This study measured the correlation between the magnitude of the presence of the abnormality gist and case difficulty based on standard presentation and reporting mechanisms for 80 cases. Half of the cases contained biopsy-proven cancer while the remainder were normal and confirmed to be cancer-free for at least two years of follow-up. In the gist experiment, seventeen breast radiologists and physicians gave an abnormality score on a scale from 0 (confident normal) to 100 (confident abnormal) to unilateral CC mammograms following a very brief, 500 millisecond presentation of the image. Independently, each mammogram was assessed by a separate sample of at least 40 radiologists using standard presentation and reporting mechanisms, with these readers asked to locate any cancers present. All readers reported at least 1000 cases annually. For each case and each category, the percentage of correct reports served as an objective measure of case difficulty (lower rate of correct report shows a more difficult case). For each of the 17 readers, the association between the abnormality scores from the gist study and detection rates from the earlier reports was examined using Spearman correlation. None of the coefficients were significantly different from zero (p<0.05). For the normal cases, the correlation coefficient between abnormality scores and detection rates for the 17 readers ranged from -0.262 to 0.258, and for cancer -0.180 to 0.309. The results suggest that the gist signal may indicate the presence of cancer, using mechanisms other than those employed in usual reporting, and might be exploited to improve breast cancer detection.
Can radiologists distinguish prior mammograms with no overt signs of cancer from women who were later diagnosed with breast cancer from the prior mammograms of women reported as normal and subsequently confirmed to be cancerfree? Twenty-three radiologists and breast physicians viewed 200 craniocaudial mammograms for a half-second and rated whether the woman would be recalled on a scale of 0 (clearly normal) to 100 (clearly abnormal). The dataset included five categories of mammograms, with each category containing 40 cases. The categories were Cancer (current cancer-containing mammograms), Prior-Vis (prior mammograms with visible cancer signs), Contra (current ‘normal’ mammograms contralateral to the cancer), Prior-Invis (priors without visible cancer signs), and Normal (priors of normal cases). For each radiologist, four pairs of analyses were performed to evaluate whether the radiologists could distinguish mammograms in each category from the normal mammograms: Cancer vs Normal, Prior-Vis vs Normal, Contra vs Normal, and Prior-Invis vs Normal. The Area under Receiver Operating Characteristic curves (AUC) was calculated for each paired grouping and each radiologist. Wilcoxon Signed Rank test showed the AUC values were above-chance for all comparisons: Cancer (z=4.20, P<0.001); Prior-Vis (z=4.11, P<0.001); Contra (z=4.17, P<0.001); Prior-Invis (z=3.71, P<0.001). The results suggest that radiologists can distinguish patients who were diagnosed with cancer from individuals without breast cancer at an above-chance level based on a half-second glimpse of mammogram even before the lesion becomes apparently visible (Prior-Invis). Apparently, something about the breast parenchyma can look abnormal before the appearance of a localized lesion
<strong>PURPOSE:</strong> To assess the performance of Quantra<sup>TM</sup> in reproducing BI-RADS<sup>®</sup> mammographic breast density (MBD) assessment. <strong>METHODS:</strong> Two methods of MBD assessment were used (Quantra<sup>TM</sup> and BI-RADS<sup>®</sup>). Volumetric breast density measurement from 292 raw projection images was performed using Quantra<sup>TM</sup>. BI-RADS<sup>®</sup> assessment was performed by three radiologists and a majority report (consensus of at least two radiologists) was generated. Interreader agreement (κ), agreement, and the sensitivity and specificity of Quantra<sup>TM</sup> in reproducing BI-RADS<sup>®</sup> rating were calculated on a four-grade (1, 2, 3, and 4) and two-grade (1–2 vs. 3–4) scale. <strong>RESULTS:</strong> The majority BI-RADS<sup>®</sup> report in the dataset consisted of 9.6% (n = 28), 35.3% (n = 103), 27.1% (n = 79), and 28.1% (n = 82) for BI-RADS® 1, 2, 3, and 4 respectively. Intra-reader agreement (κ) was 0.86 (95%CI: 0.83 – 0.91) to 0.88 (95%CI: 0.85 – 0.93) on a four-grade and 0.88 (95%CI: 0.83 – 0.92) to 0.91 (95%CI: 0.88 – 0.95) on a two-grade scale. Inter-reader agreement (κ) was substantial [0.66 (95%CI: 0.62 – 0.71) to 0.75 (95%CI: 0.70 – 0.81)] on a four-grade scale and substantial to almost perfect [0.77 (95%CI: 0.73 – 0.82) to 0.89 (95%CI: 0.84 – 0.93)] on a two-grade scale. Quantra<sup>TM</sup> correctly classified 35.7%, 91.2%, 88.6%, and 50.3% of BI-RADS<sup>®</sup> 1, 2, 3, and 4 respectively. It also demonstrated 91.3% sensitivity and 83.6% specificity in reproducing BI-RADS<sup>®</sup> on a two-grade scale (1–2 vs. 3–4). <strong>CONCLUSION:</strong> Quantra<sup>TM</sup> has limited performance in reproducing BI-RADS<sup>®</sup> rating on a four-grade scale, however, highly reproduces BI-RADS<sup>®</sup> assessment on a two-grade scale.