How fast can we scan patients with modern (digital) PET/CT systems?

PURPOSE: To seek for the minimal duration per bed position with a digital PET system without compromising image quality and lesion detection in patients requiring fast 18F-FDG PET imaging. MATERIALS AND METHODS: 19 cancer patients experiencing pain or dyspnea and 9 pediatric patients were scanned on a Vereos system. List mode data were reconstructed with decreasing time frame down to 10 s per bed position. Noise was evaluated in the liver, blood pool and muscle, and using target-to-background ratios. Five PET readers recorded image quality, number of clinically relevant foci and of involved anatomical sites in reconstructions ranging from 60 to 10 s per bed position, compared to the standard 90 s reconstruction. RESULTS: The following reconstructions, which harboured a noise not significantly higher than that of the standard reconstruction, were selected for clinical evaluation: 1iterations/10 subsets/20sec (1i10 s20sec), 1i10 s30sec, and 2i10 sPSF60sec. Only the 60 s per bed acquisition displayed similar target-to-background ratios compared to the standard reconstruction, but mean ratios were still higher than 2.0 for the 30 s reconstruction. Inter-rater agreement for the number of involved anatomical sites and detected lesion was good or almost perfect (Kappa: 0.64-0.91) for all acquisitions. In particular, kappa for the 30 s per bed acquisition was 0.78 and 0.91 for lesion and anatomical sites number, respectively. Intra-rater agreement was also excellent for the 30 s reconstruction (kappa = 0.72). Median estimated total PET acquisition time for the 1i10 s30sec, and the standard reconstruction were 4 and 12 min, respectively. CONCLUSIONS: Fast imaging is feasible with state-of-the-art PET systems. Acquisitions of 30 s per bed position are feasible with the Vereos system, requiring optimization of reconstruction parameters.

Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers.

INTRODUCTION: We aimed to investigate whether 18F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS). MATERIALS AND METHODS: On 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUVmax, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables. RESULTS: For ER expression, correlations were mainly observed with 18F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness_ER and uniformity_HISTO (ρ = -0.386, p = 0.017) and correlation_PR and entropy_GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUVmax, skewness_ER, kurtosis_ER, entropy_HISTO, and uniformity_HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness_ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness_ER, none of the parameters were independent predictors. Indeed, skewness_ER was significantly higher in youngest patients (ρ = -0.351, p = 0.031) and in clinical stage III tumors (p = 0.023). CONCLUSION: A heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics.

BACKGROUND: The aim of this study is to determine if the choice of the 18F-FDG-PET protocol, especially matrix size and reconstruction algorithm, is of importance to discriminate between immunohistochemical subtypes (luminal versus non-luminal) in breast cancer with textural features (TFs). PROCEDURES: Forty-seven patients referred for breast cancer staging in the framework of a prospective study were reviewed as part of an ancillary study. In addition to standard PET imaging (PSFWholeBody), a high-resolution breast acquisition was performed and reconstructed with OSEM and PSF (OSEMbreast/PSFbreast). PET standard metrics and TFs were extracted. For each reconstruction protocol, a prediction model for tumour classification was built using a random forests method. Spearman coefficients were used to seek correlation between PET metrics. RESULTS: PSFWholeBody showed lower numbers of voxels within VOIs than OSEMbreast and PSFbreast with median (interquartile range) equal to 130 (43-271), 316 (167-1042), 367 (107-1221), respectively (p < 0.0001). Therefore, using LifeX software, 28 (59%), 46 (98%) and 42 (89%) patients were exploitable with PSFWholeBody, OSEMbreast and PSFbreast, respectively. On matched comparisons, PSFbreast reconstruction presented better abilities than PSFwholeBody and OSEMbreast for the classification of luminal versus non-luminal breast tumours with an accuracy reaching 85.7% as compared to 67.8% for PSFwholeBody and 73.8% for OSEMbreast. PSFbreast accuracy, sensitivity, specificity, PPV and NPV were equal to 85.7%, 94.3%, 42.9%, 89.2%, 60.0%, respectively. Coarseness and ZLNU were found to be main variables of importance, appearing in all three prediction models. Coarseness was correlated with SUVmax on PSFwholeBody images (ρ = - 0.526, p = 0.005), whereas it was not on OSEMbreast (ρ = - 0.183, p = 0.244) and PSFbreast (ρ = - 0.244, p = 0.119) images. Moreover, the range of its values was higher on PSFbreast images as compared to OSEMbreast, especially in small lesions (MTV < 3 ml). CONCLUSIONS: High-resolution breast PET acquisitions, applying both small-voxel matrix and PSF modelling, appeared to improve the characterisation of breast tumours.

Assessment of alteration in liver 18F-FDG uptake due to steatosis in lymphoma patients and its impact on the Deauville score.

AIM: Our aim was (1) to evaluate the prevalence of steatosis in lymphoma patients and its evolution during treatment; (2) to evaluate the impact of hepatic steatosis on 18F-FDG liver uptake; and (3) to study how hepatic steatosis affects the Deauville score (DS) for discriminating between responders and non-responders. METHODS: Over a 1-year period, 358 PET scans from 227 patients [122 diffuse large B cell lymphoma (DLBCL), 57 Hodgkin lymphoma (HL) and 48 Follicular lymphoma (FL)] referred for baseline (n = 143), interim (n = 79) and end-of-treatment (EoT, n = 136) PET scans were reviewed. Steatosis was diagnosed on the unenhanced CT part of PET/CT examinations using a cut-off value of 42 Hounsfield units (HU). EARL-compliant SULmax were recorded on the liver and the tumour target lesion. DS were then computed. RESULTS: Prevalence of steatosis at baseline, interim and EoT PET was 15/143 (10.5%), 6/79 (7.6%) and 16/136 (11.8%), respectively (p = 0.62).Ten out of 27 steatotic patients (37.0%) displayed a steatotic liver on all examinations. Six patients (22.2%) had a disappearance of hepatic steatosis during their time-course of treatment. Only one patient developed steatosis during his course of treatment. Liver SULmax values were significantly lower in the steatosis versus non-steatotic groups of patients for interim (1.66 ± 0.36 versus 2.15 ± 0.27) and EoT (1.67 ± 0.29 versus 2.17 ± 0.30) PET. CT density was found to be an independent factor that correlated with liver SULmax, while BMI, blood glucose level and the type of chemotherapy regimen were not. Using a method based on this correlation to correct liver SULmax, all DS4 steatotic patients on interim (n = 1) and EoT (n = 2) PET moved to DS3. CONCLUSIONS: Steatosis is actually a theoretical but not practical issue in most patients but should be recognised and corrected in appropriate cases, namely, for those patients scored DS4 with a percentage difference between the target lesion and the liver background lower than 30%.

Generating harmonized SUV within the EANM EARL accreditation program: software approach versus EARL-compliant reconstruction.

BACKGROUND: Evolutions in hardware and software PET technology, such as point spread function (PSF) reconstruction, have been shown to improve diagnostic performance, but can also lead to important device-dependent and reconstruction-dependent variations in standardized uptake values (SUVs). This may preclude the multicentre use of SUVs as a prognostic or diagnostic tool or as a biomarker of the early response to antineoplastic treatments. This study compared two SUV harmonization strategies using a newer reconstruction algorithm that improves lesion detection while maintaining comparability with older systems: (1) the use of a second reconstruction compliant with harmonization standards and (2) the use of a proprietary software tool (EQ.PET). METHODS: PET data from 50 consecutive non-small cell lung cancer patients were reconstructed with PSF reconstruction for optimal tumor detection and an ordered subset expectation maximization (OSEM3D) reconstruction to mimic a former generation PET. An additional PSF reconstruction was performed with a 7 mm Gaussian filter (PSF7, first method), and, post-reconstruction, the EQ filter (same Gaussian filter) was applied to the PSF data (PSFEQ, second method) for harmonization purposes. The 7 mm kernel filter was chosen to comply with the European Association of Nuclear Medicine (EANM) standards. SUVs for all reconstructions were compared with regression analyses and/or Bland-Altman plots. RESULTS: Overall, 171 lesions were analyzed: 55 lung lesions (32.2%), 87 lymph nodes (50.9%), and 29 metastases (16.9%). In these lesions, the mean PSF7/OSEM3D ratios for SUVmax and SUVpeak were 1.02 (95% CI: 0.93-1.11) and 1.04 (95% CI: 0.95-1.14), respectively. The mean PSFEQ/OSEM3D ratios for SUVmax and SUVpeak were 1.01 (95% CI: 0.91-1.11) and 1.04 (95% CI: 0.94-1.14), respectively. When comparing PSF7 and PSFEQ, Bland-Altman analysis showed that the mean PSF7/PSFEQ ratios for SUVmax and SUVpeak were 1.01 (95% CI: 0.96-1.06) and 1.01 (95% CI: 0.97-1.04), respectively. CONCLUSION: The issue of reconstruction dependency in SUV values that hampers the comparison of data between different PET systems can be overcome using two reconstructions for harmonized quantification and optimal diagnosis or using the EQ.PET technology. Both technologies produce similar results, EQ.PET sparing reconstruction and interpretation time. Other manufacturers are encouraged to either emulate this solution or to produce a vendor-neutral approach.