These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
127 related articles for article (PubMed ID: 36458424)
1. Application of 18 F-fluorodeoxyglucose PET/CT radiomic features and machine learning to predict early recurrence of non-small cell lung cancer after curative-intent therapy. Park SB; Kim KU; Park YW; Hwang JH; Lim CH Nucl Med Commun; 2023 Feb; 44(2):161-168. PubMed ID: 36458424 [TBL] [Abstract][Full Text] [Related]
2. A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer. Lim CH; Koh YW; Hyun SH; Lee SJ Anticancer Res; 2022 Dec; 42(12):5875-5884. PubMed ID: 36456151 [TBL] [Abstract][Full Text] [Related]
3. Pre-treatment Ahn HK; Lee H; Kim SG; Hyun SH Clin Radiol; 2019 Jun; 74(6):467-473. PubMed ID: 30898382 [TBL] [Abstract][Full Text] [Related]
4. 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]
5. Machine learning based evaluation of clinical and pretreatment Nakajo M; Jinguji M; Tani A; Yano E; Hoo CK; Hirahara D; Togami S; Kobayashi H; Yoshiura T Abdom Radiol (NY); 2022 Feb; 47(2):838-847. PubMed ID: 34821963 [TBL] [Abstract][Full Text] [Related]
6. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [ Nakajo M; Jinguji M; Tani A; Kikuno H; Hirahara D; Togami S; Kobayashi H; Yoshiura T Mol Imaging Biol; 2021 Oct; 23(5):756-765. PubMed ID: 33763816 [TBL] [Abstract][Full Text] [Related]
7. Developing a primary tumor and lymph node 18F-FDG PET/CT-clinical (TLPC) model to predict lymph node metastasis of resectable T2-4 NSCLC. Wang M; Liu L; Dai Q; Jin M; Huang G J Cancer Res Clin Oncol; 2023 Jan; 149(1):247-261. PubMed ID: 36565319 [TBL] [Abstract][Full Text] [Related]
8. The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [ Nakajo M; Kawaji K; Nagano H; Jinguji M; Mukai A; Kawabata H; Tani A; Hirahara D; Yamashita M; Yoshiura T Mol Imaging Biol; 2023 Apr; 25(2):303-313. PubMed ID: 35864282 [TBL] [Abstract][Full Text] [Related]
10. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study. Tong H; Sun J; Fang J; Zhang M; Liu H; Xia R; Zhou W; Liu K; Chen X Front Immunol; 2022; 13():859323. PubMed ID: 35572597 [TBL] [Abstract][Full Text] [Related]
11. The usefulness of machine-learning-based evaluation of clinical and pretreatment Nakajo M; Nagano H; Jinguji M; Kamimura Y; Masuda K; Takumi K; Tani A; Hirahara D; Kariya K; Yamashita M; Yoshiura T Br J Radiol; 2023 Sep; 96(1149):20220772. PubMed ID: 37393538 [TBL] [Abstract][Full Text] [Related]
12. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms. Shiri I; Maleki H; Hajianfar G; Abdollahi H; Ashrafinia S; Hatt M; Zaidi H; Oveisi M; Rahmim A Mol Imaging Biol; 2020 Aug; 22(4):1132-1148. PubMed ID: 32185618 [TBL] [Abstract][Full Text] [Related]
13. Machine learning based on clinico-biological features integrated Ren C; Zhang J; Qi M; Zhang J; Zhang Y; Song S; Sun Y; Cheng J Eur J Nucl Med Mol Imaging; 2021 May; 48(5):1538-1549. PubMed ID: 33057772 [TBL] [Abstract][Full Text] [Related]
14. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on He J; Wang Q; Zhang Y; Wu H; Zhou Y; Zhao S Ann Nucl Med; 2021 May; 35(5):617-627. PubMed ID: 33738763 [TBL] [Abstract][Full Text] [Related]
15. Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer. Duan C; Chaovalitwongse WA; Bai F; Hippe DS; Wang S; Thammasorn P; Pierce LA; Liu X; You J; Miyaoka RS; Vesselle HJ; Kinahan PE; Rengan R; Zeng J; Bowen SR Phys Med Biol; 2020 Oct; 65(20):205007. PubMed ID: 33027064 [TBL] [Abstract][Full Text] [Related]
16. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Zhang R; Hohenforst-Schmidt W; Steppert C; Sziklavari Z; Schmidkonz C; Atzinger A; Kuwert T; Klink T; Sterlacci W; Hartmann A; Vieth M; Förster S Nuklearmedizin; 2022 Oct; 61(5):385-393. PubMed ID: 35768005 [TBL] [Abstract][Full Text] [Related]
17. Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding. Khodabakhshi Z; Amini M; Hajianfar G; Oveisi M; Shiri I; Zaidi H Clin Oncol (R Coll Radiol); 2023 Nov; 35(11):713-725. PubMed ID: 37599160 [TBL] [Abstract][Full Text] [Related]
18. [18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer. Mattonen SA; Davidzon GA; Bakr S; Echegaray S; Leung ANC; Vasanawala M; Horng G; Napel S; Nair VS Tomography; 2019 Mar; 5(1):145-153. PubMed ID: 30854452 [TBL] [Abstract][Full Text] [Related]
19. Prediction of mediastinal lymph node metastasis based on Yin G; Song Y; Li X; Zhu L; Su Q; Dai D; Xu W Eur Radiol; 2021 Jun; 31(6):3983-3992. PubMed ID: 33201286 [TBL] [Abstract][Full Text] [Related]
20. The efficacy of Nakajo M; Takeda A; Katsuki A; Jinguji M; Ohmura K; Tani A; Sato M; Yoshiura T Br J Radiol; 2022 Jun; 95(1134):20211050. PubMed ID: 35312337 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]