Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Mod Pathol ; 37(2): 100377, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-37926422

RESUMO

Conventional histopathology involves expensive and labor-intensive processes that often consume tissue samples, rendering them unavailable for other analyses. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to nondestructively image the autofluorescence of unstained tissue sections. We then trained a deep learning model to use autofluorescence to generate virtual histologic stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histologic features present in the real-stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study, where both real and virtual stains are scored by pathologists (D.T., A.D.B., R.K.P.). The test showed moderate-to-good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH Clinical Research Network score prediction. We showed that the end-to-end automated pathology platform is comparable with an independent panel of pathologists for NASH Clinical Research Network scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology and enable novel approaches to spatial biology research.


Assuntos
Aprendizado Profundo , Hepatopatia Gordurosa não Alcoólica , Humanos , Microscopia , Reprodutibilidade dos Testes , Patologistas
2.
Sci Rep ; 11(1): 16605, 2021 08 16.
Artigo em Inglês | MEDLINE | ID: mdl-34400666

RESUMO

Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly, time-consuming laboratory processes. Histologic subtype classification represents an established component of lung adenocarcinoma histopathology, but can be challenging and is associated with substantial inter-pathologist variability. Here we developed a deep learning system to both classify histologic patterns in lung adenocarcinoma and predict TMB status using de-identified Hematoxylin and Eosin (H&E) stained whole slide images. We first trained a convolutional neural network to map histologic features across whole slide images of lung cancer resection specimens. On evaluation using an external data source, this model achieved patch-level area under the receiver operating characteristic curve (AUC) of 0.78-0.98 across nine histologic features. We then integrated the output of this model with clinico-demographic data to develop an interpretable model for TMB classification. The resulting end-to-end system was evaluated on 172 held out cases from TCGA, achieving an AUC of 0.71 (95% CI 0.63-0.80). The benefit of using histologic features in predicting TMB is highlighted by the significant improvement this approach offers over using the clinical features alone (AUC of 0.63 [95% CI 0.53-0.72], p = 0.002). Furthermore, we found that our histologic subtype-based approach achieved performance similar to that of a weakly supervised approach (AUC of 0.72 [95% CI 0.64-0.80]). Together these results underscore that incorporating histologic patterns in biomarker prediction for lung cancer provides informative signals, and that interpretable approaches utilizing these patterns perform comparably with less interpretable, weakly supervised approaches.


Assuntos
Adenocarcinoma de Pulmão/genética , Carcinoma Pulmonar de Células não Pequenas/genética , Aprendizado Profundo , Neoplasias Pulmonares/genética , Mutação , Adenocarcinoma de Pulmão/patologia , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , Área Sob a Curva , Carcinoma Pulmonar de Células não Pequenas/patologia , Corantes , Conjuntos de Dados como Assunto , Amarelo de Eosina-(YS) , Feminino , Hematoxilina , Humanos , Neoplasias Pulmonares/patologia , Masculino , Pessoa de Meia-Idade , Curva ROC , Fatores Sexuais , Fumar , Coloração e Rotulagem
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...