Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients : proof of concept
Skarping, I. LU
; Larsson, M.
and Förnvik, D.
LU
(2022)
In European Radiology
32(5).
p.3131-3141
- Abstract
Objectives: In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images... (More)
Objectives: In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. Results: A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. Conclusions: Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. Key Points: • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.
(Less)
- author
- Skarping, I.
LU
; Larsson, M.
and Förnvik, D.
LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Artificial intelligence, Breast neoplasms, Diagnostic imaging, Neoadjuvant therapy
- in
- European Radiology
- volume
- 32
- issue
- 5
- pages
- 3131 - 3141
- publisher
- Springer
- external identifiers
-
- pmid:34652522
- scopus:85117097758
- ISSN
- 0938-7994
- DOI
- 10.1007/s00330-021-08306-w
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2021, The Author(s).
- id
- 8e4a2710-5955-44df-8b92-29f5238bf685
- date added to LUP
- 2021-11-16 18:05:47
- date last changed
- 2024-06-16 23:08:46
@article{8e4a2710-5955-44df-8b92-29f5238bf685,
abstract = {{<p>Objectives: In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. Results: A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. Conclusions: Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. Key Points: • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.</p>}},
author = {{Skarping, I. and Larsson, M. and Förnvik, D.}},
issn = {{0938-7994}},
keywords = {{Artificial intelligence; Breast neoplasms; Diagnostic imaging; Neoadjuvant therapy}},
language = {{eng}},
number = {{5}},
pages = {{3131--3141}},
publisher = {{Springer}},
series = {{European Radiology}},
title = {{Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients : proof of concept}},
url = {{http://dx.doi.org/10.1007/s00330-021-08306-w}},
doi = {{10.1007/s00330-021-08306-w}},
volume = {{32}},
year = {{2022}},
}