Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes

Alvaro Andrés Sandino; Charlems Alvarez-Jimenez; Andres Mosquera-Zamudio; Satish E. Viswanath; Eduardo Romero

doi:10.1117/12.2542360

3 January 2020 Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes

Alvaro Andrés Sandino, Charlems Alvarez-Jimenez, Andres Mosquera-Zamudio, Satish E. Viswanath, Eduardo Romero

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 1133007 (2020) https://doi.org/10.1117/12.2542360
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

Abstract

Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.

Citation Download Citation

Alvaro Andrés Sandino, Charlems Alvarez-Jimenez, Andres Mosquera-Zamudio, Satish E. Viswanath, and Eduardo Romero "Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 1133007 (3 January 2020); https://doi.org/10.1117/12.2542360

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KEYWORDS

Lung cancer

Cancer

Feature extraction

Image segmentation

Machine learning

Statistical analysis

Tissues

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