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Background PSMA-PET is increasingly used for staging prostate cancer (PCA) patients. However, it is not clear if quantitative imaging parameters of positron emission tomography (PET) have an impact on disease progression and are thus important for the prognosis of localized PCA. Methods This is a monocenter retrospective analysis of 86 consecutive patients with localized intermediate or high-risk PCA and PSMA-PET before treatment The quantitative PET parameters maximum standardized uptake value (SUVmax), tumor asphericity (ASP), PSMA tumor volume (PSMA-TV), and PSMA total lesion uptake (PSMA-TLU = PSMA-TV × SUVmean) were assessed for their prognostic significance in patients with radiotherapy or surgery. Cox regression analyses were performed for biochemical recurrence-free survival, overall survival (OS), local control, and loco-regional control (LRC). Results 67% of patients had high-risk disease, 51 patients were treated with radiotherapy, and 35 with surgery. Analysis of metric PET parameters in the whole cohort revealed a significant association of PSMA-TV (p = 0.003), PSMA-TLU (p = 0.004), and ASP (p < 0.001) with OS. Upon binarization of PET parameters, several other parameters showed a significant association with clinical outcome. When analyzing high-risk patients according to the primary treatment approach, a previously published cut-off for SUVmax (8.6) showed a significant association with LRC in surgically treated (p = 0.048), but not in primary irradiated (p = 0.34) patients. In addition, PSMA-TLU (p = 0.016) seemed to be a very promising biomarker to stratify surgical patients. Conclusion Our data confirm one previous publication on the prognostic impact of SUVmax in surgically treated patients with high-risk PCA. Our exploratory analysis indicates that PSMA-TLU might be even better suited. The missing association with primary irradiated patients needs prospective validation with a larger sample size to conclude a predictive potential.

Purpose: PET-derived metabolic tumor volume (MTV) and total lesion glycolysis of the primary tumor are known to be prognostic of clinical outcome in head and neck cancer (HNC). Including evaluation of lymph node metastases can further increase the prognostic value of PET but accurate manual delineation and classification of all lesions is time-consuming and prone to inter-observer variability. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in PET/CT investigations of HNC patients. Methods: Automated lesion delineation was performed with a residual 3D U-Net convolutional neural network (CNN) incorporating a multi-head self-attention block. 698 [18F]FDG PET/CT scans from 3 different sites and 5 public databases were used for network training and testing. An external dataset of 181 [18F]FDG PET/CT scans from 2 additional sites was employed to assess the generalizability of the network. In these data, primary tumor and metastases were interactively delineated and labeled by two experienced physicians. Performance of the trained network models was assessed by 5-fold cross-validation in the main dataset and by pooling results from the 5 developed models in the external dataset. The Dice similarity coefficient (DSC) for individual delineation tasks and the primary tumor/metastasis classification accuracy were used as evaluation metrics. Additionally, a survival analysis using univariate Cox regression was performed comparing achieved group separation for manual and automated delineation, respectively. Results: In the cross-validation experiment, delineation of all malignant lesions with the trained U-Net models achieves DSC of 0.885, 0.805, and 0.870 for primary tumor, LN metastases, and the union of both, respectively. In external testing, the DSC reaches 0.850, 0.724, and 0.823 for primary tumor, LN metastases, and the union of both, respectively. The voxel classificat

Aim: Image derived PET parameters such as metabolic tumor volume (MTV), total lesion glycolysis, and tumor asphericity of the primary tumor have been shown to be prognostic of clinical outcome of patients with head and neck cancer (HNC). Evaluation of lymph node metastases in addition to the primary tumor further increases the prognostic value of PET. Such analysis requires, however, accurate delineation and classification of all lesions which is very time-consuming when performed manually. The goal of this study is development of an automated tool for MTV delineation of primary tumor and lymph node metastases in HNC in PET/CT. Methods: Automated delineation of the HNC cancer lesions was performed with a residual 3D U-Net convolutional neural network (CNN). 698 FDG PET/CT scans from 3 different sites and 4 public databases were used for network training (N=558) and testing (N=140). In these data, primary tumor and metastases were manually delineated and accordingly labeled by an experienced physician. This manual delineation served as the ground truth for network training. Performance of the trained network model was assessed in the test data using the Dice similarity coefficient for primary tumor, metastases, and the union of all lesions, respectively. Results: The derived U-Net model is capable of accurate delineation of malignant lesions achieving a Dice coefficient of 0.847 for indiscriminative segmentation. Treating primary tumor and lymph node metastases as distinct classes yields Dice coefficients of 0.840 and 0.714 for the respective delineations. Conclusions: In this work, we present the first CNN model for MTV delineation and classification in HNC. The developed network model allows to quickly perform satisfactory delineation of (and discrimination between) primary tumor and lymph node metastases in HNC with only minimal manual corrections possibly required. It thus is able to improve and to accelerate study data evaluation in quantitative PET and does als

Deep Learning based approaches for automated analysis of tomographic image data are drawing ever increasing attention in Radiology and Nuclear Medicine. With the advent of the new generation of PET scanners with massively enlarged axial field of view (“total body PET”) the importance of integrating such approaches into clinical workflows will further increase. In the present study we report on our application of a convolutional neural network (CNN) for automated survival analysis in head and neck cancer (HNC): PET parameters such as metabolic tumor volume (MTV), total lesion glycolysis, and asphericity of the primary tumor are known to be prognostic of clinical outcome in HNC patients. Additionally including evaluation of lymph node metastases further increases the prognostic value of PET. However, accurate manual delineation and classification of all lesions is time consuming and incompatible with clinical routine. Our goal, therefore, was development and evaluation of an automated tool for MTV delineation/classification of primary tumor and lymph node metastases in HNC in PET. Automated delineation of the HNC cancer lesions was per- formed with a residual 3D U-Net convolutional neural network (CNN). 698 FDG PET/CT scans from 3 different sites and 4 public databases were used for network training and testing. In these data, primary tumor and metastases were manually delineated (with assistance of semi-automatic tools) and accordingly labeled by an experienced physician. Performance of the trained network models was assessed by 5-fold cross validation using the Dice similarity coefficient for individual delineation tasks. Additionally, survival analysis using univariate Cox regression was performed. Delineation of all malignant lesions with the trained U-Net model achieves a Dice coefficient of 0.866 when not dis- criminating between primary tumor and lymph nodes. Treating primary tumor and lymph node metastases as distinct classes yields Dice coefficients of 0.835

Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology—both imaging hardware and reconstruction software—into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and interpretation with a focus on recent developments in PET technology. Finally, we discuss the relationship between quantitative accuracy and subjective image interpretation.

Background PSMA PET is frequently used for staging of prostate cancer patients. Furthermore, there is increasing interest to use PET information for personalized local treatment approaches in surgery and radiotherapy, especially for focal treatment strategies. However, it is not well established which quantitative imaging parameters show highest correlation with clinical and histological tumor aggressiveness. Methods This is a retrospective analysis of 135 consecutive patients with non-metastatic prostate cancer and PSMA PET before any treatment. Clinical risk parameters (PSA values, Gleason score and D’Amico risk group) were correlated with quantitative PET parameters maximum standardized uptake value (SUVmax), mean SUV (SUVmean), tumor asphericity (ASP) and PSMA tumor volume (PSMA-TV). Results Most of the investigated imaging parameters were highly correlated with each other (correlation coefficients between 0.20 and 0.95). A low to moderate, however significant, correlation of imaging parameters with PSA values (0.19 to 0.45) and with Gleason scores (0.17 to 0.31) was observed for all parameters except ASP which did not show a significant correlation with Gleason score. Receiver operating characteristics for the detection of D’Amico high-risk patients showed poor to fair sensitivity and specificity for all investigated quantitative PSMA PET parameters (Areas under the curve (AUC) between 0.63 and 0.73). Comparison of AUC between quantitative PET parameters by DeLong test showed significant superiority of SUVmax compared to SUVmean for the detection of high-risk patients. None of the investigated imaging parameters significantly outperformed SUVmax. Conclusion Our data confirm prior publications with lower number of patients that reported moderate correlations of PSMA PET parameters with clinical risk factors. With the important limitation that Gleason scores were only biopsy-derived in this study, there is no indication that the investigated additional parameters

Purpose [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) parameters have shown prognostic value in nasopharyngeal carcinomas (NPC), mostly in monocenter studies. The aim of this study was to assess the prognostic impact of standard and novel PET parameters in a multicenter cohort of patients. Methods The established PET parameters metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximal standardized uptake value (SUVmax) as well as the novel parameter tumor asphericity (ASP) were evaluated in a retrospective multicenter cohort of 114 NPC patients with FDG-PET staging, treated with (chemo)radiation at 8 international institutions. Uni- and multivariable Cox regression and Kaplan-Meier analysis with respect to overall survival (OS), event-free survival (EFS), distant metastases-free survival (FFDM), and locoregional control (LRC) was performed for clinical and PET parameters. Results When analyzing metric PET parameters, ASP showed a significant association with EFS (p = 0.035) and a trend for OS (p = 0.058). MTV was significantly associated with EFS (p = 0.026), OS (p = 0.008) and LRC (p = 0.012) and TLG with LRC (p = 0.019). TLG and MTV showed a very high correlation (Spearman’s rho = 0.95), therefore TLG was subesequently not further analysed. Optimal cutoff values for defining high and low risk groups were determined by maximization of the p-value in univariate Cox regression considering all possible cutoff values. Generation of stable cutoff values was feasible for MTV (p<0.001), ASP (p = 0.023) and combination of both (MTV+ASP = occurrence of one or both risk factors, p<0.001) for OS and for MTV regarding the endpoints OS (p<0.001) and LRC (p<0.001). In multivariable Cox (age >55 years + one binarized PET parameter), MTV >11.1ml (hazard ratio (HR): 3.57, p<0.001) and ASP > 14.4% (HR: 3.2, p = 0.031) remained prognostic for OS. MTV additionally remained prognostic for LRC (HR: 4.86 p<0.001) and EFS (HR: 2.51 p = 0.004). Bootstrappi

Background Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies. Methods This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases. Results Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF4/8, PSF + TOF2/17, Q.Clear and the reference algorithm TOF4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF4/8, 2%; PSF + TOF2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF4/8, 38% reduced to 2%; PSF + TOF2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportion

Background Bayesian penalized likelihood reconstruction for PET (e.g., GE Q.Clear) aims at improving convergence of lesion activity while ensuring sufficient signal-to-noise ratio (SNR). This study evaluated reconstructed spatial resolution, maximum/peak contrast recovery (CRmax/CRpeak) and SNR of Q.Clear compared to time-of-flight (TOF) OSEM with and without point spread function (PSF) modeling. Methods The NEMA IEC Body phantom was scanned five times (3 min scan duration, 30 min between scans, background, 1.5–3.9 kBq/ml F18) with a GE Discovery MI PET/CT (3-ring detector) with spheres filled with 8-, 4-, or 2-fold the background activity concentration (SBR 8:1, 4:1, 2:1). Reconstruction included Q.Clear (beta, 150/300/450), “PSF+TOF4/16” (iterations, 4; subsets, 16; in-plane filter, 2.0 mm), “OSEM+TOF4/16” (identical parameters), “PSF+TOF2/17” (2 it, 17 ss, 2.0 mm filter), “OSEM+TOF2/17” (identical), “PSF+TOF4/8” (4 it, 8 ss, 6.4 mm), and “OSEM+TOF2/8” (2 it, 8 ss, 6.4 mm). Spatial resolution was derived from 3D sphere activity profiles. RC as (sphere activity concentration [AC]/true AC). SNR as (background mean AC/background AC standard deviation). Results Spatial resolution of Q.Clear150 was significantly better than all conventional algorithms at SBR 8:1 and 4:1 (Wilcoxon, each p < 0.05). At SBR 4:1 and 2:1, the spatial resolution of Q.Clear300/450 was similar or inferior to PSF+TOF4/16 and OSEM+TOF4/16. Small sphere CRpeak generally underestimated true AC, and it was similar for Q.Clear150/300/450 as with PSF+TOF4/16 or PSF+TOF2/17 (i.e., relative differences < 10%). Q.Clear provided similar or higher CRpeak as OSEM+TOF4/16 and OSEM+TOF2/17 resulting in a consistently better tradeoff between CRpeak and SNR with Q.Clear. Compared to PSF+TOF4/8/OSEM+TOF2/8, Q.Clear150/300/450 showed lower SNR but higher CRpeak. Conclusions Q.Clear consistently improved reconstructed spatial resolution at high and medium SBR compared to PSF+TOF and OSEM+TOF, but only with beta =

Background: Tumor hypoxia measured by dedicated tracers like [18F]fluoromisonidazole (FMISO) is a well-established prognostic factor in head and neck squamous cell carcinomas (HNSCC) treated with definitive chemoradiation (CRT). However, prevalence and characteristics of positron emission tomography (PET) measured hypoxia in patients with relapse after previous irradiation is missing. Here we report imaging findings of a prospective pilot study in HNSCC patients treated with re-irradiation. Methods: In 8 patients with recurrent HNSCC, diagnosed at a median of 18 months after initial radiotherapy/CRT, [18F]fluorodeoxyglucose (FDG)-PET/CT (n=8) and FMISO-PET/MRI (n=7) or FMISO-PET/CT (n=1) were performed. Static FMISO-PET was performed after 180 min. MRI sequences in PET/MRI included diffusion-weighted imaging with apparent diffusion coefficient (ADC) values and contrast enhanced T1w imaging (StarVIBE). Lesions (primary tumor recurrence, 4; cervical lymph node, 1; both, 3) were delineated on FDG-PET and FMISO-PET data using a background-adapted threshold-based method. SUVmax and SUVmean in FDG- and FMISO-PET were derived, as well as maximum tumor-to-muscle ratio (TMRmax) and hypoxic volume with 1.6-fold muscle SUVmean (HV1.6) in FMISO-PET. Intensity of lesional contrast enhancement was rated relative to contralateral normal tissue. Average ADC values were derived from a 2D region of interest in the tumor. Results: In FMISO-PET, median TMRmax was 1.7 (range: 1.1-1.8). Median HV1.6 was 0.05 ml (range: 0-7.3 ml). Only in 2/8 patients, HV1.6 was ≥1.0 ml. In FDG-PET, median SUVmax was 9.3 (range: 5.0-20.1). On contrast enhanced imaging four lesions showed decreased and four lesions increased contrast enhancement compared to non-pathologic reference tissue. Median average ADC was 1,060 ×106 mm2/s (range: 840-1,400 ×106 mm2/s). Conclusions: This pilot study implies that hypoxia detectable by FMISO-PET may not be as prevalent as expected among loco-regional recurrent HNSCC. A

Ziel/Aim: In patients with non-small cell lung cancer (NSCLC) undergoing treatment with curative intent, the asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) has been demonstrated as a prognostic factor. This study aimed at validation in an independent cohort with sufficient sample size. Methodik/Methods: Retrospective study in 313 NSCLC patients (203 men; median age, 67 [41-87] a) undergoing FDG-PET/CT with the same scanner prior to treatment in curative intent (always including resection of the primary tumor). 137 patients had UICC stage I, 79 patients stage II and 97 patients stage III disease (adenocarcinoma [ADC], 153; squamous cell carcinoma [SCC], 143, other, 17). Delineation of primary tumor MTV with semiautomated background-adapted threshold relative to its SUVmax. Univariable Cox regression for progression-free (PFS) and overall survival (OS) for PET parameters (MTV, ASP, SUVmax, SUVmean), clinical (UICC stage I vs. II vs. III), histological (SCC vs. ADC) and treatment variables (Rx/1 vs. R0 resection, chemotherapy yes/no, radiotherapy yes/no). Multivariable Cox of parameters significant in univariate Cox. Ergebnisse/Results: Events (progression, relapse, death) occurred in 169/313 patients, 139 patients died (median follow-up in survivors, 37 months). In multivariable Cox for OS, ASP >33.3% (hazard ratio [HR], 1.53 [95%-confidence interval, 1.02-2.3]), Rx/1 vs. R0 resection (HR, 2.47 [1.5-4.2]) and SCC vs. ADC (HR, 1.53 [1.1-2.2]) were significant. Log-rank test for ASP was significant at any cut-off from 18% upwards. Among separate UICC stages, ASP was only prognostic for OS in stage II (optimal, >19.5%; median OS, 33 vs. 59 months; p<0.01). In multivariable Cox for PFS, ASP >21.2% (HR, 1.75 [1.2-2.5]) and Rx/1 vs. R0 (HR, 2.48 [1.5-4.1]) were significant. Log-rank test for ASP was significant at any cut-off from 10-60%. Schlussfolgerungen/Conclusions: ASP was validated as an independent predictor of PFS and OS in NSCLC patients with

Background: Today, the standardized uptake value (SUV) is essentially the only means for quantitative evaluation of static [18 F-]fluorodeoxyglucose (FDG) positron emission tomography (PET) investigations. However, the SUV approach has several well-known shortcomings which adversely affect the reliability of the SUV as a surrogate of the metabolic rate of glucose consumption. The standard uptake ratio (SUR), i.e., the uptake time-corrected ratio of tumor SUV to image-derived arterial blood SUV, has been shown in the first clinical studies to overcome most of these shortcomings, to decrease test-retest variability, and to increase the prognostic value in comparison to SUV. However, it is unclear, to what extent the SUR approach is vulnerable to observer variability of the additionally required blood SUV (BSUV) determination. The goal of the present work was the investigation of the interobserver variability of image-derived BSUV. Methods: FDG PET/CT scans from 83 patients (72 male, 11 female) with non-small cell lung cancer (N = 46) or head and neck cancer (N = 37) were included. BSUV was determined by 8 individuals, each applying a dedicated delineation tool for the BSUV determination in the aorta. Two of the observers applied two further tools. Altogether, five different delineation tools were used. With each used tool, delineation was performed for the whole patient group, resulting in 12 distinct observations per patient. Intersubject variability of BSUV determination was assessed using the fractional deviations for the individual patients from the patient group average and was quantified as standard deviation (SDis ), 95% confidence interval, and range. Interobserver variability of BSUV determination was assessed using the fractional deviations of the individual observers from the observer-average for the considered patient and quantified as standard deviations (SDp , SDd ) or root mean square (RMS), 95% confidence interval, and range in each patient, each obser

Background Standardized treatment in pediatric patients with Hodgkin’s lymphoma (HL) follows risk stratification by tumor stage, erythrocyte sedimentation rate and tumor bulk. We aimed to identify quantitative parameters from pretherapeutic FDG-PET to assist prediction of response to induction chemotherapy. Methods Retrospective analysis in 50 children with HL (f:18; m:32; median age, 14.8 [4–18] a) consecutively treated according to EuroNet-PHL-C1 (n = 42) or -C2 treatment protocol (n = 8). Total metabolic tumor volume (MTV) in pretherapeutic FDG-PET was defined using a semi-automated, background-adapted threshold. Metabolic (SUVmax, SUVmean, SUVpeak, total lesion glycolysis [MTV*SUVmean]) and heterogeneity parameters (asphericity [ASP], entropy, contrast, local homogeneity, energy, and cumulative SUV-volume histograms) were derived. Early response assessment (ERA) was performed after 2 cycles of induction chemotherapy according to treatment protocol and verified by reference rating. Prediction of inadequate response (IR) in ERA was based on ROC analysis separated by stage I/II (1 and 26 patients) and stage III/IV disease (7 and 16 patients) or treatment group/level (TG/TL) 1 to 3. Results IR was seen in 28/50 patients (TG/TL 1, 6/12 patients; TG/TL 2, 10/17; TG/TL 3, 12/21). Among all PET parameters, MTV best predicted IR; ASP was the best heterogeneity parameter. AUC of MTV was 0.84 (95%-confidence interval, 0.69–0.99) in stage I/II and 0.86 (0.7–1.0) in stage III/IV. In patients of TG/TL 1, AUC of MTV was 0.92 (0.74–1.0); in TG/TL 2 0.71 (0.44–0.99), and in TG/TL 3 0.85 (0.69–1.0). Patients with high vs. low MTV had IR in 86 vs. 0% in TG/TL 1, 80 vs. 29% in TG/TL 2, and 90 vs. 27% in TG/TL 3 (cut-off, > 80 ml, > 160 ml, > 410 ml). Conclusions In this explorative study, high total MTV best predicted inadequate response to induction therapy in pediatric HL of all pretherapeutic FDG-PET parameters – in both low and high stages as well as the 3 different TG/TL.