Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis

Boita, Joana; Engen, Ruben E. van; Mackenzie, Alistair; Tingberg, Anders; Bosmans, Hilde; Bolejko, Anetta; Zackrisson, Sophia; Wallis, Matthew; Ikeda, Debra M.; Ongeval, Chantal Van; Pijnappel, Ruud M.; Broeders, Mireille J. M.; Sechopoulos, Ioannis

doi:10.1016/j.ejrad.2021.109686

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…Sensitivity was 74% when specificity was similar to mammography (Criteria #1) and specificity was 76% when a highly sensitive diagnostic was achieved (Criteria #2). As mentioned, AUC of 0.85 is considered excellent (>0.8) (41), and not only well exceeds criteria (0.73), but exceeds all AUC values identified in studies on mammography (0.54-0.84) (12,31,40,(42)(43)(44)(45)(46).…”

Section: Algorithm Robustness and Configuration Selectionmentioning

confidence: 84%

“…AUC greater than 0.7, 0.8 and 0.9 are considered acceptable, excellent and outstanding, respectively (41). The average ROC curve AUC value for mammography across eight studies was 0.73 (0.54-0.84 range), which will also be used to compare diagnostic success of the method presented in this paper (12,31,40,(42)(43)(44)(45)(46). Two overall accuracy criteria are suggested to show diagnostic potential for the algorithm presented:…”

Section: Diagnostic Criteriamentioning

confidence: 99%

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Breast cancer diagnosis using frequency decomposition of surface motion of actuated breast tissue

Fitzjohn

Zhou²,

Chase³

2022

Front. Oncol.

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This paper presents a computationally simple diagnostic algorithm for breast cancer using a non-invasive Digital Image Elasto Tomography (DIET) system. N=14 women (28 breasts, 13 cancerous) underwent a clinical trial using the DIET system following mammography diagnosis. The screening involves steady state sinusoidal vibrations applied to the free hanging breast with cameras used to capture tissue motion. Image reconstruction methods provide surface displacement data for approximately 14,000 reference points on the breast surface. The breast surface was segmented into four radial and four vertical segments. Frequency decomposition of reference point motion in each segment were compared. Segments on the same vertical band were hypothesised to have similar frequency content in healthy breasts, with significant differences indicating a tumor, based on the stiffness dependence of frequency and tumors being 4~10 times stiffer than healthy tissue. Twelve breast configurations were used to test robustness of the method. Optimal breast configuration for the 26 breasts analysed (13 cancerous, 13 healthy) resulted in 85% sensitivity and 77% specificity. Combining two opposite configurations resulted in correct diagnosis of all cancerous breasts with 100% sensitivity and 69% specificity. Bootstrapping was used to fit a smooth receiver operator characteristic (ROC) curve to compare breast configuration performance with optimal area under the curve (AUC) of 0.85. Diagnostic results show diagnostic accuracy is comparable or better than mammography, with the added benefits of DIET screening, including portability, non-invasive screening, and no breast compression, with potential to increase screening participation and equity, improving outcomes for women.

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Section: Algorithm Robustness and Configuration Selectionmentioning

confidence: 84%

Section: Diagnostic Criteriamentioning

confidence: 99%

Breast cancer diagnosis using frequency decomposition of surface motion of actuated breast tissue

Fitzjohn

Zhou²,

Chase³

2022

Front. Oncol.

View full text Add to dashboard Cite

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“…Visual evaluation can be quite subjective and influenced by image aesthetics or personal preferences. Poor agreement levels have been demonstrated between expert readers on evaluation of the depiction of various features of the normal tissue [ 32 ]. Therefore, differences in the evaluations between the software and visual assessments do not necessarily indicate a problem with the software tool, but rather may indicate a difference in the techniques utilized by humans and software to verify the same positioning criteria.…”

Section: Discussionmentioning

confidence: 99%

Using automated software evaluation to improve the performance of breast radiographers in tomosynthesis screening

Gennaro,

Povolo,

Del Genio

et al. 2023

Eur Radiol

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Objective To improve breast radiographers’ individual performance by using automated software to assess the correctness of breast positioning and compression in tomosynthesis screening. Materials and methods In this retrospective longitudinal analysis of prospective cohorts, six breast radiographers with varying experience in the field were asked to use automated software to improve their performance in breast compression and positioning. The software tool automatically analyzes craniocaudal (CC) and mediolateral oblique (MLO) views for their positioning quality by scoring them according to PGMI classifications (perfect, good, moderate, inadequate) and checking whether the compression pressure is within the target range. The positioning and compression data from the studies acquired before the start of the project were used as individual baselines, while the data obtained after the training were used to test whether conscious use of the software could help the radiographers improve their performance. The percentage of views rated perfect or good and the percentage of views in target compression were used as overall metrics to assess changes in performance. Results Following the use of the software, all radiographers significantly increased the percentage of images rated as perfect or good in both CCs and MLOs. Individual improvements ranged from 7 to 14% for CC and 10 to 16% for MLO views. Moreover, most radiographers exhibited improved compression performance in CCs, with improvements up to 16%. Conclusion Active use of a software tool to automatically assess the correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers. Clinical relevance statement This study suggests that the use of a software tool for automatically evaluating correctness of breast compression and positioning in breast cancer screening can improve the performance of radiographers on these metrics, which may ultimately lead to improved screening outcomes. Key Points • Proper breast positioning and compression are critical in breast cancer screening to ensure accurate diagnosis. • Active use of the software increased the quality of craniocaudal and mediolateral oblique views acquired by all radiographers. • Improved performance of radiographers is expected to improve screening outcomes.

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“…Responses to each question were restricted to a 7-point Likert scale In this pilot study only one experienced Medical Physics Expert was used as the reader. Interestingly Boita et al (2021) found that both physicist and radiologist instrument responses correlated well with radiologist detection performance [14].…”

Section: Methodsmentioning

confidence: 99%

Image quality in mammograms with low compressed breast thicknesses

Loveland,

Mackenzie

2024

17th International Workshop on Breast Imaging (IWBI 2024)

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Following a number of reports of poor image quality in mammograms with particularly low compressed breast thicknesses in the UK National Health Service Breast Screening Programmes a pilot study was undertaken to more formally assess and quantify the problem. Stratified random sampling was used to select images from a large database of mammograms (OPTIMAM). Visual grading characteristic curves were used to compare image quality between mammograms with compressed breast thicknesses in two ranges: 55mm to 65mm inclusive and less than or equal to 20mm. It was found that breasts with a compressed thickness of 20mm or less were on average ranked lower for image quality than breasts in the 55mm to 65mm thickness range. Evidence was found indicating that in some cases the poor image quality was a result of insufficient dose under automatic exposure control. The most extreme cases contained no useful clinical information at all and arguably the affected clients will not have benefitted from attending breast screening. There is evidence that automatic exposure control systems struggle to deliver high enough exposures for dense breasts with low compressed breast thicknesses. For such cases higher dose manual factors may be beneficial. If AI readers are adopted in the future care must be taken to ensure that under-served, small subgroups such as this are not lost in the overall performance statistics.

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Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis

Cited by 6 publications

References 34 publications

Breast cancer diagnosis using frequency decomposition of surface motion of actuated breast tissue

Breast cancer diagnosis using frequency decomposition of surface motion of actuated breast tissue

Using automated software evaluation to improve the performance of breast radiographers in tomosynthesis screening

Image quality in mammograms with low compressed breast thicknesses

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