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Radiomics Predictive Modeling From Dual-Time-Point Fdg Pet Ki Parametric Maps: Application to Chemotherapy Response in Lymphoma Publisher



Samimi R1 ; Shiri I2 ; Ahmadyar Y1 ; Van Den Hoff J3, 4 ; Kamaliasl A1 ; Rezaee A5 ; Yousefirizi F6 ; Geramifar P7 ; Rahmim A6, 8
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Authors Affiliations
  1. 1. Department of Medical Radiation Engineering, Shahid Beheshti University, Tehran, Iran
  2. 2. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, 1211, Switzerland
  3. 3. PET Center, Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, 01328, Germany
  4. 4. Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universitat Dresden, Dresden, 01307, Germany
  5. 5. Khatam PET/CT Center, Khatam Hospital, Tehran, Iran
  6. 6. Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
  7. 7. Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
  8. 8. Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada

Source: EJNMMI Research Published:2023


Abstract

Background: To investigate the use of dynamic radiomics features derived from dual-time-point (DTP-feature) [18F]FDG PET metabolic uptake rate Ki parametric maps to develop a predictive model for response to chemotherapy in lymphoma patients. Methods: We analyzed 126 lesions from 45 lymphoma patients (responding n = 75 and non-responding n = 51) treated with chemotherapy from two different centers. Static and DTP radiomics features were extracted from baseline static PET images and DTP Ki parametric maps. Spearman’s rank correlations were calculated between static and DTP features to identify features with potential additional information. We first employed univariate analysis to determine correlations between individual features, and subsequently utilized multivariate analysis to derive predictive models utilizing DTP and static radiomics features before and after ComBat harmonization. For multivariate modeling, we utilized both the minimum redundancy maximum relevance feature selection technique and the XGBoost classifier. To evaluate our model, we partitioned the patient datasets into training/validation and testing sets using an 80/20% split. Different metrics for classification including area under the curve (AUC), sensitivity (SEN), specificity (SPE), and accuracy (ACC) were reported in test sets. Results: Via Spearman’s rank correlations, there was negligible to moderate correlation between 32 out of 65 DTP features and some static features (ρ < 0.7); all the other 33 features showed high correlations (ρ ≥ 0.7). In univariate modeling, no significant difference between AUC of DTP and static features was observed. GLRLM_RLNU from static features demonstrated a strong correlation (AUC = 0.75, p value = 0.0001, q value = 0.0007) with therapy response. The most predictive DTP features were GLCM_Energy, GLCM_Entropy, and Uniformity, each with AUC = 0.73, p value = 0.0001, and q value < 0.0005. In multivariate analysis, the mean ranges of AUCs increased following harmonization. Use of harmonization plus combining DTP and static features was shown to provide significantly improved predictions (AUC = 0.97 ± 0.02, accuracy = 0.89 ± 0.05, sensitivity = 0.92 ± 0.09, and specificity = 0.88 ± 0.05). All models depicted significant performance in terms of AUC, ACC, SEN, and SPE (p < 0.05, Mann–Whitney test). Conclusions: Our results demonstrate significant value in harmonization of radiomics features as well as combining DTP and static radiomics models for predicting response to chemotherapy in lymphoma patients. © 2023, The Author(s).
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