292 related articles for article (PubMed ID: 36976336)
1. Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.
Yu FH; Miao SM; Li CY; Hang J; Deng J; Ye XH; Liu Y
Eur Radiol; 2023 Aug; 33(8):5634-5644. PubMed ID: 36976336
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study.
Gu J; Tong T; He C; Xu M; Yang X; Tian J; Jiang T; Wang K
Eur Radiol; 2022 Mar; 32(3):2099-2109. PubMed ID: 34654965
[TBL] [Abstract][Full Text] [Related]
4. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method.
Qu YH; Zhu HT; Cao K; Li XT; Ye M; Sun YS
Thorac Cancer; 2020 Mar; 11(3):651-658. PubMed ID: 31944571
[TBL] [Abstract][Full Text] [Related]
5. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
Huang JX; Shi J; Ding SS; Zhang HL; Wang XY; Lin SY; Xu YF; Wei MJ; Liu LZ; Pei XQ
Acad Radiol; 2023 Sep; 30 Suppl 2():S50-S61. PubMed ID: 37270368
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.
Li B; Li F; Liu Z; Xu F; Ye G; Li W; Zhang Y; Zhu T; Shao L; Chen C; Sun C; Qiu B; Bu H; Wang K; Tian J
Breast; 2022 Dec; 66():183-190. PubMed ID: 36308926
[TBL] [Abstract][Full Text] [Related]
8. Radiomics features based on automatic segmented MRI images: Prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy.
Ma M; Gan L; Liu Y; Jiang Y; Xin L; Liu Y; Qin N; Cheng Y; Liu Q; Xu L; Zhang Y; Wang X; Zhang X; Ye J; Wang X
Eur J Radiol; 2022 Jan; 146():110095. PubMed ID: 34890936
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer.
Jiang M; Li CL; Luo XM; Chuan ZR; Lv WZ; Li X; Cui XW; Dietrich CF
Eur J Cancer; 2021 Apr; 147():95-105. PubMed ID: 33639324
[TBL] [Abstract][Full Text] [Related]
11. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.
Li Y; Fan Y; Xu D; Li Y; Zhong Z; Pan H; Huang B; Xie X; Yang Y; Liu B
Front Oncol; 2022; 12():1041142. PubMed ID: 36686755
[TBL] [Abstract][Full Text] [Related]
12. Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.
Zhong J; Zhang C; Hu Y; Zhang J; Liu Y; Si L; Xing Y; Ding D; Geng J; Jiao Q; Zhang H; Yang G; Yao W
Eur Radiol; 2022 Sep; 32(9):6196-6206. PubMed ID: 35364712
[TBL] [Abstract][Full Text] [Related]
13. Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.
Joo S; Ko ES; Kwon S; Jeon E; Jung H; Kim JY; Chung MJ; Im YH
Sci Rep; 2021 Sep; 11(1):18800. PubMed ID: 34552163
[TBL] [Abstract][Full Text] [Related]
14. Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study.
Wang X; Hua H; Han J; Zhong X; Liu J; Chen J
Clin Breast Cancer; 2023 Aug; 23(6):e331-e344. PubMed ID: 37321954
[TBL] [Abstract][Full Text] [Related]
15. An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.
Wu L; Ye W; Liu Y; Chen D; Wang Y; Cui Y; Li Z; Li P; Li Z; Liu Z; Liu M; Liang C; Yang X; Xie Y; Wang Y
Breast Cancer Res; 2022 Nov; 24(1):81. PubMed ID: 36414984
[TBL] [Abstract][Full Text] [Related]
16. Noninvasive identification of HER2-low-positive status by MRI-based deep learning radiomics predicts the disease-free survival of patients with breast cancer.
Guo Y; Xie X; Tang W; Chen S; Wang M; Fan Y; Lin C; Hu W; Yang J; Xiang J; Jiang K; Wei X; Huang B; Jiang X
Eur Radiol; 2024 Feb; 34(2):899-913. PubMed ID: 37597033
[TBL] [Abstract][Full Text] [Related]
17. Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer.
Qi TH; Hian OH; Kumaran AM; Tan TJ; Cong TRY; Su-Xin GL; Lim EH; Ng R; Yeo MCR; Tching FLLW; Zewen Z; Hui CYS; Xin WR; Ooi SKG; Leong LCH; Tan SM; Preetha M; Sim Y; Tan VKM; Yeong J; Yong WF; Cai Y; Nei WL; ;
Breast Cancer Res Treat; 2022 May; 193(1):121-138. PubMed ID: 35262831
[TBL] [Abstract][Full Text] [Related]
18. Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients.
Zhang J; Wu Q; Yin W; Yang L; Xiao B; Wang J; Yao X
BMC Cancer; 2023 May; 23(1):431. PubMed ID: 37173635
[TBL] [Abstract][Full Text] [Related]
19. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals.
Byra M; Dobruch-Sobczak K; Piotrzkowska-Wroblewska H; Klimonda Z; Litniewski J
Phys Med Biol; 2022 Sep; 67(18):. PubMed ID: 36001984
[No Abstract] [Full Text] [Related]
20. Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound.
Zhang H; Cao W; Liu L; Meng Z; Sun N; Meng Y; Fei J
J Transl Med; 2023 May; 21(1):337. PubMed ID: 37211604
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
