164 related articles for article (PubMed ID: 36653246)
1. Development of a machine learning algorithm to predict complications of total laparoscopic anterior resection and natural orifice specimen extraction surgery in rectal cancer.
Wei R; Guan X; Liu E; Zhang W; Lv J; Huang H; Zhao Z; Chen H; Liu Z; Jiang Z; Wang X
Eur J Surg Oncol; 2023 Jul; 49(7):1258-1268. PubMed ID: 36653246
[TBL] [Abstract][Full Text] [Related]
2. Machine learning model for prediction of low anterior resection syndrome following laparoscopic anterior resection of rectal cancer: A multicenter study.
Wang Z; Shao SL; Liu L; Lu QY; Mu L; Qin JC
World J Gastroenterol; 2023 May; 29(19):2979-2991. PubMed ID: 37274801
[TBL] [Abstract][Full Text] [Related]
3. A nomogram for predicting feasibility of laparoscopic anterior resection with trans-rectal specimen extraction (NOSES) in patients with upper rectal cancer.
Zhang ZY; Zhu Z; Zhang Y; Ni L; Lu B
BMC Surg; 2021 Jun; 21(1):296. PubMed ID: 34140016
[TBL] [Abstract][Full Text] [Related]
4. Interpretable machine learning model to predict surgical difficulty in laparoscopic resection for rectal cancer.
Yu M; Yuan Z; Li R; Shi B; Wan D; Dong X
Front Oncol; 2024; 14():1337219. PubMed ID: 38380369
[TBL] [Abstract][Full Text] [Related]
5. Machine learning based prediction of recurrence after curative resection for rectal cancer.
Jeon Y; Kim YJ; Jeon J; Nam KH; Hwang TS; Kim KG; Baek JH
PLoS One; 2023; 18(12):e0290141. PubMed ID: 38100485
[TBL] [Abstract][Full Text] [Related]
6. Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting.
Zhang Y; Li L; Li Y; Zeng Z
Adv Clin Exp Med; 2023 Feb; 32(2):185-194. PubMed ID: 36226692
[TBL] [Abstract][Full Text] [Related]
7. Comparative short- and long-term outcomes of three techniques of natural orifice specimen extraction surgery for rectal cancer.
Guan X; Lu Z; Wang S; Liu E; Zhao Z; Chen H; Zhang M; Hu X; Muhammad S; Ma C; Ma X; Huang H; Jiang Z; Liu Z; Wang G; Wang X
Eur J Surg Oncol; 2020 Oct; 46(10 Pt B):e55-e61. PubMed ID: 32782201
[TBL] [Abstract][Full Text] [Related]
8. Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.
Li X; Zhou Z; Zhu B; Wu Y; Xing C
World J Surg Oncol; 2024 Apr; 22(1):111. PubMed ID: 38664824
[TBL] [Abstract][Full Text] [Related]
9. Advancing mid-rectal cancer surgery: Unveiling the potential of natural orifice specimen extraction surgery in comparison to conventional laparoscopic-assisted resection.
Muhammad S; Jiang Z; Fan T; Tang Q; Hai Y; Ehsan SBE; Bilal M; Zubayraeva AA; Gao Y; He J
Cancer Rep (Hoboken); 2024 May; 7(5):e2003. PubMed ID: 38703000
[TBL] [Abstract][Full Text] [Related]
10. Long-term Oncologic Outcomes of Laparoscopic Anterior Resections for Cancer with Natural Orifice Versus Conventional Specimen Extraction: A Case-Control Study.
Chang SC; Chen HC; Chen YC; Ke TW; Tsai YY; Wang HM; Fingerhut A; Chen WT
Dis Colon Rectum; 2020 Aug; 63(8):1071-1079. PubMed ID: 32692072
[TBL] [Abstract][Full Text] [Related]
11. Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME.
Liu Y; Zhao S; Du W; Tian Z; Chi H; Chao C; Shen W
Front Surg; 2023; 10():1125875. PubMed ID: 37035560
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.
Luo XQ; Kang YX; Duan SB; Yan P; Song GB; Zhang NY; Yang SK; Li JX; Zhang H
J Med Internet Res; 2023 Jan; 25():e41142. PubMed ID: 36603200
[TBL] [Abstract][Full Text] [Related]
13. Using Machine Learning Algorithms to Predict High-Risk Factors for Postoperative Delirium in Elderly Patients.
Liu Y; Shen W; Tian Z
Clin Interv Aging; 2023; 18():157-168. PubMed ID: 36789284
[TBL] [Abstract][Full Text] [Related]
14. Using machine learning to identify patients at high risk of developing low bone density or osteoporosis after gastrectomy: a 10-year multicenter retrospective analysis.
Zhu Y; Liu Y; Wang Q; Niu S; Wang L; Cheng C; Chen X; Liu J; Zhao S
J Cancer Res Clin Oncol; 2023 Dec; 149(19):17479-17493. PubMed ID: 37897658
[TBL] [Abstract][Full Text] [Related]
15. A Machine Learning Algorithm for Predicting the Risk of Developing to M1b Stage of Patients With Germ Cell Testicular Cancer.
Ding L; Wang K; Zhang C; Zhang Y; Wang K; Li W; Wang J
Front Public Health; 2022; 10():916513. PubMed ID: 35844840
[TBL] [Abstract][Full Text] [Related]
16. Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy.
Liu Y; Song C; Tian Z; Shen W
Int J Gen Med; 2023; 16():1909-1925. PubMed ID: 37228741
[TBL] [Abstract][Full Text] [Related]
17. Development of prediction model of low anterior resection syndrome for colorectal cancer patients after surgery based on machine-learning technique.
Huang MJ; Ye L; Yu KX; Liu J; Li K; Wang XD; Li JP
Cancer Med; 2023 Jan; 12(2):1501-1519. PubMed ID: 35899858
[TBL] [Abstract][Full Text] [Related]
18. [Analysis of 17 cases underwent laparoscopic rectal cancer surgery with transanal natural orifice specimen extraction and resection].
Zhou HT; Su H; Zhou ZX; Liu Q; Liang JW; Shan Y; Pei W; Wang Z; Wang P; Shi L; Wang XS
Zhonghua Zhong Liu Za Zhi; 2018 Mar; 40(3):206-210. PubMed ID: 29575840
[No Abstract] [Full Text] [Related]
19. A modified technique of transanal specimen extraction in the laparoscopic anterior rectal resection for upper rectal or lower sigmoid colon cancer: a retrospective study.
Yu S; Ji Y; Luo T; Xu P; Zhen Z; Deng J
BMC Surg; 2021 Feb; 21(1):82. PubMed ID: 33579251
[TBL] [Abstract][Full Text] [Related]
20. [Comparison of the mid- and long-term outcomes between natural orifice specimen extraction surgery and conventional laparoscopic surgery with abdominal auxiliary incision in the treatment of rectal cancer based on propensity score matching method].
Xu S; Zhang H
Zhonghua Wei Chang Wai Ke Za Zhi; 2021 Aug; 24(8):698-703. PubMed ID: 34412187
[No Abstract] [Full Text] [Related]
[Next] [New Search]