116 related articles for article (PubMed ID: 38880866)
1. CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency.
Bortolotto C; Pinto A; Brero F; Messana G; Cabini RF; Postuma I; Robustelli Test A; Stella GM; Galli G; Mariani M; Figini S; Lascialfari A; Filippi AR; Bottinelli OM; Preda L
Eur Radiol Exp; 2024 Jun; 8(1):71. PubMed ID: 38880866
[TBL] [Abstract][Full Text] [Related]
2. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform.
Fornacon-Wood I; Mistry H; Ackermann CJ; Blackhall F; McPartlin A; Faivre-Finn C; Price GJ; O'Connor JPB
Eur Radiol; 2020 Nov; 30(11):6241-6250. PubMed ID: 32483644
[TBL] [Abstract][Full Text] [Related]
3. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values.
Paquier Z; Chao SL; Acquisto A; Fenton C; Guiot T; Dhont J; Levillain H; Gulyban A; Bali MA; Reynaert N
Biomed Phys Eng Express; 2022 Sep; 8(6):. PubMed ID: 36049399
[TBL] [Abstract][Full Text] [Related]
4. Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms.
Carloni G; Garibaldi C; Marvaso G; Volpe S; Zaffaroni M; Pepa M; Isaksson LJ; Colombo F; Durante S; Lo Presti G; Raimondi S; Spaggiari L; de Marinis F; Piperno G; Vigorito S; Gandini S; Cremonesi M; Positano V; Jereczek-Fossa BA
Radiother Oncol; 2023 Jan; 178():109424. PubMed ID: 36435336
[TBL] [Abstract][Full Text] [Related]
5. Comparison of radiomic features in diagnostic CT images with and without contrast enhancement in the delayed phase for NSCLC patients.
Kakino R; Nakamura M; Mitsuyoshi T; Shintani T; Hirashima H; Matsuo Y; Mizowaki T
Phys Med; 2020 Jan; 69():176-182. PubMed ID: 31918370
[TBL] [Abstract][Full Text] [Related]
6. CT radiomics-based model for predicting TMB and immunotherapy response in non-small cell lung cancer.
Wang J; Wang J; Huang X; Zhou Y; Qi J; Sun X; Nie J; Hu Z; Wang S; Hong B; Wang H
BMC Med Imaging; 2024 Feb; 24(1):45. PubMed ID: 38360550
[TBL] [Abstract][Full Text] [Related]
7. Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model.
Lee S; Lee CY; Kim NY; Suh YJ; Lee HJ; Yong HS; Kim HR; Kim YJ
Eur Radiol; 2024 May; 34(5):3422-3430. PubMed ID: 37840100
[TBL] [Abstract][Full Text] [Related]
8. Rank acquisition impact on radiomics estimation (AсquIRE) in chest CT imaging: A retrospective multi-site, multi-use-case study.
Cherezov D; Viswanathan VS; Fu P; Gupta A; Madabhushi A
Comput Methods Programs Biomed; 2024 Feb; 244():107990. PubMed ID: 38194767
[TBL] [Abstract][Full Text] [Related]
9. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features.
Sun Z; Hu S; Ge Y; Wang J; Duan S; Song J; Hu C; Li Y
J Xray Sci Technol; 2020; 28(3):449-459. PubMed ID: 32176676
[TBL] [Abstract][Full Text] [Related]
10. Interchangeability of radiomic features between [18F]-FDG PET/CT and [18F]-FDG PET/MR.
Vuong D; Tanadini-Lang S; Huellner MW; Veit-Haibach P; Unkelbach J; Andratschke N; Kraft J; Guckenberger M; Bogowicz M
Med Phys; 2019 Apr; 46(4):1677-1685. PubMed ID: 30714158
[TBL] [Abstract][Full Text] [Related]
11. [Development of a radiomics signature to predict Ki-67 expression level in non-small cell lung cancer].
Gu Q; Feng Z; Liang Q; Li M; Wang W; Rong P
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2018 Nov; 43(11):1216-1222. PubMed ID: 30643066
[TBL] [Abstract][Full Text] [Related]
12. Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types.
Denzler S; Vuong D; Bogowicz M; Pavic M; Frauenfelder T; Thierstein S; Eboulet EI; Maurer B; Schniering J; Gabryś HS; Schmitt-Opitz I; Pless M; Foerster R; Guckenberger M; Tanadini-Lang S
Br J Radiol; 2021 Apr; 94(1120):20200947. PubMed ID: 33544646
[TBL] [Abstract][Full Text] [Related]
13. An [
Meng N; Feng P; Yu X; Wu Y; Fu F; Li Z; Luo Y; Tan H; Yuan J; Yang Y; Wang Z; Wang M
Eur Radiol; 2024 Jan; 34(1):318-329. PubMed ID: 37530809
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT.
Huynh E; Coroller TP; Narayan V; Agrawal V; Romano J; Franco I; Parmar C; Hou Y; Mak RH; Aerts HJ
PLoS One; 2017; 12(1):e0169172. PubMed ID: 28046060
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.
Qin Q; Shi A; Zhang R; Wen Q; Niu T; Chen J; Qiu Q; Wan Y; Sun X; Xing L
Thorac Cancer; 2020 Apr; 11(4):964-972. PubMed ID: 32061061
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis.
Haga A; Takahashi W; Aoki S; Nawa K; Yamashita H; Abe O; Nakagawa K
Radiol Phys Technol; 2018 Mar; 11(1):27-35. PubMed ID: 29209915
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
