From a41c8fbd1712b6b98a71889339c412b2285ca3f3 Mon Sep 17 00:00:00 2001
From: Carlijn Lems <72729530+carlijnlems@users.noreply.github.com>
Date: Tue, 26 Nov 2024 17:34:15 +0100
Subject: [PATCH] Update preprint to published book chapter (#7)

* Remove consortium authors

* Update preprint to published book chapter
---
 diag.bib | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/diag.bib b/diag.bib
index 6a3fbe7..665e573 100644
--- a/diag.bib
+++ b/diag.bib
@@ -30071,20 +30071,22 @@ @article{Sier20
   month = {2},
 }
 
-@article{Sili24,
+@book{Sili24,
   author = {Silina, Karina and Ciompi, Francesco},
-  title = {Hitchhiker's guide to cancer-associated lymphoid aggregates in histology images: manual and deep learning-based quantification approaches},
-  doi = {10.48550/ARXIV.2403.04142},
+  title = {Cancer-associated lymphoid aggregates in histology images: manual and deep learning-based quantification approaches},
+  doi = {10.1007/978-1-0716-4184-2_12},
   year = {2024},
-  abstract = {Quantification of lymphoid aggregates including tertiary lymphoid structures with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide recommendations for identifying lymphoid aggregates in tissue sections from routine pathology workflows such as hematoxylin and eosin staining. To overcome the intrinsic variability associated with manual image analysis (such as subjective decision making, attention span), we recently developed a deep learning-based algorithm called HookNet-TLS to detect lymphoid aggregates and germinal centers in various tissues. Here, we additionally provide a guideline for using manually annotated images for training and implementing HookNet-TLS for automated and objective quantification of lymphoid aggregates in various cancer types.},
-  url = {https://arxiv.org/abs/2403.04142},
+  volume = {2864},
+  abstract = {Quantification of lymphoid aggregates including tertiary lymphoid structures (TLS) with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide recommendations for identifying lymphoid aggregates in tissue sections from routine pathology workflows such as hematoxylin and eosin staining. To overcome the intrinsic variability associated with manual image analysis (such as subjective decision making, attention span), we recently developed a deep learning-based algorithm called HookNet-TLS to detect lymphoid aggregates and germinal centers in various tissues. Here, we additionally provide a guideline for using manually annotated images for training and implementing HookNet-TLS for automated and objective quantification of lymphoid aggregates in various cancer types.},
+  url = {https://link.springer.com/protocol/10.1007/978-1-0716-4184-2_12},
   file = {Sili24.pdf:pdf\\Sili24.pdf:PDF},
   optnote = {DIAG, RADIOLOGY},
-  journal = {arXiv:2403.04142},
-  automatic = {yes},
+  series = {Methods in Molecular Biology},
   all_ss_ids = {['9829ecb0d60c7abc69057c7d359eec3c90bc6694', '4aa8da8616c700f70d5f55a27b675674d882974d']},
   gscites = {0},
+  pages = {231-246},
   pmid = {39527225},
+  publisher = {Springer},
 }
 
 @conference{Silv17,