291 related articles for article (PubMed ID: 36525703)
1. Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis.
Jia LL; Zhao JX; Zhao LP; Tian JH; Huang G
Eur J Radiol; 2023 Jan; 158():110640. PubMed ID: 36525703
[TBL] [Abstract][Full Text] [Related]
2. Using tumor habitat-derived radiomic analysis during pretreatment
Zhao H; Su Y; Wang Y; Lyu Z; Xu P; Gu W; Tian L; Fu P
Cancer Imaging; 2024 Feb; 24(1):26. PubMed ID: 38342905
[TBL] [Abstract][Full Text] [Related]
3. Diagnostic performance of F-18 FDG PET/CT for prediction of KRAS mutation in colorectal cancer patients: a systematic review and meta-analysis.
Kim SJ; Pak K; Kim K
Abdom Radiol (NY); 2019 May; 44(5):1703-1711. PubMed ID: 30603881
[TBL] [Abstract][Full Text] [Related]
4. Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta-analysis".
Fusco R; Granata V
Eur J Radiol; 2023 Dec; 169():111192. PubMed ID: 37976763
[TBL] [Abstract][Full Text] [Related]
5. Construction of prediction model for KRAS mutation status of colorectal cancer based on CT radiomics.
Cao Y; Zhang J; Huang L; Zhao Z; Zhang G; Ren J; Li H; Zhang H; Guo B; Wang Z; Xing Y; Zhou J
Jpn J Radiol; 2023 Nov; 41(11):1236-1246. PubMed ID: 37311935
[TBL] [Abstract][Full Text] [Related]
6. Preoperative prediction of KRAS mutation status in colorectal cancer using a CT-based radiomics nomogram.
Xue T; Peng H; Chen Q; Li M; Duan S; Feng F
Br J Radiol; 2022 Jun; 95(1134):20211014. PubMed ID: 35312376
[TBL] [Abstract][Full Text] [Related]
7. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?
Yang L; Dong D; Fang M; Zhu Y; Zang Y; Liu Z; Zhang H; Ying J; Zhao X; Tian J
Eur Radiol; 2018 May; 28(5):2058-2067. PubMed ID: 29335867
[TBL] [Abstract][Full Text] [Related]
8. MRI-Based Radiomics Methods for Predicting Ki-67 Expression in Breast Cancer: A Systematic Review and Meta-analysis.
Tabnak P; HajiEsmailPoor Z; Baradaran B; Pashazadeh F; Aghebati Maleki L
Acad Radiol; 2024 Mar; 31(3):763-787. PubMed ID: 37925343
[TBL] [Abstract][Full Text] [Related]
9. Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer.
Wang Q; Xu J; Wang A; Chen Y; Wang T; Chen D; Zhang J; Brismar TB
Radiol Med; 2023 Feb; 128(2):136-148. PubMed ID: 36648615
[TBL] [Abstract][Full Text] [Related]
10. Impact of feature harmonization on radiogenomics analysis: Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images.
Shiri I; Amini M; Nazari M; Hajianfar G; Haddadi Avval A; Abdollahi H; Oveisi M; Arabi H; Rahmim A; Zaidi H
Comput Biol Med; 2022 Mar; 142():105230. PubMed ID: 35051856
[TBL] [Abstract][Full Text] [Related]
11. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.
Shiri I; Maleki H; Hajianfar G; Abdollahi H; Ashrafinia S; Hatt M; Zaidi H; Oveisi M; Rahmim A
Mol Imaging Biol; 2020 Aug; 22(4):1132-1148. PubMed ID: 32185618
[TBL] [Abstract][Full Text] [Related]
12. Response to letter by Fusco Roberta & Vincenza Granata-Re: Comments on "Current status and quality of radiomic studies for predicting KRAS mutations in colorectal cancer patients: A systematic review and meta‑analysis".
Jia LL; Lei J
Eur J Radiol; 2024 Jun; 175():111195. PubMed ID: 38669754
[No Abstract] [Full Text] [Related]
13. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.
He K; Liu X; Li M; Li X; Yang H; Zhang H
BMC Med Imaging; 2020 Jun; 20(1):59. PubMed ID: 32487083
[TBL] [Abstract][Full Text] [Related]
14. CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.
Taguchi N; Oda S; Yokota Y; Yamamura S; Imuta M; Tsuchigame T; Nagayama Y; Kidoh M; Nakaura T; Shiraishi S; Funama Y; Shinriki S; Miyamoto Y; Baba H; Yamashita Y
Eur J Radiol; 2019 Sep; 118():38-43. PubMed ID: 31439256
[TBL] [Abstract][Full Text] [Related]
15. KRAS mutation testing of tumours in adults with metastatic colorectal cancer: a systematic review and cost-effectiveness analysis.
Westwood M; van Asselt T; Ramaekers B; Whiting P; Joore M; Armstrong N; Noake C; Ross J; Severens J; Kleijnen J
Health Technol Assess; 2014 Oct; 18(62):1-132. PubMed ID: 25314637
[TBL] [Abstract][Full Text] [Related]
16. Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.
Abdurixiti M; Nijiati M; Shen R; Ya Q; Abuduxiku N; Nijiati M
Br J Radiol; 2021 Jun; 94(1122):20201272. PubMed ID: 33882244
[TBL] [Abstract][Full Text] [Related]
17. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis.
Jia LL; Zheng QY; Tian JH; He DL; Zhao JX; Zhao LP; Huang G
Front Oncol; 2022; 12():1026216. PubMed ID: 36313696
[TBL] [Abstract][Full Text] [Related]
18. Preoperative prediction of perineural invasion and KRAS mutation in colon cancer using machine learning.
Li Y; Eresen A; Shangguan J; Yang J; Benson AB; Yaghmai V; Zhang Z
J Cancer Res Clin Oncol; 2020 Dec; 146(12):3165-3174. PubMed ID: 32779023
[TBL] [Abstract][Full Text] [Related]
19. Relationship between KRAS mutations and dual time point
Mao W; Zhou J; Zhang H; Qiu L; Tan H; Hu Y; Shi H
Abdom Radiol (NY); 2019 Jun; 44(6):2059-2066. PubMed ID: 30143816
[TBL] [Abstract][Full Text] [Related]
20. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.
Doniselli FM; Pascuzzo R; Mazzi F; Padelli F; Moscatelli M; Akinci D'Antonoli T; Cuocolo R; Aquino D; Cuccarini V; Sconfienza LM
Eur Radiol; 2024 Feb; ():. PubMed ID: 38308012
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
