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A Nomogram for Predicting the Diagnosis of Central Malignant Tumors Based on Preoperative Clinical Characteristics and Laboratory Indicators:

NCT06914700 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 800

Last updated 2025-04-09

No results posted yet for this study

Summary

The purpose of this study is to develop a nomogram to predict the diagnostic probability of preoperative central lymphoma and glioma, as well as the diagnostic probability of glioblastoma and non glioblastoma in central malignant tumors. The author retrospectively analyzed patients with central lymphoma and glioma who received treatment in the neurosurgery department of Guangdong Provincial People's Hospital from 2016 to 2024. Eligible patients were randomly divided into training and validation sets in a 7:3 ratio. By integrating the least absolute shrinkage and selection operator \[LASSO\] and multivariate logistic regression analysis, the key variables for establishing a nomogram were identified. Moreover, seven models including logistic regression, decision tree, random forest, support vector machine (SVM), neural network, XGBoost, and lightGBM were used to calculate the area under the receiver operating characteristic curve (AUC)

Conditions

Sponsors & Collaborators

  • Guangdong Provincial People's Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2016-09-01
Primary Completion
2024-08-01
Completion
2024-12-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06914700 on ClinicalTrials.gov