Scholarlink

<p>Digital pathology enables objective evaluation of prognostic markers in prostate cancer through integrated morphological and molecular analysis. In this study, 72 radical prostatectomy samples were analyzed using tissue microarrays and whole-slide imaging, with tumor regions annotated by pathologists and quantified in QuPath for PTEN, Ki-67, ATM, CD8, and key histological features. PTEN quantification showed high variability and limited predictive value (AUC <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(=\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>=</mo> </math></EquationSource> </InlineEquation> 0.61). Cribriform morphology was significantly associated with advanced pathological stage, non-organ-confined tumors, and a profile of aggressiveness markers, whereas CD8<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(^{+}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mo>+</mo> </mmultiscripts> </math></EquationSource> </InlineEquation> density did not correlate, supporting an “immune-cold” phenotype. Quantitative cribriform area did not add prognostic value. A logistic regression model combining Gleason pattern 4 percentage and ATM expression predicted cribriform morphology with good accuracy (AUC <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(=\)</EquationSource> <EquationSource Format="MATHML"><math> <mo>=</mo> </math></EquationSource> </InlineEquation> 0.79). These results highlight the potential of digital pathology to refine risk stratification and identify high-risk morpho-molecular features in prostate cancer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Quantitative digital pathology reveals morphological and molecular correlates of tumor aggressiveness in prostate cancer

  • 期刊论文

  • 发表日期：

    2025年10月6日

查看全文
  • Francisco Araujo,
  • Guilherme Velozo,
  • Juliana Cordeiro,
  • Aline Ramos,
  • Samuel Ferreira,
  • Laura Cardoso,
  • Vania Melo,
  • Fábio Távora

摘要

Digital pathology enables objective evaluation of prognostic markers in prostate cancer through integrated morphological and molecular analysis. In this study, 72 radical prostatectomy samples were analyzed using tissue microarrays and whole-slide imaging, with tumor regions annotated by pathologists and quantified in QuPath for PTEN, Ki-67, ATM, CD8, and key histological features. PTEN quantification showed high variability and limited predictive value (AUC \(=\) = 0.61). Cribriform morphology was significantly associated with advanced pathological stage, non-organ-confined tumors, and a profile of aggressiveness markers, whereas CD8 \(^{+}\) + density did not correlate, supporting an “immune-cold” phenotype. Quantitative cribriform area did not add prognostic value. A logistic regression model combining Gleason pattern 4 percentage and ATM expression predicted cribriform morphology with good accuracy (AUC \(=\) = 0.79). These results highlight the potential of digital pathology to refine risk stratification and identify high-risk morpho-molecular features in prostate cancer.