Dataset Information

Deep learning performance for detection and classification of microcalcifications on mammography.

ABSTRACT:

Background

Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications.

Methods

Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for training, validation, and testing. Three neural networks (AlexNet, ResNet18, and ResNet34) were trained and evaluated using specific metrics including receiver operating characteristics area under the curve (AUC), sensitivity, and specificity. The reported metrics were computed on the test set (10% of the whole dataset).

Results

The dataset included 1,000 patients aged 21-73 years and 1,986 mammograms (180 density A, 220 density B, 380 density C, and 220 density D), with 389 malignant and 611 benign groups of microcalcifications. AlexNet achieved the best performance with 0.98 sensitivity, 0.89 specificity of, and 0.98 AUC for microcalcifications detection and 0.85 sensitivity, 0.89 specificity, and 0.94 AUC of for microcalcifications classification. For microcalcifications detection, ResNet18 and ResNet34 achieved 0.96 and 0.97 sensitivity, 0.91 and 0.90 specificity and 0.98 and 0.98 AUC, retrospectively. For microcalcifications classification, ResNet18 and ResNet34 exhibited 0.75 and 0.84 sensitivity, 0.85 and 0.84 specificity, and 0.88 and 0.92 AUC, respectively.

Conclusions

The developed AI models accurately detect and characterize microcalcifications on mammography.

Relevance statement

AI-based systems have the potential to assist radiologists in interpreting microcalcifications on mammograms. The study highlights the importance of developing reliable deep learning models possibly applied to breast cancer screening.

Key points

• A novel AI tool was developed and tested to aid radiologists in the interpretation of mammography by accurately detecting and characterizing microcalcifications. • Three neural networks (AlexNet, ResNet18, and ResNet34) were trained, validated, and tested using an annotated dataset of 1,000 patients and 1,986 mammograms. • The AI tool demonstrated high accuracy in detecting/localizing and characterizing microcalcifications on mammography, highlighting the potential of AI-based systems to assist radiologists in the interpretation of mammograms.

SUBMITTER: Pesapane F

PROVIDER: S-EPMC10630180 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Deep learning performance for detection and classification of microcalcifications on mammography.

Pesapane Filippo F Trentin Chiara C Ferrari Federica F Signorelli Giulia G Tantrige Priyan P Montesano Marta M Cicala Crispino C Virgoli Roberto R D'Acquisto Silvia S Nicosia Luca L Origgi Daniela D Cassano Enrico E

European radiology experimental 20231107 1

<h4>Background</h4>Breast cancer screening through mammography is crucial for early detection, yet the demand for mammography services surpasses the capacity of radiologists. Artificial intelligence (AI) can assist in evaluating microcalcifications on mammography. We developed and tested an AI model for localizing and characterizing microcalcifications.<h4>Methods</h4>Three expert radiologists annotated a dataset of mammograms using histology-based ground truth. The dataset was partitioned for t ...[more]

PMID: 37934382

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Dataset Information

Deep learning performance for detection and classification of microcalcifications on mammography.

Background

Methods

Results

Conclusions

Relevance statement

Key points

Publications

Deep learning performance for detection and classification of microcalcifications on mammography.

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