Publications

@article{Burrows2024-xb,

title = {Mathematical modelling and deep learning algorithms to automate  assessment of single and digitally multiplexed  immunohistochemical stains in tumoural stroma},

author = {Liam Burrows and Declan Sculthorpe and Hongrun Zhang and Obaid Rehman and Abhik Mukherjee and Ke Chen},

doi = {10.1016/j.jpi.2023.100351},

year  = {2024},

date = {2024-12-01},

urldate = {2024-12-01},

journal = {J. Pathol. Inform.},

volume = {15},

number = {100351},

pages = {100351},

publisher = {Elsevier BV},

abstract = {Whilst automated analysis of immunostains in pathology research 

 has focused predominantly on the epithelial compartment, 

 automated analysis of stains in the stromal compartment is 

 challenging and therefore requires time-consuming pathological 

 input and guidance to adjust to tissue morphometry as perceived 

 by pathologists. This study aimed to develop a robust method to 

 automate stromal stain analyses using 2 of the commonest stromal 

 stains (SMA and desmin) employed in clinical pathology practice 

 as examples. An effective computational method capable of 

 automatically assessing and quantifying tumour-associated 

 stromal stains was developed and applied on cores of colorectal 

 cancer tissue microarrays. The methodology combines both 

 mathematical models and deep learning techniques with the former 

 requiring no training data and the latter as many inputs as 

 possible. The novel mathematical model was used to produce a 

 digital double marker overlay allowing for fast automated 

 digital multiplex analysis of stromal stains. The results show 

 that deep learning methodologies in combination with 

 mathematical modelling allow for an accurate means of 

 quantifying stromal stains whilst also opening up new 

 possibilities of digital multiplex analyses.},

keywords = {Digital multiplex, Digital pathology, Machine learning, Mathematical modelling, Stromal stain, Tissue microarrays},

pubstate = {published},

tppubtype = {article}

}