Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

Lue, Kun-Han; Wu, Yifeng; Lin, Hsueh-Yi; Hsieh, Tsung-Cheng; Liu, Shu-Hsin; Chan, Sheng-Chieh; Chen, Yu-Hung

doi:10.3390/diagnostics11010036

Cited by 31 publications

(25 citation statements)

References 51 publications

(76 reference statements)

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“…The hybrid nomograms combining RS with IPI could help stratify those high-risk individuals with poorer survival outcomes, achieving significantly higher AUCs and contributing to more distinct risk stratifications than IPI alone. This is consistent with a very recent study indicating that a single radiomic feature run length non-uniformity could provide additional prognostic value to the IPI in DLBCL [35]. However, compared with the run length non-uniformity reported in their study, the RS in our study showed more significant P values in the multivariate analysis (e.g., the P value of the MBV-based RS for PFS prediction was 0.001).…”

Section: Discussionsupporting

confidence: 92%

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

Zhang

Lin

Jiang

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose This study aimed to develop a novel analytic approach based on 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) radiomic signature (RS) and International Prognostic Index (IPI) to predict the progression-free survival (PFS) and overall survival (OS) of patients with diffuse large B-cell lymphoma (DLBCL). Methods We retrospectively enrolled 152 DLBCL patients and divided them into a training cohort (n = 100) and a validation cohort (n = 52). A total of 1245 radiomic features were extracted from the total metabolic tumor volume (TMTV) and the metabolic bulk volume (MBV) of pre-treatment PET/CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to develop the RS. Cox regression analysis was used to construct hybrid nomograms based on different RS and clinical variables. The performances of hybrid nomograms were evaluated using the time-dependent receiver operator characteristic (ROC) curve and the Hosmer–Lemeshow test. The clinical utilities of prediction nomograms were determined via decision curve analysis. The predictive efficiency of different RS, clinical variables, and hybrid nomograms was compared. Results The RS and IPI were identified as independent predictors of PFS and OS, and were selected to construct hybrid nomograms. Both TMTV- and MBV-based hybrid nomograms had significantly higher values of area under the curve (AUC) than IPI in training and validation cohorts (all P < 0.05), while no significant difference was found between TMTV- and MBV-based hybrid nomograms (P > 0.05). The Hosmer–Lemeshow test showed that both TMTV- and MBV-based hybrid nomograms calibrated well in the training and validation cohorts (all P > 0.05). Decision curve analysis indicated that hybrid nomograms had higher net benefits than IPI. Conclusion The hybrid nomograms combining RS with IPI could significantly improve survival prediction in DLBCL. Radiomic analysis on MBV may serve as a potential approach for prognosis assessment in DLBCL. Trial registration NCT04317313. Registered March 16, 2020. Public site: https://clinicaltrials.gov/ct2/show/NCT04317313

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Section: Discussionsupporting

confidence: 92%

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

Zhang

Lin

Jiang

et al. 2021

Eur J Nucl Med Mol Imaging

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“…Only variables that were statistically significant predictors of death in the ROC curve analyses were selected for the survival analysis. Cut-off values with the highest summation of sensitivity and specificity were selected as the optimal cut-offs for each continuous variable [28][29][30]. The variable selection and optimal cut-off determinations are summarized in the Supplementary Table S1.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic–Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis

et al. 2021

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We investigated whether the combination of primary tumor and nodal 18F-FDG PET parameters predict survival outcomes in patients with nodal metastatic non-small cell lung cancer (NSCLC) without distant metastasis. We retrospectively extracted pre-treatment 18F-FDG PET parameters from 89 nodal-positive NSCLC patients (stage IIB–IIIC). The Cox proportional hazard model was used to identify independent prognosticators of overall survival (OS) and progression-free survival (PFS). We devised survival stratification models based on the independent prognosticators and compared the model to the American Joint Committee on Cancer (AJCC) staging system using Harrell’s concordance index (c-index). Our results demonstrated that total TLG (the combination of primary tumor and nodal total lesion glycolysis) and age were independent risk factors for unfavorable OS (p < 0.001 and p = 0.001) and PFS (both p < 0.001), while the Eastern Cooperative Oncology Group scale independently predicted poor OS (p = 0.022). Our models based on the independent prognosticators outperformed the AJCC staging system (c-index = 0.732 versus 0.544 for OS and c-index = 0.672 versus 0.521 for PFS, both p < 0.001). Our results indicate that incorporating total TLG with clinical factors may refine risk stratification in nodal metastatic NSCLC patients and may facilitate tailored therapeutic strategies in this patient group.

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“…Radiomics is an emerging field that converts digital imaging data into a high-dimensional mineable feature space using high-throughput computing [12,13]. By extracting a large number of quantitative features from tomographic images, radiomics has the potential to allow the assessment of tumor heterogeneity, which maybe correlated with clinical outcomes (Figure 1) [14][15][16]. Recent studies have reported the feasibility of radiomics in the prognosis of patients with various malignancies [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…By extracting a large number of quantitative features from tomographic images, radiomics has the potential to allow the assessment of tumor heterogeneity, which maybe correlated with clinical outcomes (Figure 1) [14][15][16]. Recent studies have reported the feasibility of radiomics in the prognosis of patients with various malignancies [15][16][17][18]. However, research using radiomics nomograms based on 18 F-FDG PET for HL is relatively limited.…”

Section: Introductionmentioning

confidence: 99%

Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes

Zhou

Zhu

Chen

et al. 2021

Contrast Media & Molecular Imaging

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Purpose. In the present study, we aimed to investigate whether the radiomic features of baseline 18F-FDG PET can predict the prognosis of Hodgkin lymphoma (HL). Methods. A total 65 HL patients (training cohort: n = 49; validation cohort: n = 16) were retrospectively enrolled in the present study. A total of 47 radiomic features were extracted from pretreatment PET images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most useful prognostic features in the training cohort. The distance between the two lesions that were the furthest apart (Dmax) was recorded. The receiver operating characteristic (ROC) curve, Kaplan–Meier method, and Cox proportional hazards model were used to assess the prognostic factors. Results. Long-zone high gray-level emphasis extracted from a gray-level zone-length matrix (LZHGEGLZLM) (HR = 9.007; p = 0.044 ) and Dmax (HR = 3.641; p = 0.048 ) were independently correlated with 2-year progression-free survival (PFS). A prognostic stratification model was established based on both risk predictors, which could distinguish three risk categories for PFS ( p = 0.0002 ). The 2-year PFS was 100.0%, 64.7%, and 33.3%, respectively. Conclusions. LZHGEGLZLM and Dmax were independent prognostic factors for survival outcomes. Besides, we proposed a prognostic stratification model that could further improve the risk stratification of HL patients.

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Prognostic Value of Baseline Radiomic Features of 18F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma

Cited by 31 publications

References 51 publications

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT

Prognostic Value of Combing Primary Tumor and Nodal Glycolytic–Volumetric Parameters of 18F-FDG PET in Patients with Non-Small Cell Lung Cancer and Regional Lymph Node Metastasis

Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes

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