Prognostic Value of 18F-Fluorocholine PET Parameters in Metastatic Castrate-Resistant Prostate Cancer Patients Treated with Docetaxel.

Background and Aim: The availability of new treatments for metastatic castrate-resistant prostate cancer (mCRPC) patients increases the need for reliable biomarkers to help clinicians to choose the better sequence strategy. The aim of the present retrospective and observational work is to investigate the prognostic value of 18F-fluorocholine (18F-FCH) positron emission tomography (PET) parameters in mCRPC. Materials and Methods: Between March 2013 and August 2016, 29 patients with mCRPC were included. They all received three-weekly docetaxel after androgen deprivation therapy, and they underwent 18F-FCH PET/computed tomography (CT) before and after the therapy. Semi-quantitative indices such as maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) with partial volume effect (PVC-SUV) correction, metabolically active tumour volume (MATV), and total lesion activity (TLA) with partial volume effect (PVC-TLA) correction were measured both in pre-treatment and post-treatment 18F-FCH PET/CT scans for each lesion. Whole-body indices were calculated as sum of values measured for each lesion (SSUVmax, SPVC-SUV, SMATV, and STLA). Progression-free survival (PFS) and overall survival (OS) were considered as clinical endpoints. Univariate and multivariate hazard ratios for whole-body 18F-FCH PET indices were performed, and p < 0.05 was considered as significant. Results: Cox regression analysis showed a statistically significant correlation between PFS, SMATV, and STLA. No correlations between OS and 18F-FCH PET parameters were defined probably due to the small sample size. Conclusions: Semi-quantitative indices such as SMATV and STLA at baseline have a prognostic role in patients treated with docetaxel for mCRPC, suggesting a potential role of 18F-FCH PET/CT imaging in clinical decision-making.

An Adaptive Thresholding Method for BTV Estimation Incorporating PET Reconstruction Parameters: A Multicenter Study of the Robustness and the Reliability.

OBJECTIVE: The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. METHOD: In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with (18)F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. RESULTS: The goodness of the model fit was high (R(2): 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. CONCLUSIONS: Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.

Partial volume corrected 18F-FDG PET mean standardized uptake value correlates with prognostic factors in breast cancer.

AIM: The aim of this paper was to assess the prognostic role of pretherapy partial volume corrected (PVC) 18F-fluorodeoxyglucose mean standardized uptake value (SUV) in breast cancer (BC). METHODS: Forty oncological patients, BC diagnosed by biopsy, with breast tumor mass diameter >1 cm measured to the mammography, designed for surgical intervention, underwent a pretherapy semi-quantitative 18F-FDG positron emission tomography/computed tomography (18F-FDG PET/CT) whole-body study for tumor staging. Mean Body-Weight Standardized Uptake Value with Correction for Partial Volume effect (PVC- SUVBW-mean) was calculated in all mammary detected lesions. Excised tissues from primitive BC were sectioned and classified according to the WHO guidelines, evaluating biological features. Univariate (Mann-Withney/Kruskal-Wallis) and multivariate (linear regression, hierarchical clustering) statistical tests were performed between PVC-SUVBW-mean and biological indexes. ROC analysis was performed. PVC-SUVBW-mean thresholds were derived allowing to distinguish groups of BC patients with different biological characteristics. Specificity and Sensitivity were also calculated. RESULTS: Statistical and multiple correlations between pretherapy 18F-FDG PET PVC-SUVBW-mean and histological type, grade, ER/PgR hormone receptors and Mib-1 cellular proliferation index were found. In our samples, PVC-SUVBW-mean <≈4 g/cc was found correlated to BC patients with Invasive Lobular Carcinoma (ILC) or well differentiated Invasive Ductal Carcinoma (IDC), a positive expression of ER and PgR and a negative expression of MiB-1, while PVC-SUVBW-mean >≈7.00 is associated to BC patients with moderately and poorly differentiated IDC, negative expression of ER and PgR and a positive expression of MiB-1. CONCLUSION: Pretherapy PVC 18F-FDG PET PVC-SUVBW-mean measurement correlates with prognostic factors in BC and could be used to stratify patients before intervention.

Metabolic impact of partial volume correction of [18F]FDG PET-CT oncological studies on the assessment of tumor response to treatment.

AIM: The aim of this work is to evaluate the metabolic impact of Partial Volume Correction (PVC) on the measurement of the Standard Uptake Value (SUV) from [18F]FDG PET-CT oncological studies for treatment monitoring purpose. METHODS: Twenty-nine breast cancer patients with bone lesions (42 lesions in total) underwent [18F]FDG PET-CT studies after surgical resection of breast cancer primitives, and before (PET-II) chemotherapy and hormone treatment. PVC of bone lesion uptake was performed on the two [18F]FDG PET-CT studies, using a method based on Recovery Coefficients (RC) and on an automatic measurement of lesion metabolic volume. Body-weight average SUV was calculated for each lesion, with and without PVC. The accuracy, reproducibility, clinical feasibility and the metabolic impact on treatment response of the considered PVC method was evaluated. RESULTS: The PVC method was found clinically feasible in bone lesions, with an accuracy of 93% for lesion sphere-equivalent diameter >1 cm. Applying PVC, average SUV values increased, from 7% up to 154% considering both PET-I and PET-II studies, proving the need of the correction. As main finding, PVC modified the therapy response classification in 6 cases according to EORTC 1999 classification and in 5 cases according to PERCIST 1.0 classification. CONCLUSION: PVC has an important metabolic impact on the assessment of tumor response to treatment by [18F]FDG PET-CT oncological studies.

Predictive value of pre-therapy (18)F-FDG PET/CT for the outcome of (18)F-FDG PET-guided radiotherapy in patients with head and neck cancer.

PURPOSE: The aim of this study was to evaluate the predictive role of pre-therapy fluorodeoxyglucose (FDG) uptake parameters of primary tumour in head and neck cancer (HNC) patients undergoing intensity-modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) on FDG-positive volume-positron emission tomography (PET) gross tumour volume (PET-GTV). METHODS: This retrospective study included 19 patients (15 men and 4 women, mean age 59.2 years, range 23-81 years) diagnosed with HNC between 2005 and 2011. Of 19 patients, 15 (79 %) had stage III-IV. All patients underwent FDG PET/CT before treatment. Metabolic indexes of primary tumour, including metabolic tumour volume (MTV), maximum and mean standardized uptake value (SUVmax, SUVmean) and total lesion glycolysis (TLG) were considered. Partial volume effect correction (PVC) was performed for SUVmean and TLG estimation. Correlations between PET/CT parameters and 2-year disease-free survival (DFS), local recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS) were assessed. Median patient follow-up was 19.2 months (range 4-24 months). RESULTS: MTV, TLG and PVC-TLG predicting patients' outcome with respect to all the considered local and distant disease control endpoints (LRFS, DMFS and DFS) were 32.4 cc, 469.8 g and 547.3 g, respectively. SUVmean and PVC-SUVmean cut-off values predictive of LRFS and DFS were 10.8 and 13.3, respectively. PVC was able to compensate errors up to 25 % in the primary HNC tumour uptake. Moreover, PVC enhanced the statistical significance of the results. CONCLUSION: FDG PET/CT uptake parameters are predictors of patients' outcome and can potentially identify patients with higher risk of treatment failure that could benefit from more aggressive approaches. Application of PVC is recommended for accurate measurement of PET parameters.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

BACKGROUND: Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). METHOD: Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. RESULTS: The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. COMPARISON WITH EXISTING METHODS: Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. CONCLUSIONS: The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.

Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer.

Specific genome copy number alterations, such as deletions and amplifications are an important factor in tumor development and progression, and are also associated with changes in gene expression. By combining analyses of gene expression and genome copy number we identified genes as candidate biomarkers of BC which were validated as prognostic factors of the disease progression. These results suggest that the proposed combined approach may become a valuable method for BC prognosis.

A partial volume effect correction tailored for 18F-FDG-PET oncological studies.

We have developed, optimized, and validated a method for partial volume effect (PVE) correction of oncological lesions in positron emission tomography (PET) clinical studies, based on recovery coefficients (RC) and on PET measurements of lesion-to-background ratio (L/B m) and of lesion metabolic volume. An operator-independent technique, based on an optimised threshold of the maximum lesion uptake, allows to define an isocontour around the lesion on PET images in order to measure both lesion radioactivity uptake and lesion metabolic volume. RC are experimentally derived from PET measurements of hot spheres in hot background, miming oncological lesions. RC were obtained as a function of PET measured sphere-to-background ratio and PET measured sphere metabolic volume, both resulting from the threshold-isocontour technique. PVE correction of lesions of a diameter ranging from 10 mm to 40 mm and for measured L/B m from 2 to 30 was performed using measured RC curves tailored at answering the need to quantify a large variety of real oncological lesions by means of PET. Validation of the PVE correction method resulted to be accurate (>89%) in clinical realistic conditions for lesion diameter > 1 cm, recovering >76% of radioactivity for lesion diameter < 1 cm. Results from patient studies showed that the proposed PVE correction method is suitable and feasible and has an impact on a clinical environment.

New positron emission tomography derived parameters as predictive factors for recurrence in resected stage I non-small cell lung cancer.

BACKGROUND: The recurrence rate for stage I non-small cell lung cancer is high, with 20-40% of patients that relapse after surgery. The aim of this study was to evaluate new F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) derived parameters, such as standardized uptake value index (SUVindex), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), as predictive factors for recurrence in resected stage I non-small cell lung cancer. METHODS: We retrospectively reviewed 99 resected stage I non-small cell lung cancer patients that were grouped by SUVindex, TLG and MTV above or below their median value. Disease free survival was evaluated as primary end point. RESULTS: The 5-year overall survival and the 5-year disease free survival rates were 62% and 73%, respectively. The median SUVindex, MTL and TLG were 2.73, 2.95 and 9.61, respectively. Patients with low SUVindex, MTV and TLG were more likely to have smaller tumors (p ≤ 0.001). Univariate analysis demonstrated that SUVindex (p = 0.027), MTV (p = 0.014) and TLG (p = 0.006) were significantly related to recurrence showing a better predictive performance than SUVmax (p = 0.031). The 5-year disease free survival rates in patients with low and high SUVindex, MTV and TLG were 84% and 59%, 86% and 62% and 88% and 60%, respectively. The multivariate analysis showed that only TLG was an independent prognostic factor (p = 0.014) with a hazard ratio of 4.782. CONCLUSION: Of the three PET-derived parameters evaluated, TLG seems to be the most accurate in stratifying surgically treated stage I non-small cell lung cancer patients according to their risk of recurrence.

A Decision Support System for the assisted diagnosis of brain tumors: a feasibility study for ¹8F-FDG PET preclinical studies.

Decision support systems for the assisted medical diagnosis offer the main feature of giving assessments which are poorly affected from arbitrary clinical reasoning. Aim of this work was to assess the feasibility of a decision support system for the assisted diagnosis of brain cancer, such approach presenting potential for early diagnosis of tumors and for the classification of the degree of the disease progression. For this purpose, a supervised learning algorithm combined with a pattern recognition method was developed and cross-validated in ¹8F-FDG PET studies of a model of a brain tumour implantation.