Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images (2018)
- Authors:
- Saltz, Joel
- Gupta, Rajarsi
- Hou, Le
- Kurc, Tahsin
- Singh, Pankaj
- Nguyen, Vu
- Samaras, Dimitris
- Shroyer, Kenneth R.
- Zhao, Tianhao
- Batiste, Rebecca
- Van Arnam, John
- Shmulevich, Ilya
- Rao, Arvind
- Lazar, Alexander J.
- Sharma, Ashish
- Thorsson, Vésteinn
- Noushmehr, Houtan
- Carlotti Júnior, Carlos Gilberto
- Santos, José Sebastião dos
- Kemp, Rafael
- Sankarankuty, Ajith Kumar
- Tirapelli, Daniela Pretti da Cunha
- USP affiliated authors: NOUSHMEHR, HOUTAN - FMRP ; CARLOTTI JUNIOR, CARLOS GILBERTO - FMRP ; SANTOS, JOSÉ SEBASTIÃO DOS - FMRP ; KEMP, RAFAEL - FMRP ; SANKARANKUTTY, AJITH KUMAR - FMRP ; TIRAPELLI, DANIELA PRETTI DA CUNHA - FMRP
- Unidade: FMRP
- DOI: 10.1016/j.celrep.2018.03.086
- Subjects: NEOPLASIAS; APRENDIZADO COMPUTACIONAL; INTELIGÊNCIA ARTIFICIAL; BIOINFORMÁTICA; LINFÓCITOS
- Keywords: Digital pathology; Immuno-oncology; Machine learning; Lymphocytes; Tumor microenvironment; Deep learning; Tumor-infiltrating lymphocytes; Artificial intelligence; Bioinformatics; Computer vision
- Language: Inglês
- Imprenta:
- Source:
- Título: Cell Reports
- ISSN: 2211-1247
- Volume/Número/Paginação/Ano: v. 23, n. 1, p. 181-193, 2018
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by-nc-nd
-
ABNT
SALTZ, Joel et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, v. 23, n. 1, p. 181-193, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.celrep.2018.03.086. Acesso em: 30 dez. 2025. -
APA
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., et al. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Reports, 23( 1), 181-193. doi:10.1016/j.celrep.2018.03.086 -
NLM
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2025 dez. 30 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086 -
Vancouver
Saltz J, Gupta R, Hou L, Kurc T, Singh P, Nguyen V, Samaras D, Shroyer KR, Zhao T, Batiste R, Van Arnam J, Shmulevich I, Rao A, Lazar AJ, Sharma A, Thorsson V, Noushmehr H, Carlotti Júnior CG, Santos JS dos, Kemp R, Sankarankuty AK, Tirapelli DP da C. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images [Internet]. Cell Reports. 2018 ; 23( 1): 181-193.[citado 2025 dez. 30 ] Available from: https://doi.org/10.1016/j.celrep.2018.03.086 - Molecular characterization and clinical relevance of metabolic expression subtypes in human cancers
- Machine learning detects pan-cancer ras pathway activation in the cancer genome atlas
- An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics
- Pathogenic germline variants in 10,389 adult cancers
- Comprehensive analysis of alternative splicing across tumors from 8,705 patients
- Scalable open science approach for mutation calling of tumor exomes using multiple genomic pipelines
- Oncogenic signaling pathways in the cancer genome atlas
- Comprehensive molecular characterization of the hippo signaling pathway in cancer
- Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types
- The immune landscape of cancer
Informações sobre o DOI: 10.1016/j.celrep.2018.03.086 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
| 002938421.pdf | Direct link |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas