Maxime W Lafarge
Overview
Explore the profile of Maxime W Lafarge including associated specialties, affiliations and a list of published articles.
Author names and details appear as published. Due to indexing inconsistencies, multiple individuals may share a name, and a single author may have variations. MedLuna displays this data as publicly available, without modification or verification
Snapshot
Snapshot
Articles
15
Citations
104
Followers
0
Related Specialties
Related Specialties
Top 10 Co-Authors
Top 10 Co-Authors
Published In
Affiliations
Affiliations
Soon will be listed here.
Recent Articles
1.
Fan F, Martinez G, DeSilvio T, Shin J, Chen Y, Jacobs J, et al.
Npj Imaging
. 2024 Jul;
2(1):15.
PMID: 38962496
Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release...
2.
Author Correction: Prediction of recurrence risk in endometrial cancer with multimodal deep learning
Volinsky-Fremond S, Horeweg N, Andani S, Barkey Wolf J, Lafarge M, de Kroon C, et al.
Nat Med
. 2024 Jul;
30(7):2092.
PMID: 38951637
No abstract available.
3.
Malla S, Byrne R, Lafarge M, Corry S, Fisher N, Tsantoulis P, et al.
Nat Genet
. 2024 Jun;
56(6):1321.
PMID: 38831011
No abstract available.
4.
Volinsky-Fremond S, Horeweg N, Andani S, Barkey Wolf J, Lafarge M, de Kroon C, et al.
Nat Med
. 2024 May;
30(7):1962-1973.
PMID: 38789645
Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed...
5.
Lafarge M, Domingo E, Sirinukunwattana K, Wood R, Samuel L, Murray G, et al.
NPJ Precis Oncol
. 2024 Apr;
8(1):89.
PMID: 38594327
The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we...
6.
Aubreville M, Stathonikos N, Donovan T, Klopfleisch R, Ammeling J, Ganz J, et al.
Med Image Anal
. 2024 Mar;
94:103155.
PMID: 38537415
Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance...
7.
Malla S, Byrne R, Lafarge M, Corry S, Fisher N, Tsantoulis P, et al.
Nat Genet
. 2024 Feb;
56(3):458-472.
PMID: 38351382
Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than...
8.
Brouwer N, Webbink L, Haddad T, Rutgers N, van Vliet S, Wood C, et al.
J Pathol
. 2023 Oct;
261(4):401-412.
PMID: 37792663
Both lymph node metastases (LNMs) and tumour deposits (TDs) are included in colorectal cancer (CRC) staging, although knowledge regarding their biological background is lacking. This study aimed to compare the...
9.
van Bergeijk S, Stathonikos N, Ter Hoeve N, Lafarge M, Nguyen T, van Diest P, et al.
J Pathol Inform
. 2023 Jun;
14:100316.
PMID: 37273455
Introduction: Breast cancer (BC) prognosis is largely influenced by histopathological grade, assessed according to the Nottingham modification of Bloom-Richardson (BR). Mitotic count (MC) is a component of histopathological grading but...
10.
Aubreville M, Stathonikos N, Bertram C, Klopfleisch R, Ter Hoeve N, Ciompi F, et al.
Med Image Anal
. 2022 Dec;
84:102699.
PMID: 36463832
The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF...