Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies
Abstract Purpose Paraneoplastic syndromes (PNS) are a group of rare disorders triggered by an immune response to malignancy, characterized by diverse neurological, muscular, and systemic symptoms. This study aims to leverage machine learning to develop a predictive model for cancer diagnosis in pati...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-01836-5 |
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| author | Alireza Maleki Mohammad Mahdi Mirza Ali Mohammadi Shahab Gholizadeh Behnaz Dalvandi Mohammad Rahimi Aidin Tarokhian |
| author_facet | Alireza Maleki Mohammad Mahdi Mirza Ali Mohammadi Shahab Gholizadeh Behnaz Dalvandi Mohammad Rahimi Aidin Tarokhian |
| author_sort | Alireza Maleki |
| collection | DOAJ |
| description | Abstract Purpose Paraneoplastic syndromes (PNS) are a group of rare disorders triggered by an immune response to malignancy, characterized by diverse neurological, muscular, and systemic symptoms. This study aims to leverage machine learning to develop a predictive model for cancer diagnosis in patients with paraneoplastic autoantibodies. Methods Demographic data included age and sex, and presenting symptoms were recorded. Laboratory data comprised serum or cerebrospinal fluid (CSF) paraneoplastic autoantibody panels. The study included participants who tested positive for at least one autoantibody. Naive Bayes model was used to predict cancer presence. Model performance was evaluated using sensitivity, specificity, likelihood ratios, predictive values, AUC-ROC, Brier score, and overall accuracy. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. A graphical user interface (GUI)-based application was developed to facilitate model use. Results The study included 116 participants, with an average age of 57.1 years and a higher proportion of females (53.4%). The most common presenting symptom was ‘‘Motor’’ (40.5%), followed by ‘‘Cognitive’’ (17.2%) and ‘‘Bulbar’’ (15.5%) symptoms. Cancer was present in 23 participants (19.8%). The Naive Bayes model demonstrated high performance with a sensitivity of 85.71% and specificity of 100.00%. The AUC-ROC was 0.9795, indicating excellent diagnostic capability. Age and the presence or absence of specific autoantibodies were significant predictors of cancer. Conclusion Machine learning models, such as the Naive Bayes classifier developed in this study, can accurately stratify cancer risk in patients with positive paraneoplastic autoantibodies. |
| format | Article |
| id | doaj-art-c3e0c4f3dc1049c295c9d496397a9861 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-c3e0c4f3dc1049c295c9d496397a98612025-01-26T12:39:54ZengSpringerDiscover Oncology2730-60112025-01-011611910.1007/s12672-025-01836-5Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodiesAlireza Maleki0Mohammad Mahdi Mirza Ali Mohammadi1Shahab Gholizadeh2Behnaz Dalvandi3Mohammad Rahimi4Aidin Tarokhian5College of Management, University of TehranDepartment of Electrical Engineering, Iran University of Science and TechnologyChaloos Razi HospitalTehran Medical Branch, Islamic Azad UniversityStudent Research Committee, Hamadan University of Medical SciencesSchool of Medicine, Hamadan University of Medical SciencesAbstract Purpose Paraneoplastic syndromes (PNS) are a group of rare disorders triggered by an immune response to malignancy, characterized by diverse neurological, muscular, and systemic symptoms. This study aims to leverage machine learning to develop a predictive model for cancer diagnosis in patients with paraneoplastic autoantibodies. Methods Demographic data included age and sex, and presenting symptoms were recorded. Laboratory data comprised serum or cerebrospinal fluid (CSF) paraneoplastic autoantibody panels. The study included participants who tested positive for at least one autoantibody. Naive Bayes model was used to predict cancer presence. Model performance was evaluated using sensitivity, specificity, likelihood ratios, predictive values, AUC-ROC, Brier score, and overall accuracy. Feature importance was assessed using SHapley Additive exPlanations (SHAP) values. A graphical user interface (GUI)-based application was developed to facilitate model use. Results The study included 116 participants, with an average age of 57.1 years and a higher proportion of females (53.4%). The most common presenting symptom was ‘‘Motor’’ (40.5%), followed by ‘‘Cognitive’’ (17.2%) and ‘‘Bulbar’’ (15.5%) symptoms. Cancer was present in 23 participants (19.8%). The Naive Bayes model demonstrated high performance with a sensitivity of 85.71% and specificity of 100.00%. The AUC-ROC was 0.9795, indicating excellent diagnostic capability. Age and the presence or absence of specific autoantibodies were significant predictors of cancer. Conclusion Machine learning models, such as the Naive Bayes classifier developed in this study, can accurately stratify cancer risk in patients with positive paraneoplastic autoantibodies.https://doi.org/10.1007/s12672-025-01836-5Machine learningParaneoplastic syndromeCancerAutoantibody |
| spellingShingle | Alireza Maleki Mohammad Mahdi Mirza Ali Mohammadi Shahab Gholizadeh Behnaz Dalvandi Mohammad Rahimi Aidin Tarokhian Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies Discover Oncology Machine learning Paraneoplastic syndrome Cancer Autoantibody |
| title | Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| title_full | Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| title_fullStr | Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| title_full_unstemmed | Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| title_short | Machine learning-assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| title_sort | machine learning assisted cancer diagnosis in patients with paraneoplastic autoantibodies |
| topic | Machine learning Paraneoplastic syndrome Cancer Autoantibody |
| url | https://doi.org/10.1007/s12672-025-01836-5 |
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