Diagnostic accuracy of 100 radiologists in detecting pulmonary nodules: a short communication

BACKGROUND: Chest X-ray is a widely used and accessible screening method for lung pathology. However, its diagnostic capabilities, particularly for visualizing pulmonary nodules, are limited. Suboptimal detection of pulmonary nodules by radiologists on chest X-rays affects the timeliness of diagnosis and therapeutic outcomes. One approach to improving lung nodule detection in chest X-rays involves adopting novel techniques, particularly those based on artificial intelligence. Nevertheless, the diagnostic value of these technologies in clinical practice remains largely unresolved due to limited data on radiologists’ performance conventional metrics. AIM: To evaluate the diagnostic performance of 100 radiologists in identifying pulmonary nodules on chest X-rays. METHODS: Each of the 100 radiologists was asked to evaluate 100 chest radiographs, of which 50 showed abnormal findings and 50 were normal. The presence of pulmonary nodules was assessed using the following scale: Absent (0 on the probability scale), likely absent (0.25), uncertain (0.50), likely present (0.75), present (1.00). Validation of nodule presence or absence was performed by three expert physicians using a binary scale (0/1) based on chest CT performed within 14 days after chest X-ray. We assessed image interpretation time, the difference in performance between radiologists and expert physicians (expressed in absolute units as D), and the primary diagnostic accuracy metrics of the radiologists. RESULTS: The study yielded the following: ROC AUC: 0.858 ± 0.059, accuracy: 0.822 ± 0.048, sensitivity: 0.779 ± 0.097, and specificity: 0.864 ± 0.095. A negligible positive correlation was observed between expert accuracy and mean image interpretation time (Spearman correlation coefficient rs = 0.189) and a low positive correlation (rs = 0.344) between image interpretation time and the D value. CONCLUSION: These results can be used to assess the quality of automated detection systems under development and to evaluate the efficacy of alternative methods and approaches for pulmonary nodule detection.