Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis

Yırgın, İnci Kızıldağ; Koyluoglu, Yilmaz Onat; Seker, Mustafa Ege; Gürdal, Sibel Özkan; Özaydın, Ayşe; Özçınar, Beyza; Cabıoğlu, Neslihan; Özmen, Vahit; Arıbal, Erkin

doi:10.1177/15330338221075172

Cited by 13 publications

(14 citation statements)

References 48 publications

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“…The maturing AI software may also increase our ability to capture cancer. A diagnostic study for AI in a retrospective simulated screening setting found the highest cancer detection rate by teaming up radiologists and AI [ 23 ]. A recently published retrospective study by Kim et al also found AI to be able to detect mammographically occult cancers in dense breasts [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Characteristics of Breast Cancer Detected through Different Imaging Modalities in a Large Cohort of Hong Kong Chinese Women: Implication of Imaging Choice on Upcoming Local Screening Program

Chan

Hung²,

Yuen³

et al. 2022

The Breast Journal

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Background. We compared the clinico-radio-pathological characteristics of breast cancer detected through mammogram (MMG) and ultrasound (USG) and discuss the implication of the choice of imaging as the future direction of our recently launched local screening program. Methods. Retrospective study of 14613 Hong Kong Chinese female patients with histologically confirmed breast cancer registered in the Hong Kong Breast Cancer Registry between January 2006 and February 2020. Patients were classified into four groups based on the mode of breast cancer detection (detectable by both mammogram and ultrasound (MMG+/USG+), mammogram only (MMG+/USG−), ultrasound only (MMG−/USG+), or not detectable by either (MMG−/USG−). Characteristics of breast cancer detected were compared, including patient demographics, breast density on MMG, mode of presentation, tumour size, histological type, and staging. Types of mammographic abnormalities were also evaluated for MMG+ subgroups. Results. 85% of the cancers were detectable by MMG, while USG detected an additional 9%. MMG+/USG+ cancers were larger, more advanced in stage, often of symptomatic presentation, and commonly manifested as mammographic mass. MMG+/USG− cancers were more likely of asymptomatic presentation, manifested as microcalcifications, and of earlier stage and to be ductal carcinoma in situ. MMG−/USG+ cancers were more likely seen in young patients and those with denser breasts and more likely of symptomatic presentation. MMG−/USG− cancers were often smaller and found in denser breasts. Conclusion. Mammogram has a good detection rate of cancers in our local population. It has superiority in detecting early cancers by detecting microcalcifications. Our current study agrees that ultrasound is one of the key adjunct tools of breast cancer detection.

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Section: Discussionmentioning

confidence: 99%

Comparison of Characteristics of Breast Cancer Detected through Different Imaging Modalities in a Large Cohort of Hong Kong Chinese Women: Implication of Imaging Choice on Upcoming Local Screening Program

Chan

Hung²,

Yuen³

et al. 2022

The Breast Journal

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“…AI framework using CNN has been employed for screening BC using DM. 34 AI enabled screening outperformed radiologist screening with an accuracy of 72.7%. Further combining AI and radiologist decision improved the detection performance of 83.6% accuracy with reduced false positive rates.…”

Section: Deep Learning-based Cad Approaches For Breast Cancer Diagnosismentioning

confidence: 99%

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Kumar¹,

Sasikala²

2023

Technol Cancer Res Treat

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Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

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“…A study conducted in Korea using a similar AI program with 435 mammograms discovered that a cut-off value of 38.03 resulted in an AUC of 0.745 (95% CI, 0.701-0.785) with 69.1% sensitivity and 69.0% specificity in identifying suspicious microcalcifications [35]. Another study carried out in Turkey with 211 mammograms demonstrated that a cut-off value of 34.5 yielded an AUC of 0.853 (95% CI, 0.801-0.905) with 72.8% sensitivity and 88.3% specificity in identifying breast carcinoma [36].…”

Section: S a W I T R I D A R M I A T I E T A L Ai On Mammography Int...mentioning

confidence: 99%

Impact of Artificial Intelligence on Mammography Interpretation by Breast Radiologists, Non-Breast Radiologists, and Senior Residents

Darmiati,

Afifi,

Billy

et al. 2023

Indonesian Journal of Cancer

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Background: Artificial intelligence (AI) is recognized to have tremendous potential to revolutionize breast cancer management through mammography. However, the extent of its impact on radiologists with different levels of experience remains largely unexplored. Therefore, this study aimed to comprehensively show how AI could assist radiologists of varying expertise including breast and non-breast radiologists, as well as senior residents, in performing mammogram interpretation.Methods: This retrospective study analyzed eligible mammograms from Cipto Mangunkusumo Hospital between January 2017 and March 2021. Mammographic readings were conducted independently by two breast radiologists, two from other subspecialties, and three senior residents, all blinded to clinical information. AI standalone performance, as well as radiologists with and without AI assistance, was measured. Results: The results showed that a total of 886 eligible mammograms were analyzed. AI standalone performance, assessed using ROC curve analysis, yielded an AUC of 0.946 (95% CI, 0.925–0.967) with sensitivity and specificity of 90.1% and 93.6%, respectively. AI assistance significantly improved the sensitivity and specificity of all radiologists, regardless of experience level, with a median increase of 19.4% (IQR, 10.4–33.5%) and 12.1% (IQR, 5.2–16.2%), respectively. Moreover, there was a trend toward a higher increase with AI assistance in dense compared to fatty breasts.Conclusions: AI proved to be a highly effective diagnostic supplement for radiologists across varying experience levels, specifically in non-breast radiologists, offering the potential to add even greater value in cases of dense breast tissue. The results were derived from a national referral tertiary hospital that generally received many breast cancer cases referred from other hospitals for further treatment. Therefore, further studies incorporating different levels of hospitals were needed.

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Diagnostic Performance of AI for Cancers Registered in A Mammography Screening Program: A Retrospective Analysis

Cited by 13 publications

References 48 publications

Comparison of Characteristics of Breast Cancer Detected through Different Imaging Modalities in a Large Cohort of Hong Kong Chinese Women: Implication of Imaging Choice on Upcoming Local Screening Program

Comparison of Characteristics of Breast Cancer Detected through Different Imaging Modalities in a Large Cohort of Hong Kong Chinese Women: Implication of Imaging Choice on Upcoming Local Screening Program

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Impact of Artificial Intelligence on Mammography Interpretation by Breast Radiologists, Non-Breast Radiologists, and Senior Residents

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