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Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts

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A Correction to this article was published on 22 June 2023

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Abstract

Objectives

To analyze the performance of deep learning in isodense/obscure masses in dense breasts. To build and validate a deep learning (DL) model using core radiology principles and analyze its performance in isodense/obscure masses. To show performance on screening mammography as well as diagnostic mammography distribution.

Methods

This was a retrospective, single-institution, multi-centre study with external validation. For model building, we took a 3-pronged approach. First, we explicitly taught the network to learn features other than density differences: such as spiculations and architectural distortion. Second, we used the opposite breast to enable the detection of asymmetries. Third, we systematically enhanced each image by piece-wise-linear transformation. We tested the network on a diagnostic mammography dataset (2569 images with 243 cancers, January to June 2018) and a screening mammography dataset (2146 images with 59 cancers, patient recruitment from January to April 2021) from a different centre (external validation).

Results

When trained with our proposed technique (and compared with baseline network), sensitivity for malignancy increased from 82.7 to 84.7% at 0.2 False positives per image (FPI) in the diagnostic mammography dataset, 67.9 to 73.8% in the subset of patients with dense breasts, 74.6 to 85.3 in the subset of patients with isodense/obscure cancers and 84.9 to 88.7 in an external validation test set with a screening mammography distribution. We showed that our sensitivity exceeded currently reported values (0.90 at 0.2 FPI) on a public benchmark dataset (INBreast).

Conclusion

Modelling traditional mammographic teaching into a DL framework can help improve cancer detection accuracy in dense breasts.

Clinical relevance statement

Incorporating medical knowledge into neural network design can help us overcome some limitations associated with specific modalities. In this paper, we show how one such deep neural network can help improve performance on mammographically dense breasts.

Key Points

• Although state-of-the-art deep learning networks achieve good results in cancer detection in mammography in general, isodense, obscure masses and mammographically dense breasts posed a challenge to deep learning networks.

• Collaborative network design and incorporation of traditional radiology teaching into the deep learning approach helped mitigate the problem.

• The accuracy of deep learning networks may be translatable to different patient distributions. We showed the results of our network on screening as well as diagnostic mammography datasets.

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Abbreviations

CABD:

Contrast adjusted for breast density

CBIS-DDSM:

Curated Breast Imaging Subset of DDSM, a publicly available dataset

CI:

Confidence interval

DICOM:

Digital imaging and communications in medicine

DL:

Deep learning

DM:

Diagnostic mammography dataset

DNN:

Deep neural network

EfficientDet:

A state-of-art object detection network

FFDM:

Full-field digital mammography

FPI:

False positives per image

FRCNN:

Faster RCNN, a state-of-art object detection network

PACS:

Picture archiving communication system

PNG:

Portable network graphics file format

RIS:

Radiology information system

RPN:

Region proposal network

SM:

Screening mammography dataset

SOTA:

State-of-the art

TI:

Thresholded image

YOLO v3:

You Only Look Once version 3, a State-of-art Object Detection Network

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Acknowledgements

We acknowledge the help provided by Dr M Kalaivani, Professor, Department of Biostatistics, AIIMS for her valuable guidance in the project. We also acknowledge the role of our data entry operator Hema Malhotra in meticulously compiling the data required for this project. In addition, we also acknowledge Dr Pankaj Hari, Professor of Pediatrics, AIIMS Delhi for his help in the biostatistical analysis.

Funding

This work was supported in part by the Department of Biotechnology, Government of India, under Grant BT/PR33193/AI/133/5/2019.

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Author notes

  1. Krithika Rangarajan and Pranjal Aggarwal have equal contributions.

Authors and Affiliations

  1. All India Institute of Medical Sciences, Ansari Nagar New Delhi, 110029, India

    Krithika Rangarajan, Akhil Baby, Chandan Pal, Arun Kumar Gupta & Smriti Hari

  2. Indian Institute of Technology, Delhi, Hauz Khas, Delhi, 110016, India

    Krithika Rangarajan, Pranjal Aggarwal, Dhruv Kumar Gupta, Rohan Dhanakshirur, Subhashis Banerjee & Chetan Arora

Authors
  1. Krithika Rangarajan
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Corresponding author

Correspondence to Krithika Rangarajan.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr Krithika Rangarajan, Assistant Professor, Radiology, AIIMS, New Delhi.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Dr M. Kalaivani, Professor, Department of Biostatistics, AIIMS kindly provided statistical advice for this manuscript.

Informed consent

Informed consent was waived by the institutional review board.

Ethical approval

Institutional Review Board approval was obtained. (IEC-247-4.05.2018). The title of the project for which ethical clearance was obtained is “Deep learning for detection and classification of abnormalities on full field digital mammography.” The current manuscript is one of the works done under this project.

Study subjects or cohorts overlap

Mammograms from study subjects are being used for the development of other AI algorithms for cancer detection on mammograms as well. However, none of the other studies deals with the detection of cancers in dense breasts and uses completely different neural networks. The current neural network is not reported on the data presented in the manuscript in any other work.

Methodology

This was a retrospective diagnostic accuracy study performed at 2 centres of one institution. These centres were the Department of Radiology, All India Institute of Medical Sciences New Delhi (centre 1) and the Department of Oncoradiology, BR Ambedkar Institute Rotary Cancer Hospital, AIIMS (centre 2), New Delhi. The DM dataset and Institutional training dataset described in the manuscript were obtained from centre 1, and the SM dataset was obtained from centre 2. The original ethical clearance obtained from IRB was for mammography and pathology information from centre 1 (IEC-247–4.05.2018). Centre 2 mammography and pathology information was allowed by IRB as an addendum to the same ethical clearance (IEC-247–4.05.2018 OP03/04.06.2021). The other 2 datasets described are publicly available datasets which are downloadable. These can be accessed from:

• The CBIS-DDSM dataset is available from https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM

• The INBreast dataset is available upon request from http://medicalresearch.inescporto.pt/breastresearch/GetINb

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The original online version of this article was revised: In this article the author name Pranjal Aggarwal was incorrectly written as Pranjal Agarwal.

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Rangarajan, K., Aggarwal, P., Gupta, D.K. et al. Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts. Eur Radiol 33, 8112–8121 (2023). https://doi.org/10.1007/s00330-023-09717-7

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