146 related articles for article (PubMed ID: 38732368)
1. A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.
Islam W; Abdoli N; Alam TE; Jones M; Mutembei BM; Yan F; Tang Q
Diagnostics (Basel); 2024 May; 14(9):. PubMed ID: 38732368
[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. Introducing radiomics model to predict active plaque in multiple sclerosis patients using magnetic resonance images.
Khajetash B; Talebi A; Bagherpour Z; Abbaspour S; Tavakoli M
Biomed Phys Eng Express; 2023 Jul; 9(5):. PubMed ID: 37379814
[TBL] [Abstract][Full Text] [Related]
4. Screening of COVID-19 based on the extracted radiomics features from chest CT images.
Rezaeijo SM; Abedi-Firouzjah R; Ghorvei M; Sarnameh S
J Xray Sci Technol; 2021; 29(2):229-243. PubMed ID: 33612539
[TBL] [Abstract][Full Text] [Related]
5. Parameter tuning in machine learning based on radiomics biomarkers of lung cancer.
Luo Y; Li Y; Zhang Y; Zhang J; Liang M; Jiang L; Guo L
J Xray Sci Technol; 2022; 30(3):477-490. PubMed ID: 35342074
[TBL] [Abstract][Full Text] [Related]
6. Predicting Histopathological Grading of Adult Gliomas Based On Preoperative Conventional Multimodal MRI Radiomics: A Machine Learning Model.
Du P; Liu X; Wu X; Chen J; Cao A; Geng D
Brain Sci; 2023 Jun; 13(6):. PubMed ID: 37371390
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Multiclassifier fusion based on radiomics features for the prediction of benign and malignant primary pulmonary solid nodules.
Shen Y; Xu F; Zhu W; Hu H; Chen T; Li Q
Ann Transl Med; 2020 Mar; 8(5):171. PubMed ID: 32309318
[TBL] [Abstract][Full Text] [Related]
9. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
Chen W; Lin G; Chen Y; Cheng F; Li X; Ding J; Zhong Y; Kong C; Chen M; Xia S; Lu C; Ji J
BMC Cancer; 2024 Apr; 24(1):418. PubMed ID: 38580939
[TBL] [Abstract][Full Text] [Related]
10. Machine learning model based on enhanced CT radiomics for the preoperative prediction of lymphovascular invasion in esophageal squamous cell carcinoma.
Wang Y; Bai G; Huang M; Chen W
Front Oncol; 2024; 14():1308317. PubMed ID: 38549935
[TBL] [Abstract][Full Text] [Related]
11. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. [Evaluation of extravascular lung water index in critically ill patients based on lung ultrasound radiomics analysis combined with machine learning].
Meng W; Zhang C; Hu J; Tang Z
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Oct; 35(10):1074-1079. PubMed ID: 37873713
[TBL] [Abstract][Full Text] [Related]
14. Five machine learning-based radiomics models for preoperative prediction of histological grade in hepatocellular carcinoma.
Wu C; Du X; Zhang Y; Zhu L; Chen J; Chen Y; Wei Y; Liu Y
J Cancer Res Clin Oncol; 2023 Nov; 149(16):15103-15112. PubMed ID: 37624395
[TBL] [Abstract][Full Text] [Related]
15. MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors.
Gu J; Yu Q; Li Q; Peng J; Lv F; Gong B; Zhang X
Front Oncol; 2022; 12():1003639. PubMed ID: 36212455
[TBL] [Abstract][Full Text] [Related]
16. Use of radiomics based on
Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
[TBL] [Abstract][Full Text] [Related]
17. The value of machine learning based radiomics model in preoperative detection of perineural invasion in gastric cancer: a two-center study.
Gao X; Cui J; Wang L; Wang Q; Ma T; Yang J; Ye Z
Front Oncol; 2023; 13():1205163. PubMed ID: 37388227
[TBL] [Abstract][Full Text] [Related]
18. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
19. Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.
Li J; Li X; Ma J; Wang F; Cui S; Ye Z
Eur Radiol; 2023 Jul; 33(7):5193-5204. PubMed ID: 36515713
[TBL] [Abstract][Full Text] [Related]
20. Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.
Guo Y; Wu J; Wang Y; Jin Y
Diagnostics (Basel); 2022 Dec; 12(12):. PubMed ID: 36553137
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]