Background & Aims: Hepatocellular carcinoma (HCC) prognosis strongly depends upon nuclear grade and the presence of microscopic vascular invasion (MVI). The aim of this study was to develop an artificial neural network (ANN) that is able to predict tumour grade and MVI on the basis of non-invasive variables. Methods: Clinical, radiological, and histological data from 250 cirrhotic patients resected (n = 200) or transplanted (n = 50) for HCC were analyzed. ANN and logistic regression models were built on a training group of 175 randomly chosen patients and tested on the remaining testing group of 75. Receiver operating characteristics curve (ROC) and k-statistics were used to analyze model accuracy in the prediction of the final histological assessment of tumour grade (G1-G2 vs. G3-G4) and MVI (absent vs. present). Results: Pathologic examination showed G3-G4 in 69.6% of cases and MVI in 74.4%. Preoperative serum alpha-fetoprotein (AFP), tumour number, size, and volume were related to tumour grade and MVI (p <0.05) and were used for ANN building, whereas, tumour number did not enter into the logistic models. In the training group, ANN area under ROC curves (AUC) for tumour grade and MVI prediction were 0.94 and 0.92, both higher (p <0.001) than those of logistic models (0.85 for both). In the testing group, ANN correctly identified 93.3% of tumour grades (k = 0.81) and 91% of MVI (k = 0.73). Logistic models correctly identified 81% of tumour grades (k = 0.55) and 85% of MVI (k = 0.57). Conclusion: ANN identifies HCC tumour grades and MVI on the basis of preoperative variables more accurately than the conventional linear model and should be used for tailoring clinical management.

Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study / CUCCHETTI, ALESSANDRO; PISCAGLIA, FABIO; D'ERRICO, ANTONIETTA; Ravaioli M; CESCON, MATTEO; Zanello M; GRAZI, GIAN LUCA; GOLFIERI, RITA; GRIGIONI, FRANCO; PINNA, ANTONIO DANIELE. - In: JOURNAL OF HEPATOLOGY. - ISSN 0168-8278. - STAMPA. - 52:(2010), pp. 880-888. [10.1016/j.jhep.2009.12.037]

Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study

GRAZI, GIAN LUCA;
2010

Abstract

Background & Aims: Hepatocellular carcinoma (HCC) prognosis strongly depends upon nuclear grade and the presence of microscopic vascular invasion (MVI). The aim of this study was to develop an artificial neural network (ANN) that is able to predict tumour grade and MVI on the basis of non-invasive variables. Methods: Clinical, radiological, and histological data from 250 cirrhotic patients resected (n = 200) or transplanted (n = 50) for HCC were analyzed. ANN and logistic regression models were built on a training group of 175 randomly chosen patients and tested on the remaining testing group of 75. Receiver operating characteristics curve (ROC) and k-statistics were used to analyze model accuracy in the prediction of the final histological assessment of tumour grade (G1-G2 vs. G3-G4) and MVI (absent vs. present). Results: Pathologic examination showed G3-G4 in 69.6% of cases and MVI in 74.4%. Preoperative serum alpha-fetoprotein (AFP), tumour number, size, and volume were related to tumour grade and MVI (p <0.05) and were used for ANN building, whereas, tumour number did not enter into the logistic models. In the training group, ANN area under ROC curves (AUC) for tumour grade and MVI prediction were 0.94 and 0.92, both higher (p <0.001) than those of logistic models (0.85 for both). In the testing group, ANN correctly identified 93.3% of tumour grades (k = 0.81) and 91% of MVI (k = 0.73). Logistic models correctly identified 81% of tumour grades (k = 0.55) and 85% of MVI (k = 0.57). Conclusion: ANN identifies HCC tumour grades and MVI on the basis of preoperative variables more accurately than the conventional linear model and should be used for tailoring clinical management.
2010
52
880
888
CUCCHETTI, ALESSANDRO; PISCAGLIA, FABIO; D'ERRICO, ANTONIETTA; Ravaioli M; CESCON, MATTEO; Zanello M; GRAZI, GIAN LUCA; GOLFIERI, RITA; GRIGIONI, FRANCO; PINNA, ANTONIO DANIELE
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1301178
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