Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images
There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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Enthalten in: |
Cancers - 15(2023), 6 vom: 08. März |
Sprache: |
Englisch |
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Beteiligte Personen: |
Veluponnar, Dinusha [VerfasserIn] |
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Links: |
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Themen: |
Artificial intelligence |
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Anmerkungen: |
Date Revised 31.03.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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doi: |
10.3390/cancers15061652 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM354855026 |
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520 | |a There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a artificial intelligence | |
650 | 4 | |a breast cancer | |
650 | 4 | |a breast surgery | |
650 | 4 | |a deep learning | |
650 | 4 | |a surgical margin | |
650 | 4 | |a tumor segmentation | |
650 | 4 | |a ultrasound | |
700 | 1 | |a de Boer, Lisanne L |e verfasserin |4 aut | |
700 | 1 | |a Geldof, Freija |e verfasserin |4 aut | |
700 | 1 | |a Jong, Lynn-Jade S |e verfasserin |4 aut | |
700 | 1 | |a Da Silva Guimaraes, Marcos |e verfasserin |4 aut | |
700 | 1 | |a Vrancken Peeters, Marie-Jeanne T F D |e verfasserin |4 aut | |
700 | 1 | |a van Duijnhoven, Frederieke |e verfasserin |4 aut | |
700 | 1 | |a Ruers, Theo |e verfasserin |4 aut | |
700 | 1 | |a Dashtbozorg, Behdad |e verfasserin |4 aut | |
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