This thesis describes the steps necessary for the addition of the tumor-stroma ratio (TSR) into clinical practice as high-risk factor besides the TNM classification. The route from laboratory... Show moreThis thesis describes the steps necessary for the addition of the tumor-stroma ratio (TSR) into clinical practice as high-risk factor besides the TNM classification. The route from laboratory biomarker development to clinical implementation is followed. During this process, the relationship of the TSR to other available biomarkers for prognostic information for breast and colon cancer patients is investigated. Additionally, the prognostic value of the TSR in lung cancer is studied. Show less
Purpose 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... Show morePurpose 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. Method 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. Conclusion 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. Show less
