A review of screening mammography: The benefits and radiation risks put into perspective

The relationship between mean glandular dose and compressed breast thickness specified for Jordan

The mean glandular dose (MGD) is a measurement used in mammography to assess the amount of radiation absorbed. By considering specific exposure radiation dose criteria, MGD ensures minimal radiation while maintaining image quality for detecting abnormalities. The relationship between MGD and compressed breast thickness (CBT) is commonly utilized in mammographic dose surveys. This study aims to estimate the MGD-CBT relationship based on patient age in Jordan through retrospective analysis. The analysis involved 3465 screening mammography images of women aged 40-80, divided into three age groups: 40-49, 50-64 and 65-80 years. Each group had a specific CBT range (16.5-156 mm). The results indicate that MGD ranges from 1.6 to 1.7 mGy across all three age groups, independent of CBT. Thus, a significant and positive correlation exists between MGD and CBT in all age groups.

RRIMS: Radiation Risk In Mammography Screening - a novel model for predicting the lifetime dose and risk of radiation-induced breast cancer from the first screening visit

Objectives

Radiation Risk In Mammography Screening (RRIMS) builds on the prototype, formerly known as Breast-iRRISC, to develop a model that aims to establish a dose and risk profile for females by calculating their lifetime mean glandular dose (MGD) for each age of screening between 40 and 75 years, using only the information from her first screening visit. This is then used to allocate her to a dose category and estimate the lifetime risk of radiation-induced breast cancer incidence and mortality for a population of females in that category.

Methods

This model training was developed using a large dataset of Hologic images containing a total of 20,232 images from 5,076 visits from 4,154 females. The female's breast characteristics and exposure parameters were extracted from the images to calculate the female's MGD throughout a lifetime of screening from just her first screening visit, using modelling of various parameters and their change through time.

Results

This development has ultimately provided a model that uses the female's first screening visit to calculate the received MGD for all ages of potential screening. This has enabled the allocation of females to either a low-, medium-, or high-dose category, ultimately followed by the lifetime effective risk (LER) estimation for any screening attendance pattern. A female in the low-dose category undergoing biennial screening from 50 to 74 years would expect a risk of radiation-induced breast cancer incidence and mortality of 8.64 and 2.61 cases per 100,000 females, respectively. Similarly, a female in the medium- or high-dose category undergoing the same regimen would expect an incidence and mortality risk of 11.76 and 3.55, and 15.08 and 4.55 cases per 100,000 females, respectively.

Conclusions

This novel approach of establishing a female's dose profile and lifetime risk from a single visit will further assist females in their informed consent on breast screening attendance and help inform policy-makers when exploring the benefits and drawbacks of various screening patterns and frequencies.

Advances in knowledge

RRIMS is a novel tool that enables the assessment of a female's lifetime dose and risk profile using only the information from her first screening visit.

Stage IV Non-breast Cancer Patients and Screening Mammography: It is Time to Stop

BACKGROUND

Patients diagnosed with metastatic cancer have shortened life expectancy with questionable benefit of routine screening mammography (SM). The aim of this study was to evaluate the incidence and consequences of continued SM in the setting of reduced survival from stage IV non-breast cancer.

METHODS

Women diagnosed with Stage IV non-breast cancer at a single institution from 2015 to 2019 were queried from the institutional tumor registry for demographics, stage IV cancer diagnosis, and survival. Incidence and timing of SM after stage IV diagnosis and further diagnostic workup were extracted from the medical record.

RESULTS

790 women with Stage IV non-breast cancer were identified, 109 (14%) had at least 1 SM, 23% required diagnostic mammography, 7% breast biopsy, and 1% breast surgery. No breast cancers were identified. SM was ordered most often in stage IV gynecological cancers (28%), with more common cancers still seeing a high percentage of patients screened (lung 10%, colorectal 15%). Study 3-year survival was 26% (95% confidence interval [CI] 23-30%), with 74% mortality during follow up and median time from Stage IV diagnosis to death of 1.2 years (CI 0.4-2.3 years). Of patients screened, 41/109 died within 2 years of undergoing SM.

CONCLUSIONS

Despite low overall survival for patients diagnosed with metastatic non-breast cancer, 14% of women underwent SM which resulted in additional imaging, biopsies, and surgery with no new breast cancers identified. Continued SM in this population offers risk without benefit of reduced breast cancer mortality and should no longer continue in women with stage IV non-breast cancer.

How well do practicing radiologists interpret the results of CAD technology? A quantitative characterization

Many studies have shown that using a computer-aided detection (CAD) system does not significantly improve diagnostic accuracy in radiology, possibly because radiologists fail to interpret the CAD results properly. We tested this possibility using screening mammography as an illustrative example. We carried out two experiments, one using 28 practicing radiologists, and a second one using 25 non-professional subjects. During each trial, subjects were shown the following four pieces of information necessary for evaluating the actual probability of cancer in a given unseen mammogram: the binary decision of the CAD system as to whether the mammogram was positive for cancer, the true-positive and false-positive rates of the system, and the prevalence of breast cancer in the relevant patient population. Based only on this information, the subjects had to estimate the probability that the unseen mammogram in question was positive for cancer. Additionally, the non-professional subjects also had to decide, based on the same information, whether to recall the patients for additional testing. Both groups of subjects similarly (and significantly) overestimated the cancer probability regardless of the categorical CAD decision, suggesting that this effect is not peculiar to either group. The misestimations were not fully attributable to causes well-known in other contexts, such as base rate neglect or inverse fallacy. Non-professional subjects tended to recall the patients at high rates, even when the actual probably of cancer was at or near zero. Moreover, the recall rates closely reflected the subjects’ estimations of cancer probability. Together, our results show that subjects interpret CAD system output poorly when only the probabilistic information about the underlying decision parameters is available to them. Our results also highlight the need for making the output of CAD systems more readily interpretable, and for providing training and assistance to radiologists in evaluating the output.