Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis (2019)
- Authors:
- USP affiliated authors: FABRO, ALEXANDRE TODOROVIC - FMRP ; MARQUES, PAULO MAZZONCINI DE AZEVEDO - FMRP
- Unidade: FMRP
- DOI: 10.1007/s11548-019-01969-3
- Subjects: RADIOMETRIA; NEOPLASIAS PULMONARES; CAVITAÇÃO
- Keywords: Deep learning; Radiomics; Distant metastasis; Lung cancer
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher place: Heidelberg
- Date published: 2019
- Source:
- Título: International Journal of Computer Assisted Radiology and Surgery
- ISSN: 1861-6410
- Volume/Número/Paginação/Ano: v. 14, suppl. 1, p. S78-S79, 2019
- Conference titles: Computer Assisted Radiology and Surgery (CARS) - International Congress and Exhibition
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
FERREIRA JUNIOR, José Raniery et al. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1007/s11548-019-01969-3. Acesso em: 28 dez. 2025. , 2019 -
APA
Ferreira Junior, J. R., Santos, M. K., Hironaka, T., Cipriano, F. E. G., Fabro, A. T., Yoshida, H., & Azevedo-Marques, P. M. de. (2019). Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis. International Journal of Computer Assisted Radiology and Surgery. Heidelberg: Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo. doi:10.1007/s11548-019-01969-3 -
NLM
Ferreira Junior JR, Santos MK, Hironaka T, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78-S79.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/s11548-019-01969-3 -
Vancouver
Ferreira Junior JR, Santos MK, Hironaka T, Cipriano FEG, Fabro AT, Yoshida H, Azevedo-Marques PM de. Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis [Internet]. International Journal of Computer Assisted Radiology and Surgery. 2019 ; 14 S78-S79.[citado 2025 dez. 28 ] Available from: https://doi.org/10.1007/s11548-019-01969-3 - Radiomics-based features for pattern recognition of lung cancer histopathology and metastases
- A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors
- Towards convolutional neural network on primary lung tumors to predict histophatological type, distant and lymph node metastasis
- CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms
- Taenia crassiceps injection into the subarachnoid space of rats simulates radiological and morphological features of racemose neurocysticercosis
- The effects of intratendinous injections of corticosteroid into achilles tendon: an experimental randomized study in a rabbit model
- The role of glucose metabolism and insulin resistance in cardiac remodelling induced by cigarette smoke exposure
- Recovery of cardiac remodeling and dysmetabolism by pancreatic islet injury improvement in diabetic rats after yacon leaf extract treatment
- AI-based radiomic approach in high-resolution CT images for differential diagnosis of idiopathic pulmonary fibrosis
- Paracoccidioidomycosis: current perspectives from Brazil
Informações sobre o DOI: 10.1007/s11548-019-01969-3 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 003007763.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
