173 related articles for article (PubMed ID: 37524093)
1. Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.
Wu P; Jiang Y; Xing H; Song W; Cui X; Wu XL; Xu G
Phys Med Biol; 2023 Aug; 68(17):. PubMed ID: 37524093
[No Abstract] [Full Text] [Related]
2. 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]
3. Deep Learning Radiomics Nomogram Based on Multiphase Computed Tomography for Predicting Axillary Lymph Node Metastasis in Breast Cancer.
Zhang J; Yin W; Yang L; Yao X
Mol Imaging Biol; 2024 Feb; 26(1):90-100. PubMed ID: 37563517
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Ultrasound images-based deep learning radiomics nomogram for preoperative prediction of
Yu J; Zhang Y; Zheng J; Jia M; Lu X
Front Endocrinol (Lausanne); 2022; 13():1062571. PubMed ID: 36605945
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram.
Zhang H; Zhao T; Zhang S; Sun J; Zhang F; Li X; Ni X
Technol Cancer Res Treat; 2023; 22():15330338231166218. PubMed ID: 36987661
[No Abstract] [Full Text] [Related]
8. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.
Chen X; Feng B; Xu K; Chen Y; Duan X; Jin Z; Li K; Li R; Long W; Liu X
Eur Radiol; 2023 Oct; 33(10):6804-6816. PubMed ID: 37148352
[TBL] [Abstract][Full Text] [Related]
9. A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.
Nie P; Yang G; Wang Y; Xu Y; Yan L; Zhang M; Zhao L; Wang N; Zhao X; Li X; Cheng N; Wang Y; Chen C; Wang N; Duan S; Wang X; Wang Z
Eur Radiol; 2023 Dec; 33(12):8858-8868. PubMed ID: 37389608
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.
Song H; Yang S; Yu B; Li N; Huang Y; Sun R; Wang B; Nie P; Hou F; Huang C; Zhang M; Wang H
Cancer Imaging; 2023 Sep; 23(1):89. PubMed ID: 37723572
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study.
Tian Z; Cheng Y; Zhao S; Li R; Zhou J; Sun Q; Wang D
Cancer Imaging; 2024 Apr; 24(1):52. PubMed ID: 38627828
[TBL] [Abstract][Full Text] [Related]
15. Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.
Zhou H; Bai HX; Jiao Z; Cui B; Wu J; Zheng H; Yang H; Liao W
Eur J Radiol; 2023 Nov; 168():111136. PubMed ID: 37832194
[TBL] [Abstract][Full Text] [Related]
16. Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.
Liu H; Zou L; Xu N; Shen H; Zhang Y; Wan P; Wen B; Zhang X; He Y; Gui L; Kong W
NPJ Breast Cancer; 2024 Mar; 10(1):22. PubMed ID: 38472210
[TBL] [Abstract][Full Text] [Related]
17. A Deep Learning Radiomics Nomogram to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Cervical Cancer: A Two-Center Study.
Zhang Y; Wu C; Xiao Z; Lv F; Liu Y
Diagnostics (Basel); 2023 Mar; 13(6):. PubMed ID: 36980381
[No Abstract] [Full Text] [Related]
18. A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.
Zheng YM; Che JY; Yuan MG; Wu ZJ; Pang J; Zhou RZ; Li XL; Dong C
Acad Radiol; 2023 Aug; 30(8):1591-1599. PubMed ID: 36460582
[TBL] [Abstract][Full Text] [Related]
19. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer.
Zhang X; Yang Z; Cui W; Zheng C; Li H; Li Y; Lu L; Mao J; Zeng W; Yang X; Zheng J; Shen J
Eur Radiol; 2021 Aug; 31(8):5924-5939. PubMed ID: 33569620
[TBL] [Abstract][Full Text] [Related]
20. An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy.
Wu Y; Ma Q; Fan L; Wu S; Wang J
Acad Radiol; 2024 Jan; 31(1):93-103. PubMed ID: 37544789
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]