110 related articles for article (PubMed ID: 38258592)
1. Application of Machine-learning based on Radiomics Features in Differential Diagnosis of Superficial Lymphadenopathy.
Lyu S; Zhang M; Yang L; Zhang B; Gao L; Yang L; Zhang Y
Curr Med Imaging; 2024 Jan; ():. PubMed ID: 38258592
[TBL] [Abstract][Full Text] [Related]
2. FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.
Eifer M; Pinian H; Klang E; Alhoubani Y; Kanana N; Tau N; Davidson T; Konen E; Catalano OA; Eshet Y; Domachevsky L
Eur Radiol; 2022 Sep; 32(9):5921-5929. PubMed ID: 35385985
[TBL] [Abstract][Full Text] [Related]
3. [Multi-class discrimination of lymphadenopathy by using dual-modal ultrasound radiomics with elastography and B-mode ultrasound].
Shi J; Jiang J; Chang W; Chen M; Zhang Q
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2019 Dec; 36(6):957-963. PubMed ID: 31875369
[TBL] [Abstract][Full Text] [Related]
4. Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.
Zhang W; Peng J; Zhao S; Wu W; Yang J; Ye J; Xu S
J Cancer Res Clin Oncol; 2022 Oct; 148(10):2773-2780. PubMed ID: 35562596
[TBL] [Abstract][Full Text] [Related]
5. Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.
Zhu Y; Yang L; Shen H
Front Oncol; 2021; 11():757111. PubMed ID: 34868967
[TBL] [Abstract][Full Text] [Related]
6. Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study.
Liu Y; Chen J; Zhang C; Li Q; Zhou H; Zeng Y; Zhang Y; Li J; Xv W; Li W; Zhu J; Zhao Y; Chen Q; Huang Y; Li H; Huang Y; Yang G; Huang P
Front Oncol; 2022; 12():856605. PubMed ID: 35656511
[TBL] [Abstract][Full Text] [Related]
7. Classification of superficial suspected lymph nodes: non-invasive radiomic model based on multiphase contrast-enhanced ultrasound for therapeutic options of lymphadenopathy.
Jiang Z; Yuan F; Zhang Q; Zhu J; Xu M; Hu Y; Hou C; Liu X
Quant Imaging Med Surg; 2024 Feb; 14(2):1507-1525. PubMed ID: 38415137
[TBL] [Abstract][Full Text] [Related]
8. Dual-Energy CT Texture Analysis With Machine Learning for the Evaluation and Characterization of Cervical Lymphadenopathy.
Seidler M; Forghani B; Reinhold C; Pérez-Lara A; Romero-Sanchez G; Muthukrishnan N; Wichmann JL; Melki G; Yu E; Forghani R
Comput Struct Biotechnol J; 2019; 17():1009-1015. PubMed ID: 31406557
[TBL] [Abstract][Full Text] [Related]
9. Using ultrasound radiomics analysis to diagnose cervical lymph node metastasis in patients with nasopharyngeal carcinoma.
Lin M; Tang X; Cao L; Liao Y; Zhang Y; Zhou J
Eur Radiol; 2023 Feb; 33(2):774-783. PubMed ID: 36070091
[TBL] [Abstract][Full Text] [Related]
10. Using T2-weighted magnetic resonance imaging-derived radiomics to classify cervical lymphadenopathy in children.
Xu Y; Chu C; Wang Q; Xiang L; Lu M; Yan W; Huang L
Pediatr Radiol; 2024 Jun; ():. PubMed ID: 38937304
[TBL] [Abstract][Full Text] [Related]
11. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images.
Jin X; Ai Y; Zhang J; Zhu H; Jin J; Teng Y; Chen B; Xie C
Eur Radiol; 2020 Jul; 30(7):4117-4124. PubMed ID: 32078013
[TBL] [Abstract][Full Text] [Related]
12. Dual-mode ultrasound radiomics and intrinsic imaging phenotypes for diagnosis of lymph node lesions.
Chen Y; Jiang J; Shi J; Chang W; Shi J; Chen M; Zhang Q
Ann Transl Med; 2020 Jun; 8(12):742. PubMed ID: 32647667
[TBL] [Abstract][Full Text] [Related]
13. Prediction of Individual Lymph Node Metastatic Status in Esophageal Squamous Cell Carcinoma Using Routine Computed Tomography Imaging: Comparison of Size-Based Measurements and Radiomics-Based Models.
Xie C; Hu Y; Han L; Fu J; Vardhanabhuti V; Yang H
Ann Surg Oncol; 2022 Dec; 29(13):8117-8126. PubMed ID: 36018524
[TBL] [Abstract][Full Text] [Related]
14. The value of multimodal ultrasound in diagnosis of cervical lymphadenopathy: can real-time elastography help identify benign and malignant lymph nodes?
Tong J; Lin T; Wen B; Chen P; Wang Y; Yu Y; Chen M; Yang G
Front Oncol; 2023; 13():1073614. PubMed ID: 38090486
[TBL] [Abstract][Full Text] [Related]
15. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer.
Li X; Yang L; Jiao X
Acad Radiol; 2023 Jul; 30(7):1281-1287. PubMed ID: 36376154
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT.
Yang Y; Zheng B; Li Y; Li Y; Ma X
Med Phys; 2023 Jan; 50(1):152-162. PubMed ID: 35925871
[TBL] [Abstract][Full Text] [Related]
17. Imaging biomarkers to stratify lymph node metastases in abdominal CT - Is radiomics superior to dual-energy material decomposition?
Mahmoudi S; Koch V; Santos DPD; Ackermann J; Grünewald LD; Weitkamp I; Yel I; Martin SS; Albrecht MH; Scholtz JE; Vogl TJ; Bernatz S
Eur J Radiol Open; 2023; 10():100459. PubMed ID: 36561422
[TBL] [Abstract][Full Text] [Related]
18. An [
Meng N; Feng P; Yu X; Wu Y; Fu F; Li Z; Luo Y; Tan H; Yuan J; Yang Y; Wang Z; Wang M
Eur Radiol; 2024 Jan; 34(1):318-329. PubMed ID: 37530809
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.
Tang Y; Yang CM; Su S; Wang WJ; Fan LP; Shu J
BMC Cancer; 2021 Nov; 21(1):1268. PubMed ID: 34819043
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
