Investigating the effectiveness of supplemental breast cancer screening tests considering radiologists' bias.
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| Title: | Investigating the effectiveness of supplemental breast cancer screening tests considering radiologists' bias. |
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| Authors: | Madadi, Mahboubeh1, Molani, Sevda2, Williams, Donna L.3 |
| Source: | IISE Transactions on Healthcare Systems Engineering. Jan-Mar2023, Vol. 13 Issue 1, p1-20. 20p. |
| Subjects: | Breast cancer diagnosis, Mammograms, Radiologists |
| Abstract: | Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (i.e., ultrasound and MRI). Since the enactment of the laws, there have been controversial debates over (i) their implementations due to the potential radiologists' bias in breast density classification of mammogram images and (ii) the necessity of supplemental screenings for all patients with high breast density. In this study, we formulate a finite-horizon, discrete-time partially observable Markov chain to investigate the effectiveness of supplemental screening and the impact of radiologists' misclassification bias on patients' outcomes. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime as the primary and the expected number of supplemental tests as the secondary patient's outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists' performances are shown to be comparable with the performance of a perfect radiologist (i.e., 100% accuracy in breast density classification). However, a significant bias in breast density classification (i.e., consistent upgrading or downgrading of breast density classes) can negatively impact a patient's health outcomes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (i.e., ultrasound and MRI). Since the enactment of the laws, there have been controversial debates over (i) their implementations due to the potential radiologists' bias in breast density classification of mammogram images and (ii) the necessity of supplemental screenings for all patients with high breast density. In this study, we formulate a finite-horizon, discrete-time partially observable Markov chain to investigate the effectiveness of supplemental screening and the impact of radiologists' misclassification bias on patients' outcomes. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime as the primary and the expected number of supplemental tests as the secondary patient's outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists' performances are shown to be comparable with the performance of a perfect radiologist (i.e., 100% accuracy in breast density classification). However, a significant bias in breast density classification (i.e., consistent upgrading or downgrading of breast density classes) can negatively impact a patient's health outcomes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 24725579 |
| DOI: | 10.1080/24725579.2022.2095466 |
