Diagnosis of focal bone lesions using neural networks

Abstract

Rationale and objectives: Use of a neural network to diagnose focal lesions of bone was evaluated.

Methods: Imaging features of 709 lesions were encoded into a predetermined database. Data were divided into four groups and were analyzed using cross-validation by a two-layer feed-forward neural network.

Results: The lesions comprised 43 different pathologic diagnoses. Overall, the network was 85% accurate in distinguishing benign from malignant lesions. With a differential list of five diagnoses, the list was internally consistent regarding benign and malignant lesions 81.9% of the time. The network correctly diagnosed 56% of the lesions by pathologic diagnosis as its first choice. It included the correct diagnosis 71.8% of the time in a differential list of three diagnoses and 87.3% of the time in a differential list of nine diagnoses.

Conclusion: Although not yet adequate for clinical use, neural network diagnosis of bone lesions is in its infancy and has important implications for the future analysis of focal bone lesions.