298 related articles for article (PubMed ID: 33880066)
1. Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer.
Liu Y; Zhang FJ; Zhao XX; Yang Y; Liang CY; Feng LL; Wan XB; Ding Y; Zhang YW
Cancer Manag Res; 2021; 13():3235-3246. PubMed ID: 33880066
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. A multiple-time-scale comparative study for the added value of magnetic resonance imaging-based radiomics in predicting pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Peng W; Wan L; Wang S; Zou S; Zhao X; Zhang H
Front Oncol; 2023; 13():1234619. PubMed ID: 37664046
[TBL] [Abstract][Full Text] [Related]
5. MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
Wang L; Wu X; Tian R; Ma H; Jiang Z; Zhao W; Cui G; Li M; Hu Q; Yu X; Xu W
Front Oncol; 2023; 13():1133008. PubMed ID: 36925913
[TBL] [Abstract][Full Text] [Related]
6. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
Li ZY; Wang XD; Li M; Liu XJ; Ye Z; Song B; Yuan F; Yuan Y; Xia CC; Zhang X; Li Q
World J Gastroenterol; 2020 May; 26(19):2388-2402. PubMed ID: 32476800
[TBL] [Abstract][Full Text] [Related]
7. MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
Yi X; Pei Q; Zhang Y; Zhu H; Wang Z; Chen C; Li Q; Long X; Tan F; Zhou Z; Liu W; Li C; Zhou Y; Song X; Li Y; Liao W; Li X; Sun L; Pei H; Zee C; Chen BT
Front Oncol; 2019; 9():552. PubMed ID: 31293979
[No Abstract] [Full Text] [Related]
8. MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer.
Fang Z; Pu H; Chen XL; Yuan Y; Zhang F; Li H
Abdom Radiol (NY); 2023 Jul; 48(7):2270-2283. PubMed ID: 37085730
[TBL] [Abstract][Full Text] [Related]
9. A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer.
Jiang H; Guo W; Yu Z; Lin X; Zhang M; Jiang H; Zhang H; Sun Z; Li J; Yu Y; Zhao S; Hu H
Acad Radiol; 2023 Sep; 30 Suppl 1():S185-S198. PubMed ID: 37394412
[TBL] [Abstract][Full Text] [Related]
10. MRI-based radiomics to predict neoadjuvant chemoradiotherapy outcomes in locally advanced rectal cancer: A multicenter study.
Xiang Y; Li S; Wang H; Song M; Hu K; Wang F; Wang Z; Niu Z; Liu J; Cai Y; Li Y; Zhu X; Geng J; Zhang Y; Teng H; Wang W
Clin Transl Radiat Oncol; 2023 Jan; 38():175-182. PubMed ID: 36471751
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. [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]
17. 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]
18. Development of a joint prediction model based on both the radiomics and clinical factors for preoperative prediction of circumferential resection margin in middle-low rectal cancer using T2WI images.
Ju Y; Zheng L; Qi W; Tian G; Lu Y
Med Phys; 2024 Apr; 51(4):2563-2577. PubMed ID: 37987563
[TBL] [Abstract][Full Text] [Related]
19. Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
Li C; Chen H; Zhang B; Fang Y; Sun W; Wu D; Su Z; Shen L; Wei Q
Cancers (Basel); 2023 Oct; 15(21):. PubMed ID: 37958309
[TBL] [Abstract][Full Text] [Related]
20. Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
Liu Z; Zhang XY; Shi YJ; Wang L; Zhu HT; Tang Z; Wang S; Li XT; Tian J; Sun YS
Clin Cancer Res; 2017 Dec; 23(23):7253-7262. PubMed ID: 28939744
[No Abstract] [Full Text] [Related]
[Next] [New Search]
