Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?
Objectives: Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clin...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
14 September 2023
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| In: |
European radiology
Year: 2024, Jahrgang: 34, Heft: 4, Pages: 2560-2573 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-023-10238-6 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-023-10238-6
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| Verfasserangaben: | Lie Cai, Chris Sidey-Gibbons, Juliane Nees, Fabian Riedel, Benedikt Schäfgen, Riku Togawa, Kristina Killinger, Joerg Heil, André Pfob, Michael Golatta |
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| 245 | 1 | 0 | |a Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer? |c Lie Cai, Chris Sidey-Gibbons, Juliane Nees, Fabian Riedel, Benedikt Schäfgen, Riku Togawa, Kristina Killinger, Joerg Heil, André Pfob, Michael Golatta |
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| 520 | |a Objectives: Response assessment to neoadjuvant systemic treatment (NAST) to guide individualized treatment in breast cancer is a clinical research priority. We aimed to develop an intelligent algorithm using multi-modal pretreatment ultrasound and tomosynthesis radiomics features in addition to clinical variables to predict pathologic complete response (pCR) prior to the initiation of therapy. Methods: We used retrospective data on patients who underwent ultrasound and tomosynthesis before starting NAST. We developed a support vector machine algorithm using pretreatment ultrasound and tomosynthesis radiomics features in addition to patient and tumor variables to predict pCR status (ypT0 and ypN0). Findings were compared to the histopathologic evaluation of the surgical specimen. The main outcome measures were area under the curve (AUC) and false-negative rate (FNR). Results: We included 720 patients, 504 in the development set and 216 in the validation set. Median age was 51.6 years and 33.6% (242 of 720) achieved pCR. The addition of radiomics features significantly improved the performance of the algorithm (AUC 0.72 to 0.81; p = 0.007). The FNR of the multi-modal radiomics and clinical algorithm was 6.7% (10 of 150 with missed residual cancer). Surface/volume ratio at tomosynthesis and peritumoral entropy characteristics at ultrasound were the most relevant radiomics. Hormonal receptors and HER-2 status were the most important clinical predictors. Conclusion: A multi-modal machine learning algorithm with pretreatment clinical, ultrasound, and tomosynthesis radiomics features may aid in predicting residual cancer after NAST. Pending prospective validation, this may facilitate individually tailored NAST regimens. Clinical relevance statement: Multi-modal radiomics using pretreatment ultrasound and tomosynthesis showed significant improvement in assessing response to NAST compared to an algorithm using clinical variables only. Further prospective validation of our findings seems warranted to enable individualized predictions of NAST outcomes. | ||
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Neoadjuvant systemic treatment | |
| 650 | 4 | |a Treatment outcome | |
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