Detecting micro-metastases in the sentinel lymph node by characterizing micro-environments

Leidy T. Molano; Ricardo A. Moncayo; Eduardo Romero M.D.

doi:10.1117/12.2606277

10 December 2021 Detecting micro-metastases in the sentinel lymph node by characterizing micro-environments

Leidy T. Molano, Ricardo A. Moncayo, Eduardo Romero M.D.

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208810 (2021) https://doi.org/10.1117/12.2606277
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

The sentinel lymph node is a predictor of breast cancer aggressiveness.¹ Patients with micro-metastasis (MM) are usually considered negative, yet their the hazard ratio has been reported to be 2.4 and 1.203.81 with a 95% confidence interval.^2–4 This work proposes an automatic detection of micro-metastasis by quantifying local cellular changes. The proposed strategy characterizes nuclei morphometry, color and texture to establish differences between MM and normal tissue. The color model is obtained from the plane [(r − b), g] while texture corresponds to the Haralick’s features from five different orders of the co-occurrence matrix.⁵ This description is complemented by the cellular area obtained from a conventional watershed segmentation. An AdaBoost model, trained with 300 patches of 350 × 350 pixels (56000 μm² ) randomly selected from 18 cases, was tested in a set of five different cases with approximately ten patches containing micro-metastasis. This approach obtained a best classification accuracy of 0.86, sensitivity of 0.89, specificity of 0, 83, and F-score of 0.86, while the baseline, a ResNet 50 model, obtained 0.74 of accuracy, 0.86 of sensitivity, 0, 63 of specificity, and F-score of 0.77 for exactly the same task.

Citation Download Citation

Leidy T. Molano, Ricardo A. Moncayo, and Eduardo Romero M.D. "Detecting micro-metastases in the sentinel lymph node by characterizing micro-environments", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208810 (10 December 2021); https://doi.org/10.1117/12.2606277

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KEYWORDS

RGB color model

Lymphatic system

Tumors

Image segmentation

Data modeling

Breast cancer

Cancer

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