Radiomics Feature Selection for Lung Cancer Subtyping and Prognosis Prediction: A Comparative Study of Ant Colony Optimization and Simulated Annealing
Keywords:
Ant Colony Optimization, Lung Cancer detection, Optimization, Feature Selection, PredictionAbstract
Lung cancer subtyping and prognosis prediction play a critical role in the development of individualised treatment strategies, which is a cornerstone of precision medicine. The field of radiomics, which focuses on the quantitative feature extraction from medical pictures, shows great promise as a means to this end. This paper presents a comparative comparison of two effective optimisation algorithms, Ant Colony Optimization (ACO) and Simulated Annealing (SA), for the goal of radiomics feature selection in lung cancer subtyping and prognosis prediction.The remarkable heterogeneity of lung cancer makes accurate subtyping difficult. Utilising a large number of features extracted from medical imaging, such as CT scans, radiomics is able to detect even the most minute of tumour characteristics. However, because to their abundance, overfitting occurs and model generalizability suffers. Feature selection is crucial to solving this problem.Natural-process-inspired ACO and SA are used to find the best radiomic features to use. Both ACO and SA are heuristic algorithms, however SA takes its cues from the metallurgical annealing process, while ACO is based on the foraging behaviour of ants. Both methods seek to reduce the dimensionality of a problem by identifying a subset of features that yields the best predicted performance.In this study, ACO and SA are applied to a sizable dataset containing information about people with lung cancer, allowing for a thorough comparison of the two methods. Accuracy in subtyping and prognosis prediction are two measures used to assess the outcomes. In addition, feature selection's reliability and durability are evaluated. The results of this study provide important insights for researchers and clinicians who want to improve the accuracy of subtyping and prognosis prediction in the era of personalised medicine by using radiomics feature selection for lung cancer.
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J. Alam, S. Alam and A. Hossan, "Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifie," 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2018, pp. 1-4, doi: 10.1109/IC4ME2.2018.8465593.
B. J. Bipin Nair, K. J. Anju and A. Jeevakumar, "Tobacco smoking induced lung cancer prediction by lc-micrornas secondary structure prediction and target comparison," 2017 2nd International Conference for Convergence in Technology (I2CT), Mumbai, India, 2017, pp. 854-857, doi: 10.1109/I2CT.2017.8226250.
L. Zhao et al., "Self-Supervised Learning Guided Transformer for Survival Prediction of Lung Cancer Using Pathological Images," 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp. 1-5, doi: 10.1109/ISBI53787.2023.10230825.
V. N. Jenipher and S. Radhika, "SVM kernel Methods with Data Normalization for Lung Cancer Survivability Prediction Application," 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 2021, pp. 1294-1299, doi: 10.1109/ICICV50876.2021.9388543.
D. Rawat, "Validating and Strengthen the Prediction Performance Using Machine Learning Models and Operational Research for Lung Cancer," 2022 IEEE International Conference on Data Science and Information System (ICDSIS), Hassan, India, 2022, pp. 1-5, doi: 10.1109/ICDSIS55133.2022.9915898.
J. Moranguinho, T. Pereira, B. Ramos, J. Morgado, J. L. Costa and H. P. Oliveira, "Attention Based Deep Multiple Instance Learning Approach for Lung Cancer Prediction using Histopathological Images," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, 2021, pp. 2852-2855, doi: 10.1109/EMBC46164.2021.9631000.
G. Sruthi, C. L. Ram, M. K. Sai, B. P. Singh, N. Majhotra and N. Sharma, "Cancer Prediction using Machine Learning," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), Gautam Buddha Nagar, India, 2022, pp. 217-221, doi: 10.1109/ICIPTM54933.2022.9754059.
Y. Balagurunathan et al., "Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge," in IEEE Transactions on Medical Imaging, vol. 40, no. 12, pp. 3748-3761, Dec. 2021, doi: 10.1109/TMI.2021.3097665.
B. H. Sai, K. Inala, A. S. Saketh and A. R. Maremolla, "Lung Cancer Stage Prediction Using Multi-Layer Perceptron and Deep Learning Classifier," 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE), Shillong, India, 2023, pp. 1-5, doi: 10.1109/ICEPE57949.2023.10201556.
P. Nanda and N. Duraipandian, "Prediction of Survival Rate from Non-Small Cell Lung Cancer using Improved Random Forest," 2020 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020, pp. 93-97, doi: 10.1109/ICICT48043.2020.9112558.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08615-w
Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y
A Jochems, Timo M. Deist, Issam El Naqa, Marc Kessler, M Chuck, R Jackson et al., "Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries", April 2017, [online] Available: www.redjournal.org.
B. Azadeh, G. Marjan, S. Reza, Leila Shahmoradi and E Hamide, "Improving the Prediction of Survival in Cancer Patients by Using Machine Learning Techniques: Experience of Gene Expression Data: A Narrative Review", Iran J Public Health, vol. 46, no. 2, pp. 165-172, Feb 2017.
K. Timor and G. Fergus, "Lung cancer prediction using machine learning and advanced imaging techniques", Transactional lung cancer research, vol. 07, 2018.
T Asha, S. Natarajan and K.N.B. Murthy, "Effective Classification Algorithms to Predict the Accuracy of Tuberculosis-A Machine Learning Approach", International Journal of Computer Science and Information Security, vol. 9, no. 7, July 2011.
R. Daniele, W. Charence, FaniDeligianni, B. Melissa, Javier Andreu-Perez and Benny Lo, "Deep Learning for Health Informatics", ieee journal of biomedical and health informatics, vol. 21, no. 1, january 2017.
M. Jaime, G. Bram van, M. Pragnya, H. Rick, H. M. Philipsen, Klaus Reither, Marianne Breuninger et al., "A Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays", IEEE transactions on medical imaging, vol. 34, no. 1, january 2015.
Mabrook Al-Rakhami, Abdu Gumaei, A. Ahmed, A. Atif and Mohammad M. Hassan, "An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings", IEEE Access, April 2019.
H. Samuel, N. K. John, B Yoganand, G. Yuhua, K Virendra, B. Satrajit et al., "Predicting Outcomes of Nonsmall Cell Lung Cancer Using CT Image Features", IEEE Access, vol. 2, Nov 2014.
K. Nobuteru, S. Jun-ichi, S. Hirofumi, S. Katsuyuki, K. Hidemasa, O. Tatsuya et al., "Dosimetric comparison of carbon ion and X-ray radiotherapy for Stage IIIA non–small cell lung cancer", Journal of Radiation Research, vol. 57, no. 5, Sept. 2016.
Y. Xu, A. Hosny, R. Zeleznik, C. Parmar, T. Coroller, I. Franco, et al., "Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging", Clinical cancer research : an official journal of the American Association for Cancer Research, vol. 25, no. 11, pp. 3266-3275, 2019, [online] Available: https://doi.org/10.1158/1078-0432.CCR-18-2495.
A. Sherstinsky, "Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network", Physica D: Nonlinear Phenomena, vol. 404, pp. 132306, 2020.
A. Ghazipour, B. Veasey, A. Seow and A. A. Amini, "Joint Learning for Deformable Registration and Malignancy Classification of Lung Nodules", 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1807-1811, 2021.
B. P. Veasey, J. Broadhead, M. Dahle, A. Seow and A. A. Amini, "Lung Nodule Malignancy Prediction From Longitudinal CT Scans With Siamese Convolutional Attention Networks", IEEE Open Journal of Engineering in Medicine and Biology, vol. 1, pp. 257-264, 2020.
J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database", 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.
X. Mei, Z. Liu, P. M. Robson, B. Marinelli, M. Huang, A. Doshi, et al., "Radimagenet: an open radiologic deep learning research dataset for effective transfer learning", Radiology: Artificial Intelligence, vol. 4, no. 5, pp. e210315, 2022.
L. Perez and J. Wang, "The Effectiveness of Data Augmentation in Image Classification using Deep Learning", Dec. 2017.
Christopher Davies, Matthew Martine, Catalina Fernández, Ana Flores, Anders Pedersen. Improving Automated Essay Scoring with Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/173
Kumar, C. ., & Muthumanickam, T. . (2023). Analysis of Unmanned Four-Wheeled Bot with AI Evaluation Feedback Linearization Method. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 138–142. https://doi.org/10.17762/ijritcc.v11i2.6138
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