162 related articles for article (PubMed ID: 37033061)
1. Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study.
Zhang Y; Yi X; Tang Z; Xie P; Yin N; Deng Q; Zhu L; Luo H; Peng K
Front Public Health; 2023; 11():1104931. PubMed ID: 37033061
[TBL] [Abstract][Full Text] [Related]
2. A clinical prediction model for predicting the risk of liver metastasis from renal cell carcinoma based on machine learning.
Wang Z; Xu C; Liu W; Zhang M; Zou J; Shao M; Feng X; Yang Q; Li W; Shi X; Zang G; Yin C
Front Endocrinol (Lausanne); 2022; 13():1083569. PubMed ID: 36686417
[TBL] [Abstract][Full Text] [Related]
3. Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma.
Feng X; Hong T; Liu W; Xu C; Li W; Yang B; Song Y; Li T; Li W; Zhou H; Yin C
Front Endocrinol (Lausanne); 2022; 13():1054358. PubMed ID: 36465636
[TBL] [Abstract][Full Text] [Related]
4. Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients.
Xu C; Liu W; Yin C; Li W; Liu J; Sheng W; Tang H; Li W; Zhang Q
Comput Math Methods Med; 2022; 2022():5676570. PubMed ID: 36226243
[TBL] [Abstract][Full Text] [Related]
5. Applying machine learning techniques to predict the risk of lung metastases from rectal cancer: a real-world retrospective study.
Qiu B; Shen Z; Yang D; Wang Q
Front Oncol; 2023; 13():1183072. PubMed ID: 37293595
[TBL] [Abstract][Full Text] [Related]
6. A machine learning-based model for predicting distant metastasis in patients with rectal cancer.
Qiu B; Shen Z; Wu S; Qin X; Yang D; Wang Q
Front Oncol; 2023; 13():1235121. PubMed ID: 37655097
[TBL] [Abstract][Full Text] [Related]
7. Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients.
Li W; Liu Y; Liu W; Tang ZR; Dong S; Li W; Zhang K; Xu C; Hu Z; Wang H; Lei Z; Liu Q; Guo C; Yin C
Front Oncol; 2022; 12():797103. PubMed ID: 35515104
[TBL] [Abstract][Full Text] [Related]
8. Application of machine learning techniques in real-world research to predict the risk of liver metastasis in rectal cancer.
Qiu B; Su XH; Qin X; Wang Q
Front Oncol; 2022; 12():1065468. PubMed ID: 36605425
[TBL] [Abstract][Full Text] [Related]
9. Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma.
Zhu J; Zheng J; Li L; Huang R; Ren H; Wang D; Dai Z; Su X
Front Med (Lausanne); 2021; 8():635771. PubMed ID: 33768105
[No Abstract] [Full Text] [Related]
10. Factors related to lymph node sampling at the time of surgery in children, adolescents, and young adults with unilateral non-metastatic renal cell carcinoma.
Saltzman AF; Stokes W; Walker J; Cost NG
J Pediatr Urol; 2019 May; 15(3):259.e1-259.e7. PubMed ID: 30819622
[TBL] [Abstract][Full Text] [Related]
11. The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm.
Le Y; Xu W; Guo W
Technol Cancer Res Treat; 2023; 22():15330338231165131. PubMed ID: 37078130
[TBL] [Abstract][Full Text] [Related]
12. An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning.
Li W; Liu W; Hussain Memon F; Wang B; Xu C; Dong S; Wang H; Hu Z; Quan X; Deng Y; Liu Q; Su S; Yin C
Comput Intell Neurosci; 2022; 2022():2220527. PubMed ID: 35571720
[TBL] [Abstract][Full Text] [Related]
13. Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.
Yi X; Zhang Y; Cai J; Hu Y; Wen K; Xie P; Yin N; Zhou X; Luo H
Int J Clin Pract; 2023; 2023():8001899. PubMed ID: 37383704
[TBL] [Abstract][Full Text] [Related]
14. Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation.
Kocak B; Yardimci AH; Bektas CT; Turkcanoglu MH; Erdim C; Yucetas U; Koca SB; Kilickesmez O
Eur J Radiol; 2018 Oct; 107():149-157. PubMed ID: 30292260
[TBL] [Abstract][Full Text] [Related]
15. Development and Validation of a Nomogram to Predict Distant Metastasis in Elderly Patients With Renal Cell Carcinoma.
Wang J; Zhanghuang C; Tan X; Mi T; Liu J; Jin L; Li M; Zhang Z; He D
Front Public Health; 2021; 9():831940. PubMed ID: 35155365
[TBL] [Abstract][Full Text] [Related]
16. Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma.
Khene ZE; Bigot P; Doumerc N; Ouzaid I; Boissier R; Nouhaud FX; Albiges L; Bernhard JC; Ingels A; Borchiellini D; Kammerer-Jacquet S; Rioux-Leclercq N; Roupret M; Acosta O; De Crevoisier R; Bensalah K;
Eur Urol Oncol; 2023 Jun; 6(3):323-330. PubMed ID: 35987730
[TBL] [Abstract][Full Text] [Related]
17. Development and validation of a nomogram to predict overall survival for patients with metastatic renal cell carcinoma.
Zheng W; Zhu W; Yu S; Li K; Ding Y; Wu Q; Tang Q; Zhao Q; Lu C; Guo C
BMC Cancer; 2020 Nov; 20(1):1066. PubMed ID: 33148204
[TBL] [Abstract][Full Text] [Related]
18. A Machine Learning-Based Predictive Model for Predicting Lymph Node Metastasis in Patients With Ewing's Sarcoma.
Li W; Zhou Q; Liu W; Xu C; Tang ZR; Dong S; Wang H; Li W; Zhang K; Li R; Zhang W; Hu Z; Shibin S; Liu Q; Kuang S; Yin C
Front Med (Lausanne); 2022; 9():832108. PubMed ID: 35463005
[TBL] [Abstract][Full Text] [Related]
19. LASSO-based machine learning models for the prediction of central lymph node metastasis in clinically negative patients with papillary thyroid carcinoma.
Feng JW; Ye J; Qi GF; Hong LZ; Wang F; Liu SY; Jiang Y
Front Endocrinol (Lausanne); 2022; 13():1030045. PubMed ID: 36506061
[TBL] [Abstract][Full Text] [Related]
20. A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis.
Ding L; Zhang C; Wang K; Zhang Y; Wu C; Xia W; Li S; Li W; Wang J
Front Surg; 2023; 10():1095545. PubMed ID: 37009612
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]