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6 January 2025 Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm
Nishta Letchumanan, Shouhei Hanaoka, Tomomi Takenaga, Yusuke Suzuki, Takahiro Nakao, Yukihiro Nomura, Takeharu Yoshikawa, Osamu Abe
Author Affiliations +
Journal of Medical Imaging, Vol. 12, Issue 1, 014501 (January 2025). https://doi.org/10.1117/1.JMI.12.1.014501
Abstract

Purpose

The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance.

Approach

We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods. In the first method, a radiomics-based approach, we utilized radiomics features and machine learning (ML) algorithms. The entire breast region was chosen as the region of interest for extracting radiomics features. Then, a binary breast image was created from which we extracted 668 features and analyzed them using various ML algorithms. In the second method, a complex convolutional neural network (CNN) with a modified ResNet architecture and various kernel sizes was applied to raw mammography images for the prediction task. A nested, stratified five-fold cross-validation was done for both parts A and B to compute accuracy, sensitivity, specificity, and area under the receiver operating curve (AUROC). Hyperparameter tuning was also done to enhance the model’s performance and reliability.

Results

The radiomics approach’s light gradient boosting model gave 68.9% accuracy, 30.7% sensitivity, 89.5% specificity, and 0.63 AUROC. The CNN method achieved an AUROC of 0.58 over 20 epochs.

Conclusion

Radiomics outperformed CNN by 0.05 in terms of AUROC. This may be due to the more straightforward interpretability and clinical relevance of predefined radiomics features compared with the complex, abstract features learned by CNNs.
© 2024 Society of Photo-Optical Instrumentation Engineers (SPIE)

Funding Statement

Citation Download Citation
Nishta Letchumanan, Shouhei Hanaoka, Tomomi Takenaga, Yusuke Suzuki, Takahiro Nakao, Yukihiro Nomura, Takeharu Yoshikawa, and Osamu Abe "Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm," Journal of Medical Imaging 12(1), 014501 (6 January 2025). https://doi.org/10.1117/1.JMI.12.1.014501
Received: 30 August 2024; Accepted: 1 December 2024; Published: 6 January 2025
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KEYWORDS
Radiomics
Deep learning
Mammography
Cooccurrence matrices
Performance modeling
Feature extraction
Data modeling
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