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.
751 related articles for article (PubMed ID: 37995359)
1. 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]
2. 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]
3. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging. Dai H; Lu M; Huang B; Tang M; Pang T; Liao B; Cai H; Huang M; Zhou Y; Chen X; Ding H; Feng ST Quant Imaging Med Surg; 2021 May; 11(5):1836-1853. PubMed ID: 33936969 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Multi-parametric assessment of cardiac magnetic resonance images to distinguish myocardial infarctions: A tensor-based radiomics feature. Wang D; Jasim Taher H; Al-Fatlawi M; Abdullah BA; Khayatovna Ismailova M; Abedi-Firouzjah R J Xray Sci Technol; 2024; 32(3):735-749. PubMed ID: 38217635 [TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Radiomics based predictive modeling of rectal toxicity in prostate cancer patients undergoing radiotherapy: CT and MRI comparison. Hassaninejad H; Abdollahi H; Abedi I; Amouheidari A; Tavakoli MB Phys Eng Sci Med; 2023 Dec; 46(4):1353-1363. PubMed ID: 37556091 [TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. MRI-based radiomics of rectal cancer: preoperative assessment of the pathological features. Ma X; Shen F; Jia Y; Xia Y; Li Q; Lu J BMC Med Imaging; 2019 Nov; 19(1):86. PubMed ID: 31747902 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. Construction of a predictive model for radiation proctitis after radiotherapy for female pelvic tumors based on machine learning. Xie H; Gong M; Zhang J; Li Q Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2022 Aug; 47(8):1065-1074. PubMed ID: 36097774 [TBL] [Abstract][Full Text] [Related]
18. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion. Wan Q; Zhou J; Xia X; Hu J; Wang P; Peng Y; Zhang T; Sun J; Song Y; Yang G; Li X Front Oncol; 2021; 11():683587. PubMed ID: 34868905 [TBL] [Abstract][Full Text] [Related]
19. Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics. Xiao DX; Zhong JP; Peng JD; Fan CG; Wang XC; Wen XL; Liao WW; Wang J; Yin XF BMC Med Imaging; 2023 Oct; 23(1):159. PubMed ID: 37845636 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]