328 related articles for article (PubMed ID: 35962933)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. 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]
13. 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]
14. 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]
15. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study.
Petresc B; Lebovici A; Caraiani C; Feier DS; Graur F; Buruian MM
Cancers (Basel); 2020 Jul; 12(7):. PubMed ID: 32674345
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Performance of Machine Learning and Texture Analysis for Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer with 3T MRI.
Bellini D; Carbone I; Rengo M; Vicini S; Panvini N; Caruso D; Iannicelli E; Tombolini V; Laghi A
Tomography; 2022 Aug; 8(4):2059-2072. PubMed ID: 36006071
[TBL] [Abstract][Full Text] [Related]
19. [Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer].
Shu Z; Fang S; Ding Z; Mao D; Pang P; Gong X
Zhonghua Wei Chang Wai Ke Za Zhi; 2018 Sep; 21(9):1051-1058. PubMed ID: 30269327
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
