Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT
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 l...
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83786-6 |
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| author | Minmini Selvam Abjasree Sadanandan Anupama Chandrasekharan Sidharth Ramesh Arunan Murali Ganapathy Krishnamurthi |
| author_facet | Minmini Selvam Abjasree Sadanandan Anupama Chandrasekharan Sidharth Ramesh Arunan Murali Ganapathy Krishnamurthi |
| author_sort | Minmini Selvam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-eeb4f8dead0743c9acf330ea61723b10 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eeb4f8dead0743c9acf330ea61723b102025-01-05T12:23:42ZengNature PortfolioScientific Reports2045-23222024-12-0114111310.1038/s41598-024-83786-6Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CTMinmini Selvam0Abjasree Sadanandan1Anupama Chandrasekharan2Sidharth Ramesh3Arunan Murali4Ganapathy Krishnamurthi5Department of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and ResearchDepartment of Engineering Design, Indian Institute of Technology MadrasDepartment of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and ResearchDepartment of Engineering Design, Indian Institute of Technology MadrasDepartment of Engineering Design, Indian Institute of Technology MadrasDepartment of Radiology and Imaging Sciences, Sri Ramachandra Institute of Higher Education and ResearchAbstract 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.https://doi.org/10.1038/s41598-024-83786-6Lung noduleChest CTRadiomicsAdenocarcinomaSquamous cell carcinomaLung carcinoma |
| spellingShingle | Minmini Selvam Abjasree Sadanandan Anupama Chandrasekharan Sidharth Ramesh Arunan Murali Ganapathy Krishnamurthi Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT Scientific Reports Lung nodule Chest CT Radiomics Adenocarcinoma Squamous cell carcinoma Lung carcinoma |
| title | Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT |
| title_full | Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT |
| title_fullStr | Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT |
| title_full_unstemmed | Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT |
| title_short | Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT |
| title_sort | radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non small cell lung cancer beyond nodule morphology in chest ct |
| topic | Lung nodule Chest CT Radiomics Adenocarcinoma Squamous cell carcinoma Lung carcinoma |
| url | https://doi.org/10.1038/s41598-024-83786-6 |
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