210 related articles for article (PubMed ID: 38255165)
1. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.
Sheng L; Yang C; Chen Y; Song B
Biomedicines; 2023 Dec; 12(1):. PubMed ID: 38255165
[TBL] [Abstract][Full Text] [Related]
2. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Kocher M; Ruge MI; Galldiks N; Lohmann P
Strahlenther Onkol; 2020 Oct; 196(10):856-867. PubMed ID: 32394100
[TBL] [Abstract][Full Text] [Related]
3. Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches.
Fritz B; Yi PH; Kijowski R; Fritz J
Invest Radiol; 2023 Jan; 58(1):3-13. PubMed ID: 36070548
[TBL] [Abstract][Full Text] [Related]
4. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.
Mao B; Ma J; Duan S; Xia Y; Tao Y; Zhang L
Eur Radiol; 2021 Jul; 31(7):4576-4586. PubMed ID: 33447862
[TBL] [Abstract][Full Text] [Related]
5. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors.
Zheng Y; Zhou D; Liu H; Wen M
Eur Radiol; 2022 Oct; 32(10):6953-6964. PubMed ID: 35484339
[TBL] [Abstract][Full Text] [Related]
6. Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.
Yap FY; Varghese BA; Cen SY; Hwang DH; Lei X; Desai B; Lau C; Yang LL; Fullenkamp AJ; Hajian S; Rivas M; Gupta MN; Quinn BD; Aron M; Desai MM; Aron M; Oberai AA; Gill IS; Duddalwar VA
Eur Radiol; 2021 Feb; 31(2):1011-1021. PubMed ID: 32803417
[TBL] [Abstract][Full Text] [Related]
7. Radiomics in liver diseases: Current progress and future opportunities.
Wei J; Jiang H; Gu D; Niu M; Fu F; Han Y; Song B; Tian J
Liver Int; 2020 Sep; 40(9):2050-2063. PubMed ID: 32515148
[TBL] [Abstract][Full Text] [Related]
8. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.
Dalal V; Carmicheal J; Dhaliwal A; Jain M; Kaur S; Batra SK
Cancer Lett; 2020 Jan; 469():228-237. PubMed ID: 31629933
[TBL] [Abstract][Full Text] [Related]
9. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.
Liu D; Liu F; Xie X; Su L; Liu M; Xie X; Kuang M; Huang G; Wang Y; Zhou H; Wang K; Lin M; Tian J
Eur Radiol; 2020 Apr; 30(4):2365-2376. PubMed ID: 31900703
[TBL] [Abstract][Full Text] [Related]
10. Machine and deep learning methods for radiomics.
Avanzo M; Wei L; Stancanello J; Vallières M; Rao A; Morin O; Mattonen SA; El Naqa I
Med Phys; 2020 Jun; 47(5):e185-e202. PubMed ID: 32418336
[TBL] [Abstract][Full Text] [Related]
11. Radiomics and deep learning in liver diseases.
Sung YS; Park B; Park HJ; Lee SS
J Gastroenterol Hepatol; 2021 Mar; 36(3):561-568. PubMed ID: 33709608
[TBL] [Abstract][Full Text] [Related]
12. Prediction of Renal Function 1 Year After Transplantation Using Machine Learning Methods Based on Ultrasound Radiomics Combined With Clinical and Imaging Features.
Zhu L; Huang R; Zhou Z; Fan Q; Yan J; Wan X; Zhao X; He Y; Dong F
Ultrason Imaging; 2023 Mar; 45(2):85-96. PubMed ID: 36932907
[TBL] [Abstract][Full Text] [Related]
13. A Comprehensive Machine Learning Benchmark Study for Radiomics-Based Survival Analysis of CT Imaging Data in Patients With Hepatic Metastases of CRC.
Stüber AT; Coors S; Schachtner B; Weber T; Rügamer D; Bender A; Mittermeier A; Öcal O; Seidensticker M; Ricke J; Bischl B; Ingrisch M
Invest Radiol; 2023 Dec; 58(12):874-881. PubMed ID: 37504498
[TBL] [Abstract][Full Text] [Related]
14. Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma.
Hsieh C; Laguna A; Ikeda I; Maxwell AWP; Chapiro J; Nadolski G; Jiao Z; Bai HX
Radiology; 2023 Nov; 309(2):e222891. PubMed ID: 37934098
[TBL] [Abstract][Full Text] [Related]
15. A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images.
Attallah O
Digit Health; 2022; 8():20552076221092543. PubMed ID: 35433024
[TBL] [Abstract][Full Text] [Related]
16. CT-Based Radiomics Analysis Before Thermal Ablation to Predict Local Tumor Progression for Colorectal Liver Metastases.
Taghavi M; Staal F; Gomez Munoz F; Imani F; Meek DB; Simões R; Klompenhouwer LG; van der Heide UA; Beets-Tan RGH; Maas M
Cardiovasc Intervent Radiol; 2021 Jun; 44(6):913-920. PubMed ID: 33506278
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer.
Qin Y; Zhu LH; Zhao W; Wang JJ; Wang H
Front Oncol; 2022; 12():913683. PubMed ID: 36016617
[TBL] [Abstract][Full Text] [Related]
19. Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application.
Peeken JC; Wiestler B; Combs SE
Recent Results Cancer Res; 2020; 216():773-794. PubMed ID: 32594406
[TBL] [Abstract][Full Text] [Related]
20. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.
Wang T; Gong J; Li Q; Chu C; Shen W; Peng W; Gu Y; Li W
Eur Radiol; 2021 Aug; 31(8):6125-6135. PubMed ID: 33486606
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]