Deep convolutional neural networks combine Raman spectral signature of serum for prostate cancer bone metastases screening.
Nanomedicine
; 29: 102245, 2020 10.
Article
en En
| MEDLINE
| ID: mdl-32592757
ABSTRACT
Prostate cancer most frequently metastasizes to bone, resulting in abnormal bone metabolism and the release of components into the blood stream. Here, we evaluated the capacity of convolutional neural networks (CNNs) to use Raman data for screening of prostate cancer bone metastases. We used label-free surface-enhanced Raman spectroscopy (SERS) to collect 1281 serum Raman spectra from 427 patients with prostate cancer, and then we constructed a CNN based on LetNet-5 to recognize prostate cancer patients with bone metastases. We then used 5-fold cross-validation method to train and test the CNN model and evaluated its actual performance. Our CNN model for bone metastases detection revealed a mean training accuracy of 99.51% ± 0.23%, mean testing accuracy of 81.70% ± 2.83%, mean testing sensitivity of 80.63% ± 5.07%, and mean testing specificity of 82.82% ± 2.94%.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Neoplasias de la Próstata
/
Neoplasias Óseas
/
Detección Precoz del Cáncer
/
Proteínas de Neoplasias
Tipo de estudio:
Diagnostic_studies
/
Screening_studies
Límite:
Humans
/
Male
Idioma:
En
Revista:
Nanomedicine
Asunto de la revista:
BIOTECNOLOGIA
Año:
2020
Tipo del documento:
Article