162 related articles for article (PubMed ID: 35810066)
21. 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]
22. Radiomics-based machine learning models in STEMI: a promising tool for the prediction of major adverse cardiac events.
Durmaz ES; Karabacak M; Ozkara BB; Kargın OA; Raimoglu U; Tokdil H; Durmaz E; Adaletli I
Eur Radiol; 2023 Jul; 33(7):4611-4620. PubMed ID: 36633675
[TBL] [Abstract][Full Text] [Related]
23. Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening.
Bülbül HM; Burakgazi G; Kesimal U; Kaba E
Jpn J Radiol; 2024 Mar; ():. PubMed ID: 38536559
[TBL] [Abstract][Full Text] [Related]
24. Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.
Koo CW; Kline TL; Yoon JH; Vercnocke AJ; Johnson MP; Suman G; Lu A; Larson NB
Br J Radiol; 2022 Dec; 95(1140):20220230. PubMed ID: 36367095
[TBL] [Abstract][Full Text] [Related]
25. An MRI-Based Radiomic Nomogram for Discrimination Between Malignant and Benign Sinonasal Tumors.
Zhang H; Wang H; Hao D; Ge Y; Wan G; Zhang J; Liu S; Zhang Y; Xu D
J Magn Reson Imaging; 2021 Jan; 53(1):141-151. PubMed ID: 32776393
[TBL] [Abstract][Full Text] [Related]
26. CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors.
Chen J; Liu L; He Z; Su D; Liu C
J Imaging Inform Med; 2024 Feb; 37(1):180-195. PubMed ID: 38343232
[TBL] [Abstract][Full Text] [Related]
27. Texture Analysis of Three-Dimensional MRI Images May Differentiate Borderline and Malignant Epithelial Ovarian Tumors.
Ye R; Weng S; Li Y; Yan C; Chen J; Zhu Y; Wen L
Korean J Radiol; 2021 Jan; 22(1):106-117. PubMed ID: 32932563
[TBL] [Abstract][Full Text] [Related]
28. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
[TBL] [Abstract][Full Text] [Related]
29. Tumor-to-bone distance and radiomic features on MRI distinguish intramuscular lipomas from well-differentiated liposarcomas.
Sudjai N; Siriwanarangsun P; Lektrakul N; Saiviroonporn P; Maungsomboon S; Phimolsarnti R; Asavamongkolkul A; Chandhanayingyong C
J Orthop Surg Res; 2023 Mar; 18(1):255. PubMed ID: 36978182
[TBL] [Abstract][Full Text] [Related]
30. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.
Liu C; Ding J; Spuhler K; Gao Y; Serrano Sosa M; Moriarty M; Hussain S; He X; Liang C; Huang C
J Magn Reson Imaging; 2019 Jan; 49(1):131-140. PubMed ID: 30171822
[TBL] [Abstract][Full Text] [Related]
31. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on
He J; Wang Q; Zhang Y; Wu H; Zhou Y; Zhao S
Ann Nucl Med; 2021 May; 35(5):617-627. PubMed ID: 33738763
[TBL] [Abstract][Full Text] [Related]
32. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer.
Xu H; Liu J; Chen Z; Wang C; Liu Y; Wang M; Zhou P; Luo H; Ren J
Eur Radiol; 2022 Jul; 32(7):4845-4856. PubMed ID: 35079887
[TBL] [Abstract][Full Text] [Related]
33. A Radiomics Nomogram for Distinguishing Benign From Malignant Round-Like Breast Tumors.
Wang L; Ding Y; Yang W; Wang H; Shen J; Liu W; Xu J; Wei R; Hu W; Ge Y; Zhang B; Song B
Front Oncol; 2022; 12():677803. PubMed ID: 35558514
[TBL] [Abstract][Full Text] [Related]
34. Development and validation of a machine learning-derived radiomics model for diagnosis of osteoporosis and osteopenia using quantitative computed tomography.
Xie Q; Chen Y; Hu Y; Zeng F; Wang P; Xu L; Wu J; Li J; Zhu J; Xiang M; Zeng F
BMC Med Imaging; 2022 Aug; 22(1):140. PubMed ID: 35941568
[TBL] [Abstract][Full Text] [Related]
35. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. 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]
38. Value of contrast-enhanced magnetic resonance imaging-T2WI-based radiomic features in distinguishing lung adenocarcinoma from lung squamous cell carcinoma with solid components >8 mm.
Yang M; Shi L; Huang T; Li G; Shao H; Shen Y; Zhu J; Ni B
J Thorac Dis; 2023 Feb; 15(2):635-648. PubMed ID: 36910079
[TBL] [Abstract][Full Text] [Related]
39. Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms.
Song XL; Ren JL; Zhao D; Wang L; Ren H; Niu J
Eur Radiol; 2021 Jan; 31(1):368-378. PubMed ID: 32767049
[TBL] [Abstract][Full Text] [Related]
40. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.
Militello C; Rundo L; Dimarco M; Orlando A; Woitek R; D'Angelo I; Russo G; Bartolotta TV
Acad Radiol; 2022 Jun; 29(6):830-840. PubMed ID: 34600805
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
