222 related articles for article (PubMed ID: 34939174)
21. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.
Bektaş M; Tuynman JB; Costa Pereira J; Burchell GL; van der Peet DL
World J Surg; 2022 Dec; 46(12):3100-3110. PubMed ID: 36109367
[TBL] [Abstract][Full Text] [Related]
22. Evaluation of machine learning-based models for prediction of clinical deterioration: A systematic literature review.
Jahandideh S; Ozavci G; Sahle BW; Kouzani AZ; Magrabi F; Bucknall T
Int J Med Inform; 2023 Jul; 175():105084. PubMed ID: 37156168
[TBL] [Abstract][Full Text] [Related]
23. Role of Repeat 18F-Fluorodeoxyglucose Positron Emission Tomography Examination in Predicting Pathologic Response Following Neoadjuvant Chemoradiotherapy for Esophageal Adenocarcinoma.
Kukar M; Alnaji RM; Jabi F; Platz TA; Attwood K; Nava H; Ben-David K; Mattson D; Salerno K; Malhotra U; Kanehira K; Gannon J; Hochwald SN
JAMA Surg; 2015 Jun; 150(6):555-62. PubMed ID: 25902198
[TBL] [Abstract][Full Text] [Related]
24. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer.
Larue RTHM; Klaassen R; Jochems A; Leijenaar RTH; Hulshof MCCM; van Berge Henegouwen MI; Schreurs WMJ; Sosef MN; van Elmpt W; van Laarhoven HWM; Lambin P
Acta Oncol; 2018 Nov; 57(11):1475-1481. PubMed ID: 30067421
[TBL] [Abstract][Full Text] [Related]
25. Application of machine learning in predicting hospital readmissions: a scoping review of the literature.
Huang Y; Talwar A; Chatterjee S; Aparasu RR
BMC Med Res Methodol; 2021 May; 21(1):96. PubMed ID: 33952192
[TBL] [Abstract][Full Text] [Related]
26. Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies.
Yuan T; Edelmann D; Fan Z; Alwers E; Kather JN; Brenner H; Hoffmeister M
Artif Intell Med; 2023 Sep; 143():102589. PubMed ID: 37673571
[TBL] [Abstract][Full Text] [Related]
27. A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.
Kawahara D; Murakami Y; Tani S; Nagata Y
Br J Radiol; 2021 Aug; 94(1124):20210525. PubMed ID: 34235955
[TBL] [Abstract][Full Text] [Related]
28.
Xi M; Liao Z; Hofstetter WL; Komaki R; Ho L; Lin SH
J Nucl Med; 2017 Nov; 58(11):1756-1763. PubMed ID: 28522744
[TBL] [Abstract][Full Text] [Related]
29. Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment
Beukinga RJ; Hulshoff JB; van Dijk LV; Muijs CT; Burgerhof JGM; Kats-Ugurlu G; Slart RHJA; Slump CH; Mul VEM; Plukker JTM
J Nucl Med; 2017 May; 58(5):723-729. PubMed ID: 27738011
[TBL] [Abstract][Full Text] [Related]
30. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.
Jian A; Jang K; Manuguerra M; Liu S; Magnussen J; Di Ieva A
Neurosurgery; 2021 Jun; 89(1):31-44. PubMed ID: 33826716
[TBL] [Abstract][Full Text] [Related]
31. Effect of whole-course nutrition management on patients with esophageal cancer undergoing concurrent chemoradiotherapy: A randomized control trial.
Qiu Y; You J; Wang K; Cao Y; Hu Y; Zhang H; Fu R; Sun Y; Chen H; Yuan L; Lyu Q
Nutrition; 2020 Jan; 69():110558. PubMed ID: 31526964
[TBL] [Abstract][Full Text] [Related]
32. Effects of S-1 combined with radiotherapy in the treatment of advanced esophageal cancer: A systematic review and meta-analysis protocol.
Wang W; Xing D; Song Y; Liu F
Medicine (Baltimore); 2018 Mar; 97(12):e0164. PubMed ID: 29561425
[TBL] [Abstract][Full Text] [Related]
33. Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics.
Jin X; Zheng X; Chen D; Jin J; Zhu G; Deng X; Han C; Gong C; Zhou Y; Liu C; Xie C
Eur Radiol; 2019 Nov; 29(11):6080-6088. PubMed ID: 31028447
[TBL] [Abstract][Full Text] [Related]
34. A systematic review of machine learning models for predicting outcomes of stroke with structured data.
Wang W; Kiik M; Peek N; Curcin V; Marshall IJ; Rudd AG; Wang Y; Douiri A; Wolfe CD; Bray B
PLoS One; 2020; 15(6):e0234722. PubMed ID: 32530947
[TBL] [Abstract][Full Text] [Related]
35. Concurrent chemoradiation therapy tailored to the older adults with esophageal cancer: state of the art and the future.
Song T; Fang M; Wu S
Clin Interv Aging; 2018; 13():2275-2287. PubMed ID: 30519009
[TBL] [Abstract][Full Text] [Related]
36. Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990-2022).
Sheehy J; Rutledge H; Acharya UR; Loh HW; Gururajan R; Tao X; Zhou X; Li Y; Gurney T; Kondalsamy-Chennakesavan S
Artif Intell Med; 2023 May; 139():102536. PubMed ID: 37100507
[TBL] [Abstract][Full Text] [Related]
37. Addition of HER2 and CD44 to
Beukinga RJ; Wang D; Karrenbeld A; Dijksterhuis WPM; Faber H; Burgerhof JGM; Mul VEM; Slart RHJA; Coppes RP; Plukker JTM
Eur Radiol; 2021 May; 31(5):3306-3314. PubMed ID: 33151397
[TBL] [Abstract][Full Text] [Related]
38. MiRNA Expression Analysis of Pretreatment Biopsies Predicts the Pathological Response of Esophageal Squamous Cell Carcinomas to Neoadjuvant Chemoradiotherapy.
Wen J; Luo K; Liu H; Liu S; Lin G; Hu Y; Zhang X; Wang G; Chen Y; Chen Z; Li Y; Lin T; Xie X; Liu M; Wang H; Yang H; Fu J
Ann Surg; 2016 May; 263(5):942-8. PubMed ID: 26445467
[TBL] [Abstract][Full Text] [Related]
39. The diffusion-weighted magnetic resonance imaging (DWI) predicts the early response of esophageal squamous cell carcinoma to concurrent chemoradiotherapy.
Wang L; Liu L; Han C; Liu S; Tian H; Li Z; Ren X; Shi G; Wang Q; Wang G
Radiother Oncol; 2016 Nov; 121(2):246-251. PubMed ID: 27838148
[TBL] [Abstract][Full Text] [Related]
40. Predictive biomarkers for response of esophageal cancer to chemo(radio)therapy: A systematic review and meta-analysis.
Li Y; Huang HC; Chen LQ; Xu LY; Li EM; Zhang JJ
Surg Oncol; 2017 Dec; 26(4):460-472. PubMed ID: 29113666
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
