A CT Algorithm Can Elevate the Differential Diagnosis of Interstitial Lung Disease by Non-specialists to Equal That of Specialist Thoracic Radiologists.

BACKGROUND:Diagnosis of diffuse parenchymal lung diseases (DPLD) on high resolution CT (HRCT) is difficult for non-expert radiologists due to varied presentation for any single disease and overlap in presentation between diseases. RATIONALE AND OBJECTIVES:To evaluate whether a pattern-based training algorithm can improve the ability of non-experts to diagnosis of DPLD. MATERIALS AND METHODS:Five experts (cardiothoracic-trained radiologists), and 25 non-experts (non-cardiothoracic-trained radiologists, radiology residents, and pulmonologists) were each assigned a semi-random subset of cases from a compiled database of DPLD HRCTs. Each reader was asked to create a top three differential for each case. The non-experts were then given a pattern-based training algorithm for identifying DPLDs. Following training, the non-experts were again asked to create a top three differential for each case that they had previously evaluated. Accuracy between groups was compared using Chi-Square analysis. RESULTS:A total of 400 and 1450 studies were read by experts and non-experts, respectively. Experts correctly placed the diagnosis as the first item on the differential versus having the correct diagnosis as one of their top three diagnoses at an overall rate of 48 and 64.3%, respectively. Pre-training, non-experts achieved a correct diagnosis/top three of 32.5 and 49.7%, respectively. Post-training, non-experts demonstrated a correct diagnosis/top three of 41.2 and 65%, a statistically significant increase (p < 0.0001). In addition, post training, there was no difference between non-experts and experts in placing the correct diagnosis within their top three differential. CONCLUSION:The diagnosis of DPLDs by HRCT imaging alone is relatively poor. However, use of a pattern-based teaching algorithm can improve non-expert interpretation and enable non-experts to include the correct diagnosis within their differential diagnoses at a rate comparable to expert cardiothoracic trained radiologists.