This study aims to review the literature on existing diagnostic reference levels (DRLs) in digital mammography and methodologies for establishing them. To this end, a systematic search through Medline, Cinahl, Web of Science, Scopus and Google scholar was conducted using search terms extracted from three terms: DRLs, digital mammography and breast screen. The search resulted in 1539 articles of which 22 were included after a screening process. Relevant data from the included studies were summarised and analysed. Differences were found in the methods utilised to establish DRLs including test subjects types, protocols followed, conversion factors employed, breast compressed thicknesses and percentile values adopted. These differences complicate comparison of DRLs among countries; hence, an internationally accepted protocol would be valuable so that international comparisons can be made.
and regression were used to study the agreement and correlation between organ and calculated doses. BlandAltman showed statistically significant bias between organ and calculated doses. The bias differed for different unit makes and models; Philips had the lowest bias, overestimating Dance method by 0.03 mGy, while general electric had the highest bias, overestimating Wu method by 0.20 mGy, the Hologic organ dose underestimated Boone method by 0.07 mGy, and the Fujifilm organ dose underestimated Dance method by 0.09 mGy. Organ dose was found to disagree with our calculated dose, yet organ dose is potentially beneficial for rapid dose audits. Conclusions drawn based on the organ dose alone come with a risk of over or underestimating the calculated dose to the patient and this error should be considered in any reported results.
MGD is dependent upon compressed breast thickness and it is recommended that DRL values should be specific to compressed breast thickness and image detector technology.
The information captured by the gist signal, which refers to radiologists’ first impression arising from an initial global image processing, is poorly understood. We examined whether the gist signal can provide complementary information to data captured by radiologists (experiment 1), or computer algorithms (experiment 2) based on detailed mammogram inspection. In the first experiment, 19 radiologists assessed a case set twice, once based on a half-second image presentation (i.e., gist signal) and once in the usual viewing condition. Their performances in two viewing conditions were compared using repeated measure correlation (rm-corr). The cancer cases (19 cases × 19 readers) exhibited non-significant trend with rm-corr = 0.012 (p = 0.82, CI: −0.09, 0.12). For normal cases (41 cases × 19 readers), a weak correlation of rm-corr = 0.238 (p < 0.001, CI: 0.17, 0.30) was found. In the second experiment, we combined the abnormality score from a state-of-the-art deep learning-based tool (DL) with the radiological gist signal using a support vector machine (SVM). To obtain the gist signal, 53 radiologists assessed images based on half-second image presentation. The SVM performance for each radiologist and an average reader, whose gist responses were the mean abnormality scores given by all 53 readers to each image was assessed using leave-one-out cross-validation. For the average reader, the AUC for gist, DL, and the SVM, were 0.76 (CI: 0.62–0.86), 0.79 (CI: 0.63–0.89), and 0.88 (CI: 0.79–0.94). For all readers with a gist AUC significantly better than chance-level, the SVM outperformed DL. The gist signal provided malignancy evidence with no or weak associations with the information captured by humans in normal radiologic reporting, which involves detailed mammogram inspection. Adding gist signal to a state-of-the-art deep learning-based tool improved its performance for the breast cancer detection.
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