A blood test-based machine learning model for predicting lung cancer risk
BackgroundThe goal of early detection is individual cancer prediction. For lung cancer (LC), age and smoking history are the primary criteria for annual low-dose CT screening, leaving other populations at risk of being overlooked. Machine learning (ML) is a promising method to identify complex patte...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1577451/full |
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| author | Lihi Schwartz Naor Matania Matanel Levi Teddy Lazebnik Teddy Lazebnik Shiri Kushnir Noga Yosef Assaf Hoogi Dekel Shlomi Dekel Shlomi |
| author_facet | Lihi Schwartz Naor Matania Matanel Levi Teddy Lazebnik Teddy Lazebnik Shiri Kushnir Noga Yosef Assaf Hoogi Dekel Shlomi Dekel Shlomi |
| author_sort | Lihi Schwartz |
| collection | DOAJ |
| description | BackgroundThe goal of early detection is individual cancer prediction. For lung cancer (LC), age and smoking history are the primary criteria for annual low-dose CT screening, leaving other populations at risk of being overlooked. Machine learning (ML) is a promising method to identify complex patterns in the data that can reveal personalized disease predictors.MethodsAn ML-based model was used on blood test data collected before the diagnosis of LC, and sociodemographic factors such as age and gender among LC patients and controls were incorporated to predict the risk for future LC diagnosis.ResultsIn addition to age and gender, we identified 22 blood tests that contributed to the model. For the entire study population, the ML model predicted LC with an accuracy of 71.2%, a sensitivity of 63%, and a positive predictive value of 67.2%. Higher accuracy was found among women than men (71.8 vs. 70.8) and among never smokers than smokers (73.6 vs. 70.1%). Age was the most significant contributor (13.6%), followed by red blood cell distribution (5.1%), creatinine (5%), gender (3.6%), and mean corpuscular hemoglobin (3.3%). A majority of the blood tests made a highly variable contribution to the complex ML model; however, some tests, such as red cell distribution width, mean corpuscular hemoglobin, prothrombin time, hematocrit, urea, and calcium, contributed slightly more to a dichotomous prediction.ConclusionBlood tests can be used in the proposed ML model to predict LC. More studies are needed in basic science fields to identify possible explanations between specific blood results and LC prediction. |
| format | Article |
| id | doaj-art-7b12e467644a445aad1bf92464c997a9 |
| institution | OA Journals |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-7b12e467644a445aad1bf92464c997a92025-08-20T02:35:49ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-06-011210.3389/fmed.2025.15774511577451A blood test-based machine learning model for predicting lung cancer riskLihi Schwartz0Naor Matania1Matanel Levi2Teddy Lazebnik3Teddy Lazebnik4Shiri Kushnir5Noga Yosef6Assaf Hoogi7Dekel Shlomi8Dekel Shlomi9Fliner Clinic, Department of Family Medicine, Dan-Petah-Tiqwa District, Clalit Health Services Community Division, Petah Tiqwa, IsraelDepartment of Computer Science, Bar Ilan University, Ramat Gan, IsraelAdelson School of Medicine, Ariel University, Ariel, IsraelDepartment of Mathematics, Ariel University, Ariel, IsraelDepartment of Cancer Biology, Cancer Institute, University College London, London, United KingdomResearch Authority, Rabin Medical Center, Beilinson Campus, Petah Tiqwa, IsraelResearch Unit, Dan-Petah-Tiqwa District, Clalit Health Services Community Division, Ramat Gan, IsraelThe School of Computer Science and The Data Science and Artificial Intelligence Research Center, Ariel University, Ariel, IsraelAdelson School of Medicine, Ariel University, Ariel, IsraelPulmonary Clinic, Dan-Petah-Tiqwa District, Clalit Health Services Community Division, Ramat Gan, IsraelBackgroundThe goal of early detection is individual cancer prediction. For lung cancer (LC), age and smoking history are the primary criteria for annual low-dose CT screening, leaving other populations at risk of being overlooked. Machine learning (ML) is a promising method to identify complex patterns in the data that can reveal personalized disease predictors.MethodsAn ML-based model was used on blood test data collected before the diagnosis of LC, and sociodemographic factors such as age and gender among LC patients and controls were incorporated to predict the risk for future LC diagnosis.ResultsIn addition to age and gender, we identified 22 blood tests that contributed to the model. For the entire study population, the ML model predicted LC with an accuracy of 71.2%, a sensitivity of 63%, and a positive predictive value of 67.2%. Higher accuracy was found among women than men (71.8 vs. 70.8) and among never smokers than smokers (73.6 vs. 70.1%). Age was the most significant contributor (13.6%), followed by red blood cell distribution (5.1%), creatinine (5%), gender (3.6%), and mean corpuscular hemoglobin (3.3%). A majority of the blood tests made a highly variable contribution to the complex ML model; however, some tests, such as red cell distribution width, mean corpuscular hemoglobin, prothrombin time, hematocrit, urea, and calcium, contributed slightly more to a dichotomous prediction.ConclusionBlood tests can be used in the proposed ML model to predict LC. More studies are needed in basic science fields to identify possible explanations between specific blood results and LC prediction.https://www.frontiersin.org/articles/10.3389/fmed.2025.1577451/fulllung cancerartificial intelligencemachine learningblood testprediction model |
| spellingShingle | Lihi Schwartz Naor Matania Matanel Levi Teddy Lazebnik Teddy Lazebnik Shiri Kushnir Noga Yosef Assaf Hoogi Dekel Shlomi Dekel Shlomi A blood test-based machine learning model for predicting lung cancer risk Frontiers in Medicine lung cancer artificial intelligence machine learning blood test prediction model |
| title | A blood test-based machine learning model for predicting lung cancer risk |
| title_full | A blood test-based machine learning model for predicting lung cancer risk |
| title_fullStr | A blood test-based machine learning model for predicting lung cancer risk |
| title_full_unstemmed | A blood test-based machine learning model for predicting lung cancer risk |
| title_short | A blood test-based machine learning model for predicting lung cancer risk |
| title_sort | blood test based machine learning model for predicting lung cancer risk |
| topic | lung cancer artificial intelligence machine learning blood test prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1577451/full |
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