Artificial intelligence to de-escalate loco-regional breast cancer treatment
In this review, we evaluate the potential and recent advancements in using artificial intelligence techniques to de-escalate loco-regional breast cancer therapy, with a special focus on surgical treatment after neoadjuvant systemic treatment (NAST). The increasing use and efficacy of NAST make the o...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
20 February 2023
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| In: |
The breast
Year: 2023, Volume: 68, Pages: 201-204 |
| ISSN: | 1532-3080 |
| DOI: | 10.1016/j.breast.2023.02.009 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.breast.2023.02.009 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0960977623000322 
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| Author Notes: | André Pfob, Joerg Heil |
| Summary: | In this review, we evaluate the potential and recent advancements in using artificial intelligence techniques to de-escalate loco-regional breast cancer therapy, with a special focus on surgical treatment after neoadjuvant systemic treatment (NAST). The increasing use and efficacy of NAST make the optimal loco-regional management of patients with pathologic complete response (pCR) a clinically relevant knowledge gap. It is hypothesized that patients with pCR do not benefit from therapeutic surgery because all tumor has already been eradicated by NAST. It is unclear, however, how residual cancer after NAST can be reliably excluded prior to surgery to identify patients eligible for omitting breast cancer surgery. Evidence from clinical trials evaluating the potential of imaging and minimally-invasive biopsies to exclude residual cancer suggests that there is a high risk of missing residual cancer. More recently, AI-based algorithms have shown promising results to reliably exclude residual cancer after NAST. This example illustrates the great potential of AI-based algorithms to further de-escalate and individualize loco-regional breast cancer treatment. |
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| Item Description: | Online verfügbar 20 February 2023, Version des Artikels 24 February 2023 Gesehen am 12.07.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1532-3080 |
| DOI: | 10.1016/j.breast.2023.02.009 |