Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study
Objective To develop and validate a prediction model of Philadelphia chromosome-positive acute lymphoblastic leukaemia (Ph+ALL).Design A single-centre retrospective study.Participants This study analysed 471 newly diagnosed patients with ALL at the Second Affiliated Hospital of Army Medical Universi...
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BMJ Publishing Group
2025-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/6/e097526.full |
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| author | Jing Zhang Cheng Zhang Xi Zhang Wuchen Yang Jingya Liu Yang Gou Xingqin Huang Maoshan Chen Dezhi Huang Shengwang Wu Shuiqing Liu Xiangui Peng |
| author_facet | Jing Zhang Cheng Zhang Xi Zhang Wuchen Yang Jingya Liu Yang Gou Xingqin Huang Maoshan Chen Dezhi Huang Shengwang Wu Shuiqing Liu Xiangui Peng |
| author_sort | Jing Zhang |
| collection | DOAJ |
| description | Objective To develop and validate a prediction model of Philadelphia chromosome-positive acute lymphoblastic leukaemia (Ph+ALL).Design A single-centre retrospective study.Participants This study analysed 471 newly diagnosed patients with ALL at the Second Affiliated Hospital of Army Medical University from January 2014 to December 2023.Methods Clinical and laboratory parameters were collected, and the important characteristic parameters were selected using BorutaShap. Multiple machine learning (ML) models were constructed and optimised by using the active learning (AL) algorithm. Performance was evaluated using the area under the curve (AUC), comprehensive indicators and decision curve analysis. The interpretability of the model was evaluated by using SHapley Additive Interpretation (SHAP), and external validation was conducted on an independent test cohort.Results 10 parameters were selected to construct multiple ML models. The CatBoost model integrated with an AL algorithm (CatBoost-AL) was found to be the most effective model for predicting Ph+ALL within the validation data set. This model achieved an AUC of 0.797 (95% CI 0.710 to 0.884), along with sensitivity, specificity and F1 score of 0.667, 0.864 and 0.777, respectively. The prediction performance of CatBoost-AL was further validated with an external testing set, where it maintained a strong AUC of 0.794 (95% CI 0.707 to 0.881). Using SHAP for global interpretability analysis, age, monocyte count, γ-glutamyl transferase, neutrophil count and alanine aminotransferase were identified as crucial parameters that significantly influence the diagnostic accuracy of CatBoost-AL.Conclusion An interpretable ML model and online prediction tool were developed to determine whether newly diagnosed patients with ALL are Ph+ALL. The key parameters identified by the optimal model provided a further understanding of Ph+ALL characteristics and were valuable for accurate diagnosis and treatment of Ph+ALL. |
| format | Article |
| id | doaj-art-a017bc7e0caf4f3f8da29d899ee315b1 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMJ Publishing Group |
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| series | BMJ Open |
| spelling | doaj-art-a017bc7e0caf4f3f8da29d899ee315b12025-08-20T02:35:19ZengBMJ Publishing GroupBMJ Open2044-60552025-06-0115610.1136/bmjopen-2024-097526Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective studyJing Zhang0Cheng Zhang1Xi Zhang2Wuchen Yang3Jingya Liu4Yang Gou5Xingqin Huang6Maoshan Chen7Dezhi Huang8Shengwang Wu9Shuiqing Liu10Xiangui Peng11State Key Laboratory of Trauma and Chemical Poisoning, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaChongqing Key Laboratory of Hematology and Microenvironment, Chongqing, ChinaDepartment of Hematology, Third Military Medical University Southwest Hospital, Chongqing, ChinaLaboratory of Radiation Biology, Department of Blood Transfusion, Laboratory Medicine Center, Third Military Medical University Second Affiliated Hospital, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaChongqing Key Laboratory of Hematology and Microenvironment, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaMedical Center of Hematology, The Second Affiliated Hospital of Army Medical University, Chongqing, ChinaObjective To develop and validate a prediction model of Philadelphia chromosome-positive acute lymphoblastic leukaemia (Ph+ALL).Design A single-centre retrospective study.Participants This study analysed 471 newly diagnosed patients with ALL at the Second Affiliated Hospital of Army Medical University from January 2014 to December 2023.Methods Clinical and laboratory parameters were collected, and the important characteristic parameters were selected using BorutaShap. Multiple machine learning (ML) models were constructed and optimised by using the active learning (AL) algorithm. Performance was evaluated using the area under the curve (AUC), comprehensive indicators and decision curve analysis. The interpretability of the model was evaluated by using SHapley Additive Interpretation (SHAP), and external validation was conducted on an independent test cohort.Results 10 parameters were selected to construct multiple ML models. The CatBoost model integrated with an AL algorithm (CatBoost-AL) was found to be the most effective model for predicting Ph+ALL within the validation data set. This model achieved an AUC of 0.797 (95% CI 0.710 to 0.884), along with sensitivity, specificity and F1 score of 0.667, 0.864 and 0.777, respectively. The prediction performance of CatBoost-AL was further validated with an external testing set, where it maintained a strong AUC of 0.794 (95% CI 0.707 to 0.881). Using SHAP for global interpretability analysis, age, monocyte count, γ-glutamyl transferase, neutrophil count and alanine aminotransferase were identified as crucial parameters that significantly influence the diagnostic accuracy of CatBoost-AL.Conclusion An interpretable ML model and online prediction tool were developed to determine whether newly diagnosed patients with ALL are Ph+ALL. The key parameters identified by the optimal model provided a further understanding of Ph+ALL characteristics and were valuable for accurate diagnosis and treatment of Ph+ALL.https://bmjopen.bmj.com/content/15/6/e097526.full |
| spellingShingle | Jing Zhang Cheng Zhang Xi Zhang Wuchen Yang Jingya Liu Yang Gou Xingqin Huang Maoshan Chen Dezhi Huang Shengwang Wu Shuiqing Liu Xiangui Peng Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study BMJ Open |
| title | Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study |
| title_full | Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study |
| title_fullStr | Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study |
| title_full_unstemmed | Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study |
| title_short | Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study |
| title_sort | development and validation of an interpretable machine learning model for predicting philadelphia chromosome positive acute lymphoblastic leukaemia using clinical and laboratory parameters a single centre retrospective study |
| url | https://bmjopen.bmj.com/content/15/6/e097526.full |
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