160 related articles for article (PubMed ID: 35993045)
1. Deep Learning Mechanism for Predicting the Axillary Lymph Node Metastasis in Patients with Primary Breast Cancer.
Ashokkumar N; Meera S; Anandan P; Murthy MYB; Kalaivani KS; Alahmadi TA; Alharbi SA; Raghavan SS; Jayadhas SA
Biomed Res Int; 2022; 2022():8616535. PubMed ID: 35993045
[TBL] [Abstract][Full Text] [Related]
2. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.
Zhou LQ; Wu XL; Huang SY; Wu GG; Ye HR; Wei Q; Bao LY; Deng YB; Li XR; Cui XW; Dietrich CF
Radiology; 2020 Jan; 294(1):19-28. PubMed ID: 31746687
[TBL] [Abstract][Full Text] [Related]
3. Role of diffusion-weighted MRI: predicting axillary lymph node metastases in breast cancer.
Chung J; Youk JH; Kim JA; Gweon HM; Kim EK; Ryu YH; Son EJ
Acta Radiol; 2014 Oct; 55(8):909-16. PubMed ID: 24234236
[TBL] [Abstract][Full Text] [Related]
4. Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images.
Park TY; Kwon LM; Hyeon J; Cho BJ; Kim BJ
Curr Oncol; 2024 Apr; 31(4):2278-2288. PubMed ID: 38668072
[No Abstract] [Full Text] [Related]
5. Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.
Gao J; Zhong X; Li W; Li Q; Shao H; Wang Z; Dai Y; Ma H; Shi Y; Zhang H; Duan S; Zhang K; Yang P; Zhao F; Zhang H; Xie H; Mao N
J Magn Reson Imaging; 2023 Jun; 57(6):1842-1853. PubMed ID: 36219519
[TBL] [Abstract][Full Text] [Related]
6. Deep learning method with a convolutional neural network for image classification of normal and metastatic axillary lymph nodes on breast ultrasonography.
Ozaki J; Fujioka T; Yamaga E; Hayashi A; Kujiraoka Y; Imokawa T; Takahashi K; Okawa S; Yashima Y; Mori M; Kubota K; Oda G; Nakagawa T; Tateishi U
Jpn J Radiol; 2022 Aug; 40(8):814-822. PubMed ID: 35284996
[TBL] [Abstract][Full Text] [Related]
7. Sentinel lymph node as a new marker for therapeutic planning in breast cancer patients.
Gipponi M; Bassetti C; Canavese G; Catturich A; Di Somma C; Vecchio C; Nicolò G; Schenone F; Tomei D; Cafiero F
J Surg Oncol; 2004 Mar; 85(3):102-11. PubMed ID: 14991881
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. In vitro diagnosis of axillary lymph node metastases in breast cancer by spectrum analysis of radio frequency echo signals.
Tateishi T; Machi J; Feleppa EJ; Oishi R; Jucha J; Yanagihara E; McCarthy LJ; Noritomi T; Shirouzu K
Ultrasound Med Biol; 1998 Oct; 24(8):1151-9. PubMed ID: 9833584
[TBL] [Abstract][Full Text] [Related]
10. Apparent diffusion coefficient ratio between axillary lymph node with primary tumor to detect nodal metastasis in breast cancer patients.
Luo N; Su D; Jin G; Liu L; Zhu X; Xie D; Liu Y
J Magn Reson Imaging; 2013 Oct; 38(4):824-8. PubMed ID: 23440958
[TBL] [Abstract][Full Text] [Related]
11. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
Gu J; Tong T; Xu D; Cheng F; Fang C; He C; Wang J; Wang B; Yang X; Wang K; Tian J; Jiang T
Cancer; 2023 Feb; 129(3):356-366. PubMed ID: 36401611
[TBL] [Abstract][Full Text] [Related]
12. Diagnostic Accuracy of Axillary Ultrasonography Compared with Intra-operative Pathological Findings in Patients with Breast Cancer.
Rezvani A; Zahergivar A; Iranpour P; Akrami M; Kazemi S
Asian Pac J Cancer Prev; 2018 Dec; 19(12):3615-3621. PubMed ID: 30583690
[TBL] [Abstract][Full Text] [Related]
13. Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI.
Ren T; Cattell R; Duanmu H; Huang P; Li H; Vanguri R; Liu MZ; Jambawalikar S; Ha R; Wang F; Cohen J; Bernstein C; Bangiyev L; Duong TQ
Clin Breast Cancer; 2020 Jun; 20(3):e301-e308. PubMed ID: 32139272
[TBL] [Abstract][Full Text] [Related]
14. Preoperative MRI improves prediction of extensive occult axillary lymph node metastases in breast cancer patients with a positive sentinel lymph node biopsy.
Loiselle C; Eby PR; Kim JN; Calhoun KE; Allison KH; Gadi VK; Peacock S; Storer BE; Mankoff DA; Partridge SC; Lehman CD
Acad Radiol; 2014 Jan; 21(1):92-8. PubMed ID: 24331270
[TBL] [Abstract][Full Text] [Related]
15. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.
Guo X; Liu Z; Sun C; Zhang L; Wang Y; Li Z; Shi J; Wu T; Cui H; Zhang J; Tian J; Tian J
EBioMedicine; 2020 Oct; 60():103018. PubMed ID: 32980697
[TBL] [Abstract][Full Text] [Related]
16. Correlation between the contrast-enhanced ultrasound image features and axillary lymph node metastasis of primary breast cancer and its diagnostic value.
Xu H; Xu GL; Li XD; Su QH; Dong CZ
Clin Transl Oncol; 2021 Jan; 23(1):155-163. PubMed ID: 32488804
[TBL] [Abstract][Full Text] [Related]
17. Prospective evaluation of fluorine-18-FDG PET in presurgical staging of the axilla in breast cancer.
Crippa F; Agresti R; Seregni E; Greco M; Pascali C; Bogni A; Chiesa C; De Sanctis V; Delledonne V; Salvadori B; Leutner M; Bombardieri E
J Nucl Med; 1998 Jan; 39(1):4-8. PubMed ID: 9443729
[TBL] [Abstract][Full Text] [Related]
18. [Qualitative and quantitative PET/MRI in the evaluation of axillary lymph node metastasis in patients with breast cancer].
Chen S; Huo LQ; Luo S; Wang XX; Xu SJ; Ma Y; Gu HF; Gu J; Lu GM; Zhang LJ
Zhonghua Yi Xue Za Zhi; 2023 Aug; 103(31):2433-2439. PubMed ID: 37599218
[No Abstract] [Full Text] [Related]
19. Initial experience with sentinel node biopsy in breast cancer at the National Cancer Center Hospital East.
Imoto S; Hasebe T
Jpn J Clin Oncol; 1999 Jan; 29(1):11-5. PubMed ID: 10073145
[TBL] [Abstract][Full Text] [Related]
20. Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning.
Liu Z; Ni S; Yang C; Sun W; Huang D; Su H; Shu J; Qin N
Comput Biol Med; 2021 Sep; 136():104715. PubMed ID: 34388460
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
