BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1079 related articles for article (PubMed ID: 28295386)

  • 1. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
    Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
    Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.
    Beig N; Khorrami M; Alilou M; Prasanna P; Braman N; Orooji M; Rakshit S; Bera K; Rajiah P; Ginsberg J; Donatelli C; Thawani R; Yang M; Jacono F; Tiwari P; Velcheti V; Gilkeson R; Linden P; Madabhushi A
    Radiology; 2019 Mar; 290(3):783-792. PubMed ID: 30561278
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
    Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
    Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.
    Khorrami M; Bera K; Thawani R; Rajiah P; Gupta A; Fu P; Linden P; Pennell N; Jacono F; Gilkeson RC; Velcheti V; Madabhushi A
    Eur J Cancer; 2021 May; 148():146-158. PubMed ID: 33743483
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis.
    Niehaus R; Raicu DS; Furst J; Armato S
    J Digit Imaging; 2015 Dec; 28(6):704-17. PubMed ID: 25708891
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images.
    Dhara AK; Mukhopadhyay S; Dutta A; Garg M; Khandelwal N
    J Digit Imaging; 2016 Aug; 29(4):466-75. PubMed ID: 26738871
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.
    Madero Orozco H; Vergara Villegas OO; Cruz Sánchez VG; Ochoa Domínguez Hde J; Nandayapa Alfaro Mde J
    Biomed Eng Online; 2015 Feb; 14():9. PubMed ID: 25888834
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.
    Way TW; Sahiner B; Chan HP; Hadjiiski L; Cascade PN; Chughtai A; Bogot N; Kazerooni E
    Med Phys; 2009 Jul; 36(7):3086-98. PubMed ID: 19673208
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.
    Wu YJ; Liu YC; Liao CY; Tang EK; Wu FZ
    Sci Rep; 2021 Jan; 11(1):66. PubMed ID: 33462251
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.
    Dhara AK; Mukhopadhyay S; Dutta A; Garg M; Khandelwal N
    J Digit Imaging; 2017 Feb; 30(1):63-77. PubMed ID: 27678255
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.
    Alilou M; Orooji M; Beig N; Prasanna P; Rajiah P; Donatelli C; Velcheti V; Rakshit S; Yang M; Jacono F; Gilkeson R; Linden P; Madabhushi A
    Sci Rep; 2018 Oct; 8(1):15290. PubMed ID: 30327507
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
    Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
    Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.
    Aoyama M; Li Q; Katsuragawa S; Li F; Sone S; Doi K
    Med Phys; 2003 Mar; 30(3):387-94. PubMed ID: 12674239
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.
    Gong J; Liu J; Hao W; Nie S; Wang S; Peng W
    Phys Med Biol; 2019 Jul; 64(13):135015. PubMed ID: 31167172
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy.
    Li F; Aoyama M; Shiraishi J; Abe H; Li Q; Suzuki K; Engelmann R; Sone S; Macmahon H; Doi K
    AJR Am J Roentgenol; 2004 Nov; 183(5):1209-15. PubMed ID: 15505279
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.
    Cascio D; Magro R; Fauci F; Iacomi M; Raso G
    Comput Biol Med; 2012 Nov; 42(11):1098-109. PubMed ID: 23020972
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
    Suzuki K; Li F; Sone S; Doi K
    IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automated lung nodule classification following automated nodule detection on CT: a serial approach.
    Armato SG; Altman MB; Wilkie J; Sone S; Li F; Doi K; Roy AS
    Med Phys; 2003 Jun; 30(6):1188-97. PubMed ID: 12852543
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework.
    Tavakoli MB; Orooji M; Teimouri M; Shahabifar R
    BMC Res Notes; 2021 Mar; 14(1):87. PubMed ID: 33750438
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
    Messay T; Hardie RC; Rogers SK
    Med Image Anal; 2010 Jun; 14(3):390-406. PubMed ID: 20346728
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 54.