Digital Breast Tomosynthesis (DBT) increases breast cancer detection rates but produces a significantly greater number of images for screeners to read compared to traditional two-dimensional (2-D) mammograms. Putting screeners at risk of fatigue and therefore error in detecting cancers. The aim of this study was to explore if screeners showed differences in subjective fatigue, blink metrics and diagnostic accuracy during a DBT reading session with and without breaks. Prospective study including 45 participants from 6 different hospital sites around England between December 2020 to April 2022. Non-intrusive, screen mounted eye tracking cameras (60Hz sampling rate) were set up in the participant’s natural reading environment. Forty DBT cases were read in a random order (47.5% malignant, 12.5% benign, 40% normal). Each breast was rated as normal or benign (return to screen) or indeterminate, suspicious or highly suspicious (recall). Twenty-one participants had a break at approximately 40 minutes into the session. Participants without a break showed a significantly greater difference in subjective fatigue before and after the reporting session (44% vs 33%, p=0.037). Furthermore, those without breaks exhibited significantly greater blinks per minute (15.75 vs 13.25, p<0.001) and blink duration (milliseconds) (296 vs 286, p<0.001). There was no significant difference in overall accuracy between the cohorts (p=0.921). Blink metrics have the potential to be used in identifying early onset of fatigue during reading sessions.
The final stage in the medical imaging diagnostic system is the radiologist’s interpretation of the images, though research on the factors influencing performance in digital breast tomosynthesis (DBT) is inconclusive. This study seeks to understand the performance of radiologists in reading DBT images and the parameters impacting observer performance in three different countries. The study used a DBT mammogram test to compare the performance of radiologists from Australia, China and Iran in reading thirty-five DBT cases. A range of performance metrics including specificity, sensitivity, lesion sensitivity, ROC AUC and JAFROC FOM were generated for each radiologist upon the conclusion of the test set. The radiologists also provided demographic information relating to their experience in reading digital mammograms and DBT. Each country had a greater percentage of radiologists that have completed a breast imaging fellowship compared to those that have not. Australia had a greater percentage of radiologists that have completed training in DBT reading (Australia=88.2%), while China and Iran had a smaller percentage of radiologists that have not completed training in DBT reading (China=37%, Iran=40%). Significant differences were identified between the three countries in specificity (p=.001), lesion sensitivity (p=.016), ROC (p<.001) and JAFROC (p<.001). Australia had the highest mean value for all performance metrics, while China had the lowest mean value for all performance metrics. Australian radiologists have a moderate positive correlation between lesion sensitivity and the number of years reading DBT images (r=.513, p=.042). Iranian radiologists who read more than 20 DBT cases per week obtained significantly higher performance in lesion sensitivity 73.3% vs. 51.8%; p=.032) than the ones who read less than 20 DBT cases per week.
Deep convolutional neural networks (CNNs) have demonstrated high accuracy in a wide range of computer vision applications, including medical and biological imaging. Many CNNs are fully supervised learning algorithms, and their performance is directly associated with the quality of the training data labels, which are human-defined. In this work, we investigate the fidelity of human-defined truth for cell detection, segmentation, and classification tasks in multiplex microscopy images. We compare manual annotations from human readers on three tasks. Readers were asked to (1) segment all cells in single-channel fluorescence images of a pannuclear stain (DAPI), (2) segment cells in two-channel fluorescence images (CD20/DAPI), only identifying cells with both nuclear signal (DAPI) and signal from a cell surface marker (CD20), and (3) segment two separate cell classes in three-channel fluorescence images (CD3/CD4/DAPI). In this third task, readers were asked to identify cells that had nuclear signal and were CD3+/CD4- and CD3+/CD4+. By comparing these manual segmentations within and between readers, we demonstrate that human readers show the least variability in single-channel DAPI segmentation (p<<0.05, F test for equal variance). We also compared the agreement of human readers with one another to the agreement of an object-detection network, Yolov5, on cell detection in DAPI images. All pairwise comparisons of human readers with other human readers yielded an average F1-score of 0.83±0.14, and comparisons of Yolov5 with human readers yielded an average F1-score of 0.84±0.12 (p=0.26, Welch’s T test). We therefore demonstrate that out of the provided tasks, DAPI detection provides the highest fidelity ground truth, and were unable to show a difference between Yolov5 and human readers in this task.
Computer-aided diagnostic schemes have been developed with the primary aim of supporting diagnostic accuracy in the investigation of mammography images. A robust mammograms database is a priority requirement to assist in testing the effectiveness of techniques associated with these schemes. However, such datasets–with information on radiological and biopsy reports, different types of findings and good quality images-are difficult to be available, mainly due to restrictions of different radiology centers and hospitals or limited to the research team involved. Aiming to overcome these issues, we are developing an alternative based on images of a structured breast phantom, previously developed in our group. Although having been validated in terms of their physical characteristics, the investigation on the images produced from this phantom exposure on a digital mammography system is an important task with regard to their appearance compared to actual breasts images. For this purpose, a software was developed for managing comparative tests between images from actual breasts and from this phantom, considering internal regions of interest. The intention is to evaluate how much the simulated and real images are confused according to the human visual perception of different observers, seeking to validate the use of this breast phantom for structuring a new mammography images database aimed at evaluation tests of CAD schemes in mammography. Results were promising showing a variation of 50-60% in the rate of correct answers among observers, indicating a reasonable level of confusion between the two types of images.
Previous studies reported that the cancer subtypes radiologists struggling to detect successfully varied across countries in mammography interpretation. However, little is known whether such variation is also in radiologists’ perception of local cancer-free areas. This study compared the cancer-free areas incorrectly flagged as cancer by radiologists from two populations in reading dense screening mammograms. We collected reading data from 20 Chinese and 16 Australian radiologists who previously evaluated 60 dense screening cases. For each cohort, findings from all readers were pooled together, and the local cancer-free areas classified as cancer were identified. Particularly the areas misclassified by readers from both cohorts were recognized and displayed on the mammograms as overlaps. For each overlap, we counted the error rate, the proportion of readers failing to distinguish between normality and abnormality, as a measure of the actual difficulty level for each reader cohort. Afterward, the Spearman correlation was performed to explore whether the calculated cohort-specific difficulty levels were correlated. A similar analysis was conducted on two geographically-distant groups within China. Results showed that between Chinese and Australian radiologists, the correlation was only found in the cancer-free views of cancer cases (r=0.902, p=0.004). However, between the two groups within China, we found strong correlations in both cancer-containing (r=0.833, p=0.333) and cancer-free views (r=0.955, p=0.022) of cancer cases, despite an insignificant correlation in normal cases. In conclusion, radiologists from different populations display various error-making patterns in reading dense screening mammograms, while those with similar demographic characteristics share the diagnosis to a certain degree.
