In a recent article in Cancer, Cho and colleagues1 suggested that the Cancer of the Liver Italian Program (CLIP) system was the best prognostic system for predicting the survival of patients with hepatocellular carcinoma (HCC) who were undergoing transcatheter arterial chemoembolization, and further validation studies will be performed. We agree with the authors.

Predicting mortality is an important component of the decision-making process, and scoring systems (Child-Pugh, CLIP, Model for Endstage Liver Disease, Barcelona Clinic Liver Cancer, etc) long have been accepted for establishing patient outcomes in HCC. Although these models perform well in predicting the mortality or morbidity in the original cohorts on which the models were developed, they may underestimate or overestimate mortality or morbidity in different populations.1

However, in the midst of such uncertainty, fuzzy logic may play an important role in the decision-making process. Fuzzy logic is the science of reasoning, thinking, and inference that recognizes and uses the real-world phenomenon that everything is a matter of degree. In the simplest terms, fuzzy-logic theory is an extension of binary theory that does not use crisp definitions and distinctions. Instead of assuming that everything must be defined in to black and white (binary view), fuzzy logic is a method that captures and uses the concept of fuzziness in a computationally effective manner.

Neural networks and fuzzy logic are 2 complementary technologies. Neural networks can learn (ie, adapt) from data and feedback, but understanding the pattern learned by neural networks is difficult. Conversely, fuzzy rule-based models are easy to comprehend, because they use linguistic terms and the structure of if/then rules. Unlike neural networks, fuzzy logic does not come with a learning algorithm (ie, the system cannot learn from feedback). The combination of neural networks and fuzzy logic has created a new term, a neurofuzzy system. Neural networks, fuzzy systems, and the combination of both already have been applied successfully to computer-aided diagnosis—eg, detection of microcalcification, automatic detection of distorted plethysmogram pulses in neonates and pediatric patients, detection of erythematosquamous diseases, and lung-nodule detection—and have been useful for predicting the presence of prostate cancer.2

In conclusion, slight variations in population characteristics can change predictive values of the scoring systems. However, neurofuzzy systems can incorporate data from many clinical, biologic, and genetic variables and can provide better risk estimations for hepatocellular carcinoma.


  1. Top of page

Mesut Tez MD*, Baris Zulfikaroglu MD†, * Gazi University School of Medicine, Department of Surgery, Ankara, Turkey, † Ankara Numune Hospital, Department of Fifth Surgery, Ankara, Turkey.