174 related articles for article (PubMed ID: 37272242)
1. Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer.
Jung JJ; Kim EK; Kang E; Kim JH; Kim SH; Suh KJ; Kim SM; Jang M; Yun B; Park SY; Lim C; Han W; Shin HC
J Breast Cancer; 2023 Aug; 26(4):353-362. PubMed ID: 37272242
[TBL] [Abstract][Full Text] [Related]
2. Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?
Anderson AB; Grazal CF; Balazs GC; Potter BK; Dickens JF; Forsberg JA
Clin Orthop Relat Res; 2020 Jul; 478(7):0-1618. PubMed ID: 32282466
[TBL] [Abstract][Full Text] [Related]
3. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
4. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study.
Liu Y; Wang Y; Wang Y; Xie Y; Cui Y; Feng S; Yao M; Qiu B; Shen W; Chen D; Du G; Chen X; Liu Z; Li Z; Yang X; Liang C; Wu L
EClinicalMedicine; 2022 Oct; 52():101562. PubMed ID: 35928032
[TBL] [Abstract][Full Text] [Related]
5. Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records.
Rahimian F; Salimi-Khorshidi G; Payberah AH; Tran J; Ayala Solares R; Raimondi F; Nazarzadeh M; Canoy D; Rahimi K
PLoS Med; 2018 Nov; 15(11):e1002695. PubMed ID: 30458006
[TBL] [Abstract][Full Text] [Related]
6. Nomogram for predicting axillary lymph node pathological response in node-positive breast cancer patients after neoadjuvant chemotherapy.
Wang W; Wang X; Liu J; Zhu Q; Wang X; Wang P
Chin Med J (Engl); 2021 Dec; 135(3):333-340. PubMed ID: 35108228
[TBL] [Abstract][Full Text] [Related]
7. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
[TBL] [Abstract][Full Text] [Related]
8. Development of a web-based calculator to predict three-month mortality among patients with bone metastases from cancer of unknown primary: An internally and externally validated study using machine-learning techniques.
Cui Y; Wang Q; Shi X; Ye Q; Lei M; Wang B
Front Oncol; 2022; 12():1095059. PubMed ID: 36568149
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.
Wei Q; Chen Z; Tang Y; Chen W; Zhong L; Mao L; Hu S; Wu Y; Deng K; Yang W; Liu X
Eur Radiol; 2023 Mar; 33(3):1906-1917. PubMed ID: 36355199
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Development and External Validation of
Lim CH; Choi JY; Choi JH; Lee JH; Lee J; Lim CW; Kim Z; Woo SK; Park SB; Park JM
Cancers (Basel); 2023 Jul; 15(15):. PubMed ID: 37568658
[TBL] [Abstract][Full Text] [Related]
13. Deep learning-based predictive model for pathological complete response to neoadjuvant chemotherapy in breast cancer from biopsy pathological images: a multicenter study.
Zeng H; Qiu S; Zhuang S; Wei X; Wu J; Zhang R; Chen K; Wu Z; Zhuang Z
Front Physiol; 2024; 15():1279982. PubMed ID: 38357498
[No Abstract] [Full Text] [Related]
14. Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model.
Holm CE; Grazal CF; Raedkjaer M; Baad-Hansen T; Nandra R; Grimer R; Forsberg JA; Petersen MM; Skovlund Soerensen M
SAGE Open Med; 2022; 10():20503121221076387. PubMed ID: 35154743
[TBL] [Abstract][Full Text] [Related]
15. Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study.
Huang Y; Zhu T; Zhang X; Li W; Zheng X; Cheng M; Ji F; Zhang L; Yang C; Wu Z; Ye G; Lin Y; Wang K
EClinicalMedicine; 2023 Apr; 58():101899. PubMed ID: 37007742
[TBL] [Abstract][Full Text] [Related]
16. Development and Validation of a Prognostic Classification Model Predicting Postoperative Adverse Outcomes in Older Surgical Patients Using a Machine Learning Algorithm: Retrospective Observational Network Study.
Choi JY; Yoo S; Song W; Kim S; Baek H; Lee JS; Yoon YS; Yoon S; Lee HY; Kim KI
J Med Internet Res; 2023 Nov; 25():e42259. PubMed ID: 37955965
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Prognosis prediction and risk stratification of transarterial chemoembolization or intraarterial chemotherapy for unresectable hepatocellular carcinoma based on machine learning.
Liu W; Wei R; Chen J; Li Y; Pang H; Zhang W; An C; Li C
Eur Radiol; 2024 Jan; ():. PubMed ID: 38291256
[TBL] [Abstract][Full Text] [Related]
19. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
20. Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.
Meti N; Saednia K; Lagree A; Tabbarah S; Mohebpour M; Kiss A; Lu FI; Slodkowska E; Gandhi S; Jerzak KJ; Fleshner L; Law E; Sadeghi-Naini A; Tran WT
JCO Clin Cancer Inform; 2021 Jan; 5():66-80. PubMed ID: 33439725
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
