Deep learning pathological microscopic features in endemic nasopharyngeal cancer: Prognostic value and protentional role for individual induction chemotherapy.
Cancer Med
; 9(4): 1298-1306, 2020 02.
Article
en En
| MEDLINE
| ID: mdl-31860791
ABSTRACT
BACKGROUND:
To explore the prognostic value and the role for treatment decision of pathological microscopic features in patients with nasopharyngeal carcinoma (NPC) using the method of deep learning.METHODS:
The pathological microscopic features were extracted using the software QuPath (version 0.1.3. Queen's University) in the training cohort (Guangzhou training cohort, n = 843). We used the neural network DeepSurv to analyze the pathological microscopic features (DSPMF) and then classified patients into high-risk and low-risk groups through the time-dependent receiver operating characteristic (ROC). The prognosis accuracy of the pathological feature was validated in a validation cohort (n = 212). The primary endpoint was progression-free survival (PFS).RESULTS:
We found 429 pathological microscopic features in the H&E image. Patients with high-risk scores in the training cohort had shorter 5-year PFS (HR 10.03, 6.06-16.61; P < .0001). The DSPMF (C-index 0.723) had the higher C-index than the EBV DNA (C-index 0.612) copies and the N stage (C-index 0.593). Furthermore, induction chemotherapy (ICT) plus concomitant chemoradiotherapy (CCRT) had better 5-year PFS to those received CCRT (P < .0001) in the high-risk group.CONCLUSION:
The DSPMF is a reliable prognostic tool for survival risk in patients with NPC and might be able to guide the treatment decision.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Nasofaringe
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Neoplasias Nasofaríngeas
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Quimioradioterapia
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Aprendizaje Profundo
Tipo de estudio:
Diagnostic_studies
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Etiology_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
País/Región como asunto:
Asia
Idioma:
En
Revista:
Cancer Med
Año:
2020
Tipo del documento:
Article
País de afiliación:
China