176 related articles for article (PubMed ID: 34449713)
21. Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
Hamerla G; Meyer HJ; Schob S; Ginat DT; Altman A; Lim T; Gihr GA; Horvath-Rizea D; Hoffmann KT; Surov A
Magn Reson Imaging; 2019 Nov; 63():244-249. PubMed ID: 31425811
[TBL] [Abstract][Full Text] [Related]
22. Can diffusion-weighted magnetic resonance imaging predict tumor recurrence of uterine cervical cancer after concurrent chemoradiotherapy?
Bae JM; Kim CK; Park JJ; Park BK
Abdom Radiol (NY); 2016 Aug; 41(8):1604-10. PubMed ID: 27056747
[TBL] [Abstract][Full Text] [Related]
23. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
24. Radiomic features of cervical cancer on T2-and diffusion-weighted MRI: Prognostic value in low-volume tumors suitable for trachelectomy.
Wormald BW; Doran SJ; Ind TE; D'Arcy J; Petts J; deSouza NM
Gynecol Oncol; 2020 Jan; 156(1):107-114. PubMed ID: 31685232
[TBL] [Abstract][Full Text] [Related]
25. Pre-treatment diffusion-weighted MR imaging for predicting tumor recurrence in uterine cervical cancer treated with concurrent chemoradiation: value of histogram analysis of apparent diffusion coefficients.
Heo SH; Shin SS; Kim JW; Lim HS; Jeong YY; Kang WD; Kim SM; Kang HK
Korean J Radiol; 2013; 14(4):616-25. PubMed ID: 23901319
[TBL] [Abstract][Full Text] [Related]
26. Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram.
Wang T; Gao T; Guo H; Wang Y; Zhou X; Tian J; Huang L; Zhang M
Eur Radiol; 2020 Jun; 30(6):3585-3593. PubMed ID: 32065284
[TBL] [Abstract][Full Text] [Related]
27. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
28. Volumetric assessment of apparent diffusion coefficient predicts outcome following chemoradiation for cervical cancer.
Ho JC; Fang P; Cardenas CE; Mohamed ASR; Fuller CD; Allen PK; Bhosale PR; Frumovitz MM; Jhingran A; Klopp AH
Radiother Oncol; 2019 Jun; 135():58-64. PubMed ID: 31015171
[TBL] [Abstract][Full Text] [Related]
29. Diffusion-Weighted Magnetic Resonance Imaging as a Predictor of Outcome in Cervical Cancer After Chemoradiation.
Ho JC; Allen PK; Bhosale PR; Rauch GM; Fuller CD; Mohamed AS; Frumovitz M; Jhingran A; Klopp AH
Int J Radiat Oncol Biol Phys; 2017 Mar; 97(3):546-553. PubMed ID: 28011045
[TBL] [Abstract][Full Text] [Related]
30. Treatment response evaluation using the mean apparent diffusion coefficient in cervical cancer patients treated with definitive chemoradiotherapy.
Onal C; Erbay G; Guler OC
J Magn Reson Imaging; 2016 Oct; 44(4):1010-9. PubMed ID: 26919800
[TBL] [Abstract][Full Text] [Related]
31. Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs.
Gao Y; Kalbasi A; Hsu W; Ruan D; Fu J; Shao J; Cao M; Wang C; Eilber FC; Bernthal N; Bukata S; Dry SM; Nelson SD; Kamrava M; Lewis J; Low DA; Steinberg M; Hu P; Yang Y
Phys Med Biol; 2020 Aug; 65(17):175006. PubMed ID: 32554891
[TBL] [Abstract][Full Text] [Related]
32. 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]
33. Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer.
Hou L; Zhou W; Ren J; Du X; Xin L; Zhao X; Cui Y; Zhang R
Front Oncol; 2020; 10():1393. PubMed ID: 32974143
[No Abstract] [Full Text] [Related]
34. Discriminating malignant from benign testicular masses using machine-learning based radiomics signature of appearance diffusion coefficient maps: Comparing with conventional mean and minimum ADC values.
Fan C; Sun K; Min X; Cai W; Lv W; Ma X; Li Y; Chen C; Zhao P; Qiao J; Lu J; Guo Y; Xia L
Eur J Radiol; 2022 Mar; 148():110158. PubMed ID: 35066342
[TBL] [Abstract][Full Text] [Related]
35. Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas.
Zheng Y; Chen L; Liu M; Wu J; Yu R; Lv F
Int J Hyperthermia; 2021; 38(1):1349-1358. PubMed ID: 34486913
[TBL] [Abstract][Full Text] [Related]
36. Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis.
Park SH; Hahm MH; Bae BK; Chong GO; Jeong SY; Na S; Jeong S; Kim JC
Radiat Oncol; 2020 Apr; 15(1):86. PubMed ID: 32312283
[TBL] [Abstract][Full Text] [Related]
37. Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer.
Chen H; He Y; Zhao C; Zheng L; Pan N; Qiu J; Zhang Z; Niu X; Yuan Z
Acta Radiol; 2021 May; 62(5):679-686. PubMed ID: 32640886
[TBL] [Abstract][Full Text] [Related]
38. 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]
39. Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Shi L; Zhang Y; Nie K; Sun X; Niu T; Yue N; Kwong T; Chang P; Chow D; Chen JH; Su MY
Magn Reson Imaging; 2019 Sep; 61():33-40. PubMed ID: 31059768
[TBL] [Abstract][Full Text] [Related]
40. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors.
Xu X; Wang H; Du P; Zhang F; Li S; Zhang Z; Yuan J; Liang Z; Zhang X; Guo Y; Liu Y; Lu H
J Magn Reson Imaging; 2019 Dec; 50(6):1893-1904. PubMed ID: 30980695
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]