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Differentiating adenocarcinoma and squamous cell carcinoma in lung cancer using semi automated segmentation and
R Vijitha1, W M I S Wickramasinghe1, P A S Perera1
1Department of Radiography & Radiotherapy, Faculty of Allied Health Sciences, General Sir John Kotelawala Defence University, Ratmalana, Sri Lanka.
Introduction:
Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are frequently observed forms of non-small cell lung cancer (NSCLC), playing a significant role in global cancer mortality. This research categorizes NSCLC subtypes by analyzing image details using computer-assisted semi-automatic segmentation and radiomic features in model development.
Method:
This study includes 80 patients with 50 AD and 30 SCC which were analyzed using 3D Slicer software and extracted 107 quantitative radiomic features per patient. After eliminating correlated attributes, LASSO binary logistic regression model and 10-fold cross-validation were used for feature selection. The Shapiro-Wilk test assessed radiomic score normality, and the Mann-Whitney U test compared score distributions. Random Forest (RF) and Support Vector Machine (SVM) classification models were implemented for subtype classification.
Results:
Receiver-Operator Characteristic (ROC) curves evaluated the radiomics score, showing a moderate predictive ability with training set area under curve (AUC) of 0.679 (95 % CI, 0.541-0.871) and validation set AUC of 0.560 (95 % CI, 0.342-0.778). Rad-Score distributions were normal for AD and not normal for SCC. RF and SVM classification models, which are based on selected features, resulted RF accuracy (95 % CI) of 0.73 and SVM accuracy (95 % CI) of 0.87, with respective AUC values of 0.54 and 0.87.
Conclusion:
These findings enhance the understanding that the two subtypes of NSCLC can be differentiated.
Implications For Practice:
The study demonstrated radiomic analysis improves diagnostic accuracy and offers a non-invasive alternative. However, the AUCs and ROC curves for the machine learning models must be critically evaluated to ensure clinical acceptability. If robust, these models could reduce the need for biopsies and enhance personalized treatment planning. Further research is needed to validate these findings and integrate radiomics into NSCLC clinical practice.
