Abstract
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing primary there is from SCC presenting as lung nodules on Computed Tomography (CT) scans. We studied individuals with histopathologically proven adenocarcinoma or SCC type NSCLC tumours, detected as lung nodules on Chest CT. The workflow comprised manual nodule segmentation, regions of interest creation, preprocessing data, feature extraction, and nodule classification using machine learning algorithms. The dataset comprised 46 adenocarcinoma and 28 SCC cases. For feature extraction, 101 radiomic features were extracted from the tumour regions using the 'pyradiomics' module in Python. After extensive experimentation with various feature importance techniques, the top 10 most significant radiomic features for differentiating between adenocarcinoma and squamous cell carcinoma (SCC) were identified. The Synthetic Minority Over-Sampling Technique was used to achieve a balanced distribution. Lung nodules were classified using 13 machine-learning algorithms, including Linear Discriminant Analysis, Random Forest, AdaBoost, and eXtreme Gradient Boosting. The Multilayer Perceptron (MLP) Classifier with Rectified Linear Unit (ReLu) activation was the most accurate (83% accuracy) with 83% precision and 86% sensitivity in distinguishing SCC from adenocarcinoma. It achieved a balanced F1 score of 83%, indicating well-rounded performance in both precision and sensitivity. The average Area Under the Curve score was 88%, representing good discrimination between the two classes of lung nodules. Radiomics is a powerful non-invasive tool that could potentially add to visual information obtained on CT. The MLP Classifier with ReLu activation showed good accuracy in distinguishing primary lung adenocarcinoma from SCC nodules. However, widespread multicentre trials are required to realize the full potential of radiomics in lung nodules.