A comparison of logistic regression and artificial neural networks in predicting central lymph node metastases in papillary thyroid microcarcinoma


COD: 01_2805 Categorie: ,

Sabri Özden, Sadettin Er, Bari Saylam, Bari Dog˘u Yildiz, Kazim S¸enol, Mesut Tez

Ann Ital Chir, 2018 89, 2: 113-117
Epub Ahead of Print – February 9

La mia nuova descrizione qui!

OBJECTIVE: Prophylactic central lymph node dissection(CLND) is a controversial issue in papillary thyroid microcarcinoma(
PTMC) patients without lymphatic metastasis. Artificial neural network(ANN) has been proposed as an alternative
statistical technique for predicting complex biologic phenomena. Our aim is to develop an ANN model in predicting
central lymph node metastases(CLNM) in patients with PTMC, in comparison to traditional logistic regression(LR) analysis.
STUDY DESIGN: Eighty patients who underwent total thyroidectomy plus CLND for PTMC were included in the study.
The factors associated with CLNM were determined by using both ANN model and LR analysis. The predictive performances
of these two statistical models were compared.
RESULTS: Twenty (25%) patients had CLNM. In univariate analysis, age >45 years, tumor diameter >7 mm, and multifocality
were the associated parameters with CLNM. These parameters were used to create LR and ANN models. LR
test revealed tumor diameter >7 mm and multifocality as independent factors for CLNM. ANN (AUC: 0.786) had a
higher predictive value for CLNM, in comparison to LR model (AUC: 0.750).
CONCLUSIONS: Tumor diameter >7 mm and multifocality are the independent prognostic indicators of CLNM in patients
with PTMC. ANN model has higher predictive value for determining central metastasis, in comparison to LR analysis.


Utilizzando il sito, accetti l'utilizzo dei cookie da parte nostra. maggiori informazioni

Questo sito utilizza i cookie per fornire la migliore esperienza di navigazione possibile. Continuando a utilizzare questo sito senza modificare le impostazioni dei cookie o cliccando su "Accetta" permetti il loro utilizzo.