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Ziel/Aim: Patients with locally advanced non-small-cell lung cancer (NSCLC) have a high risk of developing distant metastases. It has been shown that immunotherapy after radiochemotherapy can significantly improve the prognosis. Therefore, biomarkers for individualized therapy escalation are urgently needed. One such biomarker could be the total metabolic volume of primary tumor and lymph node (LN) metastases. However, delineation of LN metastases with currently available methods is time consuming and error-prone. The goal of this study was to investigate to which extend this delineation can be performed with deep learning methods. Methodik/Methods: Automated delineation was performed with a pretrained 3D U-Net convolutional neural network (CNN) previously derived for a different head and neck cancer delineation task. 517 [18F]FDG PET/CT scans of NSCLC patients were used for further network training and testing using a 5-fold cross-validation scheme. In these data, manual delineation and labeling of primary tumor and metastases was performed by an experienced physician serving as the ground truth for network training and testing. Ergebnisse/Results: The derived CNN models are capable of accurate delineation, achieving a Dice similarity coefficient of 0.854. Sensitivity of lesion detection was 0.841 and positive predictive value was 0.847. Accuracy of lesion classification as primary tumor or LN metastases was 82.2%. Schlussfolgerungen/Conclusions: In this work, we present a CNN able to perform delineation of and discrimination between primary tumor and lymph node metastases in NSCLC with only minimal manual corrections possibly required. It thus is able to accelerate study data evaluation in quantitative PET and does also have potential for clinical application.

Aim/Introduction: Patients with locally advanced non-small-cell lung cancer (NSCLC) have a high risk of developing distant metastases. It has been shown that applying immunotherapy after radiochemotherapy can significantly improve the prognosis for affected patients. In this context, biomarkers for individualized therapy escalation are urgently needed. One such biomarker could be the total metabolic volume of primary tumor and lymph node (LN) metastases (total tumor burden, TTB). However, delineation of tumor lesions with conventional methods is time consuming and error-prone, especially for the LN metastases. The goal of this study was to investigate feasibility of such delineation with deep learning methods. Materials and Methods: Automated delineation was performed with a 3D U-Net convolutional neural network (CNN) developed with the nnU-Net software package [1]. The default nnU-Net training parameters were modified to provide better training stability with small lesion targets as well as to better balance sensitivity vs. positive predictive value (PPV) of lesion detection. A dataset consisting of 517 [18F]FDG-PET/CT scans of NSCLC patients was used for the network training and testing following 5-fold cross-validation scheme. In these data, the ground truth labels were defined via manual delineation and labeling of primary tumor and metastases by an experienced physician. Results: The derived CNN models were capable of accurate delineation, achieving a mean (median) Dice similarity coefficient of 0.831 (0.891). The sensitivity and PPV of lesion detection was 0.974/0.829/0.887 and 0.963/0.741/0.824 for primary tumor/LN metastases/union of both, respectively. Accuracy of lesion classification as primary tumor or LN metastases was 92.1%. Manually and automatically derived TTBs were highly correlated with R2=0.96 and a mean absolute difference of 5.4 ml (after rejecting 1% of the outliers). Conclusion: In this work, we present CNN models able to perform delineation

MOTIVATION: Patients with locally advanced non-small-cell lung cancer (NSCLC) have a high risk of developing distant metastases. It was shown that immunotherapy after radiochemotherapy significantly improves the prognosis. Therefore, biomarkers to identify such patients are urgently needed. Here, we investigated the prognostic utility of total tumor burden (TTB) in NSCLC for prediction of distant metastases. METHODS: Altogether, 165 patients (65+/-9)y, 100m) with newly diagnosed NSCLC were included. All patients received FDG-PET/CT prior to definitive radiochemotherapy. In the PET images, the metabolically active volume (MTV) of the primary tumor and of all FDG avid lymph nodes was delineated with an adaptive threshold method. TTB was computed as the cumulative volume of primary tumor and lymph nodes. Survival analysis with respect to freedom from distant metastases (FFDM) was performed. RESULTS: Survival analysis revealed MTV and TTB as prognostic factors for FFDM (P=0.004 and P<0.001, respectively). Hazard ratio (HR) for TTB was significantly higher than HR for MTV (1.9 vs. 2.5, P=0.007). CONCLUSIONS: In the investigated group of patients, the inclusion of lymph nodes into MTV computation significantly increased the prognostic value of FDG-PET. Further investigations are necessary to confirm these preliminary results.

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.

Ziel/Aim The aim of the study was an independent evaluation of the prognostic value of a gene expression signature (EPPI) and the PET-derived tumor asphericity (ASP) in non-small cell lung cancer (NSCLC) patients. Methodik/Methods This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. Altogether 253 NSCLC patients were included, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data was available for 120 patients. Univariate and multivariate Cox regression and Kaplan-Meier analysis were calculated for the primary endpoint progression-free survival (PFS) and additional endpoints. Ergebnisse/Results In the whole cohort a significant association with PFS was observed for ASP (p<0.001) and EPPI (p=0.012). On multivariate testing, EPPI remained significantly associated with PFS (p=0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p=0.012). In stage II patients, ASP was significantly associated with PFS (p=0.009) and a previously published cutoff value for ASP (19.5%) was successfully validated (p=0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p=0.033). Exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. Schlussfolgerungen/Conclusions The combination of EPPI and ASP seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.

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

The aim of this retrospective multicenter study was an independent validation of a gene expression signature ECM-related prognostic and predictive indicator (EPPI) and the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non-small cell lung cancer (NSCLC) patients. The whole cohort comprised 253 NSCLC patients, all treated with surgery. Clinical and PET parameters were available for all patients, additional gene expression data for 120 patients. Univariate and multivariate Cox regression and Kaplan-Meier analyses were calculated for progression-free survival (PFS). A significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, ASP was significantly associated with PFS (p = 0.012), and EPPI (p = 0.018) in patients with additional gene data. In stage II patients, ASP was significantly associated with PFS (p = 0.009) and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). EPPI showed a significant association with PFS in stage II patients, too (p = 0.033). Exploratory combination of ASP and EPPI showed potentially improved stratification. We report the first successful validation of EPPI and ASP in stage II NSCLC patients, combination of both parameters seems encouraging.

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

Purpose 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is utilized for staging and treatment planning of head and neck squamous cell carcinomas (HNSCC). Some older publications on the prognostic relevance showed inconclusive results, most probably due to small study sizes. This study evaluates the prognostic and potentially predictive value of FDG-PET in a large multi-center analysis. Methods Original analysis of individual FDG-PET and patient data from 16 international centers (8 institutional datasets, 8 public repositories) with 1104 patients. All patients received curative intent radiotherapy/chemoradiation (CRT) and pre-treatment FDG-PET imaging. Primary tumors were semi-automatically delineated for calculation of SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Cox regression analyses were performed for event-free survival (EFS), overall survival (OS), loco-regional control (LRC) and freedom from distant metastases (FFDM). Results FDG-PET parameters were associated with patient outcome in the whole cohort regarding clinical endpoints (EFS, OS, LRC, FFDM), in uni- and multivariate Cox regression analyses. Several previously published cut-off values were successfully validated. Subgroup analyses identified tumor- and human papillomavirus (HPV) specific parameters. In HPV positive oropharynx cancer (OPC) SUVmax was well suited to identify patients with excellent LRC for organ preservation. Patients with SUVmax of 14 or less were unlikely to develop loco-regional recurrence after definitive CRT. In contrast FDG PET parameters deliver only limited prognostic information in laryngeal cancer. Conclusion FDG-PET parameters bear considerable prognostic value in HNSCC and potential predictive value in subgroups of patients, especially regarding treatment de-intensification and organ-preservation. The potential predictive value needs further validation in appropriate control groups. Further research on advanced imaging appro

Bronchialkarzinome sind in Deutschland die dritthäufigste Tumorerkrankung. Trotz erheblicher therapeutischer Fortschritte ist die Prognose der Lungentumoren nach wie vor ungünstig, die relative 5-Jahres- Überlebensrate nach Diagnose eines Bron- chialkarzinoms beträgt lediglich 16 % [3]. Hinsichtlich der Tumorart werden nicht- kleinzellige (NSCLC) und kleinzellige Bron- chialkarzinome unterschieden. Bei fortgeschrittenen NSCLC besteht die Standardtherapie aus einer gleichzei- tig durchgeführten Radiochemotherapie, gefolgt von einer Immuntherapie. In Metaanalysen konnte die Überlegenheit der simultanen Radiochemotherapie ge- genüber einem sequenziellen Vorgehen gezeigt werden.

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 To compare the dosimetric results of an in silico study among intensity-modulated photon (IMRT) and robust multi-field optimized intensity-modulated proton (rMFO-IMPT) treatment techniques using a dose-escalated simultaneously integrated boost (SIB) approach in locally recurrent or locally advanced pancreatic cancer patients. Material and Methods For each of 15 locally advanced pancreatic cancer patients, a volumetric modulated arc therapy (VMAT), a Tomotherapy (TOMO), and an rMFO-IMPT treatment plan was optimized on free-breathing treatment planning computed tomography (CT) images. For the photon treatment plans, doses of 66Gy and 51Gy, both as SIB in 30 fractions, were prescribed to the gross tumor volume (GTV) and to the planning target volume (PTV), respectively. For the proton plans, a dose prescription of 66Gy(RBE) to the GTV and of 51Gy(RBE) to the clinical target volume (CTV) was planned. For each SIB-treatment plan, doses to the targets and OARs were evaluated and statistically compared. Results All treatment techniques reached the prescribed doses to the GTV and CTV or PTV. The stomach and the bowel, of the latter in particular the duodenum and the small bowel, were found to be frequently exposed to doses exceeding 50Gy, irrespective of the treatment technique. For doses below 50Gy, the IMPT technique was statistically significant superior to both IMRT techniques regarding decreasing dose to the OARs, e.g. volume of the bowel receiving 15Gy (V15Gy) was reduced for rMFO compared to VMAT (p=0.003) and TOMO (p<0.001). Conclusion With all photon and proton techniques investigated, the radiation dose to gastrointestinal OARs remained critical when treating patients with unresectable locally advanced or locally recurrent pancreatic cancer using a dose-escalated SIB approach.

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.

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

We have investigated the prognostic value of two novel interim 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) parameters in patients undergoing chemoradiation (CRT) for esophageal squamous cell carcinoma (ESCC): one tumor parameter (maximal standardized uptake ratio rSUR) and one normal tissue parameter (change of FDG uptake within irradiated nontumor‐affected esophagus ∆ SUVNTO). PET data of 134 European and Chinese patients were analyzed. Parameter establishment was based on 36 patients undergoing preoperative CRT plus surgery, validation was performed in 98 patients receiving definitive CRT. Patients received PET imaging prior and during fourth week of CRT. Clinical parameters, baseline PET parameters, and interim PET parameters (rSUR and ∆ SUVNTO) were analyzed and compared to event‐free survival (EFS), overall survival (OS), loco‐regional control (LRC) and freedom from distant metastases (FFDM). Combining rSUR and ∆ SUVNTO revealed a strong prognostic impact on EFS, OS, LRC and FFDM in patients undergoing preoperative CRT. In the definitive CRT cohort, univariate analysis with respect to EFS revealed several staging plus both previously established interim PET parameters as significant prognostic factors. Multivariate analyses revealed only rSUR and ∆ SUVNTO as independent prognostic factors (p = 0.003, p = 0.008). Combination of these parameters with the cutoff established in preoperative CRT revealed excellent discrimination of patients with a long or short EFS (73% vs . 17% at 2 years, respectively) and significantly discriminated all other endpoints (OS, p < 0.001; LRC, p < 0.001; FFDM, p = 0.02), even in subgroups. Combined use of interim FDG‐PET derived parameters ∆ SUVNTO and rSUR seems to have predictive potential, allowing to select responders for definitive CRT and omission of surgery.

Background and purpose: Tumor hypoxia plays an important role in head and neck squamous cell carcinomas (HNSCC). Various positron emission tomography (PET) tracers promise non-invasive assessment of tumor hypoxia. So far, the applicability of hypoxia PET is hampered by monocentric imaging trials with few patients. Materials and methods: Multicenter individual patient data based meta-analysis of the original PET data from four prospective imaging trials was performed. All patients had localized disease and were treated with curatively intended radio(-chemo)therapy. Hypoxia PET imaging was performed with 18F-Fluoromisonidazole (FMISO, 102 patients) or 18F-Fluoroazomycin-arabinoside (FAZA, 51 patients). Impact of hypoxia PET parameters on loco-regional control (LRC) and overall survival (OS) was analyzed by uni- and multivariable Cox regression. Results: Baseline characteristics between participating centers differed significantly, especially regarding T stage (p<0.001), tumor volume (p<0.001) and p16 status (p=0.009). The commonly used hypoxia parameters, maximal tumor-to-muscle ratio (TMRmax) and hypoxic volume with 1.6 threshold (HV1.6), showed a strong association with LRC (p=0.001) and OS (p<0.001). These findings were irrespective of the radiotracer and the same cut-off values could be applied for FMISO and FAZA (TMRmax>2.0 or HV1.6>1.5 ml). The effect size of TMRmax was similar for subgroups of patients defined by radiotracer, p16 status and FDG-PET parameters for LRC and OS, respectively. Conclusion: PET measured hypoxia is robust and has a strong impact on LRC and OS in HNSCC. The most commonly investigated tracers FMISO and FAZA can probably be used equivalently in multicenter trials. Optimal strategies to improve the dismal outcome of hypoxic tumors remain elusive.

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

Detection of patients with esophageal squamous cell carcinoma (ESCC) who do not benefit from standard chemoradiation (CRT) is an important medical need. Radiomics using 18-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a promising approach. In this retrospective study of 184 patients with locally advanced ESCC. 152 patients from one center were grouped into a training cohort (n = 100) and an internal validation cohort (n = 52). External validation was performed with 32 patients treated at a second center. Primary endpoint was disease-free survival (DFS), secondary endpoints were overall survival (OS) and local control (LC). FDG-PET radiomics features were selected by Lasso-Cox regression analyses and a separate radiomics signature was calculated for each endpoint. In the training cohort radiomics signatures containing up to four PET derived features were able to identify non-responders in regard of all endpoints (DFS p < 0.001, LC p = 0.003, OS p = 0.001). After successful internal validation of the cutoff values generated by the training cohort for DFS (p = 0.025) and OS (p = 0.002), external validation using these cutoffs was successful for DFS (p = 0.002) but not for the other investigated endpoints. These results suggest that pre-treatment FDG-PET features may be useful to detect patients who do not respond to CRT and could benefit from alternative treatment.

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

Purpose The prognosis for patients with inoperable esophageal carcinoma is still poor and the reliability of individual therapy outcome prediction based on clinical parameters is not convincing. In a recent publication, we were able to show that PET can provide independent prognostic information in such a patient group and that the tumor-to-blood standard uptake ratio (SUR) can improve the prognostic value of tracer uptake values. The present investigation addresses the question of whether the distinctly improved prognostic value of SUR can be confirmed in a similar patient group that was examined and treated at a different site. Methods 18F-FDG PET/CT was performed in 147 consecutive patients (115 male, 32 female, mean age: 62 years) with newly diagnosed esophageal squamous cell carcinoma prior to definitive radiochemotherapy. In the PET images, the metabolic active volume (MTV) of the primary tumor was delineated with an adaptive threshold method. For the resulting ROIs, SUVmax and total lesion glycolysis (TLG = MTV × SUVmean) were computed. The blood SUV was determined by manually delineating the aorta in the low-dose CT. SUR values were computed as ratio of tumor SUV and blood SUV. Univariate Cox regression and Kaplan–Meier analysis with respect to overall survival (OS), distant-metastases-free survival (DM), and locoregional control (LRC) was performed. Additionally, a multivariate Cox regression including clinically relevant parameters was performed. Results Univariate Cox regression revealed MTV, TLG, and SURmax as significant prognostic factors for OS. MTV as well as TLG were significant prognostic factors for LRC while SURmax showed only a trend for significance. None of the PET parameters was prognostic for DM. In univariate analysis, SUVmax was not prognostic for any of the investigated clinical endpoints. In multivariate analysis (T-stage, N-stage, MTV, and SURmax), MTV was an independent prognostic factor for OS and showed a trend for significance for L