Firmbach Daniel, Benz Michaela, Kuritcyn Petr, Bruns Volker, Lang-Schwarz Corinna, Stuebs Frederik A, Merkel Susanne, Leikauf Leah-Sophie, Braunschweig Anna-Lea, Oldenburger Angelika, Gloßner Laura, Abele Niklas, Eck Christine, Matek Christian, Hartmann Arndt, Geppert Carol I
Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany.
Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany.
Cancers (Basel). 2023 May 9;15(10):2675. doi: 10.3390/cancers15102675.
The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.
肿瘤-基质比（TSR）已多次被证明是不同癌症类型生存预测的预后因素。然而，客观可靠地确定肿瘤-基质比仍然具有挑战性。我们提出了一种易于适应的深度学习模型，用于将结肠癌患者苏木精和伊红（H&E）染色的全切片图像（WSIs）中的肿瘤区域准确分割为五个不同类别（肿瘤、基质、坏死、黏液和背景）。在存在坏死或黏液区域的情况下也可以确定肿瘤-基质比。我们采用了一种少样本模型，最终目标是使我们的方法易于适应相关分割任务或其他原发性疾病，并将结果与一种成熟的先进方法（U-Net）进行比较。两种模型分别以86.5%和86.7%的总体准确率取得了相似的结果，这表明适应性不会导致准确率显著下降。此外，我们全面比较了人类观察者的TSR估计值，并详细检查了差异和评分者间的可靠性。在对TSR估计进行首次调查的基础上增加了一项关于分割质量的第二次调查，我们发现人类观察者的TSR估计并不像以前认为的那样是可靠的基本事实。
