The Efficacy of Machine Learning Models in Lung Cancer Risk Prediction with Explainability

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2024 Jun 13

PMID 38870229

Authors

Refat Khan Pathan

Israt Jahan Shorna

Md Sayem Hossain

Mayeen Uddin Khandaker

Huda I Almohammed

Zuhal Y Hamd

Affiliations

Soon will be listed here.

Abstract

Among many types of cancers, to date, lung cancer remains one of the deadliest cancers around the world. Many researchers, scientists, doctors, and people from other fields continuously contribute to this subject regarding early prediction and diagnosis. One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple machine learning models on a numerical dataset of lung cancer-relevant parameters and compares performance and accuracy. After comparison, each model has been explained using different methods. The main contribution of this research is to give logical explanations of why the model reached a particular decision to achieve trust. This research has also been compared with a previous study that worked with a similar dataset and took expert opinions regarding their proposed model. We also showed that our research achieved better results than their proposed model and specialist opinion using hyperparameter tuning, having an improved accuracy of almost 100% in all four models.

Citing Articles

Correction: The efficacy of machine learning models in lung cancer risk prediction with explainability.

PLoS One. 2024; 19(9):e0310604.

PMID: 39264905 PMC: 11392339. DOI: 10.1371/journal.pone.0310604.

Machine Learning Constructed Based on Patient Plaque and Clinical Features for Predicting Stent Malapposition: A Retrospective Study.

Xia Q, Deng C, Yang S, Gu N, Shen Y, Shi B Clin Cardiol. 2024; 47(8):e24332.

PMID: 39119892 PMC: 11310765. DOI: 10.1002/clc.24332.

References

van der Aalst C, Ten Haaf K, de Koning H . Lung cancer screening: latest developments and unanswered questions. Lancet Respir Med. 2016; 4(9):749-761. DOI: 10.1016/S2213-2600(16)30200-4. View

Ardila D, Kiraly A, Bharadwaj S, Choi B, Reicher J, Peng L . End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019; 25(6):954-961. DOI: 10.1038/s41591-019-0447-x. View

Huo T, Xie Y, Fang Y, Wang Z, Liu P, Duan Y . Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography. Front Oncol. 2023; 13:1125637. PMC: 9946454. DOI: 10.3389/fonc.2023.1125637. View

Lu M, Raghu V, Mayrhofer T, Aerts H, Hoffmann U . Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model. Ann Intern Med. 2020; 173(9):704-713. PMC: 9200444. DOI: 10.7326/M20-1868. View

Pathan R, Alam F, Yasmin S, Hamd Z, Aljuaid H, Khandaker M . Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling. Healthcare (Basel). 2022; 10(12). PMC: 9777990. DOI: 10.3390/healthcare10122367. View

McKinney S, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H . International evaluation of an AI system for breast cancer screening. Nature. 2020; 577(7788):89-94. DOI: 10.1038/s41586-019-1799-6. View

Wani N, Kumar R, Bedi J . DeepXplainer: An interpretable deep learning based approach for lung cancer detection using explainable artificial intelligence. Comput Methods Programs Biomed. 2023; 243:107879. DOI: 10.1016/j.cmpb.2023.107879. View

Huang S, Yang J, Fong S, Zhao Q . Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett. 2019; 471:61-71. DOI: 10.1016/j.canlet.2019.12.007. View

Rajadurai P, How S, Liam C, Sachithanandan A, Soon S, Tho L . Lung Cancer in Malaysia. J Thorac Oncol. 2020; 15(3):317-323. DOI: 10.1016/j.jtho.2019.10.021. View

10.

Thanoon M, Zulkifley M, Mohd Zainuri M, Abdani S . A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images. Diagnostics (Basel). 2023; 13(16). PMC: 10453592. DOI: 10.3390/diagnostics13162617. View

11.

Xie Y, Meng W, Li R, Wang Y, Qian X, Chan C . Early lung cancer diagnostic biomarker discovery by machine learning methods. Transl Oncol. 2020; 14(1):100907. PMC: 7683339. DOI: 10.1016/j.tranon.2020.100907. View

12.

Bi W, Hosny A, Schabath M, Giger M, Birkbak N, Mehrtash A . Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019; 69(2):127-157. PMC: 6403009. DOI: 10.3322/caac.21552. View

13.

Cassidy A, Myles J, van Tongeren M, Page R, Liloglou T, Duffy S . The LLP risk model: an individual risk prediction model for lung cancer. Br J Cancer. 2007; 98(2):270-6. PMC: 2361453. DOI: 10.1038/sj.bjc.6604158. View

14.

Ahmad A, Mayya A . A new tool to predict lung cancer based on risk factors. Heliyon. 2020; 6(2):e03402. PMC: 7044659. DOI: 10.1016/j.heliyon.2020.e03402. View

15.

Zhou J, Hu B, Feng W, Zhang Z, Fu X, Shao H . An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digit Med. 2023; 6(1):119. PMC: 10322969. DOI: 10.1038/s41746-023-00866-z. View

16.

Yount S, Beaumont J, Rosenbloom S, Cella D, Patel J, Hensing T . A brief symptom index for advanced lung cancer. Clin Lung Cancer. 2011; 13(1):14-23. PMC: 4486486. DOI: 10.1016/j.cllc.2011.03.033. View

17.

Said Y, Alsheikhy A, Shawly T, Lahza H . Medical Images Segmentation for Lung Cancer Diagnosis Based on Deep Learning Architectures. Diagnostics (Basel). 2023; 13(3). PMC: 9914913. DOI: 10.3390/diagnostics13030546. View

18.

Bach P, Kattan M, Thornquist M, Kris M, Tate R, Barnett M . Variations in lung cancer risk among smokers. J Natl Cancer Inst. 2003; 95(6):470-8. DOI: 10.1093/jnci/95.6.470. View

19.

Dwivedi K, Rajpal A, Rajpal S, Kumar V, Agarwal M, Kumar N . Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning. Comput Methods Programs Biomed. 2023; 243:107864. DOI: 10.1016/j.cmpb.2023.107864. View

20.

Katki H, Kovalchik S, Berg C, Cheung L, Chaturvedi A . Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening. JAMA. 2016; 315(21):2300-11. PMC: 4899131. DOI: 10.1001/jama.2016.6255. View