304 related articles for article (PubMed ID: 35719934)
1. MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.
Jian L; Liu Y; Xie Y; Jiang S; Ye M; Lin H
Front Oncol; 2022; 12():876664. PubMed ID: 35719934
[TBL] [Abstract][Full Text] [Related]
2. Comparison of Biexponential and Monoexponential Model of Diffusion-Weighted Imaging for Distinguishing between Common Renal Cell Carcinoma and Fat Poor Angiomyolipoma.
Ding Y; Zeng M; Rao S; Chen C; Fu C; Zhou J
Korean J Radiol; 2016; 17(6):853-863. PubMed ID: 27833401
[TBL] [Abstract][Full Text] [Related]
3. A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively.
Jia H; Jiang X; Zhang K; Shang J; Zhang Y; Fang X; Gao F; Li N; Dong J
J Magn Reson Imaging; 2022 Sep; 56(3):658-667. PubMed ID: 35090079
[TBL] [Abstract][Full Text] [Related]
4. Preoperative detection of lymphovascular invasion in rectal cancer using intravoxel incoherent motion imaging based on radiomics.
Wong C; Liu T; Zhang C; Li M; Zhang H; Wang Q; Fu Y
Med Phys; 2024 Jan; 51(1):179-191. PubMed ID: 37929807
[TBL] [Abstract][Full Text] [Related]
5. [Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].
Hu SX; Yang K; Wang XR; Wen DG; Xia CC; Li X; Li ZL
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Mar; 52(2):311-318. PubMed ID: 33829708
[TBL] [Abstract][Full Text] [Related]
6. Small (< 4 cm) Renal Tumors With Predominantly Low Signal Intensity on T2-Weighted Images: Differentiation of Minimal-Fat Angiomyolipoma From Renal Cell Carcinoma.
Park JJ; Kim CK
AJR Am J Roentgenol; 2017 Jan; 208(1):124-130. PubMed ID: 27824487
[TBL] [Abstract][Full Text] [Related]
7. A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.
Nie P; Yang G; Wang Z; Yan L; Miao W; Hao D; Wu J; Zhao Y; Gong A; Cui J; Jia Y; Niu H
Eur Radiol; 2020 Feb; 30(2):1274-1284. PubMed ID: 31506816
[TBL] [Abstract][Full Text] [Related]
8. A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.
Deng B; Wang Q; Liu Y; Yang Y; Gao X; Dai H
Abdom Radiol (NY); 2024 Feb; ():. PubMed ID: 38349392
[TBL] [Abstract][Full Text] [Related]
9. Elaboration of Multiparametric MRI-Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non-Small-Cell Lung Cancer.
Tang X; Bai G; Wang H; Guo F; Yin H
J Magn Reson Imaging; 2022 Aug; 56(2):579-589. PubMed ID: 35040525
[TBL] [Abstract][Full Text] [Related]
10. [Prediction of platinum-based chemotherapy sensitivity for epithelial ovarian cancer by multi-sequence MRI-based radiomic nomogram].
Mao MM; Li HM; Shi J; Qiu QS; Feng F
Zhonghua Yi Xue Za Zhi; 2022 Jan; 102(3):201-208. PubMed ID: 35042289
[No Abstract] [Full Text] [Related]
11. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients.
Li C; Yin J
Front Oncol; 2021; 11():671354. PubMed ID: 34041033
[TBL] [Abstract][Full Text] [Related]
12. Application of BOLD
Deng Y; Pan L; Xing W; Zhou Z; Chen J
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2021 Sept 28; 46(9):1010-1017. PubMed ID: 34707012
[TBL] [Abstract][Full Text] [Related]
13. Multi-Parametric Magnetic Resonance Imaging-Based Radiomics Analysis of Cervical Cancer for Preoperative Prediction of Lymphovascular Space Invasion.
Huang G; Cui Y; Wang P; Ren J; Wang L; Ma Y; Jia Y; Ma X; Zhao L
Front Oncol; 2021; 11():663370. PubMed ID: 35096556
[TBL] [Abstract][Full Text] [Related]
14. Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.
Ji X; Zhang J; Shi W; He D; Bao J; Wei X; Huang Y; Liu Y; Chen JC; Gao X; Tang Y; Xia W
Phys Eng Sci Med; 2021 Sep; 44(3):745-754. PubMed ID: 34075559
[TBL] [Abstract][Full Text] [Related]
15. Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation.
Matsumoto S; Arita Y; Yoshida S; Fukushima H; Kimura K; Yamada I; Tanaka H; Yagi F; Yokoyama M; Matsuoka Y; Oya M; Tateishi U; Jinzaki M; Fujii Y
Abdom Radiol (NY); 2022 Jun; 47(6):2178-2186. PubMed ID: 35426498
[TBL] [Abstract][Full Text] [Related]
16. Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?
Ma Y; Cao F; Xu X; Ma W
Abdom Radiol (NY); 2020 Aug; 45(8):2500-2507. PubMed ID: 31980867
[TBL] [Abstract][Full Text] [Related]
17. Role of combined clinical-radiomics model based on contrast-enhanced MRI in predicting the malignancy of breast non-mass enhancements without an additional diffusion-weighted imaging sequence.
Li Y; Yang Z; Lv W; Qin Y; Tang C; Yan X; Yin T; Ai T; Xia L
Quant Imaging Med Surg; 2023 Sep; 13(9):5974-5985. PubMed ID: 37711822
[TBL] [Abstract][Full Text] [Related]
18. MRI-based radiomics analysis to predict preoperative lymph node metastasis in papillary thyroid carcinoma.
Hu W; Wang H; Wei R; Wang L; Dai Z; Duan S; Ge Y; Wu PY; Song B
Gland Surg; 2020 Oct; 9(5):1214-1226. PubMed ID: 33224796
[TBL] [Abstract][Full Text] [Related]
19. Application Value of Magnetic Resonance Radiomics and Clinical Nomograms in Evaluating the Sensitivity of Neoadjuvant Chemotherapy for Nasopharyngeal Carcinoma.
Hu C; Zheng D; Cao X; Pang P; Fang Y; Lu T; Chen Y
Front Oncol; 2021; 11():740776. PubMed ID: 34790570
[TBL] [Abstract][Full Text] [Related]
20. A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).
Feng S; Gong M; Zhou D; Yuan R; Kong J; Jiang F; Zhang L; Chen W; Li Y
Transl Oncol; 2023 Mar; 29():101627. PubMed ID: 36731307
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
