Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy
(2024) In European Journal of Radiology 178.- Abstract
Purpose: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT. Methods: A total of 149 women (mean age 53 years, pCR rate 22 %) with breast cancer treated with NACT at Skane University Hospital, Sweden, between 2014 and 2019, were prospectively included in this observational cohort study (ClinicalTrials.gov: NCT02306096). DBT images from both the cancer and contralateral healthy breasts acquired at three time points: pre-NACT,... (More)
Purpose: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT. Methods: A total of 149 women (mean age 53 years, pCR rate 22 %) with breast cancer treated with NACT at Skane University Hospital, Sweden, between 2014 and 2019, were prospectively included in this observational cohort study (ClinicalTrials.gov: NCT02306096). DBT images from both the cancer and contralateral healthy breasts acquired at three time points: pre-NACT, after two cycles of NACT, and after the completion of six cycles of NACT (pre-surgery) were analyzed. The deep learning AI system used to predict pCR consisted of a backbone 3D ResNet and an attention and prediction module. The GradCAM method was used to obtain insights into the model decision basis through a quantitative analysis of the importance maps on the validation set. Moreover, specific model choices were motivated by ablation studies. Results: The AI model reached an AUC of 0.83 (95% CI: 0.63–1.00) (test set). The spatial correlation of importance maps for input volumes from the same patient but at different time points was high, possibly indicating that the model focuses on the same areas during decision-making. Conclusions: We demonstrate a high discriminative performance of our algorithm for predicting pCR/non-pCR. Availability of larger datasets would permit more comprehensive training of the models and more rigorous evaluation of their prediction performance for future patients.
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- author
- Förnvik, Daniel LU ; Borgquist, Signe LU ; Larsson, Måns ; Zackrisson, Sophia LU and Skarping, Ida LU
- organization
-
- Medical Radiation Physics, Malmö (research group)
- LUCC: Lund University Cancer Centre
- Breast cancer prevention & intervention (research group)
- Breastcancer
- Radiology Diagnostics, Malmö (research group)
- EpiHealth: Epidemiology for Health
- LTH Profile Area: Photon Science and Technology
- LU Profile Area: Light and Materials
- The Liquid Biopsy and Tumor Progression in Breast Cancer (research group)
- Breast cancer treatment
- publishing date
- 2024-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial intelligence, Breast cancer, Breast tomosynthesis, Imaging, Mammography, Neoadjuvant chemotherapy
- in
- European Journal of Radiology
- volume
- 178
- article number
- 111624
- publisher
- Elsevier
- external identifiers
-
- pmid:39029241
- scopus:85198925932
- ISSN
- 0720-048X
- DOI
- 10.1016/j.ejrad.2024.111624
- language
- English
- LU publication?
- yes
- id
- 18ca84d7-6be3-4c49-86b1-e321606516e8
- date added to LUP
- 2024-09-03 15:09:19
- date last changed
- 2024-09-03 15:09:39
@article{18ca84d7-6be3-4c49-86b1-e321606516e8, abstract = {{<p>Purpose: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT. Methods: A total of 149 women (mean age 53 years, pCR rate 22 %) with breast cancer treated with NACT at Skane University Hospital, Sweden, between 2014 and 2019, were prospectively included in this observational cohort study (ClinicalTrials.gov: NCT02306096). DBT images from both the cancer and contralateral healthy breasts acquired at three time points: pre-NACT, after two cycles of NACT, and after the completion of six cycles of NACT (pre-surgery) were analyzed. The deep learning AI system used to predict pCR consisted of a backbone 3D ResNet and an attention and prediction module. The GradCAM method was used to obtain insights into the model decision basis through a quantitative analysis of the importance maps on the validation set. Moreover, specific model choices were motivated by ablation studies. Results: The AI model reached an AUC of 0.83 (95% CI: 0.63–1.00) (test set). The spatial correlation of importance maps for input volumes from the same patient but at different time points was high, possibly indicating that the model focuses on the same areas during decision-making. Conclusions: We demonstrate a high discriminative performance of our algorithm for predicting pCR/non-pCR. Availability of larger datasets would permit more comprehensive training of the models and more rigorous evaluation of their prediction performance for future patients.</p>}}, author = {{Förnvik, Daniel and Borgquist, Signe and Larsson, Måns and Zackrisson, Sophia and Skarping, Ida}}, issn = {{0720-048X}}, keywords = {{Artificial intelligence; Breast cancer; Breast tomosynthesis; Imaging; Mammography; Neoadjuvant chemotherapy}}, language = {{eng}}, publisher = {{Elsevier}}, series = {{European Journal of Radiology}}, title = {{Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy}}, url = {{http://dx.doi.org/10.1016/j.ejrad.2024.111624}}, doi = {{10.1016/j.ejrad.2024.111624}}, volume = {{178}}, year = {{2024}}, }