Decoding Lung Cancer Radiogenomics: A Custom Clustering/Classification Methodology to Simultaneously Identify Important Imaging Features and Relevant Genes

• Published in: Applied sciences Vol. 15; no. 7; p. 4053
• Main Authors: Provenzano, Destie, Lichtenberger, John P., Goyal, Sharad, Rao, Yuan James
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2025
• Subjects: Algorithms; Biomarkers; Cancer; Classification; Clustering; CT imaging; Data collection; deep clustering; Deep learning; Gene mutations; Genes; Genetic markers; Genomes; Lung cancer; Machine learning; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical prognosis; Medical research; Metastasis; Methods; Mutation; Oncology, Experimental; Patients; Pilot projects; radiogenomics; Radiomics; ResNet; Tomography; Tumors; Variables
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15074053

Background: This study evaluated a custom algorithm that sought to perform a radiogenomic analysis on lung cancer genetic and imaging data, specifically by using machine learning to see whether a custom clustering/classification method could simultaneously identify features from imaging data that correspond to genetic markers. Methods: CT imaging data and genetic mutation data for 281 subjects with NSCLC were collected from the CPTAC-LUAD and TCGA-LUSC databases on TCIA. The algorithm was run as follows: (1) genetic clusters were initialized using random clusters, binary matrix factorization, or k-means; (2) image classification was run on CT data for these genetic clusters; (3) misclassified subjects were re-classified based on the image classification algorithm; and (4) the algorithm was run until an accuracy of 90% or no improvement after 10 runs. Input genetic mutations were evaluated for potential medical treatments and severity to provide clinical relevance. Results: The image classification algorithm was able to achieve a >90% accuracy after nine algorithm runs and grouped subjects from a starting five clusters to four final clusters, where final image classification accuracy was better than every initial clustered accuracy. These clusters were stable across all three test runs. A total of thirty-eight genes from the top hundred across each subject were identified with specific severity or treatment data; twelve of these genes are listed. Conclusion: This small pilot study presented a potential way to identify genetic patterns from image data and presented a methodology that could group images with no labels or only partial labels for future problems.