Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based on <sup>18</sup>F-FDG PET radiomics and clinical factors.

Authors

Lu D,Zhu X,Mu X,Huang X,Wei F,Qin L,Liu Q,Fu W,Deng Y

Affiliations (3)

  • Department of Nuclear Medicine, Liuzhou Workers' Hospital, Liuzhou, 545000, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Department of Nuclear Medicine, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Department of Nuclear Medicine, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi Zhuang Autonomous Region, People's Republic of China. [email protected].

Abstract

This study aimed to develop and validate a nomogram combining <sup>18</sup>F-FDG PET radiomics and clinical factors to non-invasively predict bone marrow involvement (BMI) in patients with lymphoma. A radiomics nomogram was developed using monocentric data, randomly divided into a training set (70%) and a test set (30%). Bone marrow biopsy (BMB) served as the gold standard for BMI diagnosis. Independent clinical risk factors were identified through univariate and multivariate logistic regression analyses to construct a clinical model. Radiomics features were extracted from PET and CT images and selected using least absolute shrinkage and selection operator (LASSO) regression, yielding a radiomics score (Rad<sub>score</sub>) for each patient. Models based on clinical factors, CT Rad<sub>score</sub>, and PET Rad<sub>score</sub> were established and evaluated using eight machine learning algorithms to identify the optimal prediction model. A combined model was constructed and presented as a nomogram. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). A total of 160 patients were included, of whom 70 had BMI based on BMB results. The training group comprised 112 patients (BMI: 56, without BMI: 56), while the test group included 48 patients (BMI: 14, without BMI: 34). Independent risk factors, including the number of extranodal involvements and B symptoms, were incorporated into the clinical model. In the clinical model, CT Rad<sub>score</sub>, and PET Rad<sub>score</sub>, the AUCs in the test set were 0.820 (95% CI: 0.705-0.935), 0.538 (95% CI: 0.351-0.723), and 0.836 (95% CI: 0.686-0.986). Due to the limited diagnostic performance of CT Rad<sub>score</sub>, the nomogram was constructed using PET Rad<sub>score</sub> and the clinical model. The radiomics nomogram achieved AUCs of 0.916 (95% CI: 0.865-0.967) in the training set and 0.863 (95% CI: 0.763-0.964) in the test set. Calibration curves and DCA confirmed the nomogram's discrimination, calibration, and clinical utility in both sets. By integrating PET Rad<sub>score</sub>, the number of extranodal involvements, and B symptoms, this <sup>18</sup>F-FDG PET radiomics-based nomogram offers a non-invasive method to predict bone marrow status in lymphoma patients, providing nuclear medicine physicians with valuable decision support for pre-treatment evaluation.

Topics

NomogramsFluorodeoxyglucose F18LymphomaBone MarrowPositron Emission Tomography Computed TomographyImage Processing, Computer-AssistedPositron-Emission TomographyJournal ArticleValidation Study

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