Dr. Melissa L. Robinson Profile

Dr. Melissa L. Robinson

at Univ of Sydney

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Publications (7)

Proceedings Article | 29 March 2024 Presentation + Paper

Exploring varied time intervals on diagnostic performances of radiologists and trainees via test sets

Phuong Dung (Yun) Trieu, Somphone Siviengphanom, Melissa Barron, Sarah Lewis

Proceedings Volume 12929, 129290M (2024) https://doi.org/10.1117/12.3008282

KEYWORDS: Education and training, Mammography, Radiology, Cancer detection

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Proceedings Article | 29 March 2024 Presentation + Paper

Artificial intelligence can improve cancer detection in a double reading screening mammography scenario

Zhengqiang Jiang, Ziba Gandomkar, Phuong Dung (Yun) Trieu, Seyedamir Tavakoli Taba, Melissa Barron, Sarah Lewis

Proceedings Volume 12929, 1292903 (2024) https://doi.org/10.1117/12.3006376

KEYWORDS: Cancer, Artificial intelligence, Mammography, Tumor growth modeling, Cancer detection, Machine learning, Performance modeling, Breast, Statistical modeling, Histograms

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Proceedings Article | 3 April 2023 Poster + Paper

Diagnostic performance characteristics of Chinese radiologists in breast cancer detection with FFDM versus DBT images

Jia Lin Chua, Qin Xiao, Tong Li, Yajia Gu, Melissa Robinson, Kriscia Tapia, Sarah Lewis, Phuong Trieu

Proceedings Volume 12467, 1246715 (2023) https://doi.org/10.1117/12.2654277

KEYWORDS: Digital breast tomosynthesis, Cancer, Cancer detection, Breast cancer, Education and training, Mammography, Breast, Diagnostics, Architectural distortion, Breast density

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Proceedings Article | 3 April 2023 Presentation + Paper

How do you solve a problem like concordance? a study of radiologists’ clinical annotations for mammographic AI training

Zhengqiang Jiang, Phuong Trieu, Ziba Gandomkar, Seyedamir Tavakoli Taba, Melissa Barron, Sarah Lewis

Proceedings Volume 12467, 124670M (2023) https://doi.org/10.1117/12.2654147

KEYWORDS: Mammography, Current controlled current source, Artificial intelligence, Education and training, Breast density, Cancer, Cancer detection, Breast cancer

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SPIE Journal Paper | 27 March 2023

Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density

Phuong Dung Trieu, Qin Xiao, Yajia Gu, Sarah J. Lewis, Melissa L. Barron, Kriscia Tapia, Patrick C. Brennan, Tong Li

JMI, Vol. 10, Issue 02, 025502, (March 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.2.025502

KEYWORDS: Digital breast tomosynthesis, Breast density, Mammography, Breast, Cancer, Education and training, Diagnostics, Breast cancer, Cancer detection, Radiology

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PurposeThis study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density.ApproachEighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann–Whitney U test. The Spearman rank test was used to explore the association between radiologists’ performances and their work experience in mammogram interpretation.ResultsThere were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set (P < 0.0001); in high breast density, Shanghai radiologists’ performances in lesion sensitivity and JAFROC were also lower than Australian radiologists (P < 0.0001). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists.ConclusionThere were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists.

Proceedings Article | 13 July 2022 Paper

Prior mammogram review may affect the performance of radiology trainees in identifying breast cancers and normal cases

Sarah Lewis, Tong Li, Natacha Borecky, Patrick Brennan, Melissa Barron, Phuong D. (Yun) Trieu

Proceedings Volume 12286, 1228611 (2022) https://doi.org/10.1117/12.2624189

KEYWORDS: Radiology, Mammography, Breast, Cancer, Breast cancer, Diagnostics, Imaging systems, Tissues, Visualization, Roads

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Proceedings Article | 4 April 2022 Presentation + Paper

Diagnostic performances of radiology trainees in reading digital breast tomosynthesis and the synthesized view

Phuong Dung (Yun) Trieu, Jennifer Noakes, Natacha Borecky, Tong Li, Patrick Brennan, Melissa Barron, Sarah Lewis

Proceedings Volume 12035, 120350H (2022) https://doi.org/10.1117/12.2611268

KEYWORDS: Digital breast tomosynthesis, Breast, Radiology, Diagnostics, Cancer, Mammography, Pathology, Breast cancer, Receivers, Medical imaging

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