125 related articles for article (PubMed ID: 37983902)
21. Convolutional neural network-automated hepatobiliary phase adequacy evaluation may optimize examination time.
Cunha GM; Hasenstab KA; Higaki A; Wang K; Delgado T; Brunsing RL; Schlein A; Schwartzman A; Hsiao A; Sirlin CB; Fowler KJ
Eur J Radiol; 2020 Mar; 124():108837. PubMed ID: 31958630
[TBL] [Abstract][Full Text] [Related]
22. Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score.
Ha R; Chang P; Mutasa S; Karcich J; Goodman S; Blum E; Kalinsky K; Liu MZ; Jambawalikar S
J Magn Reson Imaging; 2019 Feb; 49(2):518-524. PubMed ID: 30129697
[TBL] [Abstract][Full Text] [Related]
23. Histogram analysis of quantitative pharmacokinetic parameters on DCE-MRI: correlations with prognostic factors and molecular subtypes in breast cancer.
Nagasaka K; Satake H; Ishigaki S; Kawai H; Naganawa S
Breast Cancer; 2019 Jan; 26(1):113-124. PubMed ID: 30069785
[TBL] [Abstract][Full Text] [Related]
24. Accelerated pharmacokinetic map determination for dynamic contrast enhanced MRI using frequency-domain based Tofts model.
Vajuvalli NN; Nayak KN; Geethanath S
Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():2404-7. PubMed ID: 25570474
[TBL] [Abstract][Full Text] [Related]
25. Comparing the Performances of Magnetic Resonance Imaging Size vs Pharmacokinetic Parameters to Predict Response to Neoadjuvant Chemotherapy and Survival in Patients With Breast Cancer.
Dogan BE; Yuan Q; Bassett R; Guvenc I; Jackson EF; Cristofanilli M; Whitman GJ
Curr Probl Diagn Radiol; 2019; 48(3):235-240. PubMed ID: 29685400
[TBL] [Abstract][Full Text] [Related]
26. Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer.
Luo HB; Liu YY; Wang CH; Qing HM; Wang M; Zhang X; Chen XY; Xu GH; Zhou P; Ren J
PLoS One; 2021; 16(3):e0247074. PubMed ID: 33647031
[TBL] [Abstract][Full Text] [Related]
27. The Tofts model in frequency domain: fast and robust determination of pharmacokinetic maps for dynamic contrast enhancement MRI.
Vajuvalli NN; Chikkemenahally DK; Nayak KN; Bhosale MG; Geethanath S
Phys Med Biol; 2016 Dec; 61(24):8462-8475. PubMed ID: 27845923
[TBL] [Abstract][Full Text] [Related]
28. Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.
Zhang Y; Chen JH; Lin Y; Chan S; Zhou J; Chow D; Chang P; Kwong T; Yeh DC; Wang X; Parajuli R; Mehta RS; Wang M; Su MY
Eur Radiol; 2021 Apr; 31(4):2559-2567. PubMed ID: 33001309
[TBL] [Abstract][Full Text] [Related]
29. Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.
Huang R; Zhou Z; Wang X; Cao X
Contrast Media Mol Imaging; 2022; 2022():3790269. PubMed ID: 35677026
[TBL] [Abstract][Full Text] [Related]
30. Comparison of diffusion kurtosis imaging and dynamic contrast enhanced MRI in prediction of prognostic factors and molecular subtypes in patients with breast cancer.
Wang W; Lv S; Xun J; Wang L; Zhao F; Wang J; Zhou Z; Chen Y; Sun Z; Zhu L
Eur J Radiol; 2022 Sep; 154():110392. PubMed ID: 35679701
[TBL] [Abstract][Full Text] [Related]
31. Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
Wang Y; Choi EJ; Choi Y; Zhang H; Jin GY; Ko SB
Ultrasound Med Biol; 2020 May; 46(5):1119-1132. PubMed ID: 32059918
[TBL] [Abstract][Full Text] [Related]
32. Robust face recognition based on multi-task convolutional neural network.
Ge H; Dai Y; Zhu Z; Wang B
Math Biosci Eng; 2021 Aug; 18(5):6638-6651. PubMed ID: 34517549
[TBL] [Abstract][Full Text] [Related]
33. Application of whole-lesion histogram analysis of pharmacokinetic parameters in dynamic contrast-enhanced MRI of breast lesions with the CAIPIRINHA-Dixon-TWIST-VIBE technique.
Li Z; Ai T; Hu Y; Yan X; Nickel MD; Xu X; Xia L
J Magn Reson Imaging; 2018 Jan; 47(1):91-96. PubMed ID: 28577335
[TBL] [Abstract][Full Text] [Related]
34. Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.
Huang ZH; Chen L; Sun Y; Liu Q; Hu P
J Transl Med; 2024 Mar; 22(1):226. PubMed ID: 38429796
[TBL] [Abstract][Full Text] [Related]
35. Multi-input deep learning architecture for predicting breast tumor response to chemotherapy using quantitative MR images.
El Adoui M; Drisis S; Benjelloun M
Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1491-1500. PubMed ID: 32556920
[TBL] [Abstract][Full Text] [Related]
36. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.
Zhang Y; Chan S; Park VY; Chang KT; Mehta S; Kim MJ; Combs FJ; Chang P; Chow D; Parajuli R; Mehta RS; Lin CY; Chien SH; Chen JH; Su MY
Acad Radiol; 2022 Jan; 29 Suppl 1(Suppl 1):S135-S144. PubMed ID: 33317911
[TBL] [Abstract][Full Text] [Related]
37. Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer.
Fan M; Zhang P; Wang Y; Peng W; Wang S; Gao X; Xu M; Li L
Eur Radiol; 2019 Aug; 29(8):4456-4467. PubMed ID: 30617495
[TBL] [Abstract][Full Text] [Related]
38. Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data.
Rastogi A; Yalavarthy PK
Med Phys; 2024 Jan; ():. PubMed ID: 38214325
[TBL] [Abstract][Full Text] [Related]
39. Accuracy and precision of quantitative DCE-MRI parameters: How should one estimate contrast concentration?
Wake N; Chandarana H; Rusinek H; Fujimoto K; Moy L; Sodickson DK; Kim SG
Magn Reson Imaging; 2018 Oct; 52():16-23. PubMed ID: 29777820
[TBL] [Abstract][Full Text] [Related]
40. Diagnosis of Spinal Lesions Using Heuristic and Pharmacokinetic Parameters Measured by Dynamic Contrast-Enhanced MRI.
Lang N; Yuan H; Yu HJ; Su MY
Acad Radiol; 2017 Jul; 24(7):867-875. PubMed ID: 28162875
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
