Literature DB >> 32076815

Tumor proportion in colon cancer: results from a semiautomatic image analysis approach.

Benedikt Martin1, Bettina Monika Banner2, Eva-Maria Schäfer2, Patrick Mayr2,3, Matthias Anthuber4, Gerhard Schenkirsch5, Bruno Märkl2.   

Abstract

The tumor stroma ratio (TSR) is a promising prognostic biomarker in colon cancer, which could provide additional risk stratification for therapy adaption. The objective of our study was the investigation of the prognostic significance of TSR at different tumor sites in a simple semiautomatic approach with the open-source program ImageJ. We investigated 206 pT3 and pT4 adenocarcinomas of no special type. According to our established thresholds, 31 tumors (15%) were classified as low tumor proportion (TP) (≤ 15% TP), 42 tumors (20%) were classified as high TP (≥ 54% TP), and 133 tumors (65%) were classified as medium TP. High and low TP were associated with an adverse overall survival in comparison to medium TP (p = 0.001 and p = 0.03). Furthermore, the TP was an independent risk factor of occurrence of distant metastasis next to T status, microsatellite status, and tumor budding. The 5-year survival rate was 49% in patients with high TP, 48% in patients with low TP, and 68% in patients with medium TP (p = 0.042, n = 160). Patients with a high TP had less often tumor budding (p = 0.012), lymphovascular invasion (p = 0.049), and less harvested lymph nodes (p = 0.042) in comparison to low TP tumors. The results provide first evidence that a high tumor proportion/low stroma proportion is also associated with an adverse prognosis and that this subgroup might be difficult to identify with other classical histopathologic characteristics that are linked to an adverse prognosis.

Entities:  

Keywords:  Biomarker; Colon cancer; ImageJ; Tumor stroma ratio

Mesh:

Year:  2020        PMID: 32076815      PMCID: PMC7985049          DOI: 10.1007/s00428-020-02764-1

Source DB:  PubMed          Journal:  Virchows Arch        ISSN: 0945-6317            Impact factor:   4.064


  27 in total

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Authors:  Benedikt Martin; Eva Schäfer; Elzbieta Jakubowicz; Patrick Mayr; Regina Ihringer; Matthias Anthuber; Gerhard Schenkirsch; Tina Schaller; Bruno Märkl
Journal:  Virchows Arch       Date:  2018-04-06       Impact factor: 4.064

2.  The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer.

Authors:  J H Park; C H Richards; D C McMillan; P G Horgan; C S D Roxburgh
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Journal:  Histopathology       Date:  2017-04-03       Impact factor: 5.087

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Journal:  Nat Cell Biol       Date:  2010-04-25       Impact factor: 28.824

5.  The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients.

Authors:  N P West; M Dattani; P McShane; G Hutchins; J Grabsch; W Mueller; D Treanor; P Quirke; H Grabsch
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Journal:  Eur J Cancer       Date:  2010-01-13       Impact factor: 9.162

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Journal:  Nat Biotechnol       Date:  2011-11-13       Impact factor: 54.908

8.  The prognostic value of tumour stroma ratio and tumour budding in stage II colon cancer. A nationwide population-based study.

Authors:  Ann Christina Eriksen; Flemming B Sørensen; Jan Lindebjerg; Henrik Hager; René dePont Christensen; Sanne Kjær-Frifeldt; Torben F Hansen
Journal:  Int J Colorectal Dis       Date:  2018-05-21       Impact factor: 2.571

9.  The carcinoma-stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage.

Authors:  Wilma E Mesker; Jan M C Junggeburt; Karoly Szuhai; Pieter de Heer; Hans Morreau; Hans J Tanke; Rob A E M Tollenaar
Journal:  Cell Oncol       Date:  2007       Impact factor: 6.730

10.  Scoring the tumor-stroma ratio in colon cancer: procedure and recommendations.

Authors:  G W van Pelt; S Kjær-Frifeldt; J H J M van Krieken; R Al Dieri; H Morreau; R A E M Tollenaar; F B Sørensen; W E Mesker
Journal:  Virchows Arch       Date:  2018-07-20       Impact factor: 4.064

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2.  Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images.

Authors:  Stefan Schiele; Tim Tobias Arndt; Benedikt Martin; Silvia Miller; Svenja Bauer; Bettina Monika Banner; Eva-Maria Brendel; Gerhard Schenkirsch; Matthias Anthuber; Ralf Huss; Bruno Märkl; Gernot Müller
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