Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer.
J Gynecol Oncol
; 29(5): e66, 2018 Sep.
Article
en En
| MEDLINE
| ID: mdl-30022630
ABSTRACT
OBJECTIVE:
Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival.METHODS:
This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon.RESULTS:
Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included disease-free interval (DFI) (importance 0.231), retroperitoneal recurrence (importance 0.178), residual disease at primary surgical treatment (importance 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance 0.306). Other important variables included CC (importance 0.217), and FIGO stage at presentation (importance 0.100).CONCLUSION:
According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process.Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Neoplasias Ováricas
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Redes Neurales de la Computación
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Procedimientos Quirúrgicos de Citorreducción
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Recurrencia Local de Neoplasia
Tipo de estudio:
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
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Female
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Humans
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Middle aged
Idioma:
En
Revista:
J Gynecol Oncol
Año:
2018
Tipo del documento:
Article
País de afiliación:
Italia