Literature DB >> 32466698

Imaging Mass Spectrometry Is an Accurate Tool in Differentiating Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Proof-of-concept Study.

Hsiang-Chih Lu1, Nathan Heath Patterson2, Audra M Judd2, Michelle L Reyzer2, Jennifer K Sehn1.   

Abstract

Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) are relatively common tumors that can have significant risk for mortality. Treatment and prognostication in renal cell carcinoma (RCC) are dependent upon correct histologic typing. ccRCC and chRCC are generally straightforward to diagnose based on histomorphology alone. However, high-grade ccRCC and chRCC can sometimes resemble each other morphologically, particularly in small biopsies. Multiple immunostains and/or colloidal iron stain are sometimes required to differentiate the two. Imaging mass spectrometry (IMS) allows simultaneous spatial mapping of thousands of biomarkers, using formalin-fixed paraffin-embedded tissue sections. In this study, we evaluate the ability of IMS to differentiate between World Health Organization/International Society for Urological Pathology grade 3 ccRCC and chRCC. IMS spectra from a training set of 14 ccRCC and 13 chRCC were evaluated via support vector machine algorithm with a linear kernel for machine learning, building a classification model. The classification model was applied to a separate validation set of 6 ccRCC and 6 chRCC, with 19 to 20, 150-μm diameter tumor foci in each case sampled by IMS. Most evaluated tumor foci were classified correctly as ccRCC versus chRCC (99% accuracy, kappa=0.98), demonstrating that IMS is an accurate tool in differentiating high-grade ccRCC and chRCC.

Entities:  

Keywords:  chromophobe renal cell carcinoma; clear cell renal cell carcinoma; imaging mass spectrometry; kidney neoplasms; renal cell cancer

Mesh:

Year:  2020        PMID: 32466698     DOI: 10.1369/0022155420931417

Source DB:  PubMed          Journal:  J Histochem Cytochem        ISSN: 0022-1554            Impact factor:   2.479


  2 in total

1.  The synergism of spatial metabolomics and morphometry improves machine learning-based renal tumour subtype classification.

Authors:  Verena M Prade; Na Sun; Jian Shen; Annette Feuchtinger; Thomas Kunzke; Achim Buck; Peter Schraml; Holger Moch; Kristina Schwamborn; Michael Autenrieth; Jürgen E Gschwend; Franziska Erlmeier; Arndt Hartmann; Axel Walch
Journal:  Clin Transl Med       Date:  2022-02

Review 2.  Application of Mass Spectrometry Imaging in Uro-Oncology: Discovering Potential Biomarkers.

Authors:  Péter Czétány; Stefánia Gitta; András Balló; Alexandra Sulc; Gábor Máté; Árpád Szántó; László Márk
Journal:  Life (Basel)       Date:  2022-03-03
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.