528 related articles for article (PubMed ID: 35439946)
1. Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.
Wang J; Chen J; Zhou R; Gao Y; Li J
BMC Cancer; 2022 Apr; 22(1):420. PubMed ID: 35439946
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.
Wang J; Liu X; Hu B; Gao Y; Chen J; Li J
Abdom Radiol (NY); 2021 May; 46(5):1805-1815. PubMed ID: 33151359
[TBL] [Abstract][Full Text] [Related]
3. Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.
Li Z; Ma X; Shen F; Lu H; Xia Y; Lu J
BMC Med Imaging; 2021 Feb; 21(1):30. PubMed ID: 33593304
[TBL] [Abstract][Full Text] [Related]
4. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Cui Y; Yang X; Shi Z; Yang Z; Du X; Zhao Z; Cheng X
Eur Radiol; 2019 Mar; 29(3):1211-1220. PubMed ID: 30128616
[TBL] [Abstract][Full Text] [Related]
5. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.
Zhang Z; Jiang X; Zhang R; Yu T; Liu S; Luo Y
Diagn Interv Radiol; 2021 May; 27(3):308-314. PubMed ID: 34003118
[TBL] [Abstract][Full Text] [Related]
6. Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI.
Yardimci AH; Kocak B; Sel I; Bulut H; Bektas CT; Cin M; Dursun N; Bektas H; Mermut O; Yardimci VH; Kilickesmez O
Jpn J Radiol; 2023 Jan; 41(1):71-82. PubMed ID: 35962933
[TBL] [Abstract][Full Text] [Related]
7. Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Zhou X; Yu Y; Feng Y; Ding G; Liu P; Liu L; Ren W; Zhu Y; Cao W
Radiat Oncol; 2023 Oct; 18(1):175. PubMed ID: 37891611
[TBL] [Abstract][Full Text] [Related]
8. Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study.
Abbaspour S; Barahman M; Abdollahi H; Arabalibeik H; Hajainfar G; Babaei M; Iraji H; Barzegartahamtan M; Ay MR; Mahdavi SR
Biomed Phys Eng Express; 2023 Dec; 10(1):. PubMed ID: 37995359
[No Abstract] [Full Text] [Related]
9. MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study.
Defeudis A; Mazzetti S; Panic J; Micilotta M; Vassallo L; Giannetto G; Gatti M; Faletti R; Cirillo S; Regge D; Giannini V
Eur Radiol Exp; 2022 May; 6(1):19. PubMed ID: 35501512
[TBL] [Abstract][Full Text] [Related]
10. Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer.
Cheng Y; Luo Y; Hu Y; Zhang Z; Wang X; Yu Q; Liu G; Cui E; Yu T; Jiang X
Abdom Radiol (NY); 2021 Nov; 46(11):5072-5085. PubMed ID: 34302510
[TBL] [Abstract][Full Text] [Related]
11. Prediction of locally advanced rectal cancer response to neoadjuvant chemoradiation therapy using volumetric multiparametric MRI-based radiomics.
El Homsi M; Bane O; Fauveau V; Hectors S; Vietti Violi N; Sylla P; Ko HB; Cuevas J; Carbonell G; Nehlsen A; Vanguri R; Viswanath S; Jambawalikar S; Shaish H; Taouli B
Abdom Radiol (NY); 2024 Mar; 49(3):791-800. PubMed ID: 38150143
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. MRI-based delta-radiomics are predictive of pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Wan L; Peng W; Zou S; Ye F; Geng Y; Ouyang H; Zhao X; Zhang H
Acad Radiol; 2021 Nov; 28 Suppl 1():S95-S104. PubMed ID: 33189550
[TBL] [Abstract][Full Text] [Related]
14. [A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].
Zhang XY; Zhu HT; Li XT; Li YJ; Li ZW; Wang WH; Wu AW; Sun YS; Zhang L
Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Mar; 25(3):228-234. PubMed ID: 35340172
[No Abstract] [Full Text] [Related]
15. Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.
Abbaspour S; Abdollahi H; Arabalibeik H; Barahman M; Arefpour AM; Fadavi P; Ay M; Mahdavi SR
Abdom Radiol (NY); 2022 Nov; 47(11):3645-3659. PubMed ID: 35951085
[TBL] [Abstract][Full Text] [Related]
16. Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.
Wan L; Sun Z; Peng W; Wang S; Li J; Zhao Q; Wang S; Ouyang H; Zhao X; Zou S; Zhang H
J Magn Reson Imaging; 2022 Oct; 56(4):1130-1142. PubMed ID: 35142001
[TBL] [Abstract][Full Text] [Related]
17. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning.
Peng J; Wang W; Jin H; Qin X; Hou J; Yang Z; Shu Z
BMC Cancer; 2023 Apr; 23(1):365. PubMed ID: 37085830
[TBL] [Abstract][Full Text] [Related]
18. Machine learning-based response assessment in patients with rectal cancer after neoadjuvant chemoradiotherapy: radiomics analysis for assessing tumor regression grade using T2-weighted magnetic resonance images.
Lee YD; Kim HG; Seo M; Moon SK; Park SJ; You MW
Int J Colorectal Dis; 2024 May; 39(1):78. PubMed ID: 38789861
[TBL] [Abstract][Full Text] [Related]
19. MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy.
Shin J; Seo N; Baek SE; Son NH; Lim JS; Kim NK; Koom WS; Kim S
Radiology; 2022 May; 303(2):351-358. PubMed ID: 35133200
[TBL] [Abstract][Full Text] [Related]
20. Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.
Liu X; Zhang D; Liu Z; Li Z; Xie P; Sun K; Wei W; Dai W; Tang Z; Ding Y; Cai G; Tong T; Meng X; Tian J
EBioMedicine; 2021 Jul; 69():103442. PubMed ID: 34157487
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
