173 related articles for article (PubMed ID: 36698037)
1. MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER + /HER2 - Early Breast Cancer Patients.
Chiacchiaretta P; Mastrodicasa D; Chiarelli AM; Luberti R; Croce P; Sguera M; Torrione C; Marinelli C; Marchetti C; Domenico A; Cocco G; Di Credico A; Russo A; D'Eramo C; Corvino A; Colasurdo M; Sensi SL; Muzi M; Caulo M; Delli Pizzi A
J Digit Imaging; 2023 Jun; 36(3):1071-1080. PubMed ID: 36698037
[TBL] [Abstract][Full Text] [Related]
2. Multiparametric MR Imaging Radiomics Signatures for Assessing the Recurrence Risk of ER+/HER2- Breast Cancer Quantified With 21-Gene Recurrence Score.
Chen Y; Tang W; Liu W; Li R; Wang Q; Shen X; Gong J; Gu Y; Peng W
J Magn Reson Imaging; 2023 Aug; 58(2):444-453. PubMed ID: 36440706
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Radiomics signature on 3T dynamic contrast-enhanced magnetic resonance imaging for estrogen receptor-positive invasive breast cancers: Preliminary results for correlation with Oncotype DX recurrence scores.
Nam KJ; Park H; Ko ES; Lim Y; Cho HH; Lee JE
Medicine (Baltimore); 2019 Jun; 98(23):e15871. PubMed ID: 31169691
[TBL] [Abstract][Full Text] [Related]
5. Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study.
Mao N; Yin P; Zhang H; Zhang K; Song X; Xing D; Chu T
Br J Radiol; 2021 Nov; 94(1127):20210348. PubMed ID: 34520235
[TBL] [Abstract][Full Text] [Related]
6. Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer.
Kim HJ; Choi WJ; Kim HH; Cha JH; Shin HJ; Chae EY
Clin Imaging; 2021 Jul; 75():131-137. PubMed ID: 33548871
[TBL] [Abstract][Full Text] [Related]
7. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
Li H; Zhu Y; Burnside ES; Drukker K; Hoadley KA; Fan C; Conzen SD; Whitman GJ; Sutton EJ; Net JM; Ganott M; Huang E; Morris EA; Perou CM; Ji Y; Giger ML
Radiology; 2016 Nov; 281(2):382-391. PubMed ID: 27144536
[TBL] [Abstract][Full Text] [Related]
8. Association between partial-volume corrected SUVmax and Oncotype DX recurrence score in early-stage, ER-positive/HER2-negative invasive breast cancer.
Lee SH; Ha S; An HJ; Lee JS; Han W; Im SA; Ryu HS; Kim WH; Chang JM; Cho N; Moon WK; Cheon GJ
Eur J Nucl Med Mol Imaging; 2016 Aug; 43(9):1574-84. PubMed ID: 27209424
[TBL] [Abstract][Full Text] [Related]
9. Radiomics-based machine learning differentiates "ground-glass" opacities due to COVID-19 from acute non-COVID-19 lung disease.
Delli Pizzi A; Chiarelli AM; Chiacchiaretta P; Valdesi C; Croce P; Mastrodicasa D; Villani M; Trebeschi S; Serafini FL; Rosa C; Cocco G; Luberti R; Conte S; Mazzamurro L; Mereu M; Patea RL; Panara V; Marinari S; Vecchiet J; Caulo M
Sci Rep; 2021 Aug; 11(1):17237. PubMed ID: 34446812
[TBL] [Abstract][Full Text] [Related]
10. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
11. A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.
Saha A; Harowicz MR; Wang W; Mazurowski MA
J Cancer Res Clin Oncol; 2018 May; 144(5):799-807. PubMed ID: 29427210
[TBL] [Abstract][Full Text] [Related]
12. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.
Hussain L; Huang P; Nguyen T; Lone KJ; Ali A; Khan MS; Li H; Suh DY; Duong TQ
Biomed Eng Online; 2021 Jun; 20(1):63. PubMed ID: 34183038
[TBL] [Abstract][Full Text] [Related]
13. Breast cancer subtype intertumor heterogeneity: MRI-based features predict results of a genomic assay.
Sutton EJ; Oh JH; Dashevsky BZ; Veeraraghavan H; Apte AP; Thakur SB; Deasy JO; Morris EA
J Magn Reson Imaging; 2015 Nov; 42(5):1398-406. PubMed ID: 25850931
[TBL] [Abstract][Full Text] [Related]
14. Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using radiomics of pretreatment dynamic contrast-enhanced MRI.
Yoshida K; Kawashima H; Kannon T; Tajima A; Ohno N; Terada K; Takamatsu A; Adachi H; Ohno M; Miyati T; Ishikawa S; Ikeda H; Gabata T
Magn Reson Imaging; 2022 Oct; 92():19-25. PubMed ID: 35636571
[TBL] [Abstract][Full Text] [Related]
15. Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer.
Bian X; Du S; Yue Z; Gao S; Zhao R; Huang G; Guo L; Peng C; Zhang L
J Magn Reson Imaging; 2023 Nov; 58(5):1603-1614. PubMed ID: 36763035
[TBL] [Abstract][Full Text] [Related]
16. Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.
Huang Y; Wei L; Hu Y; Shao N; Lin Y; He S; Shi H; Zhang X; Lin Y
Front Oncol; 2021; 11():706733. PubMed ID: 34490107
[TBL] [Abstract][Full Text] [Related]
17. MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer.
Romeo V; Cuocolo R; Sanduzzi L; Carpentiero V; Caruso M; Lama B; Garifalos D; Stanzione A; Maurea S; Brunetti A
Cancers (Basel); 2023 Mar; 15(6):. PubMed ID: 36980724
[TBL] [Abstract][Full Text] [Related]
18. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.
Zheng G; Peng J; Shu Z; Jin H; Han L; Yuan Z; Qin X; Hou J; He X; Gong X
J Cancer Res Clin Oncol; 2024 Mar; 150(3):147. PubMed ID: 38512406
[TBL] [Abstract][Full Text] [Related]
19. Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics.
Zhang L; Zhou XX; Liu L; Liu AY; Zhao WJ; Zhang HX; Zhu YM; Kuai ZX
J Magn Reson Imaging; 2023 Nov; 58(5):1590-1602. PubMed ID: 36661350
[TBL] [Abstract][Full Text] [Related]
20. Diffusion-weighted MRI of estrogen receptor-positive, HER2-negative, node-negative breast cancer: association between intratumoral heterogeneity and recurrence risk.
Kim JY; Kim JJ; Hwangbo L; Lee JW; Lee NK; Nam KJ; Choo KS; Kang T; Park H; Son Y; Grimm R
Eur Radiol; 2020 Jan; 30(1):66-76. PubMed ID: 31385051
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
