Pain-preventing strategies in mammography: an observational study of simultaneously recorded pain and breast mechanics throughout the entire breast compression cycle

Groot, Jerry E. de; Broeders, Mireille J. M.; Grimbergen, C.A.; Heeten, Gerard J. den

doi:10.1186/s12905-015-0185-2

Cited by 32 publications

(27 citation statements)

References 34 publications

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“…Previous personal experience, anxiety and insufficient attention given by the radiographer are related to the pain experienced during mammography. [10][11][12] High pain scores may even lead to the decision to stop participating in the programme. 13 Therefore, it is important for radiographers to explain the procedure well and reassure females before and during mammography.…”

Section: Discussionmentioning

confidence: 99%

Mammography in females with an implanted medical device: impact on image quality, pain and anxiety

Paap¹,

Witjes²,

Landsveld-Verhoeven³

et al. 2016

BJR

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Objective: To assess the image quality of mammograms in females with an implanted medical device (IMD), to evaluate pain and anxiety during mammography in these females and to investigate the experience of radiographers. Methods: Image quality was evaluated by two radiographers and one radiologist in the images of females with an IMD participating in the Dutch screening programme (clients). Pain and anxiety were scored using a Numeric Rating Scale in both clients visiting a screening organization and patients from the Isala Hospital, Zwolle. Experience of screening radiographers was collected with a questionnaire. Results: Images of the breast with IMD showed reduced contrast in craniocaudal (CC) and mediolateral-oblique (MLO) views [by both the radiographers and radiologist (range: 11-29%)], less projected breast tissue [only radiographers; CC lateral side: 25.5%, 95% confidence interval (CI): 18.7-32.2] and reduced projection of the pectoral muscle (only radiographers; MLO width pectoral muscle: 31.5%, 95% CI: 24.4-38.7). Clients experienced more pain and anxiety during mammography in the breast with IMD compared to the breast without IMD in the breast (pain difference CC: 0.48 6 0.16, p 5 0.003; pain difference MLO: 0.46 6 0.16, p 5 0.004; anxiety difference 1.30 6 0.22; p , 0.001). Patients experienced more pain (1.05 6 0.12; p , 0.001) and anxiety (1.22 6 0.15; p , 0.001) after placement of IMD. Radiographers are more cautious, more anxious and use less compression during mammography of breasts with IMD. Conclusion: Image quality in a breast with an IMD could be improved by projecting more breast tissue on the mammogram, thereby including (part of) the IMD between the paddles, if required. In addition, radiographers should pay sufficient attention to reducing discomfort both before and during the screening examination. Advances in knowledge: Little is known about the quality of mammography in females with an IMD or how these females and radiographers experience the screening examination. The results of our study showed that having an IMD could result in a suboptimal mammogram and increased discomfort.

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Section: Discussionmentioning

confidence: 99%

Mammography in females with an implanted medical device: impact on image quality, pain and anxiety

Paap¹,

Witjes²,

Landsveld-Verhoeven³

et al. 2016

BJR

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“…Forced compression using 15 daN resulted in an average reduction of breast thickness of 57.8 % of the original thickness of the uncompressed breast, thus allowing an average dose reduction of 17 % compared to that achieved using 10 daN. De Groot et al [26] describe similar curve progressions for the mammographic compression process.…”

Section: Discussionmentioning

confidence: 95%

Ist eine Individualisierung der mammografischen Brustkompression sinnvoll? – Untersuchungen zu Schmerzangaben bei der Mammografie in Bezug auf Kompressionskraft und Fläche der komprimierten Brust

Feder

Grunert²

2016

Fortschr Röntgenstr

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ABTR ACTPurpose The aim of this paper is to determine how the presence of pain during mammographic compression could be reduced. To this end, we examine its relationship with compression force, surface-area of the compressed breast, breast density (ACR) and former operations. Materials and MethodsIn 199 women 765 mammograms were performed. Women were asked to rate the level of pain on a scale of 0 -10 (0: no, 10: highest pain). The surface-area of the breast under compression captured by the mammograms was measured using planimetry. 52 of the 199 women were asked to identify the area of the upper body with the highest level of pain.Results The thickness of the compressed breast was 65.2 % of the uncompressed breast at a force of 10 daN (57.8 % at 15 daN). When the force was increased from 10 daN to 15 daN, the average glandular dose (AGD) declined by 17 %. Tolerance of compression was associated with the size of the breast. More than 50 % of the mammograms with a small compression less than 9 daN were associated with higher level of pain. In the oblique projection, 60 % of the women specified the axilla as the area of maximum pain.Conclusion Women with larger breasts tolerated a greater force of compression. This implies a need for individualised examination depending on the size of the breast. Women with increased pain susceptibility terminated the compression early regardless of a small compression less than 9 daN. More than 50 % of the women identified areas outside breast as especially painful. Therefore, during examination, the areas around the breast should also be taken into consideration in order to minimize unnecessary discomfort.Key Points ▪ With increased mammographic compression force, the effectiveness of breast thickness reduction declined. ▪ A compression force of 15 daN enabled an additional reduction by 17 % in average glandular dose (AGD) compared to 10 daN. ▪ Tolerance of increased compression force was related to breast surface area. ▪ Women with increased susceptibility of pain terminated the compression at a low force of less than 9 daN ▪ Pain relating to the mammographic procedure was identified outside the breast by more than 50 % of the women. Citation Format

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“…The exposure time was set to the average exposure time of one second during mammography image acquisition. 6,15 Tissue elasticity was modeled as a pulling force that directs the next particle position to be closer to the previous position. 13,14 The two dominating breast tissue types are fat and fibroglandular tissues.…”

Section: B Simulated Motion-blur Modelmentioning

confidence: 99%

Automatic detection of simulated motion blur in mammograms

Kamona

Loew

2020

Medical Physics

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Purpose: To use machine-learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision-making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. Methods: Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point-spread-function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super-sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine-learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six-way classification. Results: For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. Conclusions: Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists' lesion detection performance. As of this date, no other study has investigated the ability of machine-learning classifiers and BM operators to detect motion blur in mammograms.

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Pain-preventing strategies in mammography: an observational study of simultaneously recorded pain and breast mechanics throughout the entire breast compression cycle

Cited by 32 publications

References 34 publications

Mammography in females with an implanted medical device: impact on image quality, pain and anxiety

Mammography in females with an implanted medical device: impact on image quality, pain and anxiety

Ist eine Individualisierung der mammografischen Brustkompression sinnvoll? – Untersuchungen zu Schmerzangaben bei der Mammografie in Bezug auf Kompressionskraft und Fläche der komprimierten Brust

Automatic detection of simulated motion blur in mammograms

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