Improvement of Cancer Detection on Mammograms via BREAST Test Sets

Trieu, Phuong Dung; Tapia, Kriscia; Frazer, Helen; Lee, Warwick; Brennan, Patrick C.

doi:10.1016/j.acra.2018.12.017

Cited by 37 publications

(22 citation statements)

References 11 publications

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“…44 The group initially developed 'BREAST Test Sets' for use in improving mammogram breast cancer detection by radiologists and radiology trainees. 45 In 2020, the platform expanded to find a role in chest imaging during the COVID-19 pandemic (CovED), which provided a free, cloud-based, fully online learning tool to all clinicians (not just radiologists) across the globe, containing a set of Lung Computed Tomography scans to assist in the recognition of imaging findings associated with COVID-19. Both BREAST and CovED platforms provide readers with immediate and personalised feedback.…”

Section: The Role Of Online Medical Imaging Resourcesmentioning

confidence: 99%

Medical imaging education opportunities for junior doctors and non‐radiologist clinicians: A review

Ayesa

Katelaris²,

Brennan

et al. 2021

J Med Imag Rad Onc

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Medical imaging plays a critical role in clinical decision-making across disciplines, and as such, there is frequent need for non-radiologist clinicians to interact with medical imaging. This review examines the literature about the delivery of medical imaging education to non-radiologist clinicians, spanning junior doctors, advanced trainees and specialists. Knowledge of medical imaging among non-radiologist clinicians is paramount to the quality of patient care, with calls for formal implementation of radiology education into nonimaging specialty training programmes. Overall, there is a demand across non-imaging disciplines for greater formalised medical imaging education. Concerns are raised that too great a reliance on informal methods of teaching radiology, for example in ward settings, results in greater variation in the quality and volume of educational opportunities and risks the perpetuation of erroneous attitudes and practices. The evolution of the medical imaging workplace and increasing utilisation of remote reporting has distanced the collaborative relationship between radiologists and their non-imaging colleagues, diminishing opportunities for ad hoc learning and engagement in larger formalised educational collaborations. Ideally, radiologists should be directly involved in the development and delivery of medical imaging education to post-graduate doctors to not only benefit patient care but also foster interspecialty relationships and respect. Evidence supports the value of structured radiological teaching opportunities, including tutorials, lectures and electronic resources, in improving medical imaging skills among non-radiologist clinicians. There is wide scope for growth in the e-learning arena to address this demand for quality and accessible imaging education for our non-radiology colleagues.

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Section: The Role Of Online Medical Imaging Resourcesmentioning

confidence: 99%

Medical imaging education opportunities for junior doctors and non‐radiologist clinicians: A review

Ayesa

Katelaris²,

Brennan

et al. 2021

J Med Imag Rad Onc

View full text Add to dashboard Cite

show abstract

“…Over the last eight years, BREAST has engaged more than 80% of breastscreen clinicians (radiologists, breast physicians, registrars) across all states in Australia and been evaluated as one of the best professional development tools for radiologists 11 . It has also been shown that the regular completion of the BREAST test sets improved radiologists’ test based performances 12 , 13 . In recent years, BREAST has been introduced to Vietnam, a developing country in South East Asia, through workshops for radiologists, radiology residents and data showed that Vietnamese radiologists displayed a low mammographic cancer detection of 48% compared with 81% for their counterparts in Australia and Singapore 4 .…”

Section: Introductionmentioning

confidence: 97%

Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback

Trieu

Lewis

et al. 2021

Sci Rep

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It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers’ weakness and strength are identified.

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“…A mammogram is a safe and reasonably accurate x-ray image of a breast. For women at risk of breast cancer, mammogram screening should be done routinely starting at the age of forty to screen for any early signs of the disease [3,4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Pectoral Muscles Detection and Removal in Mammogram Images

Fadhil

Dawood

2021

eijs

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The main aim of the Computer-Aided Detection/Diagnosis system is to assist the radiologists in examining the digital mammograms. Digital mammogram is the most popular screening technique for early detection of breast cancer. One of the problems in breast mammogram analysis is the presence of pectoral muscles region with high intensity in the upper right or left side of most Media-Lateral Oblique views of mammogram images. Therefore, it is important to remove this pectoral muscle from the image for accurate diagnosis results. The proposed method consists of three main steps. In the first step, noise is reduced using Median filtering. In the second step, artifacts removal and breast region extraction are performed using Otsu method. Finally, the pectoral muscle is extracted and removed using the proposed Split Orientation Local Thresholding (SOLTH) algorithm. For this study, a total of 110 mammogram images from the Mini-Mias database (MIAS) were used to evaluate the proposed method’s performance. The experimental results of automatic pectoral muscle detection and removal were observed by radiologist and showed 90.9% accuracy of acceptable results.

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Improvement of Cancer Detection on Mammograms via BREAST Test Sets

Cited by 37 publications

References 11 publications

Medical imaging education opportunities for junior doctors and non‐radiologist clinicians: A review

Medical imaging education opportunities for junior doctors and non‐radiologist clinicians: A review

Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback

Automatic Pectoral Muscles Detection and Removal in Mammogram Images

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