Department of Surgery, Leiden University Medical Centre, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
2Nd Department of Pathology, Semmelweis University, Budapest, Hungary.
Breast Cancer Res Treat. 2022 Jun;193(3):545-553. doi: 10.1007/s10549-022-06587-3. Epub 2022 Apr 16.
The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes.
This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described.
Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.
利用大型回顾性队列,肿瘤-基质比(TSR)已反复证明与乳腺癌患者的预后相关。然而,验证 TSR 的研究往往在方法学上存在差异,从而阻碍了比较和统一的结果。
本文详细描述了一种使用苏木精和伊红(H&E)染色的核心活检和切除组织的简单、统一的 TSR 评分方法,特别针对乳腺癌。报告了在评分过程中可能遇到的组织学挑战,包括克服这些挑战的建议。此外,还描述了淋巴结 TSR 估计、数字图像评分以及使用人工智能自动评估 TSR 的过程。
肿瘤活检和切除组织的数字化评分为确定患者的预后和对治疗的反应提供了有趣的未来前景。TSR 方法相对简单、快速且廉价,为其在常规诊断中的应用提供了巨大潜力,但这需要高质量的验证研究。