399 related articles for article (PubMed ID: 35810561)
41. Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures.
Cui Y; Li Z; Xiang M; Han D; Yin Y; Ma C
Radiat Oncol; 2022 Dec; 17(1):212. PubMed ID: 36575480
[TBL] [Abstract][Full Text] [Related]
42. Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT.
Kirienko M; Sollini M; Silvestri G; Mognetti S; Voulaz E; Antunovic L; Rossi A; Antiga L; Chiti A
Contrast Media Mol Imaging; 2018; 2018():1382309. PubMed ID: 30510492
[TBL] [Abstract][Full Text] [Related]
43. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [
de Jesus FM; Yin Y; Mantzorou-Kyriaki E; Kahle XU; de Haas RJ; Yakar D; Glaudemans AWJM; Noordzij W; Kwee TC; Nijland M
Eur J Nucl Med Mol Imaging; 2022 Apr; 49(5):1535-1543. PubMed ID: 34850248
[TBL] [Abstract][Full Text] [Related]
44. Integrating manual diagnosis into radiomics for reducing the false positive rate of
Kang F; Mu W; Gong J; Wang S; Li G; Li G; Qin W; Tian J; Wang J
Eur J Nucl Med Mol Imaging; 2019 Dec; 46(13):2770-2779. PubMed ID: 31321483
[TBL] [Abstract][Full Text] [Related]
45. Convolutional Neural Networks in Predicting Nodal and Distant Metastatic Potential of Newly Diagnosed Non-Small Cell Lung Cancer on FDG PET Images.
Tau N; Stundzia A; Yasufuku K; Hussey D; Metser U
AJR Am J Roentgenol; 2020 Jul; 215(1):192-197. PubMed ID: 32348182
[No Abstract] [Full Text] [Related]
46. Nomograms based on SUVmax of
Li H; Shao G; Zhang Y; Chen X; Du C; Wang K; Gao Z
Cancer Imaging; 2021 Jan; 21(1):9. PubMed ID: 33419476
[TBL] [Abstract][Full Text] [Related]
47. The prognostic value of radiomic features from pre- and post-treatment
Kim SJ; Choi JY; Ahn YC; Ahn MJ; Moon SH
Sci Rep; 2023 May; 13(1):8462. PubMed ID: 37231092
[TBL] [Abstract][Full Text] [Related]
48. Evaluating Outcome Prediction via Baseline, End-of-Treatment, and Delta Radiomics on PET-CT Images of Primary Mediastinal Large B-Cell Lymphoma.
Yousefirizi F; Gowdy C; Klyuzhin IS; Sabouri M; Tonseth P; Hayden AR; Wilson D; Sehn LH; Scott DW; Steidl C; Savage KJ; Uribe CF; Rahmim A
Cancers (Basel); 2024 Mar; 16(6):. PubMed ID: 38539425
[TBL] [Abstract][Full Text] [Related]
49. Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images.
Peng L; Hong X; Yuan Q; Lu L; Wang Q; Chen W
Ann Nucl Med; 2021 Apr; 35(4):458-468. PubMed ID: 33543393
[TBL] [Abstract][Full Text] [Related]
50. 18F-FDG PET/CT Image Deep Learning Predicts Colon Cancer Survival.
Tian M; Li Y; Chen H
Contrast Media Mol Imaging; 2023; 2023():2986379. PubMed ID: 37181405
[TBL] [Abstract][Full Text] [Related]
51. 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]
52. 2-[
Manafi-Farid R; Karamzade-Ziarati N; Vali R; Mottaghy FM; Beheshti M
Methods; 2021 Apr; 188():84-97. PubMed ID: 32497604
[TBL] [Abstract][Full Text] [Related]
53. Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm.
Amini M; Hajianfar G; Hadadi Avval A; Nazari M; Deevband MR; Oveisi M; Shiri I; Zaidi H
Clin Oncol (R Coll Radiol); 2022 Feb; 34(2):114-127. PubMed ID: 34872823
[TBL] [Abstract][Full Text] [Related]
54. Deep learning for [
Häggström I; Leithner D; Alvén J; Campanella G; Abusamra M; Zhang H; Chhabra S; Beer L; Haug A; Salles G; Raderer M; Staber PB; Becker A; Hricak H; Fuchs TJ; Schöder H; Mayerhoefer ME
Lancet Digit Health; 2024 Feb; 6(2):e114-e125. PubMed ID: 38135556
[TBL] [Abstract][Full Text] [Related]
55. Novel Functional Radiomics for Prediction of Cardiac Positron Emission Tomography Avidity in Lung Cancer Radiotherapy.
Choi W; Jia Y; Kwak J; Werner-Wasik M; Dicker AP; Simone NL; Storozynsky E; Jain V; Vinogradskiy Y
JCO Clin Cancer Inform; 2024 Mar; 8():e2300241. PubMed ID: 38452302
[TBL] [Abstract][Full Text] [Related]
56.
Cui Y; Jiang Y; Deng X; Long W; Liu B; Fan W; Li Y; Zhang X
Acad Radiol; 2023 Jul; 30(7):1408-1418. PubMed ID: 36437191
[TBL] [Abstract][Full Text] [Related]
57. Value of pre-therapy
Zhang J; Zhao X; Zhao Y; Zhang J; Zhang Z; Wang J; Wang Y; Dai M; Han J
Eur J Nucl Med Mol Imaging; 2020 May; 47(5):1137-1146. PubMed ID: 31728587
[TBL] [Abstract][Full Text] [Related]
58. Machine learning-based diagnostic method of pre-therapeutic
Yoo J; Cheon M; Park YJ; Hyun SH; Zo JI; Um SW; Won HH; Lee KH; Kim BT; Choi JY
Eur Radiol; 2021 Jun; 31(6):4184-4194. PubMed ID: 33241521
[TBL] [Abstract][Full Text] [Related]
59. Assessment of indeterminate pulmonary nodules detected in lung cancer screening: Diagnostic accuracy of FDG PET/CT.
Garcia-Velloso MJ; Bastarrika G; de-Torres JP; Lozano MD; Sanchez-Salcedo P; Sancho L; Nuñez-Cordoba JM; Campo A; Alcaide AB; Torre W; Richter JA; Zulueta JJ
Lung Cancer; 2016 Jul; 97():81-6. PubMed ID: 27237032
[TBL] [Abstract][Full Text] [Related]
60.
Li H; Xu C; Xin B; Zheng C; Zhao Y; Hao K; Wang Q; Wahl RL; Wang X; Zhou Y
Theranostics; 2019; 9(16):4730-4739. PubMed ID: 31367253
[No Abstract] [Full Text] [Related]
[Previous] [Next] [New Search]