Automated grading of non-small cell lung cancer by fuzzy rough nearest neighbour method

Abstract

Lung cancer is one of the most lethal diseases across the world. Most lung cancers belong to the category of non-small cell lung cancer (NSCLC). Many studies have so far been carried out to avoid the hazards and bias of manual classification of NSCLC tumors. A few of such studies were intended towards automated nodal staging using the standard machine learning algorithms. Many others tried to classify tumors as either benign or malignant. None of these studies considered the pathological grading of NSCLC. Automated grading may perfectly depict the dissimilarity between normal tissue and cancer affected tissue. Such automation may save patients from undergoing a painful biopsy and may also help radiologists or oncologists in grading the tumor or lesion correctly. The present study aims at the automated grading of NSCLC tumors using the fuzzy rough nearest neighbour (FRNN) method. The dataset was extracted from The Cancer Imaging Archive and it comprised PET/CT images of NSCLC tumors of 211 patients. Accelerated segment test (FAST) and histogram oriented gradients methods were used to detect and extract features from the segmented images. Gray level co-occurrence matrix (GLCM) features were also considered in the study. The features along with the clinical grading information were fed into four machine learning algorithms: FRNN, logistic regression, multi-layer perceptron, and support vector machine. The results were thoroughly compared in the light of various evaluation-metrics. The confusion matrix was found balanced, and the outcome was found more cost-effective for FRNN. Results were also compared with various other leading studies done earlier in this field. The proposed FRNN model performed satisfactorily during the experiment. Further exploration of FRNN may be very helpful for radiologists and oncologists in planning the treatment for NSCLC. More varieties of cancers may be considered while conducting similar studies.