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Breast cancer; Deep learning; Longitudinal mammography; Risk prediction; Temporal patterns; Personalized screening; Early detection

Introduction

Breast cancer is a significant global health issue and a leading cause of mortality among women. Early detection plays a critical role in improving patient outcomes by enabling timely intervention and treatment. Mammography, a widely used imaging technique for breast cancer screening, has been instrumental in detecting breast abnormalities. However, accurately predicting an individual’s risk of developing breast cancer remains a challenge. Traditional risk assessment models for breast cancer incorporate various factors such as age, family history, hormonal profile, and lifestyle choices. While these models have proven useful, they often lack the ability to capture the dynamic changes in breast tissue over time. Longitudinal mammography examinations, which involve capturing mammograms at multiple time points for each patient, provide a unique opportunity to extract temporal patterns and trends in breast tissue changes.

In recent years, deep learning, a subfield of artificial intelligence, has demonstrated remarkable success in various medical imaging applications. Deep learning algorithms, particularly convolutional neural networks (CNNs), can effectively learn complex patterns and features from large-scale datasets. By leveraging the power of deep learning, it may be possible to analyze longitudinal mammography examinations and develop a predictive model for breast cancer risk assessment. The objective of this study is to explore the potential of deep learning techniques in analyzing longitudinal mammography examinations for predicting breast cancer risk. By utilizing a large dataset of mammograms obtained from multiple time points for each patient, we aim to develop a deep learning model that can effectively capture temporal changes in breast tissue and identify individuals at higher risk of developing breast cancer. The proposed deep learning model will be trained on a comprehensive dataset of longitudinal mammograms, incorporating information from previous screenings. By considering temporal patterns and trends, the model can potentially provide more accurate and personalized risk predictions compared to traditional risk assessment methods. Additionally, the model’s performance will be evaluated on an independent dataset, and its results will be compared with existing risk assessment models. The outcomes of this research hold significant potential for improving breast cancer risk prediction and personalized screening strategies [1-4].

Methodology

Dataset acquisition: Obtain a large dataset of longitudinal mammography examinations, including mammograms from multiple time points for each patient. Ensure the dataset contains information on the patients’ breast cancer status (e.g., cancer diagnosis or follow-up information). Ensure proper anonymization and adherence to ethical guidelines regarding patient data privacy.

Preprocessing: Standardize the mammography images to a consistent format and resolution. Apply preprocessing techniques such as noise reduction, contrast enhancement, and normalization to improve image quality and consistency.

Data Split: Divide the dataset into training, validation, and testing sets. Consider stratified sampling to ensure an appropriate distribution of breast cancer cases and controls across the sets.

Deep learning model architecture: Design a deep learning model, preferably a convolutional neural network (CNN), to capture the temporal patterns and trends in longitudinal mammography examinations. Experiment with various architectures, including standard CNN architectures such as VGG, ResNet, or custom-designed architectures. Consider incorporating recurrent neural network (RNN) components to model temporal dependencies explicitly, if deemed necessary.

Training: Train the deep learning model using the training set. Utilize techniques such as data augmentation (e.g., random rotations, flips, and shifts) to enhance model generalization and robustness. Employ an appropriate optimization algorithm (e.g., Adam or RMSprop) to optimize the model’s parameters. Define a suitable loss function, such as binary cross-entropy, tailored to the binary classification task of predicting breast cancer risk.

Model evaluation: Evaluate the trained model using the validation set to monitor its performance and prevent overfitting. Track relevant metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC).

Performance comparison: Compare the performance of the deep learning model with traditional risk assessment methods (e.g., Gail model, Tyrer-Cuzick model) using the testing set. Measure the model’s ability to accurately predict breast cancer risk and assess its potential for improving risk assessment compared to traditional methods.

Interpretability and visualization: Employ techniques for model interpretability to understand the features and patterns contributing to risk prediction. Visualize the learned features or attention maps to gain insights into the model’s decision-making process [5-8].

Discussion

The use of deep learning techniques for predicting breast cancer risk based on longitudinal mammography examinations holds great promise in enhancing early detection and personalized screening strategies. This discussion focuses on the implications, advantages, limitations, and potential future directions of this approach. One of the significant advantages of utilizing deep learning in this context is its ability to capture temporal patterns and trends in breast tissue changes over time. By analyzing longitudinal mammography examinations, the deep learning model can potentially identify subtle changes and evolving patterns that may indicate an increased risk of developing breast cancer. This approach goes beyond traditional risk assessment models, which often rely on static factors and may overlook the dynamic nature of breast tissue changes. The integration of deep learning with longitudinal mammography examinations has the potential to improve the accuracy and precision of breast cancer risk prediction. By considering a patient’s entire screening history, the model can capture the cumulative effect of breast tissue changes, allowing for more personalized risk assessments. This information can guide healthcare professionals in tailoring screening protocols and interventions based on an individual’s specific risk profile. Furthermore, deep learning models have demonstrated remarkable performance in various medical imaging applications, including breast cancer detection and classification. Leveraging the power of convolutional neural networks (CNNs), these models can learn intricate features and patterns from large-scale datasets, leading to more accurate predictions. The use of transfer learning and domain adaptation techniques can potentially enhance the model’s performance across different populations and datasets. Despite these advantages, there are several considerations and limitations to address. First, obtaining a large and diverse dataset of longitudinal mammography examinations with corresponding clinical outcomes can be challenging. The availability of such comprehensive datasets may vary across healthcare systems and may require collaborations and data sharing among institutions. Additionally, ensuring proper data anonymization and privacy protection is crucial to maintain patient confidentiality. Interpretability of deep learning models is another important aspect to consider. While deep learning models have demonstrated exceptional performance, their decision-making process often lacks transparency, making it challenging to understand the specific features and patterns influencing risk predictions. Addressing this interpretability challenge is crucial to gain the trust and acceptance of healthcare professionals and patients. Moreover, the integration of deep learning models into clinical practice requires validation and verification in real-world settings. Collaborating with healthcare professionals and conducting prospective studies can validate the model’s predictions and evaluate its impact on clinical decision-making, patient outcomes, and resource allocation. Future research directions may focus on integrating additional clinical and demographic variables into the deep learning models. By combining longitudinal mammography examinations with other risk factors such as genetic information, hormonal profiles, and lifestyle factors, more comprehensive and accurate risk prediction models can be developed. Additionally, exploring the use of multimodal data, such as combining mammography with other imaging modalities like MRI or ultrasound, may further enhance the predictive performance [9-12].

Conclusion

In conclusion, the deep learning-based prediction of breast cancer risk using longitudinal mammography examinations has the potential to revolutionize personalized breast cancer screening and prevention strategies. By leveraging the temporal information from these examinations, deep learning models can capture subtle changes in breast tissue and provide more accurate risk assessments. However, further research and collaboration are needed to address data availability, interpretability, and validation challenges, ultimately enabling the seamless integration of these models into clinical practice and improving patient outcomes.

Acknowledgment

None

Conflict of Interest

None

1. Meng QY, Cong HL, Hu H, Xu FJ (2020) . Materials Today Bio 7: 100056.

, ,

2.  Bianchini G, De Angelis C, Licata L, Gianni L (2022) . Nat Rev Clin Oncol 19: 91-113.

, ,

3. Al-Mahmood S, Sapiezynski J, Garbuzenko OB,  Minko T (2018) . Drug Deliv Transl Res 8: 1483-1507.

, ,

4. Chaudhary LN (2020) . Semin Oncol 47: 201-208.
5. Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L (2016) . Nat Rev Clin Oncol 13: 674-690.
6. Melero I, Castanon E, Alvarez M, Champiat S, Marabelle A (2021) . Nat Rev Clin Oncol 18: 558-576.

, ,

7. Cabral H, Kinoh H, Kataoka K (2020) . Acc Chem Res 53: 2765-2776.

, ,

8. Lee H, Kim SW, Kwon DY, Kang HW, Jung MJ, et al. (2021) . Tissue Eng Regen Med 18: 941-951.

, ,

9. Grassi ES, Ghiandai V, Persani L (2021) . J Clin Med 10: 1455.

, ,

10. Choi JS, Heang Oh S, Kim YM, Lim JY (2020) . Tissue Eng Regen Med 17: 651-658.

, ,

11. Sinn HP, Kreipe H (2013).Breast Care (Basel)8: 149–154.

, ,

12. Li CI, Uribe DJ, Daling JR (2005).Br J Cancer93: 1046–1052.

, ,