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.
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.
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
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
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