How mammographic breast density affects radiologists' visual search patterns

Surgical and Radiology Trainees' Proficiency in Reading Mammograms: the Importance of Education for Cancer Localisation

Medical imaging with mammography plays a very important role in screening and diagnosis of breast cancer, Australia's most common female cancer. The visualisation of cancers on mammograms often forms a diagnosis and guidance for radiologists and breast surgeons, and education platforms that provide real cases in a simulated testing environment have been shown to improve observer performance for radiologists. This study reports on the performance of surgical and radiology trainees in locating breast cancers. An enriched test set of 20 mammography cases (6 cancer and 14 cancer free) was created, and 18 surgical trainees and 32 radiology trainees reviewed the cases via the Breast Screen Reader Assessment Strategy (BREAST) platform and marked any lesions identifiable. Further analysis of performance with high- and low-density cases was undertaken, and standard metrics including sensitivity and specificity. Radiology trainees performed significantly better than surgical trainees in terms of specificity (0.72 vs. 0.35; P < 0.01). No significant differences were observed between the surgical and radiology trainees in sensitivity or lesion sensitivity. Mixed results were obtained with participants regarding breast density, with higher density cases generally having lower performance. The higher specificity of the radiology trainees compared to the surgical trainees likely represents less exposure to negative mammography cases. The use of high-fidelity simulated self-test environments like BREAST is able to benchmark, understand and build strategies for improving cancer education in a safe environment, including identifying challenging scenarios like breast density for enhanced training.

Evaluating Recalibrating AI Models for Breast Cancer Diagnosis in a New Context: Insights from Transfer Learning, Image Enhancement and High-Quality Training Data Integration

This paper investigates the adaptability of four state-of-the-art artificial intelligence (AI) models to the Australian mammographic context through transfer learning, explores the impact of image enhancement on model performance and analyses the relationship between AI outputs and histopathological features for clinical relevance and accuracy assessment. A total of 1712 screening mammograms (n = 856 cancer cases and n = 856 matched normal cases) were used in this study. The 856 cases with cancer lesions were annotated by two expert radiologists and the level of concordance between their annotations was used to establish two sets: a 'high-concordances subset' with 99% agreement of cancer location and an 'entire dataset' with all cases included. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of Globally aware Multiple Instance Classifier (GMIC), Global-Local Activation Maps (GLAM), I&H and End2End AI models, both in the pretrained and transfer learning modes, with and without applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The four AI models with and without transfer learning in the high-concordance subset outperformed those in the entire dataset. Applying the CLAHE algorithm to mammograms improved the performance of the AI models. In the high-concordance subset with the transfer learning and CLAHE algorithm applied, the AUC of the GMIC model was highest (0.912), followed by the GLAM model (0.909), I&H (0.893) and End2End (0.875). There were significant differences (p < 0.05) in the performances of the four AI models between the high-concordance subset and the entire dataset. The AI models demonstrated significant differences in malignancy probability concerning different tumour size categories in mammograms. The performance of AI models was affected by several factors such as concordance classification, image enhancement and transfer learning. Mammograms with a strong concordance with radiologists' annotations, applying image enhancement and transfer learning could enhance the accuracy of AI models.

Reducing ' probably benign ' assessments in normal mammograms: The role of radiologist experience

Rationale and objectives

to investigate the relationship between radiologists' experience in reporting mammograms, their caseloads, and the classification of category '3' or 'Probably Benign' on normal mammograms.

Materials and Methods

A total of 92 board-certified radiologists participated. Self-reported parameters related to experience, including age, years since qualifying as a radiologist, years of experience reading mammograms, number of mammograms read per year, and hours spent reading mammograms per week, were documented. To assess the radiologists' accuracy, "Probably Benign fractions" was calculated by dividing the number of "Probably Benign findings" given by each radiologist in the normal cases by the total number of normal cases Probably Benign fractions were correlated with various factors, such as the radiologists' experience.

Results

The results of the statistical analysis revealed a significant negative correlation between radiologist experience and 'Probably Benign' fractions for normal images. Specifically, for normal cases, the number of mammograms read per year (r = -0.29, P = 0.006) and the number of mammograms read over the radiologist's lifetime (r = -0.21, P = 0.049) were both negatively correlated with 'Probably Benign' fractions.

Conclusion

The results indicate that a relationship exists between increased reading volumes and reduced assessments of 'Probably Benign' in normal mammograms. The implications of these findings extend to the effectiveness of screening programs and the recall rates.

Does mammographic density remain a radiological challenge in the digital era?

BACKGROUND

The low subject contrast between cancerous and fibroglandular tissue could obscure breast abnormalities.

PURPOSE

To investigate radiologists' performance for detection of breast cancer in low and high mammographic density (MD) when cases are digitally acquired.

MATERIAL AND METHODS

A test set of 60 digital mammography cases, of which 20 were cancerous, were examined by 17 radiologists. Mammograms were categorized as low (≤50%) or high (>50%) MD and rated for suspicion of malignancy using the Royal Australian and New Zealand College of Radiology (RANZCR) classification system. Radiologist demographics including cases read per year, age, subspecialty, and years of reporting were recorded. Radiologist performance was analyzed by the following metrics: sensitivity; specificity; area under the receiver operating characteristic (ROC) curve (AUC), location sensitivity, and jackknife free-response ROC (JAFROC) figure of merit (FOM).

RESULTS

Comparing high to low MD cases, radiologists showed a significantly higher sensitivity (P = 0.015), AUC (P = 0.003), location sensitivity (P = 0.002), and JAFROC FOM (P = 0.001). In high compared to low MD cases, radiologists with <1000 annual reads and radiologists with no mammographic subspecialty had significantly higher AUC, location sensitivity, and JAFROC FOM. Radiologists with ≥1000 annual reads and radiologists with mammography subspecialty demonstrated a significant increase in location sensitivity in high compared to low MD cases.

CONCLUSION

In this experimental situation, radiologists' performance was higher when reading cases with high compared to low MD. Experienced radiologists were able to precisely localize lesions in breasts with higher MD. Further studies in unselected screening materials are needed to verify the results.

Interpretative characteristics and case features associated with the performances of radiologists in reading mammograms: A study from a non-screening population in Asia

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.

The roles of clinical audit and test sets in promoting the quality of breast screening: a scoping review

Breast screening programmes enhance the probability of early breast cancer detection in many countries worldwide; however, the success of these efforts is highly dependent on the ability of breast screen readers to detect abnormalities in the screened population, which has low prevalence. Therefore, this task can be challenging. Clinical audit is a key quality assurance measure that aims to keep the screen reading performance within acceptable standards. Auditing, nonetheless, is a lengthy process, and its accuracy is dependent on available clinical data, which often can be limited. Mammographic standardised test sets are a different screen reading evaluation approach that provides participants with instant feedback based on a simulated environment. Although a test set provides unique evaluative qualities, its ability to represent clinical performance is debated. This article describes the distinctive roles of clinical audit and test sets in measuring and improving the quality of breast screening and highlights the relationship between test sets and clinical performance.

What do we know about volumetric medical image interpretation?: a review of the basic science and medical image perception literatures

Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for abnormalities in volumetric images. Although there is extensive literature on two-dimensional medical image perception, it is an open question whether the conclusions drawn from these images can be generalized to volumetric images. Importantly, volumetric images have distinct characteristics (e.g., scrolling through depth, smooth-pursuit eye-movements, motion onset cues, etc.) that should be considered in future research. In this manuscript, we will review the literature on medical image perception and discuss relevant findings from basic science that can be used to generate predictions about expertise in volumetric image interpretation. By better understanding search through volumetric images, we may be able to identify common sources of error, characterize the optimal strategies for searching through depth, or develop new training and assessment techniques for radiology residents.

Can Digital Breast Tomosynthesis Replace Full-Field Digital Mammography? A Multireader, Multicase Study of Wide-Angle Tomosynthesis

OBJECTIVE. The purpose of this study was to test the hypothesis whether two-view wide-angle digital breast tomosynthesis (DBT) can replace full-field digital mammography (FFDM) for breast cancer detection. SUBJECTS AND METHODS. In a multireader multicase study, bilateral two-view FFDM and bilateral two-view wide-angle DBT images were independently viewed for breast cancer detection in two reading sessions separated by more than 1 month. From a pool of 764 patients undergoing screening and diagnostic mammography, 330 patient-cases were selected. The endpoints were the mean ROC AUC for the reader per breast (breast level), ROC AUC per patient (subject level), noncancer recall rates, sensitivity, and specificity. RESULTS. Twenty-nine of 31 readers performed better with DBT than FFDM regardless of breast density. There was a statistically significant improvement in readers' mean diagnostic accuracy with DBT. The subject-level AUC increased from 0.765 (standard error [SE], 0.027) for FFDM to 0.835 (SE, 0.027) for DBT (p = 0.002). Breast-level AUC increased from 0.818 (SE, 0.019) for FFDM to 0.861 (SE, 0.019) for DBT (p = 0.011). The noncancer recall rate per patient was reduced by 19% with DBT (p < 0.001). Masses and architectural distortions were detected more with DBT (p < 0.001); calcifications trended lower (p = 0.136). Accuracy for detection of invasive cancers was significantly greater with DBT (p < 0.001). CONCLUSION. Reader performance in breast cancer detection is significantly higher with wide-angle two-view DBT independent of FFDM, verifying the robustness of DBT as a sole view. However, results of perception studies in the vision sciences support the inclusion of an overview image.

An investigation into the mammographic appearances of missed breast cancers when recall rates are reduced

OBJECTIVE

This study investigated whether certain mammographic appearances of breast cancer are missed when radiologists read at lower recall rates.

METHODS

5 radiologists read 1 identical test set of 200 mammographic (180 normal cases and 20 abnormal cases) 3 times and were requested to adhere to 3 different recall rate conditions: free recall, 15% and 10%. The radiologists were asked to mark the locations of suspicious lesions and provide a confidence rating for each decision. An independent expert radiologist identified the various types of cancers in the test set, including the presence of calcifications and the lesion location, including specific mammographic density.

RESULTS

Radiologists demonstrated lower sensitivity and receiver operating characteristic area under the curve for non-specific density/asymmetric density (H = 6.27, p = 0.04 and H = 7.35, p = 0.03, respectively) and mixed features (H = 9.97, p = 0.01 and H = 6.50, p = 0.04, respectively) when reading at 15% and 10% recall rates. No significant change was observed on cancer characterized with stellate masses (H = 3.43, p = 0.18 and H = 1.23, p = 0.54, respectively) and architectural distortion (H = 0.00, p = 1.00 and H = 2.00, p = 0.37, respectively). Across all recall conditions, stellate masses were likely to be recalled (90.0%), whereas non-specific densities were likely to be missed (45.6%).

CONCLUSION

Cancers with a stellate mass were more easily detected and were more likely to continue to be recalled, even at lower recall rates. Cancers with non-specific density and mixed features were most likely to be missed at reduced recall rates. Advances in knowledge: Internationally, recall rates vary within screening mammography programs considerably, with a range between 1% and 15%, and very little is known about the type of breast cancer appearances found when radiologists interpret screening mammograms at these various recall rates. Therefore, understanding the lesion types and the mammographic appearances of breast cancers that are affected by readers' recall decisions should be investigated.

Fractal analysis of visual search activity for mass detection during mammographic screening

PURPOSE

The objective of this study was to assess the complexity of human visual search activity during mammographic screening using fractal analysis and to investigate its relationship with case and reader characteristics.

METHODS

The study was performed for the task of mammographic screening with simultaneous viewing of four coordinated breast views as typically done in clinical practice. Eye-tracking data and diagnostic decisions collected for 100 mammographic cases (25 normal, 25 benign, 50 malignant) from 10 readers (three board certified radiologists and seven Radiology residents), formed the corpus for this study. The fractal dimension of the readers' visual scanning pattern was computed with the Minkowski-Bouligand box-counting method and used as a measure of gaze complexity. Individual factor and group-based interaction ANOVA analysis was performed to study the association between fractal dimension, case pathology, breast density, and reader experience level. The consistency of the observed trends depending on gaze data representation was also examined.

RESULTS

Case pathology, breast density, reader experience level, and individual reader differences are all independent predictors of the complexity of visual scanning pattern when screening for breast cancer. No higher order effects were found to be significant.

CONCLUSIONS

Fractal characterization of visual search behavior during mammographic screening is dependent on case properties and image reader characteristics.

Impact of Patient Photographs on Radiologists' Visual Search of Chest Radiographs

RATIONALE AND OBJECTIVES

To increase detection of mislabeled medical imaging studies, evidence shows it may be useful to include patient photographs during interpretation. This study examined how inclusion of photographs impacts visual search.

MATERIALS AND METHODS

Ten radiologists participated. Average age was 43.00 years and average years Board-certified was 9.70, with 2 residents, 1 general, 2 abdominal, 4 cardiothoracic, and 1 pediatric radiologist. They viewed 21 portable chest radiographs with and without a simultaneously acquired photograph of the patient while visual search was recorded. Their task was to note placement of lines and tubes.

RESULTS

Presence of the photograph reduced the number of fixations (chest radiograph only mean 98.68; chest with photograph present 80.81; photograph 10.59; p < 0.0001) and total dwell (chest radiograph only mean 30.84 seconds; chest radiograph with photograph present 25.68; photograph 3.93; p < 0.0001) on the chest radiograph as a result of periodically looking at the photograph. Overall viewing time did not increase with addition of the photograph because time not spent on the radiograph was spent on the photograph. On average, readers scanned from the radiograph to the photographs about four times during search. Men and non-cardiothoracic radiologists spent significantly more time scanning all the images, including the photographs. Average preference for having photographs was 6.10 on a 0-10 scale, and neck and chest were preferred as areas to include in the photograph.

CONCLUSION

Photographs may help with certain image interpretation tasks and may help personalize the reading experience for radiologists without increasing interpretation time.

Breast Density Knowledge and Awareness: A Review of Literature

OBJECTIVES

We reviewed the literature on breast density knowledge and breast density awareness to explore what challenges are faced by this area of research.

METHOD

A review of PubMED, PsycINFO, and CINAHL databases was performed. Studies were published in peer-reviewed journals (in all years available) and written in English. The broad search terms used were ["breast density"] AND ["knowledge" OR "awareness"]. Eligible articles were included in the final analysis after meeting the following inclusion criteria: (1) the records had to quantitatively examine and report breast density knowledge and awareness, (2) the number of participants in the sample had to be clearly specified, and (3) studies reporting differences in breast density knowledge and awareness between racial and ethnic groups were included in the review.

RESULTS

Of the 277 articles identified, only 5 met inclusion criteria and addressed breast density knowledge and awareness. Characteristics of studies and results were examined.

CONCLUSIONS

There is insufficient evidence to determine a pattern of breast density knowledge and awareness in women. More quality studies are needed that focus on how well women understand the relationship between breast density, breast cancer risk, and breast cancer screening, especially in diverse populations.