Differentiating small (<=1 cm) focal liver lesions as metastases or cysts by means of Computed Tomography: a case-study to illustrate a fuzzy logic-based method to assess the impact of diagnostic confidence on radiological diagnosis

Purpose. To quantify the impact of diagnostic confidence on radiological diagnosis with a fuzzy logic-based method. Materials and Methods. Twenty-two oncologic patients with 20 cysts and 30 metastases ≤1 cm in size found at 64-row computed tomography were included. Two readers (R1/R2) expressed diagnoses as a subjective level of confidence in malignancy within the interval [0,1] rather than on a “crisp” basis (malignant/benign); confidence in benignancy was . When cross-tabulating data according to the standard of reference, table cells resulted from the aggregation between and final diagnosis. We then assessed (i) readers diagnostic performance on a fuzzy and crisp basis; (ii) the “divergence” (%) as a measure of how confidence impacted on crisp diagnosis. Results. Diagnoses expressed with lower confidence increased fuzzy false positives compared to crisp ones (from 0 to 0.2 for R1; from 1 to 2.4 for R2). Crisp/fuzzy accuracy was 94.0%/93.6% (R1) and 94.0/91.6% (R2). (%) was larger in the case of the less experienced reader (R2) (up to +7.95% for specificity). According to simulations, (%) was negative/positive depending on the level of confidence in incorrect diagnoses. Conclusion. Fuzzy evaluation shows a measurable effect of uncertainty on radiological diagnoses.