231 related articles for article (PubMed ID: 36739228)
1. MRI-Based Radiomic Models Outperform Radiologists in Predicting Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
Wen L; Liu J; Hu P; Bi F; Liu S; Jian L; Zhu S; Nie S; Cao F; Lu Q; Yu X; Liu K
Acad Radiol; 2023 Sep; 30 Suppl 1():S176-S184. PubMed ID: 36739228
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. 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]
6. Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
Fu J; Zhong X; Li N; Van Dams R; Lewis J; Sung K; Raldow AC; Jin J; Qi XS
Phys Med Biol; 2020 Apr; 65(7):075001. PubMed ID: 32092710
[TBL] [Abstract][Full Text] [Related]
7. [Predictive value of combination of MRI tumor regression grade and apparent diffusion coefficient for pathological complete remission after neoadjuvant treatment of locally advanced rectal cancer].
Xu N; Huang FC; Li WL; Luan X; Jiang YM; He B
Zhonghua Wei Chang Wai Ke Za Zhi; 2021 Apr; 24(4):359-365. PubMed ID: 33878826
[No Abstract] [Full Text] [Related]
8. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.
Shaish H; Aukerman A; Vanguri R; Spinelli A; Armenta P; Jambawalikar S; Makkar J; Bentley-Hibbert S; Del Portillo A; Kiran R; Monti L; Bonifacio C; Kirienko M; Gardner KL; Schwartz L; Keller D
Eur Radiol; 2020 Nov; 30(11):6263-6273. PubMed ID: 32500192
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
[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. Radiomic Features of Primary Rectal Cancers on Baseline T
Antunes JT; Ofshteyn A; Bera K; Wang EY; Brady JT; Willis JE; Friedman KA; Marderstein EL; Kalady MF; Stein SL; Purysko AS; Paspulati R; Gollamudi J; Madabhushi A; Viswanath SE
J Magn Reson Imaging; 2020 Nov; 52(5):1531-1541. PubMed ID: 32216127
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.
Li Y; Liu W; Pei Q; Zhao L; Güngör C; Zhu H; Song X; Li C; Zhou Z; Xu Y; Wang D; Tan F; Yang P; Pei H
Cancer Med; 2019 Dec; 8(17):7244-7252. PubMed ID: 31642204
[TBL] [Abstract][Full Text] [Related]
15. Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Wan L; Zhang C; Zhao Q; Meng Y; Zou S; Yang Y; Liu Y; Jiang J; Ye F; Ouyang H; Zhao X; Zhang H
Abdom Radiol (NY); 2019 Sep; 44(9):2978-2987. PubMed ID: 31327039
[TBL] [Abstract][Full Text] [Related]
16. [Construction of a model based on multipoint full-layer puncture biopsy for predicting pathological complete response after neoadjuvant therapy for locally advanced rectal cancer].
Jin Y; Zhai ZW; Sun LT; Xia PD; Hu H; Jiang CQ; Zhao BC; Qu H; Qian Q; Dai Y; Yao HW; Wang ZJ; Han JG
Zhonghua Wei Chang Wai Ke Za Zhi; 2024 Apr; 27(4):403-411. PubMed ID: 38644246
[No Abstract] [Full Text] [Related]
17. 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]
18. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).
Crimì F; Capelli G; Spolverato G; Bao QR; Florio A; Milite Rossi S; Cecchin D; Albertoni L; Campi C; Pucciarelli S; Stramare R
Radiol Med; 2020 Dec; 125(12):1216-1224. PubMed ID: 32410063
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
