Dataset Information

Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [18F]FDG PET to Predict Survival in Hodgkin Lymphoma.

ABSTRACT:

Aim

To investigate the respective influence of software tool and total metabolic tumor volume (TMTV0) calculation method on prognostic stratification of baseline 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]FDG-PET) in newly diagnosed Hodgkin lymphoma (HL).

Methods

59 patients with newly diagnosed HL were retrospectively included. [18F]FDG-PET was performed before any treatment. Four sets of TMTV0 were calculated with Beth Israel (BI) software: based on an absolute threshold selecting voxel with standardized uptake value (SUV) >2.5 (TMTV02.5), applying a per-lesion threshold of 41% of the SUV max (TMTV041) and using a per-patient adapted threshold based on SUV max of the liver (>125% and >140% of SUV max of the liver background; TMTV0125 and TMTV0140). TMTV041 was also determined with commercial software for comparison of software tools. ROC curves were used to determine the optimal threshold for each TMTV0 to predict treatment failure.

Results

Median follow-up was 39 months. There was an excellent correlation between TMTV041 determined with BI and with the commercial software (r = 0.96, p<0.0001). The median TMTV0 value for TMTV041, TMTV02.5, TMTV0125 and TMTV0140 were respectively 160 (used as reference), 210 ([28;154] p = 0.005), 183 ([-4;114] p = 0.06) and 143 ml ([-58;64] p = 0.9). The respective optimal TMTV0 threshold and area under curve (AUC) for prediction of progression free survival (PFS) were respectively: 313 ml and 0.70, 432 ml and 0.68, 450 ml and 0.68, 330 ml and 0.68. There was no significant difference between ROC curves. High TMTV0 value was predictive of poor PFS in all methodologies: 4-years PFS was 83% vs 42% (p = 0.006) for TMTV02.5, 83% vs 41% (p = 0.003) for TMTV041, 85% vs 40% (p<0.001) for TMTV0125 and 83% vs 42% (p = 0.004) for TMTV0140.

Conclusion

In newly diagnosed HL, baseline metabolic tumor volume values were significantly influenced by the choice of the method used for determination of volume. However, no significant differences were found in term of prognosis.

SUBMITTER: Kanoun S

PROVIDER: S-EPMC4608733 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [18F]FDG PET to Predict Survival in Hodgkin Lymphoma.

Kanoun Salim S Tal Ilan I Berriolo-Riedinger Alina A Rossi Cédric C Riedinger Jean-Marc JM Vrigneaud Jean-Marc JM Legrand Louis L Humbert Olivier O Casasnovas Olivier O Brunotte François F Cochet Alexandre A

PloS one 20151016 10

<h4>Aim</h4>To investigate the respective influence of software tool and total metabolic tumor volume (TMTV0) calculation method on prognostic stratification of baseline 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]FDG-PET) in newly diagnosed Hodgkin lymphoma (HL).<h4>Methods</h4>59 patients with newly diagnosed HL were retrospectively included. [18F]FDG-PET was performed before any treatment. Four sets of TMTV0 were calculated with Beth Israel (BI) software: based on an ab ...[more]

PMID: 26473950

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Project description:PurposeFor pediatric lymphoma, quantitative FDG PET/CT imaging features such as metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies. However, feature extraction is difficult and time-consuming in cases of high disease burden. The purpose of this study was to fully automate the measurement of PET imaging features in PET/CT images of pediatric lymphoma.Methods18F-FDG PET/CT baseline images of 100 pediatric Hodgkin lymphoma patients were retrospectively analyzed. Two nuclear medicine physicians identified and segmented FDG avid disease using PET thresholding methods. Both PET and CT images were used as inputs to a three-dimensional patch-based, multi-resolution pathway convolutional neural network architecture, DeepMedic. The model was trained to replicate physician segmentations using an ensemble of three networks trained with 5-fold cross-validation. The maximum SUV (SUVmax), MTV, total lesion glycolysis (TLG), surface-area-to-volume ratio (SA/MTV), and a measure of disease spread (Dmaxpatient) were extracted from the model output. Pearson's correlation coefficient and relative percent differences were calculated between automated and physician-extracted features.ResultsMedian Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78-0.91). Automated SUVmax values matched exactly the physician determined values in 81/100 cases, with Pearson's correlation coefficient (R) of 0.95. Automated MTV was strongly correlated with physician MTV (R = 0.88), though it was slightly underestimated with a median (IQR) relative difference of - 4.3% (- 10.0-5.7%). Agreement of TLG was excellent (R = 0.94), with median (IQR) relative difference of - 0.4% (- 5.2-7.0%). Median relative percent differences were 6.8% (R = 0.91; IQR 1.6-4.3%) for SA/MTV, and 4.5% (R = 0.51; IQR - 7.5-40.9%) for Dmaxpatient, which was the most difficult feature to quantify automatically.ConclusionsAn automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.

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 aim of this prospective investigation was to assess the association of parameters derived from baseline (18)F-FDG PET/CT with overall survival (OS) in men with castrate-resistant metastatic prostate cancer.Eighty-seven men with castrate-resistant metastatic prostate cancer underwent (18)F-FDG PET/CT and were followed prospectively for OS. Median follow-up in patients who were alive was 22.2 mo (range, 1.6-62.5 mo). OS was defined as the time between the PET/CT imaging or the start of chemotherapy, whichever was later, and death, with patients who were alive censored at the last follow-up date. PET parameters included maximum standardized uptake value (SUV(max)) of the most active lesion, sum of SUV(max), and average SUV(max) of all metabolically active lesions, after subtraction of patient-specific background-liver average SUV. Comparison of OS was based on univariate and multivariable Cox regression analyses of continuous PET parameters adjusted for standard clinical parameters (age, serum prostate-specific antigen level, alkaline phosphatase, use of pain medication, prior chemotherapy, and Gleason score at initial diagnosis). Survival curves based on Kaplan-Meier estimates are presented.Among the 87 patients, 61 were dead at the time of last follow-up. Median OS was 16.5 mo (95% confidence interval [CI], 12.1-23.4 mo), and the OS probability at 24 mo was 39% ± 6%. For the univariate analysis, the hazard ratios associated with each unit increase were 1.01 (95% CI, 1.006-1.02) for sum of SUV(max) (P = 0.002), 1.11 (95% CI, 1.03-1.18) for maximum SUV(max) (P = 0.010), and 1.13 (95% CI, 0.99-1.30) for average SUV(max) (P = 0.095). For the multivariable analysis adjusting for relevant clinical parameters, the continuous parameter sum of SUV(max) remained significant (P = 0.053), with a hazard ratio of 1.01 (95% CI, 1.001-1.02). When sum of SUV(max) was grouped into quartile ranges, there was poorer survival probability for the patients in the fourth-quartile range than for those in the first-quartile range, with a univariate hazard ratio of 3.8 (95% CI, 1.8-7.9).Sum of SUV(max) derived from (18)F-FDG PET/CT contributes independent prognostic information on OS in men with castrate-resistant metastatic prostate cancer, and this information may be useful in assessing the comparative effectiveness of various conventional and emerging treatment strategies.

Dataset Information

Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [18F]FDG PET to Predict Survival in Hodgkin Lymphoma.

Aim

Methods

Results

Conclusion

Publications

Influence of Software Tool and Methodological Aspects of Total Metabolic Tumor Volume Calculation on Baseline [18F]FDG PET to Predict Survival in Hodgkin Lymphoma.

Similar Datasets

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