57 related articles for article (PubMed ID: 33712393)
1. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging.
Zhan C; Hu Y; Wang X; Liu H; Xia L; Ai T
Acad Radiol; 2022 Jan; 29 Suppl 1():S107-S115. PubMed ID: 33712393
[TBL] [Abstract][Full Text] [Related]
2. Radiomics in cone-beam breast CT for the prediction of axillary lymph node metastasis in breast cancer: a multi-center multi-device study.
Zhu Y; Ma Y; Zhai Z; Liu A; Wang Y; Zhang Y; Li H; Zhao M; Han P; Yin L; He N; Wu Y; Sechopoulos I; Ye Z; Caballo M
Eur Radiol; 2024 Apr; 34(4):2576-2589. PubMed ID: 37782338
[TBL] [Abstract][Full Text] [Related]
3. A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.
Cai R; Deng L; Zhang H; Zhang H; Wu Q
Radiat Oncol; 2024 May; 19(1):63. PubMed ID: 38802938
[TBL] [Abstract][Full Text] [Related]
4. Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer.
Wang Y; Shang Y; Guo Y; Hai M; Gao Y; Wu Q; Li S; Liao J; Sun X; Wu Y; Wang M; Tan H
Front Oncol; 2024; 14():1357145. PubMed ID: 38567148
[TBL] [Abstract][Full Text] [Related]
5. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.
Zhang B; Yu Y; Mao Y; Wang H; Lv M; Su X; Wang Y; Li Z; Zhang Z; Bian T; Wang Q
Acad Radiol; 2024 Mar; 31(3):800-811. PubMed ID: 37914627
[TBL] [Abstract][Full Text] [Related]
6. Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.
Zhou H; Hua Z; Gao J; Lin F; Chen Y; Zhang S; Zheng T; Wang Z; Shao H; Li W; Liu F; Li Q; Chen J; Wang X; Zhao F; Qu N; Xie H; Ma H; Zhang H; Mao N
J Magn Reson Imaging; 2024 May; 59(5):1710-1722. PubMed ID: 37497811
[TBL] [Abstract][Full Text] [Related]
7. A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.
Lai J; Chen Z; Liu J; Zhu C; Huang H; Yi Y; Cai G; Liao N
Int J Surg; 2024 Apr; 110(4):2162-2177. PubMed ID: 38215256
[TBL] [Abstract][Full Text] [Related]
8. Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.
Liu W; Chen W; Xia J; Lu Z; Fu Y; Li Y; Tan Z
BMC Med Imaging; 2024 Apr; 24(1):91. PubMed ID: 38627678
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning Prediction of Lymph Node Metastasis in Breast Cancer: Performance of a Multi-institutional MRI-based 4D Convolutional Neural Network.
Polat DS; Nguyen S; Karbasi P; Hulsey K; Cobanoglu MC; Wang L; Montillo A; Dogan BE
Radiol Imaging Cancer; 2024 May; 6(3):e230107. PubMed ID: 38607282
[TBL] [Abstract][Full Text] [Related]
10. Radiomics analysis of intratumoral and different peritumoral regions from multiparametric MRI for evaluating HER2 status of breast cancer: A comparative study.
Zhou J; Yu X; Wu Q; Wu Y; Fu C; Wang Y; Hai M; Tan H; Wang M
Heliyon; 2024 Apr; 10(7):e28722. PubMed ID: 38623231
[TBL] [Abstract][Full Text] [Related]
11. Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis.
Chen D; Liu X; Hu C; Hao R; Wang O; Xiao Y
Future Oncol; 2022 Dec; ():1-14. PubMed ID: 36475996
[TBL] [Abstract][Full Text] [Related]
12. Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction.
Han X; Gong Z; Guo Y; Tang W; Wei X
Eur J Radiol; 2024 Jun; 175():111441. PubMed ID: 38537607
[TBL] [Abstract][Full Text] [Related]
13. Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models.
Lin Y; Wang J; Li M; Zhou C; Hu Y; Wang M; Zhang X
Breast; 2024 Apr; 76():103737. PubMed ID: 38696854
[TBL] [Abstract][Full Text] [Related]
14. Editorial for "Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI".
Morrell GR
J Magn Reson Imaging; 2024 Jun; 59(6):2250-2251. PubMed ID: 37855435
[No Abstract] [Full Text] [Related]
15. Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art.
Calabrese A; Santucci D; Landi R; Beomonte Zobel B; Faiella E; de Felice C
J Cancer Res Clin Oncol; 2021 Jun; 147(6):1587-1597. PubMed ID: 33758997
[TBL] [Abstract][Full Text] [Related]
16. Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status.
Li C; Song L; Yin J
J Magn Reson Imaging; 2021 Sep; 54(3):703-714. PubMed ID: 33955619
[TBL] [Abstract][Full Text] [Related]
17. An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies.
Pötsch N; Dietzel M; Kapetas P; Clauser P; Pinker K; Ellmann S; Uder M; Helbich T; Baltzer PAT
Eur Radiol; 2021 Aug; 31(8):5866-5876. PubMed ID: 33744990
[TBL] [Abstract][Full Text] [Related]
18. Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models.
Kayadibi Y; Kocak B; Ucar N; Akan YN; Akbas P; Bektas S
Acad Radiol; 2022 Jan; 29 Suppl 1():S116-S125. PubMed ID: 33744071
[TBL] [Abstract][Full Text] [Related]
19. Breast Tumor Characterization Using [
Krajnc D; Papp L; Nakuz TS; Magometschnigg HF; Grahovac M; Spielvogel CP; Ecsedi B; Bago-Horvath Z; Haug A; Karanikas G; Beyer T; Hacker M; Helbich TH; Pinker K
Cancers (Basel); 2021 Mar; 13(6):. PubMed ID: 33809057
[No Abstract] [Full Text] [Related]
20. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach.
Kim JP; Kim J; Jang H; Kim J; Kang SH; Kim JS; Lee J; Na DL; Kim HJ; Seo SW; Park H
Sci Rep; 2021 Mar; 11(1):6954. PubMed ID: 33772041
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]