Association of metastatic pattern in breast cancer with tumor and patient-specific factors: a nationwide autopsy study using artificial intelligence
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AQYWN2RFW" target="_blank" >RIV/00216208:11320/25:QYWN2RFW - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180442264&doi=10.1007%2fs12282-023-01534-6&partnerID=40&md5=d8295ec9da5436d2c2bceee3654d4386" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180442264&doi=10.1007%2fs12282-023-01534-6&partnerID=40&md5=d8295ec9da5436d2c2bceee3654d4386</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s12282-023-01534-6" target="_blank" >10.1007/s12282-023-01534-6</a>
Alternative languages
Result language
angličtina
Original language name
Association of metastatic pattern in breast cancer with tumor and patient-specific factors: a nationwide autopsy study using artificial intelligence
Original language description
Background: Metastatic spread is characterized by considerable heterogeneity in most cancers. With increasing treatment options for patients with metastatic disease, there is a need for insight into metastatic patterns of spread in breast cancer patients using large-scale studies. Methods: Records of 2622 metastatic breast cancer patients who underwent autopsy (1974–2010) were retrieved from the nationwide Dutch pathology databank (PALGA). Natural language processing (NLP) and manual information extraction (IE) were applied to identify the tumors, patient characteristics, and locations of metastases. Results: The accuracy (0.90) and recall (0.94) of the NLP model outperformed manual IE (on 132 randomly selected patients). Adenocarcinoma no special type more frequently metastasizes to the lung (55.7%) and liver (51.8%), whereas, invasive lobular carcinoma mostly spread to the bone (54.4%) and liver (43.8%), respectively. Patients with tumor grade III had a higher chance of developing bone metastases (61.6%). In a subgroup of patients, we found that ER+/HER2+ patients were more likely to metastasize to the liver and bone, compared to ER−/HER2+ patients. Conclusion: This is the first large-scale study that demonstrates that artificial intelligence methods are efficient for IE from Dutch databanks. Different histological subtypes show different frequencies and combinations of metastatic sites which may reflect the underlying biology of metastatic breast cancer. © The Author(s), under exclusive licence to The Japanese Breast Cancer Society 2023.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Breast Cancer
ISSN
1340-6868
e-ISSN
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Volume of the periodical
31
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
9
Pages from-to
263-271
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85180442264