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}

}

@article{Sculthorpe2024-ie,

title = {Advantages of automated immunostain analyses for complex  membranous immunostains: An exemplar investigating loss of  E-cadherin expression in colorectal cancer},

author = {Declan Sculthorpe and Amy Denton and Dewi Rusnita and Wakkas Fadhil and Mohammad Ilyas and Abhik Mukherjee},

doi = {10.1016/j.prp.2024.155470},

year  = {2024},

date = {2024-08-01},

urldate = {2024-08-01},

journal = {Pathol. Res. Pract.},

volume = {260},

number = {155470},

pages = {155470},

publisher = {Elsevier BV},

abstract = {As pathology moves towards digitisation, biomarker profiling 

 through automated image analysis provides potentially objective 

 and time-efficient means of assessment. This study set out to 

 determine how a complex membranous immunostain, E-cadherin, 

 assessed using an automated digital platform fares in comparison 

 to manual evaluation in terms of clinical correlations and 

 prognostication. Tissue microarrays containing 1000 colorectal 

 cancer samples, stained with clinical E-cadherin antibodies were 

 assessed through both manual scoring and automated image 

 analysis. Both manual and automated scores were correlated to 

 clinicopathological and survival data. E-cadherin data generated 

 through digital image analysis was superior to manual evaluation 

 when investigating for clinicopathological correlations in 

 colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = \<0.001), higher T-stage (p = \<0.001), higher grade (p = \<0.001), N2 nodal stage (p = \<0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) 

 survival status. Manual assessment was only correlated with 

 higher grade tumours, though other correlations become apparent 

 only when assessed for morphological expression pattern 

 (circumferential, basolateral, parallel) irrespective of 

 intensity. Digital assessment of E-cadherin is effective for 

 prognostication of colorectal cancer and may potentially offer 

 benefits of improved objectivity, accuracy, and economy of time. 

 Incorporating tools to assess patterns of staining may further 

 improve such digital assessment in the future.},

keywords = {biomarker analysis, colorectal cancer, Digital pathology, Image   analysis, immunohistochemistry},

pubstate = {published},

tppubtype = {article}

}