Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Kai, Chiharu; Ishizuka, Shigehiro; Otsuka, Tsunehiro; Nara, Miyako; Kondo, Satoshi; Futamura, Hitoshi; Kodama, Naoki; Kasai, S.

doi:10.3390/cancers15102794

Cited by 3 publications

(4 citation statements)

References 29 publications

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“…We used an AI system developed using a convolutional neural network that had previously shown a high correlation ( 17 ) for estimating the mammary gland content ratio and identifying factors that indicate the possibility of non-visibles. The main problems in clinical research when analyzing big data are that it is time-consuming and involves large inter- and intra-observer variations.…”

Section: Discussionmentioning

confidence: 99%

“…As the volume of mammary tissue varies with age ( 14 ), and the sensitivity for detecting abnormal lesions is related to compressed breast thickness (CBT) ( 15 , 16 ), we also hypothesized that age and CBT are related to the mammary gland content ratio in controls and non-visibles. Owing to the need for a tool to evaluate large data volumes, we developed an artificial intelligence (AI) system to estimate the mammary gland content ratio as a continuous value on mammograms ( 17 ). We had previously found a high correlation between the mammary gland content ratio generated by AI and that by a specialist ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

“…Owing to the need for a tool to evaluate large data volumes, we developed an artificial intelligence (AI) system to estimate the mammary gland content ratio as a continuous value on mammograms ( 17 ). We had previously found a high correlation between the mammary gland content ratio generated by AI and that by a specialist ( 17 ). The strength of the AI-generated mammary gland content ratio is that it is reproducible and quantifiable, making it suitable for the evaluation of extensive data.…”

Section: Introductionmentioning

confidence: 99%

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Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence

Kai,

Otsuka,

Nara

et al. 2024

Front. Oncol.

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BackgroundMammography is the modality of choice for breast cancer screening. However, some cases of breast cancer have been diagnosed through ultrasonography alone with no or benign findings on mammography (hereby referred to as non-visibles). Therefore, this study aimed to identify factors that indicate the possibility of non-visibles based on the mammary gland content ratio estimated using artificial intelligence (AI) by patient age and compressed breast thickness (CBT).MethodsWe used AI previously developed by us to estimate the mammary gland content ratio and quantitatively analyze 26,232 controls and 150 non-visibles. First, we evaluated divergence trends between controls and non-visibles based on the average estimated mammary gland content ratio to ensure the importance of analysis by age and CBT. Next, we evaluated the possibility that mammary gland content ratio ≥50% groups affect the divergence between controls and non-visibles to specifically identify factors that indicate the possibility of non-visibles. The images were classified into two groups for the estimated mammary gland content ratios with a threshold of 50%, and logistic regression analysis was performed between controls and non-visibles.ResultsThe average estimated mammary gland content ratio was significantly higher in non-visibles than in controls when the overall sample, the patient age was ≥40 years and the CBT was ≥40 mm (p < 0.05). The differences in the average estimated mammary gland content ratios in the controls and non-visibles for the overall sample was 7.54%, the differences in patients aged 40–49, 50–59, and ≥60 years were 6.20%, 7.48%, and 4.78%, respectively, and the differences in those with a CBT of 40–49, 50–59, and ≥60 mm were 6.67%, 9.71%, and 16.13%, respectively. In evaluating mammary gland content ratio ≥50% groups, we also found positive correlations for non-visibles when controls were used as the baseline for the overall sample, in patients aged 40–59 years, and in those with a CBT ≥40 mm (p < 0.05). The corresponding odds ratios were ≥2.20, with a maximum value of 4.36.ConclusionThe study findings highlight an estimated mammary gland content ratio of ≥50% in patients aged 40–59 years or in those with ≥40 mm CBT could be indicative factors for non-visibles.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence

Kai,

Otsuka,

Nara

et al. 2024

Front. Oncol.

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“…To evaluate the mammograms by breast size, we divided the test cases into four groups of 125 cases each (breast area: Groups 1–4) in decreasing order of the percentage of breast area to the entire image. To evaluate the mammograms by mammary tissue volume, we divided the test cases into four groups of 125 cases each (mammary gland content ratio: Groups 1–4) in decreasing order of the outputted value by our developed AI for estimating the mammary gland content ratio [ 22 ], which could be quantitatively evaluated with a high correlation by an expert doctor who developed the guidelines. To evaluate the mammograms by breast volume, we divided the test cases into four groups of 125 cases each (compressed breast thickness: Group 1–4) in decreasing order of the thickness between the compression and detection plates.…”

Section: Methodsmentioning

confidence: 99%

Development of a Subtraction Processing Technology for Assistance in the Comparative Interpretation of Mammograms

Kai,

Kondo,

Otsuka

et al. 2024

Diagnostics

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A comparative interpretation of mammograms has become increasingly important, and it is crucial to develop subtraction processing and registration methods for mammograms. However, nonrigid image registration has seldom been applied to subjects constructed with soft tissue only, such as mammograms. We examined whether subtraction processing for the comparative interpretation of mammograms can be performed using nonrigid image registration. As a preliminary study, we evaluated the results of subtraction processing by applying nonrigid image registration to normal mammograms, assuming a comparative interpretation between the left and right breasts. Mediolateral-oblique-view mammograms were taken from noncancer patients and divided into 1000 cases for training, 100 cases for validation, and 500 cases for testing. Nonrigid image registration was applied to align the horizontally flipped left-breast mammogram with the right one. We compared the sum of absolute differences (SAD) of the difference of bilateral images (Difference Image) with and without the application of nonrigid image registration. Statistically, the average SAD was significantly lower with the application of nonrigid image registration than without it (without: 0.0692; with: 0.0549 (p < 0.001)). In four subgroups using the breast area, breast density, compressed breast thickness, and Difference Image without nonrigid image registration, the average SAD of the Difference Image was also significantly lower with nonrigid image registration than without it (p < 0.001). Nonrigid image registration was found to be sufficiently useful in aligning bilateral mammograms, and it is expected to be an important tool in the development of a support system for the comparative interpretation of mammograms.

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Evaluation of subtraction processing for mammograms analyzed by breast density and thickness

Kai,

Kondo,

Otsuka

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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In recent years, with the development of digital technology, radiologists compare bilateral and temporal mammograms easily to use the findings of difference as references for diagnosis. In this study, we examined the potential for clinical effectiveness of a subtraction processing system for mammograms in the comparative interpretation. We aimed to evaluate subgroup analysis on right and left subtraction-processed mammograms using VoxelMorph for registration. Breast density and thickness were used for subgroup analysis, which affected the depiction of the mammogram's mammary glands. We used normal mammograms with mediolateral oblique view divided into 1,000 cases for training, 100 cases for validation, and 500 cases for testing. The horizontally flipped left mammogram was aligned with the right mammogram using VoxelMorph. The 500 cases for testing were classified into four groups equally using the breast density and thickness determined from right mammograms. Based on the evaluation using the average sum of absolute difference (hereafter “SAD”) between the right mammogram and the transformed left mammogram as the objective index, it turned out that the highest density group had the lowest average of SAD (0.0482) and the thinnest compressed breast thickness group had the lowest average of SAD (0.0381). The results of this study showed that the accuracy of subtraction processing was high, especially in dense breasts. The high accuracy in dense breasts, where false-negative findings are common, suggests the potential for clinical effectiveness of the subtraction processing system for mammograms.

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Automated Estimation of Mammary Gland Content Ratio Using Regression Deep Convolutional Neural Network and the Effectiveness in Clinical Practice as Explainable Artificial Intelligence

Cited by 3 publications

References 29 publications

Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence

Identifying factors that indicate the possibility of non-visible cases on mammograms using mammary gland content ratio estimated by artificial intelligence

Development of a Subtraction Processing Technology for Assistance in the Comparative Interpretation of Mammograms

Evaluation of subtraction processing for mammograms analyzed by breast density and thickness

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