296 related articles for article (PubMed ID: 28554551)
1. Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy.
Danala G; Thai T; Gunderson CC; Moxley KM; Moore K; Mannel RS; Liu H; Zheng B; Qiu Y
Acad Radiol; 2017 Oct; 24(10):1233-1239. PubMed ID: 28554551
[TBL] [Abstract][Full Text] [Related]
2. Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.
Qiu Y; Tan M; McMeekin S; Thai T; Ding K; Moore K; Liu H; Zheng B
Acta Radiol; 2016 Sep; 57(9):1149-55. PubMed ID: 26663390
[TBL] [Abstract][Full Text] [Related]
3. Applying a computer-aided scheme to detect a new radiographic image marker for prediction of chemotherapy outcome.
Wang Y; Qiu Y; Thai T; Moore K; Liu H; Zheng B
BMC Med Imaging; 2016 Aug; 16(1):52. PubMed ID: 27581075
[TBL] [Abstract][Full Text] [Related]
4. Improving lung cancer prognosis assessment by incorporating synthetic minority oversampling technique and score fusion method.
Yan S; Qian W; Guan Y; Zheng B
Med Phys; 2016 Jun; 43(6):2694-2703. PubMed ID: 27277016
[TBL] [Abstract][Full Text] [Related]
5. A New Approach to Evaluate Drug Treatment Response of Ovarian Cancer Patients Based on Deformable Image Registration.
Tan M; Li Z; Qiu Y; McMeekin SD; Thai TC; Ding K; Moore KN; Liu H; Zheng B
IEEE Trans Med Imaging; 2016 Jan; 35(1):316-25. PubMed ID: 26336119
[TBL] [Abstract][Full Text] [Related]
6. Applying Quantitative Radiographic Image Markers to Predict Clinical Complications After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study.
Danala G; Desai M; Ray B; Heidari M; Maryada SKR; Prodan CI; Zheng B
Ann Biomed Eng; 2022 Apr; 50(4):413-425. PubMed ID: 35112157
[TBL] [Abstract][Full Text] [Related]
7. Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker.
Zargari A; Du Y; Heidari M; Thai TC; Gunderson CC; Moore K; Mannel RS; Liu H; Zheng B; Qiu Y
Phys Med Biol; 2018 Aug; 63(15):155020. PubMed ID: 30010611
[TBL] [Abstract][Full Text] [Related]
8. Fusion of quantitative imaging features and serum biomarkers to improve performance of computer-aided diagnosis scheme for lung cancer: A preliminary study.
Gong J; Liu JY; Jiang YJ; Sun XW; Zheng B; Nie SD
Med Phys; 2018 Dec; 45(12):5472-5481. PubMed ID: 30317652
[TBL] [Abstract][Full Text] [Related]
9. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy.
Aghaei F; Tan M; Hollingsworth AB; Qian W; Liu H; Zheng B
Med Phys; 2015 Nov; 42(11):6520-8. PubMed ID: 26520742
[TBL] [Abstract][Full Text] [Related]
10. Developing a Quantitative Ultrasound Image Feature Analysis Scheme to Assess Tumor Treatment Efficacy Using a Mouse Model.
Mirniaharikandehei S; VanOsdol J; Heidari M; Danala G; Sethuraman SN; Ranjan A; Zheng B
Sci Rep; 2019 May; 9(1):7293. PubMed ID: 31086267
[TBL] [Abstract][Full Text] [Related]
11. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Gong J; Liu J; Hao W; Nie S; Zheng B; Wang S; Peng W
Eur Radiol; 2020 Apr; 30(4):1847-1855. PubMed ID: 31811427
[TBL] [Abstract][Full Text] [Related]
12. Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study.
Yin R; Guo Y; Wang Y; Zhang Q; Dou Z; Wang Y; Qi L; Chen Y; Zhang C; Li H; Jian X; Ma W
Acad Radiol; 2023 Sep; 30 Suppl 2():S192-S201. PubMed ID: 37336707
[TBL] [Abstract][Full Text] [Related]
13. Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.
Aghaei F; Tan M; Hollingsworth AB; Zheng B
J Magn Reson Imaging; 2016 Nov; 44(5):1099-1106. PubMed ID: 27080203
[TBL] [Abstract][Full Text] [Related]
14. Prediction of the survival of patients with advanced-stage ovarian cancer patients undergoing interval cytoreduction with the use of computed tomography reevaluation after neoadjuvant chemotherapy.
Li JY; Wang R
J Obstet Gynaecol Res; 2023 Nov; 49(11):2700-2710. PubMed ID: 37528642
[TBL] [Abstract][Full Text] [Related]
15. Quantitative measurement of adiposity using CT images to predict the benefit of bevacizumab-based chemotherapy in epithelial ovarian cancer patients.
Wang Y; Thai T; Moore K; Ding K; McMeekin S; Liu H; Zheng B
Oncol Lett; 2016 Jul; 12(1):680-686. PubMed ID: 27347200
[TBL] [Abstract][Full Text] [Related]
16. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
17. Prediction of response to neoadjuvant chemotherapy in advanced gastric cancer: A radiomics nomogram analysis based on CT images and clinicopathological features.
Tan X; Yang X; Hu S; Ge Y; Wu Q; Wang J; Sun Z
J Xray Sci Technol; 2023; 31(1):49-61. PubMed ID: 36314190
[TBL] [Abstract][Full Text] [Related]
18. Application Values of 2D and 3D Radiomics Models Based on CT Plain Scan in Differentiating Benign from Malignant Ovarian Tumors.
Li S; Liu J; Xiong Y; Han Y; Pang P; Luo P; Fan B
Biomed Res Int; 2022; 2022():5952296. PubMed ID: 35224097
[TBL] [Abstract][Full Text] [Related]
19. CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer.
Wan S; Zhou T; Che R; Li Y; Peng J; Wu Y; Gu S; Cheng J; Hua X
J Ovarian Res; 2023 Jan; 16(1):1. PubMed ID: 36597144
[TBL] [Abstract][Full Text] [Related]
20. Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.
Gai T; Thai T; Jones M; Jo J; Zheng B
J Xray Sci Technol; 2022; 30(2):377-388. PubMed ID: 35095015
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
