Literature DB >> 31206364

Novel Internationally Verified Method Reports Desmoplastic Reaction as the Most Significant Prognostic Feature For Disease-specific Survival in Stage II Colorectal Cancer.

Ines P Nearchou1, Yoshiki Kajiwara2, Satsuki Mochizuki2, David J Harrison1, Peter D Caie1, Hideki Ueno2.   

Abstract

Multiple histopathologic features have been reported as candidates for predicting aggressive stage II colorectal cancer (CRC). These include tumor budding (TB), poorly differentiated clusters (PDC), Crohn-like lymphoid reaction and desmoplastic reaction (DR) categorization. Although their individual prognostic significance has been established, their association with disease-specific survival (DSS) has not been compared in stage II CRC. This study aimed to evaluate and compare the prognostic value of the above features in a Japanese (n=283) and a Scottish (n=163) cohort, as well as to compare 2 different reporting methodologies: analyzing each feature from across every tissue slide from the whole tumor and a more efficient methodology reporting each feature from a single slide containing the deepest tumor invasion. In the Japanese cohort, there was an excellent agreement between the multi-slide and single-slide methodologies for TB, PDC, and DR (κ=0.798 to 0.898) and a good agreement when assessing Crohn-like lymphoid reaction (κ=0.616). TB (hazard ratio [HR]=1.773; P=0.016), PDC (HR=1.706; P=0.028), and DR (HR=2.982; P<0.001) based on the single-slide method were all significantly associated with DSS. DR was the only candidate feature reported to be a significant independent prognostic factor (HR=2.982; P<0.001) with both multi-slide and single-slide methods. The single-slide result was verified in the Scottish cohort, where multivariate Cox regression analysis reported that DR was the only significant independent feature (HR=1.778; P=0.002) associated with DSS. DR was shown to be the most significant of all the analyzed histopathologic features to predict disease-specific death in stage II CRC. We further show that analyzing the features from a single-slide containing the tumor's deepest invasion is an efficient and quicker method of evaluation.

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Year:  2019        PMID: 31206364     DOI: 10.1097/PAS.0000000000001304

Source DB:  PubMed          Journal:  Am J Surg Pathol        ISSN: 0147-5185            Impact factor:   6.394


  11 in total

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4.  A Comparison of Methods for Studying the Tumor Microenvironment's Spatial Heterogeneity in Digital Pathology Specimens.

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8.  The Comparable Microenvironment Shared by Colorectal Adenoma and Carcinoma: An Evidence of Stromal Proteomics.

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9.  Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning.

Authors:  Ines P Nearchou; Hideki Ueno; Yoshiki Kajiwara; Kate Lillard; Satsuki Mochizuki; Kengo Takeuchi; David J Harrison; Peter D Caie
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