These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
716 related articles for article (PubMed ID: 33894058)
1. 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]
2. 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]
3. An investigation of machine learning methods in delta-radiomics feature analysis. Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
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. 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]
8. Multiparametric MRI-based radiomic model for predicting lymph node metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Wei Q; Chen L; Hou X; Lin Y; Xie R; Yu X; Zhang H; Wen Z; Wu Y; Liu X; Chen W Insights Imaging; 2024 Jun; 15(1):163. PubMed ID: 38922456 [TBL] [Abstract][Full Text] [Related]
9. 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]
11. Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer. Li X; Li C; Wang H; Jiang L; Chen M PeerJ; 2024; 12():e17683. PubMed ID: 39026540 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies. Orhan K; Driesen L; Shujaat S; Jacobs R; Chai X Biomed Res Int; 2021; 2021():6656773. PubMed ID: 34327235 [TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. 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] [Next] [New Search]