Dataset Information

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

ABSTRACT: This study investigates whether baseline ¹⁸F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent ¹⁸F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinkage and selection operator regression were used to reduce the dimensionality within radiomic features. Cox proportional hazards model was used to determine the prognostic factors for progression-free survival (PFS) and overall survival (OS). A prognostic stratification model was built in the training cohort and validated in the validation cohort using Kaplan-Meier survival analysis. In the training cohort, run length non-uniformity (RLN), extracted from a gray level run length matrix (GLRLM), was independently associated with PFS (hazard ratio (HR) = 15.7, p = 0.007) and OS (HR = 8.64, p = 0.040). The International Prognostic Index was an independent prognostic factor for OS (HR = 2.63, p = 0.049). A prognostic stratification model was devised based on both risk factors, which allowed identification of three risk groups for PFS and OS in the training (p < 0.001 and p < 0.001) and validation (p < 0.001 and p = 0.020) cohorts. Our results indicate that the baseline ¹⁸F-FDG PET radiomic feature, RLN_GLRLM, is an independent prognostic factor for survival outcomes. Furthermore, we propose a prognostic stratification model that may enable tailored therapeutic strategies for patients with DLBCL.

SUBMITTER: Lue KH

PROVIDER: S-EPMC7824203 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Lue Kun-Han KH Wu Yi-Feng YF Lin Hsin-Hon HH Hsieh Tsung-Cheng TC Liu Shu-Hsin SH Chan Sheng-Chieh SC Chen Yu-Hung YH

Diagnostics (Basel, Switzerland) 20201228 1

This study investigates whether baseline 18F-FDG PET radiomic features can predict survival outcomes in patients with diffuse large B-cell lymphoma (DLBCL). We retrospectively enrolled 83 patients diagnosed with DLBCL who underwent 18F-FDG PET scans before treatment. The patients were divided into the training cohort (n = 58) and the validation cohort (n = 25). Eighty radiomic features were extracted from the PET images for each patient. Least absolute shrinka ...[more]

PMID: 33379166

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Project description:Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p ≤ 0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P < 0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P < 0.001); TLG, 42.24 versus 9.90 (P < 0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.

Project description:Purpose:In the present study, we aimed to investigate whether the metabolic parameters on baseline 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) could be used to predict prognosis in peripheral T-cell lymphomas (PTCL). Methods:A total of 51 nodal PTCL patients who underwent baseline 18F-FDG PET/CT were retrospectively evaluated in the present study. Total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), and maximum standardized uptake value (SUVmax) were also assessed. Besides, the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) was also included. Log-rank test and Cox regression analysis were used to evaluate progression-free survival (PFS) and overall survival (OS). Results:The median follow-up was 18 months. Patients with low TLG, TMTV, and SUVmax levels had a significantly better clinical outcome than those with high TLG, TMTV, and SUVmax levels. The 2-year PFS rates of the high- and low-TMTV groups were 34.62% and 80%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (p < 0.001), whereas the corresponding 2-year OS rates were 46.15% and 84.00%, respectively (n = 10), intermediate-risk group with TMTV > 62.405 or NCCN-IPI score of 4-8 (2-year PFS and OS were 52.4% and 66.7%, respectively, n = 10), intermediate-risk group with TMTV > 62.405 or NCCN-IPI score of 4-8 (2-year PFS and OS were 52.4% and 66.7%, respectively, n = 10), intermediate-risk group with TMTV > 62.405 or NCCN-IPI score of 4-8 (2-year PFS and OS were 52.4% and 66.7%, respectively. Conclusions:Baseline TMTV and TLG were independent predictors of PFS and OS in PTCL patients, and SUVmax and NCCN-IPI scores were also independent predictors of OS. Moreover, the combination of TMTV and NCCN-IPI scores improved patient risk-stratification at the initial stage and might contribute to the adjustment of the therapeutic regime. This trial is registered with ChiCTR1900025526.

Project description:The current standard front-line therapy for patients with diffuse large-B cell lymphoma (DLBCL)-rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP)-is found to be ineffective in up to one-third of them. Thus, their early identification is an important step towards testing alternative treatment options. In this retrospective study, we assessed the ability of 18F-FDG PET/CT imaging features (radiomic + PET conventional parameters) plus clinical data, alone or in combination with genomic parameters to predict complete response to first-line treatment. Imaging features were extracted from images prior treatment. Lesions were segmented as a whole to reflect tumor burden. Multivariate logistic regression predictive models for response to first-line treatment trained with clinical and imaging features, or with clinical, imaging, and genomic features were developed. For imaging feature selection, a manual selection approach or a linear discriminant analysis (LDA) for dimensionality reduction were applied. Confusion matrices and performance metrics were obtained to assess model performance. Thirty-three patients (median [range] age, 58 [49-69] years) were included, of whom 23 (69.69%) achieved long-term complete response. Overall, the inclusion of genomic features improved prediction ability. The best performance metrics were obtained with the combined model including genomic data and built applying the LDA method (AUC of 0.904, and 90% of balanced accuracy). The amplification of BCL6 was found to significantly contribute to explain response to first-line treatment in both manual and LDA models. Among imaging features, radiomic features reflecting lesion distribution heterogeneity (GLSZM_GrayLevelVariance, Sphericity and GLCM_Correlation) were predictors of response in manual models. Interestingly, when the dimensionality reduction was applied, the whole set of imaging features-mostly composed of radiomic features-significantly contributed to explain response to front-line therapy. A nomogram predictive for response to first-line treatment was constructed. In summary, a combination of imaging features, clinical variables and genomic data was able to successfully predict complete response to first-line treatment in DLBCL patients, with the amplification of BCL6 as the genetic marker retaining the highest predictive value. Additionally, a panel of imaging features may provide important information when predicting treatment response, with lesion dissemination-related radiomic features deserving especial attention.

Project description:At present, 18F-fluorodesoxyglucose (18FDG) positron emission tomography (PET)/computed tomography (CT) cannot be used to omit a bone marrow biopsy (BMB) among initial staging procedures in follicular lymphoma (FL). The additional diagnostic value of skeletal textural features on baseline 18FDG-PET/CT in diffuse large B-cell lymphoma (DLBCL) patients has given promising results. The aim of this study is to evaluate the value of 18FDG-PET/CT radiomics for the diagnosis of bone marrow involvement (BMI) in FL patients. This retrospective bicentric study enrolled newly diagnosed FL patients addressed for baseline 18FDG PET/CT. For visual assessment, examinations were considered positive in cases of obvious bone focal uptakes. For textural analysis, the skeleton volumes of interest (VOIs) were automatically extracted from segmented CT images and analysed using LifeX software. BMB and visual assessment were taken as the gold standard: BMB -/PET - patients were considered as bone-NEGATIVE patients, whereas BMB +/PET -, BMB -/PET + and BMB +/PET + patients were considered bone-POSITIVE patients. A LASSO regression algorithm was used to select features of interest and to build a prediction model. Sixty-six consecutive patients were included: 36 bone-NEGATIVE (54.5%) and 30 bone-POSITIVE (45.5%). The LASSO regression found variance_GLCM, correlation_GLCM, joint entropy_GLCM and busyness_NGLDM to have nonzero regression coefficients. Based on ROC analysis, a cut-off equal to - 0.190 was found to be optimal for the diagnosis of BMI using PET pred.score. The corresponding sensitivity, specificity, PPV and NPV values were equal to 70.0%, 83.3%, 77.8% and 76.9%, respectively. When comparing the ROC AUCs with using BMB alone, visual PET assessment or PET pred.score, a significant difference was found between BMB versus visual PET assessments (p = 0.010) but not between BMB and PET pred.score assessments (p = 0.097). Skeleton texture analysis is worth exploring to improve the performance of 18FDG-PET/CT for the diagnosis of BMI at baseline in FL patients.

Project description:OBJECTIVE:We aimed to improve prediction of outcome for patients with colorectal liver metastases, via prognostic models incorporating PET-derived measures, including radiomic features that move beyond conventional standard uptake value (SUV) measures. PATIENTS AND METHODS:A range of parameters including volumetric and heterogeneity measures were derived from FDG PET images of 52 patients with colorectal intrahepatic-only metastases (29 males and 23 females; mean age 62.9 years [SD 9.8; range 32-82]). The patients underwent PET/CT imaging as part of the clinical workup prior to final decision on treatment. Univariate and multivariate models were implemented, which included statistical considerations (to discourage false discovery and overfitting), to predict overall survival (OS), progression-free survival (PFS) and event-free survival (EFS). Kaplan-Meier survival analyses were performed, where the subjects were divided into high-risk and low-risk groups, from which the hazard ratios (HR) were computed via Cox proportional hazards regression. RESULTS:Commonly-invoked SUV metrics performed relatively poorly for different prediction tasks (SUVmax HR = 1.48, 0.83 and 1.16; SUVpeak HR = 2.05, 1.93, and 1.64, for OS, PFS and EFS, respectively). By contrast, the number of liver metastases and metabolic tumor volume (MTV) each performed well (with respective HR values of 2.71, 2.61 and 2.42, and 2.62, 1.96 and 2.29, for OS, PFS and EFS). Total lesion glycolysis (TLG) also resulted in similar performance as MTV. Multivariate prognostic modeling incorporating different features (including those quantifying intra-tumor heterogeneity) resulted in further enhanced prediction. Specifically, HR values of 4.29, 4.02 and 3.20 (p-values = 0.00004, 0.0019 and 0.0002) were obtained for OS, PFS and EFS, respectively. CONCLUSIONS:PET-derived measures beyond commonly invoked SUV parameters hold significant potential towards improved prediction of clinical outcome in patients with liver metastases, especially when utilizing multivariate models.

Dataset Information

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

Publications

Prognostic Value of Baseline Radiomic Features of <sup>18</sup>F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma.

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