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Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.

Publication ,  Journal Article
Li, H; Robinson, K; Lan, L; Baughan, N; Chan, C-W; Embury, M; Whitman, GJ; El-Zein, R; Bedrosian, I; Giger, ML
Published in: Cancers (Basel)
April 4, 2023

The identification of women at risk for sporadic breast cancer remains a clinical challenge. We hypothesize that the temporal analysis of annual screening mammograms, using a long short-term memory (LSTM) network, could accurately identify women at risk of future breast cancer. Women with an imaging abnormality, which had been biopsy-confirmed to be cancer or benign, who also had antecedent imaging available were included in this case-control study. Sequences of antecedent mammograms were retrospectively collected under HIPAA-approved guidelines. Radiomic and deep-learning-based features were extracted on regions of interest placed posterior to the nipple in antecedent images. These features were input to LSTM recurrent networks to classify whether the future lesion would be malignant or benign. Classification performance was assessed using all available antecedent time-points and using a single antecedent time-point in the task of lesion classification. Classifiers incorporating multiple time-points with LSTM, based either on deep-learning-extracted features or on radiomic features, tended to perform statistically better than chance, whereas those using only a single time-point failed to show improved performance compared to chance, as judged by area under the receiver operating characteristic curves (AUC: 0.63 ± 0.05, 0.65 ± 0.05, 0.52 ± 0.06 and 0.54 ± 0.06, respectively). Lastly, similar classification performance was observed when using features extracted from the affected versus the contralateral breast in predicting future unilateral malignancy (AUC: 0.63 ± 0.05 vs. 0.59 ± 0.06 for deep-learning-extracted features; 0.65 ± 0.05 vs. 0.62 ± 0.06 for radiomic features). The results of this study suggest that the incorporation of temporal information into radiomic analyses may improve the overall classification performance through LSTM, as demonstrated by the improved discrimination of future lesions as malignant or benign. Further, our data suggest that a potential field effect, changes in the breast extending beyond the lesion itself, is present in both the affected and contralateral breasts in antecedent imaging, and, thus, the evaluation of either breast might inform on the future risk of breast cancer.

Duke Scholars

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Published In

Cancers (Basel)

DOI

ISSN

2072-6694

Publication Date

April 4, 2023

Volume

15

Issue

7

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
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ICMJE
MLA
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Li, H., Robinson, K., Lan, L., Baughan, N., Chan, C.-W., Embury, M., … Giger, M. L. (2023). Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers (Basel), 15(7). https://doi.org/10.3390/cancers15072141
Li, Hui, Kayla Robinson, Li Lan, Natalie Baughan, Chun-Wai Chan, Matthew Embury, Gary J. Whitman, Randa El-Zein, Isabelle Bedrosian, and Maryellen L. Giger. “Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.Cancers (Basel) 15, no. 7 (April 4, 2023). https://doi.org/10.3390/cancers15072141.
Li H, Robinson K, Lan L, Baughan N, Chan C-W, Embury M, et al. Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers (Basel). 2023 Apr 4;15(7).
Li, Hui, et al. “Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction.Cancers (Basel), vol. 15, no. 7, Apr. 2023. Pubmed, doi:10.3390/cancers15072141.
Li H, Robinson K, Lan L, Baughan N, Chan C-W, Embury M, Whitman GJ, El-Zein R, Bedrosian I, Giger ML. Temporal Machine Learning Analysis of Prior Mammograms for Breast Cancer Risk Prediction. Cancers (Basel). 2023 Apr 4;15(7).

Published In

Cancers (Basel)

DOI

ISSN

2072-6694

Publication Date

April 4, 2023

Volume

15

Issue

7

Location

Switzerland

Related Subject Headings

  • 3211 Oncology and carcinogenesis
  • 1112 Oncology and Carcinogenesis