Colorectal cancer histopathology image analysis: A comparative study of prognostic values of automatically extracted morphometric nuclear features in multispectral and red-blue-green imagery

• Wenlou Liu ^[3] ; Aiping Qu ^[3] ; Jingping Yuan ^[1] ; Linwei Wang ^[2] ; Jiamei Chen ^[1] ; Xiuli Zhang ^[4] ; Hongmei Wang ^[3] ; Zhengxiang Han ^[3] ; Yan Li ^[5]
  1. [1] Renmin Hospital of Wuhan University

     Renmin Hospital of Wuhan University

     China

     
  2. [2] Zhongnan Hospital of Wuhan University

     Zhongnan Hospital of Wuhan University

     China

     
  3. [3] Affiliated Hospital of Xuzhou Medical University, Xuzhou, South China
  4. [4] Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
  5. [5] Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China

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• Localización: Histology and histopathology: cellular and molecular biology, ISSN-e 1699-5848, ISSN 0213-3911, Vol. 39, Nº. 10, 2024, págs. 1303-1316
• Idioma: inglés
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• Resumen

  • Objectives. Multispectral imaging (MSI) has been utilized to predict the prognosis of colorectal cancer (CRC) patients, however, our understanding of the prognostic value of nuclear morphological parameters of bright-field MSI in CRC is still limited.

    This study was designed to compare the efficiency of MSI and standard red-green-blue (RGB) images in predicting the prognosis of CRC.

    Methods. We compared the efficiency of MS and conventional RGB images on the quantitative assessment of hematoxylin-eosin (HE) stained histopathology images. A pipeline was developed using a pixel-wise support vector machine (SVM) classifier for gland-stroma segmentation, and a marker-controlled watershed algorithm was used for nuclei segmentation.

    The correlation between extracted morphological parameters and the five-year disease-free survival (5- DFS) was analyzed.

    Results. Forty-seven nuclear morphological parameters were extracted in total. Based on KaplanMeier analysis, eight features derived from MS images and seven featured derived from RGB images were significantly associated with 5-DFS, respectively.

    Compared with RGB images, MSI showed higher accuracy, precision, and Dice index in nuclei segmentation. Multivariate analysis indicated that both integrated parameters 1 (factors negatively correlated with CRC prognosis including nuclear number, circularity, eccentricity, major axis length) and 2 (factors positively correlated with CRC prognosis including nuclear average area, area perimeter, total area/total perimeter ratio, average area/perimeter ratio) in MS images were independent prognostic factors of 5-DFS, in contrast with only integrated parameter 1 (P<0.001) in RGB images. More importantly, the quantification of HE-stained MS images displayed higher accuracy in predicting 5-DFS compared with RGB images (76.9% vs 70.9%).

    Conclusions. Quantitative evaluation of HE-stained MS images could yield more information and better predictive performance for CRC prognosis than conventional RGB images, thereby contributing to precision oncology.