Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images

AbstractThis paper describes a methodology that extracts morphological features from histological breast cancer images stained for Hematoxilyn and Eosin (H&E). Cellularity was estimated and the correlation between features and the residual tumour size cellularity after a Neo-Adjuvant treatment (NAT) was examined. Images from whole slide imaging (WSI) were processed automatically with traditional computer vision methods to extract twenty two morphological parameters from the nuclei, epithelial region and the global image. The methodology was applied to a set of images from breast cancer under NAT. The data came from the BreastPathQ Cancer Cellularity Challenge 2019, and consisted of 2579 patches of 255×255 pixels of H&E histopatological samples from NAT treatment patients. The methodology automatically implements colour separation, segmentation and morphological analysis using traditional algorithms (K-means grouping, watershed segmentation, Otsu’s binarisation). Linear regression methods were applied to determine strongest correlation between the parameters and the cancer cellularity. The morphological parameters showed correlation with the residual tumour cancer cellularity. The strongest correlations corresponded to the stroma concentration value (r = −0.9786) and value from HSV image colour space (r = −0.9728), both from a global image parameters.

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Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images

Journal of Imaging ◽

10.3390/jimaging6100101 ◽

2020 ◽

Vol 6 (10) ◽

pp. 101

Author(s):

Mauricio Alberto Ortega-Ruiz ◽

Cefa Karabağ ◽

Victor García Garduño ◽

Constantino Carlos Reyes-Aldasoro

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Morphological Features ◽

Training Data ◽

Morphological Operations ◽

Morphological Parameters ◽

Learning Approaches ◽

Residual Cancer Burden ◽

Histological Images ◽

Treated Breast

This paper describes a methodology that extracts key morphological features from histological breast cancer images in order to automatically assess Tumour Cellularity (TC) in Neo-Adjuvant treatment (NAT) patients. The response to NAT gives information on therapy efficacy and it is measured by the residual cancer burden index, which is composed of two metrics: TC and the assessment of lymph nodes. The data consist of whole slide images (WSIs) of breast tissue stained with Hematoxylin and Eosin (H&E) released in the 2019 SPIE Breast Challenge. The methodology proposed is based on traditional computer vision methods (K-means, watershed segmentation, Otsu’s binarisation, and morphological operations), implementing colour separation, segmentation, and feature extraction. Correlation between morphological features and the residual TC after a NAT treatment was examined. Linear regression and statistical methods were used and twenty-two key morphological parameters from the nuclei, epithelial region, and the full image were extracted. Subsequently, an automated TC assessment that was based on Machine Learning (ML) algorithms was implemented and trained with only selected key parameters. The methodology was validated with the score assigned by two pathologists through the intra-class correlation coefficient (ICC). The selection of key morphological parameters improved the results reported over other ML methodologies and it was very close to deep learning methodologies. These results are encouraging, as a traditionally-trained ML algorithm can be useful when limited training data are available preventing the use of deep learning approaches.

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Evaluation of Residual Cellularity and Proliferation on Preoperatively Treated Breast Cancer: A Comparison between Image Analysis and Light Microscopy Analysis

Analytical Cellular Pathology ◽

10.1155/1998/630295 ◽

1998 ◽

Vol 16 (2) ◽

pp. 83-93 ◽

Cited By ~ 3

Author(s):

Valentina Corletto ◽

Paolo Verderio ◽

Roberto Giardini ◽

Sonia Cipriani ◽

Silvana Di Palma ◽

...

Keyword(s):

Breast Cancer ◽

Image Analysis ◽

Light Microscopy ◽

Proliferation Index ◽

Residual Tumour ◽

Intraobserver Agreement ◽

Microscopy Analysis ◽

Light Microscopy Analysis ◽

Treated Breast ◽

Treated Breast Cancer

Histopathology has been suggested as a reliable method for tumour reduction evaluation of preoperatively treated breast cancer. Immunocytochemistry can be used to enhance the visibility of residual tumour cellularity and in the evaluation of its proliferative activity. We compared Image Analysis (IA) with Light Microscopy Analysis (LMA) on sections of breast carcinomas treated with preoperative chemo‐ or chemo/radiotherapy in the evaluation of the Neoplastic Cell Density (NCD) (69 cases) and the Proliferation Index (PI) (35 cases). NCD was expressed as the immunoreactive area to cytokeratin over the total original neoplastic area and PI was expressed as the number of immunostained tumoural nuclei with MIB1 MoAb over the total of tumoural nuclei. The intraobserver agreement and that between IA and LMA for both indices were estimated by the common (Kw) and the jackknife weighted kappa statistic (K˜w). The extent of agreement of each considered category was also assessed by means of the category‐specific kappa statistics (Kcs). The intraobserver agreement within LMA for NCD and PI and that between IA and LMA for PI were both satisfactory. Upon evaluation of the NCD, the agreement between IA and LMA showed unsatisfactory results, especially when the ratio between the residual tumour cells and the background was critical.

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