160 related articles for article (PubMed ID: 37102270)
1. Using 3D deep features from CT scans for cancer prognosis based on a video classification model: A multi-dataset feasibility study.
Chen J; Wee L; Dekker A; Bermejo I
Med Phys; 2023 Jul; 50(7):4220-4233. PubMed ID: 37102270
[TBL] [Abstract][Full Text] [Related]
2. A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features.
Diao Z; Jiang H
Comput Biol Med; 2024 May; 174():108461. PubMed ID: 38626509
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Generative models improve radiomics performance in different tasks and different datasets: An experimental study.
Chen J; Bermejo I; Dekker A; Wee L
Phys Med; 2022 Jun; 98():11-17. PubMed ID: 35468494
[TBL] [Abstract][Full Text] [Related]
5. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics.
Kadoya N; Tanaka S; Kajikawa T; Tanabe S; Abe K; Nakajima Y; Yamamoto T; Takahashi N; Takeda K; Dobashi S; Takeda K; Nakane K; Jingu K
Med Phys; 2020 Jun; 47(5):2197-2205. PubMed ID: 32096876
[TBL] [Abstract][Full Text] [Related]
6. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.
Tunali I; Hall LO; Napel S; Cherezov D; Guvenis A; Gillies RJ; Schabath MB
Med Phys; 2019 Nov; 46(11):5075-5085. PubMed ID: 31494946
[TBL] [Abstract][Full Text] [Related]
7. Deep learning PET/CT-based radiomics integrates clinical data: A feasibility study to distinguish between tuberculosis nodules and lung cancer.
Zhang X; Dong X; Saripan MIB; Du D; Wu Y; Wang Z; Cao Z; Wen D; Liu Y; Marhaban MH
Thorac Cancer; 2023 Jul; 14(19):1802-1811. PubMed ID: 37183577
[TBL] [Abstract][Full Text] [Related]
8. The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features.
Li Y; Reyhan M; Zhang Y; Wang X; Zhou J; Zhang Y; Yue NJ; Nie K
Med Phys; 2022 Mar; 49(3):1648-1659. PubMed ID: 35103332
[TBL] [Abstract][Full Text] [Related]
9. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
[TBL] [Abstract][Full Text] [Related]
10. Combining handcrafted features with latent variables in machine learning for prediction of radiation-induced lung damage.
Cui S; Luo Y; Tseng HH; Ten Haken RK; El Naqa I
Med Phys; 2019 May; 46(5):2497-2511. PubMed ID: 30891794
[TBL] [Abstract][Full Text] [Related]
11. Identification of Precise 3D CT Radiomics for Habitat Computation by Machine Learning in Cancer.
Prior O; Macarro C; Navarro V; Monreal C; Ligero M; Garcia-Ruiz A; Serna G; Simonetti S; Braña I; Vieito M; Escobar M; Capdevila J; Byrne AT; Dienstmann R; Toledo R; Nuciforo P; Garralda E; Grussu F; Bernatowicz K; Perez-Lopez R
Radiol Artif Intell; 2024 Mar; 6(2):e230118. PubMed ID: 38294307
[TBL] [Abstract][Full Text] [Related]
12. Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.
Zhong J; Xia Y; Chen Y; Li J; Lu W; Shi X; Feng J; Yan F; Yao W; Zhang H
Eur Radiol; 2023 Feb; 33(2):812-824. PubMed ID: 36197579
[TBL] [Abstract][Full Text] [Related]
13. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion.
Wan Q; Zhou J; Xia X; Hu J; Wang P; Peng Y; Zhang T; Sun J; Song Y; Yang G; Li X
Front Oncol; 2021; 11():683587. PubMed ID: 34868905
[TBL] [Abstract][Full Text] [Related]
14. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients.
Sugai Y; Kadoya N; Tanaka S; Tanabe S; Umeda M; Yamamoto T; Takeda K; Dobashi S; Ohashi H; Takeda K; Jingu K
Radiat Oncol; 2021 Apr; 16(1):80. PubMed ID: 33931085
[TBL] [Abstract][Full Text] [Related]
15. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.
Wang X; Zhang L; Yang X; Tang L; Zhao J; Chen G; Li X; Yan S; Li S; Yang Y; Kang Y; Li Q; Wu N
Eur J Radiol; 2020 Aug; 129():109150. PubMed ID: 32604042
[TBL] [Abstract][Full Text] [Related]
16. Screening of COVID-19 based on the extracted radiomics features from chest CT images.
Rezaeijo SM; Abedi-Firouzjah R; Ghorvei M; Sarnameh S
J Xray Sci Technol; 2021; 29(2):229-243. PubMed ID: 33612539
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study.
Jin W; Shen L; Tian Y; Zhu H; Zou N; Zhang M; Chen Q; Dong C; Yang Q; Jiang L; Huang J; Yuan Z; Ye X; Luo Q
Biomark Res; 2023 Nov; 11(1):102. PubMed ID: 37996894
[TBL] [Abstract][Full Text] [Related]
19. Differentiation of Lung Metastases Originated From Different Primary Tumors Using Radiomics Features Based on CT Imaging.
Shang H; Li J; Jiao T; Fang C; Li K; Yin D; Zeng Q
Acad Radiol; 2023 Jan; 30(1):40-46. PubMed ID: 35577699
[TBL] [Abstract][Full Text] [Related]
20. Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.
Michallek F; Genske U; Niehues SM; Hamm B; Jahnke P
Eur Radiol; 2022 Jul; 32(7):4587-4595. PubMed ID: 35174400
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
