BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

251 related articles for article (PubMed ID: 29969358)

  • 1. Rectal wall MRI radiomics in prostate cancer patients: prediction of and correlation with early rectal toxicity.
    Abdollahi H; Mahdavi SR; Mofid B; Bakhshandeh M; Razzaghdoust A; Saadipoor A; Tanha K
    Int J Radiat Biol; 2018 Sep; 94(9):829-837. PubMed ID: 29969358
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.
    Abdollahi H; Mofid B; Shiri I; Razzaghdoust A; Saadipoor A; Mahdavi A; Galandooz HM; Mahdavi SR
    Radiol Med; 2019 Jun; 124(6):555-567. PubMed ID: 30607868
    [TBL] [Abstract][Full Text] [Related]  

  • 3. MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity.
    Abdollahi H; Tanha K; Mofid B; Razzaghdoust A; Saadipoor A; Khalafi L; Bakhshandeh M; Mahdavi SR
    J Med Imaging Radiat Sci; 2019 Jun; 50(2):252-260. PubMed ID: 31176433
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Magnetic resonance imaging radiomic feature analysis of radiation-induced femoral head changes in prostate cancer radiotherapy.
    Abdollahi H; Mahdavi SR; Shiri I; Mofid B; Bakhshandeh M; Rahmani K
    J Cancer Res Ther; 2019 Mar; 15(Supplement):S11-S19. PubMed ID: 30900614
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.
    Shiradkar R; Ghose S; Jambor I; Taimen P; Ettala O; Purysko AS; Madabhushi A
    J Magn Reson Imaging; 2018 Dec; 48(6):1626-1636. PubMed ID: 29734484
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.
    Zhou X; Yi Y; Liu Z; Cao W; Lai B; Sun K; Li L; Zhou Z; Feng Y; Tian J
    Ann Surg Oncol; 2019 Jun; 26(6):1676-1684. PubMed ID: 30887373
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.
    Ginsburg SB; Algohary A; Pahwa S; Gulani V; Ponsky L; Aronen HJ; Boström PJ; Böhm M; Haynes AM; Brenner P; Delprado W; Thompson J; Pulbrock M; Taimen P; Villani R; Stricker P; Rastinehad AR; Jambor I; Madabhushi A
    J Magn Reson Imaging; 2017 Jul; 46(1):184-193. PubMed ID: 27990722
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison.
    Hassaninejad H; Abdollahi H; Abedi I; Amouheidari A; Tavakoli MB
    Phys Eng Sci Med; 2023 Dec; 46(4):1353-1363. PubMed ID: 37556091
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CT imaging markers to improve radiation toxicity prediction in prostate cancer radiotherapy by stacking regression algorithm.
    Mostafaei S; Abdollahi H; Kazempour Dehkordi S; Shiri I; Razzaghdoust A; Zoljalali Moghaddam SH; Saadipoor A; Koosha F; Cheraghi S; Mahdavi SR
    Radiol Med; 2020 Jan; 125(1):87-97. PubMed ID: 31552555
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Changes in ADC and T2-weighted MRI-derived radiomic features in patients treated with focal salvage HDR prostate brachytherapy for local recurrence after previous external-beam radiotherapy.
    Lee SL; Ravi A; Morton G; Loblaw A; Tseng CL; Haider M; Murgic J; Nicolae A; Semple M; Chung HT
    Brachytherapy; 2019; 18(5):567-573. PubMed ID: 31126856
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Machine-learning with region-level radiomic and dosimetric features for predicting radiotherapy-induced rectal toxicities in prostate cancer patients.
    Yang Z; Noble DJ; Shelley L; Berger T; Jena R; McLaren DB; Burnet NG; Nailon WH
    Radiother Oncol; 2023 Jun; 183():109593. PubMed ID: 36870609
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
    Bickelhaupt S; Paech D; Kickingereder P; Steudle F; Lederer W; Daniel H; Götz M; Gählert N; Tichy D; Wiesenfarth M; Laun FB; Maier-Hein KH; Schlemmer HP; Bonekamp D
    J Magn Reson Imaging; 2017 Aug; 46(2):604-616. PubMed ID: 28152264
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
    Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
    Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values.
    Bonekamp D; Kohl S; Wiesenfarth M; Schelb P; Radtke JP; Götz M; Kickingereder P; Yaqubi K; Hitthaler B; Gählert N; Kuder TA; Deister F; Freitag M; Hohenfellner M; Hadaschik BA; Schlemmer HP; Maier-Hein KH
    Radiology; 2018 Oct; 289(1):128-137. PubMed ID: 30063191
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study.
    Filograna L; Lenkowicz J; Cellini F; Dinapoli N; Manfrida S; Magarelli N; Leone A; Colosimo C; Valentini V
    Radiol Med; 2019 Jan; 124(1):50-57. PubMed ID: 30191445
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
    Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
    Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.
    Viswanath SE; Chirra PV; Yim MC; Rofsky NM; Purysko AS; Rosen MA; Bloch BN; Madabhushi A
    BMC Med Imaging; 2019 Feb; 19(1):22. PubMed ID: 30819131
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma.
    Wang H; Song B; Ye N; Ren J; Sun X; Dai Z; Zhang Y; Chen BT
    Eur J Radiol; 2020 Jan; 122():108755. PubMed ID: 31783344
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Clinically significant prostate cancer detection on MRI: A radiomic shape features study.
    Cuocolo R; Stanzione A; Ponsiglione A; Romeo V; Verde F; Creta M; La Rocca R; Longo N; Pace L; Imbriaco M
    Eur J Radiol; 2019 Jul; 116():144-149. PubMed ID: 31153556
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Changes in Magnetic Resonance Imaging Radiomic Features in Response to Androgen Deprivation Therapy in Patients with Intermediate- and High-risk Prostate Cancer.
    Tharmalingam H; Tsang YM; Alonzi R; Beasley W; Taylor NJ; McWilliam A; Padhani A; Choudhury A; Hoskin PJ
    Clin Oncol (R Coll Radiol); 2022 Jun; 34(6):e246-e253. PubMed ID: 35033410
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.