma: The optimal cut-off value for elective neck dissection.
    van Lanschot CGF; Klazen YP; de Ridder MAJ; Mast H; Ten Hove I; Hardillo JA; Monserez DA; Sewnaik A; Meeuwis CA; Keereweer S; Aaboubout Y; Barroso EM; van der Toom QM; Bakker Schut TC; Wolvius EB; Baatenburg de Jong RJ; Puppels GJ; Koljenović S
    Oral Oncol; 2020 Dec; 111():104940. PubMed ID: 32769035
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER-based population study.
    Kwak MS; Eun YG; Lee JW; Lee YC
    Head Neck; 2021 Aug; 43(8):2316-2324. PubMed ID: 33792112
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Utility of PET-CT in detecting nodal metastasis in cN0 early stage oral cavity squamous cell carcinoma.
    Zhang H; Seikaly H; Biron VL; Jeffery CC
    Oral Oncol; 2018 May; 80():89-92. PubMed ID: 29706193
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.
    Karadaghy OA; Shew M; New J; Bur AM
    JAMA Otolaryngol Head Neck Surg; 2019 Dec; 145(12):1115-1120. PubMed ID: 31045212
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A model to predict nodal metastasis in patients with oral squamous cell carcinoma.
    De Silva RK; Siriwardena BSMS; Samaranayaka A; Abeyasinghe WAMUL; Tilakaratne WM
    PLoS One; 2018; 13(8):e0201755. PubMed ID: 30091996
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine learning and treatment outcome prediction for oral cancer.
    Chu CS; Lee NP; Adeoye J; Thomson P; Choi SW
    J Oral Pathol Med; 2020 Nov; 49(10):977-985. PubMed ID: 32740951
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 18F-FDG PET and CT/MRI in oral cavity squamous cell carcinoma: a prospective study of 124 patients with histologic correlation.
    Ng SH; Yen TC; Liao CT; Chang JT; Chan SC; Ko SF; Wang HM; Wong HF
    J Nucl Med; 2005 Jul; 46(7):1136-43. PubMed ID: 16000282
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Preoperative predictors of occult nodal disease in cT1N0 oral cavity squamous cell carcinoma: Review of 2623 cases.
    Zhan KY; Morgan PF; Neskey DM; Kim JJ; Huang AT; Garrett-Mayer E; Day TA
    Head Neck; 2018 Sep; 40(9):1967-1976. PubMed ID: 29761586
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Comparison of machine learning algorithms for the prediction of five-year survival in oral squamous cell carcinoma.
    Alkhadar H; Macluskey M; White S; Ellis I; Gardner A
    J Oral Pathol Med; 2021 Apr; 50(4):378-384. PubMed ID: 33220109
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Development and validation of a multivariable prediction model for the identification of occult lymph node metastasis in oral squamous cell carcinoma.
    Mermod M; Jourdan EF; Gupta R; Bongiovanni M; Tolstonog G; Simon C; Clark J; Monnier Y
    Head Neck; 2020 Aug; 42(8):1811-1820. PubMed ID: 32057148
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer.
    Alabi RO; Elmusrati M; Sawazaki-Calone I; Kowalski LP; Haglund C; Coletta RD; Mäkitie AA; Salo T; Almangush A; Leivo I
    Int J Med Inform; 2020 Apr; 136():104068. PubMed ID: 31923822
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development of a New Outcome Prediction Model in Early-stage Squamous Cell Carcinoma of the Oral Cavity Based on Histopathologic Parameters With Multivariate Analysis: The Aditi-Nuzhat Lymph-node Prediction Score (ANLPS) System.
    Arora A; Husain N; Bansal A; Neyaz A; Jaiswal R; Jain K; Chaturvedi A; Anand N; Malhotra K; Shukla S
    Am J Surg Pathol; 2017 Jul; 41(7):950-960. PubMed ID: 28346327
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.
    Alabi RO; Youssef O; Pirinen M; Elmusrati M; Mäkitie AA; Leivo I; Almangush A
    Artif Intell Med; 2021 May; 115():102060. PubMed ID: 34001326
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Lymph node yield, depth of invasion, and survival in node-negative oral cavity cancer.
    Zenga J; Divi V; Stadler M; Massey B; Campbell B; Shukla M; Awan M; Schultz CJ; Shreenivas A; Wong S; Jackson RS; Pipkorn P
    Oral Oncol; 2019 Nov; 98():125-131. PubMed ID: 31586894
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development and validation of machine learning-based risk prediction models of oral squamous cell carcinoma using salivary autoantibody biomarkers.
    Tseng YJ; Wang YC; Hsueh PC; Wu CC
    BMC Oral Health; 2022 Nov; 22(1):534. PubMed ID: 36424594
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.
    Zhou Z; Maquilan GM; Thomas K; Wachsmann J; Wang J; Folkert MR; Albuquerque K
    Technol Cancer Res Treat; 2020; 19():1533033820983804. PubMed ID: 33357081
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.