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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.09596 |
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| _version_ | 1866916126194139136 |
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| author | Flyckt, Ricco Noel Hansen Sjodsholm, Louise Henriksen, Margrethe Høstgaard Bang Brasen, Claus Lohman Ebrahimi, Ali Hilberg, Ole Hansen, Torben Frøstrup Wiil, Uffe Kock Jensen, Lars Henrik Peimankar, Abdolrahman |
| author_facet | Flyckt, Ricco Noel Hansen Sjodsholm, Louise Henriksen, Margrethe Høstgaard Bang Brasen, Claus Lohman Ebrahimi, Ali Hilberg, Ole Hansen, Torben Frøstrup Wiil, Uffe Kock Jensen, Lars Henrik Peimankar, Abdolrahman |
| contents | Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach Flyckt, Ricco Noel Hansen Sjodsholm, Louise Henriksen, Margrethe Høstgaard Bang Brasen, Claus Lohman Ebrahimi, Ali Hilberg, Ole Hansen, Torben Frøstrup Wiil, Uffe Kock Jensen, Lars Henrik Peimankar, Abdolrahman Machine Learning Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals. |
| title | Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.09596 |