Interpretative characteristics and case features associated with the performances of radiologists in reading mammograms: A study from a non‐screening population in Asia. Issue 1 (7th September 2020) - Trieu, Phuong Dung (Yun); Puslednik, Louise; Colley, Brooke; Brennan, Anna; Rodriguez, Veruska Cediel; Cook, Nicholas; Dean, Kaitlin; Dryburgh, Sarah; Lowe, Hayden; Mahon, Charlotte; McGowan, Saxon; O'Brien, Joshua; et al.
Abstract: Aims: To explore radiologist characteristics and case features associated with diagnostic performances in cancer detection on mammograms in a South East Asian population. Methods: Fifty‐three radiologists reported 60 mammographic examinations which consisted of 40 normal and 20 cancer‐containing cases at the BREAST workshops. Radiologists were asked to examine each mammogram using the BIRADS on diagnostic monitors. Differences in reader characteristics and case features between correct and incorrect decisions were assessed separately for cancer and normal cases. Univariate and multivariate logistic regressions were applied to generate odds ratios (OR) for significant factors related to correct decisions. Results: Radiologists who spent ≥10 hours/week reporting mammograms had a higher possibility of detecting cancer lesions (OR = 1.6; P = 0.01). A higher rate of accuracy in reporting negative cases was associated with female radiologists (OR = 1.4; P = 0.002), radiologists who read ≤20 mammograms per week (OR = 1.5; P < 0.0001), had completed training course (OR = 1.7; P < 0.0001) or wore eyeglasses (OR = 1.4; P = 0.01). Cancer cases with breast density >50% (OR = 2.1; P < 0.0001), having abnormal lesions ≥9 mm (OR = 1.8; P < 0.0001), or displaying calcifications, a discrete mass or nonspecific density (OR = 1.6; P < 0.0001) were recorded with a higher detection rate by radiologists than other cases. Lesions located on the right breasts (OR = 1.8; P < 0.0001) or found in the lower inner, upper outer or mixed locations (OR = 2.7; P < 0.0001) were also recorded with a better diagnostic possibility compared with other lesions. Conclusion: This work identified key features related to diagnostic accuracy of breast cancer on mammograms in a nonscreening population, which is helpful for developing appropriate strategies to improve breast cancer detectability of radiologists.