| Autor: |
Marta Nowak, Faiz Jabbar, Ann-Katrin Rodewald, Luciana Gneo, Tijana Tomasevic, Andrea Harkin, Tim Iveson, Mark Saunders, Rachel Kerr, Karin Oein, Noori Maka, Jennifer Hay, Joanne Edwards, Ian Tomlinson, Owen Sansom, Caroline Kelly, Francesco Pezzella, David Kerr, Alistair Easton, Enric Domingo, Viktor H. Koelzer, David N. Church, Bengt Glimelius, Ismail Gogenur, Emma Jaeger, Hannah Morgan, Clare Orange, Claire Palles, Campbell Roxburgh |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Cell Reports Medicine, Vol 5, Iss 9, Pp 101727- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2666-3791 |
| DOI: |
10.1016/j.xcrm.2024.101727 |
| Popis: |
Summary: Testing for DNA mismatch repair deficiency (MMRd) is recommended for all colorectal cancers (CRCs). Automating this would enable precision medicine, particularly if providing information on etiology not captured by deep learning (DL) methods. We present AIMMeR, an AI-based method for determination of mismatch repair (MMR) protein expression at a single-cell level in routine pathology samples. AIMMeR shows an area under the receiver-operator curve (AUROC) of 0.98, and specificity of ≥75% at 98% sensitivity against pathologist ground truth in stage II/III in two trial cohorts, with positive predictive value of ≥98% for the commonest pattern of somatic MMRd. Lower agreement with microsatellite instability (MSI) testing (AUROC 0.86) reflects discordance between MMR and MSI PCR rather than AIMMeR misclassification. Analysis of the SCOT trial confirms MMRd prognostic value in oxaliplatin-treated patients; while MMRd does not predict differential benefit of chemotherapy duration, it correlates with difference in relapse by regimen (PInteraction = 0.04). AIMMeR may help reduce pathologist workload and streamline diagnostics in CRC. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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