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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

Correction to: Strahlenther Onkol 2021 https://doi.org/10.1007/s00066-021-01812-2

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

This comprehensive review written by experts in their field gives an overview on the current status of incorporating positron emission tomography (PET) into radiation treatment planning. Moreover, it highlights ongoing studies for treatment individualisation and per-treatment tumour response monitoring for various primary tumours. Novel tracers and image analysis methods are discussed. The authors believe this contribution to be of crucial value for experts in the field as well as for policy makers deciding on the reimbursement of this powerful imaging modality.

Purpose: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor’s glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods: Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spill-over from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results: The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean ± SD) = (-0.5± 2.2)% with a 95% confidence interval of [−5.1, 3.8]% and a total range of [-10.0, 12.0]%. For four test cases the derived ROIs were unusable (<1 ml). Conclusion: CNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting.

Aim: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor's glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods: Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), namely U-Net. 632 FDG PET/CT scans from 4 different sites were used for network training (N=208) and testing (N=424). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spill-over from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results: The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Notably, using both CT and PET data as input for network training allows the trained network to derive unbiased BSUVs by detecting and excluding aorta segments affected by attenuation artifacts or spill-over. Comparison of manually (M) and automatically (A) derived BSUVs shows excellent concordance: the mean paired M-A difference in the 424 test cases is (mean +/- SD)=(0.2 +/- 3.1)% with a 95% confidence interval of [-6.6, 5.7]%. For a single test case the M-A difference exceeded 10%. Conclusion: CNNs offer a viable approach for automatic BSUV determination. Our trained network exhibits a performance comparable to an experienced human observer and might already be considered suitable for supe

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

Ziel/Aim: Bei Patienten mit pulmonalen oder gastrointestinalen neuroendokrinen Tumoren (NET) kann der teils nebenwirkungsreiche mTOR-Inhibitor Everolimus das progressionsfreie Überleben (PFS) verlängern. Ziel dieser Studie war die Evaluation der läsionalen Asphärizität (ASP) in der prätherapeutischen Somatostatinrezeptor (SSR)-Bildgebung hinsichtlich einer weiteren Spezifizierung von Patientensubgruppen die von der Therapie im Besonderen profitieren. Methodik/Methods: Retrospektive, bizentrische Analyse von 30 Patienten (m=17; w=13; medianes Alter, 59 [38-75] Jahre) mit einer prätherapeutischen [111In-DTPA0]octreotid Szintigraphie. Die funktionellen Volumina von bis zu 3 führenden Läsionen je Patient (n=74) wurden mittels halbautomatischer, hintergrundadaptierter Segmentierung erhoben (ROVER, Version 2.1.20) und jeweils die sphärische Heterogenität des SSR-Besatzes - die ASP - berechnet. Für die maximal gemessene ASP je Patient erfolgten Kaplan-Meier-Analysen inklusive log-rank-Tests sowie eine univariate Cox Regression bzgl. des PFS. Ergebnisse/Results: Alle 30 Patienten waren unter Everolimus progredient oder entwickelten ein Rezidiv. In der univariaten Analyse erwies sich die ASP [Median, 12,7%; Spannweite, 1,1–43,5] als Prädiktor des PFS (p=0,033; Hazard Ratio [HR], 2,391 je eine Einheit). Patienten mit einer hohen ASP (>14%) zeigten ein medianes PFS von 6,7 Monaten (95%-Konfidenzintervall [CI], 2,1-11,4 Monate) gegenüber 14,4 Monaten (95%-CI, 12,5-16,3 Monate; p=0,028) für eine ASP ≤14%. Schlussfolgerungen/Conclusions: Mittels ASP kann im untersuchten Patientenkollektiv eine Prädiktion des PFS erreicht werden. In Bezug auf eine Nutzen-/Risiko-Abwägung zur Indikationsstellung der Everolimusgabe erscheint der Einsatz der prätherapeutischen ASP bzgl. einer optimalen Patientenauswahl bedeutsam; Patienten mit einer ASP >14% hatten ein signifikant kürzeres PFS.