W R Reinus1, A J Wilson, B Kalman, S Kwasny. 1. Mallinckrodt Institute of Radiology, Jewish Hospital, Washington University Medical Center, St. Louis, MO 63110.
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.
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.
Authors: Claudio E von Schacky; Nikolas J Wilhelm; Valerie S Schäfer; Yannik Leonhardt; Matthias Jung; Pia M Jungmann; Maximilian F Russe; Sarah C Foreman; Felix G Gassert; Florian T Gassert; Benedikt J Schwaiger; Carolin Mogler; Carolin Knebel; Ruediger von Eisenhart-Rothe; Marcus R Makowski; Klaus Woertler; Rainer Burgkart; Alexandra S Gersing Journal: Eur Radiol Date: 2022-04-09 Impact factor: 7.034