344 related articles for article (PubMed ID: 36057553)
1. The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma.
Ma Y; Li J; Xu X; Zhang Y; Lin Y
BMC Cancer; 2022 Sep; 22(1):949. PubMed ID: 36057553
[TBL] [Abstract][Full Text] [Related]
2. The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma.
Yuan H; Xu X; Tu S; Chen B; Wei Y; Ma Y
BMC Gastroenterol; 2022 Nov; 22(1):463. PubMed ID: 36384504
[TBL] [Abstract][Full Text] [Related]
3. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.
Jiang C; Luo Y; Yuan J; You S; Chen Z; Wu M; Wang G; Gong J
Eur Radiol; 2020 Jul; 30(7):4050-4057. PubMed ID: 32112116
[TBL] [Abstract][Full Text] [Related]
4. Marginal radiomics features as imaging biomarkers for pathological invasion in lung adenocarcinoma.
Cho HH; Lee G; Lee HY; Park H
Eur Radiol; 2020 May; 30(5):2984-2994. PubMed ID: 31965255
[TBL] [Abstract][Full Text] [Related]
5. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Sun Y; Li C; Jin L; Gao P; Zhao W; Ma W; Tan M; Wu W; Duan S; Shan Y; Li M
Eur Radiol; 2020 Jul; 30(7):3650-3659. PubMed ID: 32162003
[TBL] [Abstract][Full Text] [Related]
6. Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study.
Wu G; Woodruff HC; Sanduleanu S; Refaee T; Jochems A; Leijenaar R; Gietema H; Shen J; Wang R; Xiong J; Bian J; Wu J; Lambin P
Eur Radiol; 2020 May; 30(5):2680-2691. PubMed ID: 32006165
[TBL] [Abstract][Full Text] [Related]
7. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
[TBL] [Abstract][Full Text] [Related]
8. A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma.
Yuan H; Peng Y; Xu X; Tu S; Wei Y; Ma Y
Cancer Manag Res; 2022; 14():2409-2418. PubMed ID: 35971393
[TBL] [Abstract][Full Text] [Related]
9. Radiomics and Clinical Characters Based Gaussian Naive Bayes (GNB) Model for Preoperative Differentiation of Pulmonary Pure Invasive Mucinous Adenocarcinoma From Mixed Mucinous Adenocarcinoma.
Zhang J; Hao L; Xu Q; Gao F
Technol Cancer Res Treat; 2024; 23():15330338241258415. PubMed ID: 38819419
[No Abstract] [Full Text] [Related]
10. Computed tomography-based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers.
Li J; Li X; Ma J; Wang F; Cui S; Ye Z
Eur Radiol; 2023 Jul; 33(7):5193-5204. PubMed ID: 36515713
[TBL] [Abstract][Full Text] [Related]
11. Use of radiomics based on
Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
[TBL] [Abstract][Full Text] [Related]
12. CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma.
Zhang J; Hao L; Li M; Xu Q; Shi G
Technol Cancer Res Treat; 2023; 22():15330338231174306. PubMed ID: 37278046
[No Abstract] [Full Text] [Related]
13. A radiomics study to predict invasive pulmonary adenocarcinoma appearing as pure ground-glass nodules.
Cai J; Liu H; Yuan H; Wu Y; Xu Q; Lv Y; Li J; Fu J; Ye J
Clin Radiol; 2021 Feb; 76(2):143-151. PubMed ID: 33187676
[TBL] [Abstract][Full Text] [Related]
14. Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.
Khorrami M; Bera K; Thawani R; Rajiah P; Gupta A; Fu P; Linden P; Pennell N; Jacono F; Gilkeson RC; Velcheti V; Madabhushi A
Eur J Cancer; 2021 May; 148():146-158. PubMed ID: 33743483
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer.
Hyun SH; Ahn MS; Koh YW; Lee SJ
Clin Nucl Med; 2019 Dec; 44(12):956-960. PubMed ID: 31689276
[TBL] [Abstract][Full Text] [Related]
17. Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma.
Wang Y; Liu F; Mo Y; Huang C; Chen Y; Chen F; Zhang X; Yin Y; Liu Q; Zhang L
Thorac Cancer; 2022 Jun; 13(12):1806-1813. PubMed ID: 35538917
[TBL] [Abstract][Full Text] [Related]
18. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules.
She Y; Zhang L; Zhu H; Dai C; Xie D; Xie H; Zhang W; Zhao L; Zou L; Fei K; Sun X; Chen C
Eur Radiol; 2018 Dec; 28(12):5121-5128. PubMed ID: 29869172
[TBL] [Abstract][Full Text] [Related]
19. [Evaluation of extravascular lung water index in critically ill patients based on lung ultrasound radiomics analysis combined with machine learning].
Meng W; Zhang C; Hu J; Tang Z
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Oct; 35(10):1074-1079. PubMed ID: 37873713
[TBL] [Abstract][Full Text] [Related]
20. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
