Literature DB >> 33780631

The Potential of Digital Image Analysis to Determine Tumor Cell Content in Biobanked Formalin-Fixed, Paraffin-Embedded Tissue Samples.

Christine Greene1, Edwina O'Doherty1, Fatima Abdullahi Sidi2, Victoria Bingham2, Natalie C Fisher2, Matthew P Humphries2, Stephanie G Craig2, Louise Harewood2, Stephen McQuaid1, Claire Lewis1, Jacqueline James1,2.   

Abstract

Introduction: Best practices dictate that biobanks ensure accurate determination of tumor content before supplying formalin-fixed, paraffin-embedded (FFPE) tissue samples to researchers for nucleic acid extraction and downstream molecular testing. It is advisable that trained and competent individuals, who understand the requirements of the downstream molecular tests, perform the microscopic morphological examination. However, the special skills, time, and costs associated with these assessments can be prohibitive, especially in large case cohorts requiring extensive pathological review. Determination of tumor content reliably by digital image analysis (DIA) could represent a significant advantage if validated, utilized, and deployed by biobanks. Materials and
Methods: Whole slide digital scanned images of colorectal, lung, and breast cancer specimens were created. The scanned images were imported into the DIA software QuPath and digital annotations were completed by biobank technicians, under the direction of trained histopathology senior scientists. Automated cell detection was conducted and tumor epithelial cells were classified and quantified.
Results: DIA scores were highly concordant with the manual assessment for 376 of 435 samples (86%). A detailed review of discordant cases indicated digital scores had a higher accuracy than the manual estimation.
Conclusion: Automated digital quantification has the potential to replace visual estimations with reduced subjectivity and increased reliability compared with manual tumor estimations. We recommend the use of DIA by biobanks involved in provision of FFPE tissue samples, especially in large research studies requiring high volumes of cases to be analyzed.

Entities:  

Keywords:  image analysis; molecular testing; tumor annotation

Mesh:

Substances:

Year:  2021        PMID: 33780631     DOI: 10.1089/bio.2020.0105

Source DB:  PubMed          Journal:  Biopreserv Biobank        ISSN: 1947-5543            Impact factor:   2.300


  3 in total

1.  Integrin αvβ6 as a Target for Tumor-Specific Imaging of Vulvar Squamous Cell Carcinoma and Adjacent Premalignant Lesions.

Authors:  Bertine W Huisman; Merve Cankat; Tjalling Bosse; Alexander L Vahrmeijer; Robert Rissmann; Jacobus Burggraaf; Cornelis F M Sier; Mariette I E van Poelgeest
Journal:  Cancers (Basel)       Date:  2021-11-29       Impact factor: 6.639

2.  Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.

Authors:  Mustafa Umit Oner; Jianbin Chen; Egor Revkov; Anne James; Seow Ye Heng; Arife Neslihan Kaya; Jacob Josiah Santiago Alvarez; Angela Takano; Xin Min Cheng; Tony Kiat Hon Lim; Daniel Shao Weng Tan; Weiwei Zhai; Anders Jacobsen Skanderup; Wing-Kin Sung; Hwee Kuan Lee
Journal:  Patterns (N Y)       Date:  2021-12-09

3.  Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

Authors:  Bolesław L Osinski; Aïcha BenTaieb; Irvin Ho; Ryan D Jones; Rohan P Joshi; Andrew Westley; Michael Carlson; Caleb Willis; Luke Schleicher; Brett M Mahon; Martin C Stumpe
Journal:  Mod Pathol       Date:  2022-10-05       Impact factor: 8.209

  3 in total

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