18 related articles for article (PubMed ID: 38658211)
1. Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.
Wang Y; Gao J; Yin Z; Wen Y; Sun M; Han R
Front Oncol; 2024; 14():1384105. PubMed ID: 38803533
[TBL] [Abstract][Full Text] [Related]
2. An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.
Chen H; Liu X; Luo X; Fu J; Zhou K; Wang N; Li Y; Geng D
Front Aging Neurosci; 2024; 16():1397896. PubMed ID: 38832074
[TBL] [Abstract][Full Text] [Related]
3. Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.
Wang J; Yang Y; Xie Z; Mao G; Gao C; Niu Z; Ji H; He L; Zhu X; Shi H; Xu M
Acad Radiol; 2024 Jun; ():. PubMed ID: 38845293
[TBL] [Abstract][Full Text] [Related]
4. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
Cui Y; Zhang J; Li Z; Wei K; Lei Y; Ren J; Wu L; Shi Z; Meng X; Yang X; Gao X
EClinicalMedicine; 2022 Apr; 46():101348. PubMed ID: 35340629
[TBL] [Abstract][Full Text] [Related]
5. Ultrasound radiomics-based nomogram to predict lymphovascular invasion in invasive breast cancer: a multicenter, retrospective study.
Du Y; Cai M; Zha H; Chen B; Gu J; Zhang M; Liu W; Liu X; Liu X; Zong M; Li C
Eur Radiol; 2024 Jan; 34(1):136-148. PubMed ID: 37518678
[TBL] [Abstract][Full Text] [Related]
6. Preoperative prediction of lymphovascular invasion in patients with T1 breast invasive ductal carcinoma based on radiomics nomogram using grayscale ultrasound.
Xu ML; Zeng SE; Li F; Cui XW; Liu GF
Front Oncol; 2022; 12():1071677. PubMed ID: 36568215
[TBL] [Abstract][Full Text] [Related]
7. Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer.
Xu M; Yang H; Sun J; Hao H; Li X; Liu G
Acad Radiol; 2024 May; 31(5):1748-1761. PubMed ID: 38097466
[TBL] [Abstract][Full Text] [Related]
8. Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.
Zhang Y; Wu C; Du J; Xiao Z; Lv F; Liu Y
Abdom Radiol (NY); 2024 Jan; 49(1):258-270. PubMed ID: 37987856
[TBL] [Abstract][Full Text] [Related]
9. Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.
Zhang D; Zhou W; Lu WW; Qin XC; Zhang XY; Wang JL; Wu J; Luo YH; Duan YY; Zhang CX
Acad Radiol; 2024 Apr; ():. PubMed ID: 38658211
[TBL] [Abstract][Full Text] [Related]
10.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
11.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
12.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
13.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
14.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
15.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
16.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
17.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
18.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]