Clinical Assessment of 177Lu-DOTATATE Quantification by Comparison of SUV-Based Parameters Measured on Both Post-PRRT SPECT/CT and 68Ga-DOTATOC PET/CT in Patients With Neuroendocrine Tumors: A Feasibility Study

The Impact of Posttreatment Imaging in Peptide Receptor Radionuclide Therapy

Posttreatment imaging of γ-emissions after peptide receptor radionuclide therapy (PRRT) can be used to perform quantitative dosimetry as well as assessment response using qualitative measures. We aimed to assess the impact of qualitative posttreatment imaging on the management of patients undergoing PRRT. Methods: In this retrospective study, we evaluated 100 patients with advanced well-differentiated neuroendocrine tumors undergoing PRRT, who had posttreatment SPECT/CT imaging at 24 h. First, we evaluated the qualitative assessment of response at each cycle. Then using a chart review, we determined the impact on management from the posttreatment imaging. The changes in management were categorized as major or minor, and the cycles at which these changes occurred were noted. Additionally, tumor grade was also evaluated. Results: Of the 100 sequential patients reviewed, most (80% after cycle 2, 79% after cycle 3, and 73% after cycle 4) showed qualitatively stable disease during PRRT. Management changes were observed in 27% (n = 27) of patients; 78% of those (n = 21) were major, and 30% (n = 9) were minor. Most treatment changes occurred after cycle 2 (33% major, 67% minor) and cycle 3 (62% major, 33% minor). Higher tumor grade correlated with increased rate of changes in management (P = 0.006). Conclusion: In this retrospective study, qualitative analysis of posttreatment SPECT/CT imaging informed changes in management in 27% of patients. Patients with higher-grade tumors had a higher rate of change in management, and most of the management changes occurred after cycles 2 and 3. Incorporating posttreatment imaging into standard PRRT workflows could potentially enhance patient management.

A radiomic- and dosiomic-based machine learning regression model for pretreatment planning in 177 Lu-DOTATATE therapy

BACKGROUND

Standardized patient-specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose-effect relationship. Data sets of consistent and reliable inter-center dosimetry findings are required to characterize this relationship.

PURPOSE

We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177 Lu-DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data.

METHODS

Pretreatment and posttreatment data for 20 patients with NETs treated with 177 Lu-DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects.

RESULTS

We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68 Ga-DOTATOC positron emission tomography (PET)/CT and posttherapy 177 Lu-DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68 Ga-DOTATOC PET/CT and any posttherapy 177 Lu-DOTATATE treatment cycle SPECT/CT scans as well as any 177 Lu-DOTATATE SPECT/CT treatment cycle and the consequent 177 Lu-DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from -0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68 Ga-DOTATOC PET/CT and first 177 Lu-DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%-96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet-based features proved to have high correlated predictive value, whereas non-linear-based ML regression algorithms proved to be more capable than the linear-based of producing precise prediction in our case.

CONCLUSIONS

The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision-making, especially regarding dose escalation issues.

[18F] AlF-NOTA-FAPI-04 PET/CT can predict treatment response and survival in patients receiving chemotherapy for inoperable pancreatic ductal adenocarcinoma

PURPOSE

We investigated whether uptake of [18F] AlF-NOTA-FAPI-04 on positron emission tomography/computed tomography (PET/CT) could predict treatment response and survival in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

We prospectively evaluated 47 patients with histopathologically confirmed primary PDAC who provided pretreatment [18F] AlF-NOTA-FAPI-04 scans to detect fibroblast activation protein (FAP) on the tumor surface by uptake of [18F] AlF-NOTA-FAPI-04. PDAC specimens were immunohistochemically stained with cancer-associated fibroblast (CAF) markers. We obtained a second PET scan after one cycle of chemotherapy to study changes in FAPI uptake variables from before to during treatment. Correlations between baseline PET variables and CAF-related immunohistochemical markers were assessed with Spearman's rank test. Cox regression and Kaplan-Meier methods were used to assess relationships between disease progression and potential predictors. Receiver operating characteristic (ROC) curve analysis was used to define the optimal cut-off points for distinguishing patients according to good response vs. poor response per RECIST v.1.1.

RESULTS

The FAPI PET variables maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion FAP expression (TLF) were positively correlated with CAF markers (FAP, α-smooth muscle actin, vimentin, S100A4, and platelet-derived growth factor receptor α/β, all P < 0.05). MTV was associated with survival in patients with inoperable PDAC (all P < 0.05). Cox multivariate regression showed that MTV was associated with overall survival (MTV hazard ratio [HR] = 1.016, P = 0.016). Greater changes from before to during chemotherapy in SUVmax, MTV, and TLF were associated with good treatment response (all P < 0.05). ΔMTV, ΔTLF, and ΔSUVmax had larger areas under the curve than ΔCA19-9 for predicting treatment response. Kaplan-Meier analysis showed that the extent of change in MTV and TLF from before to after treatment predicted progression-free survival, with cut-off values (based on medians) of - 4.95 for ΔMTV (HR = 8.09, P = 0.013) and - 77.83 for ΔTLF (HR = 4.62, P = 0.012).

CONCLUSIONS

A higher baseline MTV on [18F] AlF-NOTA-FAPI-04 scans was associated with poorer survival in patients with inoperable PDAC. ΔMTV was more sensitive for predicting response than ΔCA19-9. These results are clinically meaningful for identifying patients with PDAC who are at high risk of disease progression.

Predicting [177Lu]Lu-HA-DOTATATE kidney and tumor accumulation based on [68Ga]Ga-HA-DOTATATE diagnostic imaging using semi-physiological population pharmacokinetic modeling

BACKGROUND

Prediction of [177Lu]Lu-HA-DOTATATE kidney and tumor uptake based on diagnostic [68Ga]Ga-HA-DOTATATE imaging would be a crucial step for precision dosing of [177Lu]Lu-HA-DOTATATE. In this study, the population pharmacokinetic (PK) differences between [177Lu]Lu-HA-DOTATATE and [68Ga]Ga-HA-DOTATATE were assessed and subsequently [177Lu]Lu-HA-DOTATATE was predicted based on [68Ga]Ga-HA-DOTATATE imaging.

METHODS

A semi-physiological nonlinear mixed-effects model was developed for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE, including six compartments (representing blood, spleen, kidney, tumor lesions, other somatostatin receptor expressing organs and a lumped rest compartment). Model parameters were fixed based on a previously developed physiologically based pharmacokinetic model for [68Ga]Ga-HA-DOTATATE. For [177Lu]Lu-HA-DOTATATE, PK parameters were based on literature values or estimated based on scan data (four time points post-injection) from nine patients. Finally, individual [177Lu]Lu-HA-DOTATATE uptake into tumors and kidneys was predicted based on individual [68Ga]Ga-HA-DOTATATE scan data using Bayesian estimates. Predictions were evaluated compared to observed data using a relative prediction error (RPE) for both area under the curve (AUC) and absorbed dose. Lastly, to assess the predictive value of diagnostic imaging to predict therapeutic exposure, individual prediction RPEs (using Bayesian estimation) were compared to those from population predictions (using the population model).

RESULTS

Population uptake rate parameters for spleen, kidney and tumors differed by a 0.29-fold (15% relative standard error (RSE)), 0.49-fold (15% RSE) and 1.43-fold (14% RSE), respectively, for [177Lu]Lu-HA-DOTATATE compared to [68Ga]Ga-HA-DOTATATE. Model predictions adequately described observed data in kidney and tumors for both peptides (based on visual inspection of goodness-of-fit plots). Individual predictions of tumor uptake were better (RPE AUC -40 to 28%) compared to kidney predictions (RPE AUC -53 to 41%). Absorbed dose predictions were less predictive for both tumor and kidneys (RPE tumor and kidney -51 to 44% and -58 to 82%, respectively). For most patients, [177Lu]Lu-HA-DOTATATE tumor accumulation predictions based on individual PK parameters estimated from diagnostic imaging outperformed predictions based on population parameters.

CONCLUSION

Our semi-physiological PK model indicated clear differences in PK parameters for [68Ga]Ga-HA-DOTATATE and [177Lu]Lu-HA-DOTATATE. Diagnostic images provided additional information to individually predict [177Lu]Lu-HA-DOTATATE tumor uptake compared to using a population approach. In addition, individual predictions indicated that many aspects, apart from PK differences, play a part in predicting [177Lu]Lu-HA-DOTATATE distribution.

99mTc-PYP SPECT and SPECT/CT quantitation for diagnosing cardiac transthyretin amyloidosis

BACKGROUND

We investigated quantitative 99mTc-pyrophosphate (PYP) SPECT/CT reproducibility and accuracy for diagnosing cardiac transthyretin amyloidosis (ATTR), and whether SPECT/CT improved visual and quantitative results compared to SPECT-only.

METHODS

Data were reviewed for 318 patients with suspected ATTR who underwent PYP SPECT/CT. Myocardial-to-blood pool count (MBP) ratios were computed and repeated independently > 1 month later. A physician independently scored LV myocardial-to-rib uptake on SPECT/CT as: 0 (negative), 1 < rib (equivocal), 2 = rib (positive) or 3 > rib (positive), and the image quality as: 1 (poor), 2 (adequate), and 3 (good). SPECT-only MBP ratios and visual scores were assessed separately for a subgroup of the first sequential 191 patients.

RESULTS

25% of patients had positive myocardial uptake (myocardial-to-rib uptake score of ≥ 2). SPECT/CT MBP ratios were reproducible (1.35 ± .68 vs 1.33 ± .74, p = .09) and corresponded with visual scores ≥ 2 (ROC AUC = 99 ± 1%) more accurately than SPECT-only MBPs (93 ± 3%, p = .02). SPECT/CT image quality was better than that of SPECT-only (2.7 ± .5 vs 2.1 ± .5, p < .0001) with fewer equivocal results (2.6% vs 22.5%, p < .0001).

CONCLUSION

SPECT/CT produces MBP ratios that are reproducible and accurately identify a positive scan, with better image quality and fewer equivocal cases than SPECT-only.

177Lu-DOTATATE Theranostics: Predicting Renal Dosimetry From Pretherapy 68Ga-DOTATATE PET and Clinical Biomarkers

PURPOSE

Pretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression.

PATIENTS AND METHODS

Pretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning-based tools. Dosimetry was performed by coupling the multi-time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD).

RESULTS

The median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2-1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models.

CONCLUSIONS

Pretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.

Correlations between [68Ga]Ga-DOTA-TOC Uptake and Absorbed Dose from [177Lu]Lu-DOTA-TATE

PURPOSE

The aim of this paper was to investigate correlations between pre- therapeutic [⁶⁸Ga]Ga-DOTA-TOC uptake and absorbed dose to tumours from therapy with [¹⁷⁷Lu]Lu-DOTA-TATE.

METHODS

This retrospective study included 301 tumours from 54 GEP-NET patients. The tumours were segmented on pre-therapeutic [⁶⁸Ga]Ga-DOTA-TOC PET/CT, and post-therapy [¹⁷⁷Lu]Lu-DOTA-TATE SPECT/CT images, using a fixed 40% threshold. The SPECT/CT images were used for absorbed dose calculations by assuming a linear build-up from time zero to day one, and mono-exponential wash-out after that. Both SUV_mean and SUV_max were measured from the PET images. A linear absorbed-dose prediction model was formed with SUV_mean as the independent variable, and the accuracy was tested with a split 70-30 training-test set.

RESULTS

Mean SUV_mean and SUV_max from [⁶⁸Ga]Ga-DOTA-TOC PET was 24.0 (3.6-84.4) and 41.0 (6.7-146.5), and the mean absorbed dose from [¹⁷⁷Lu]Lu-DOTA-TATE was 26.9 Gy (2.4-101.9). A linear relationship between SUV_mean and [¹⁷⁷Lu]Lu-DOTA-TATE activity concentration at 24 h post injection was found (R² = 0.44, p < 0.05). In the prediction model, a root mean squared error and a mean absolute error of 1.77 and 1.33 Gy/GBq, respectively, were found for the test set.

CONCLUSIONS

There was a high inter- and intra-patient variability in tumour measurements, both for [⁶⁸Ga]Ga-DOTA-TOC SUVs and absorbed doses from [¹⁷⁷Lu]Lu-DOTA-TATE. Depending on the required accuracy, [⁶⁸Ga]Ga-DOTA-TOC PET imaging may estimate the [¹⁷⁷Lu]Lu-DOTA-TATE uptake. However, there could be a high variance between predicted and actual absorbed doses.

A physiologically based pharmacokinetic model for [68Ga]Ga-(HA-)DOTATATE to predict whole-body distribution and tumor sink effects in GEP-NET patients

BACKGROUND

Little is known about parameters that have a relevant impact on (dis)similarities in biodistribution between various ⁶⁸Ga-labeled somatostatin analogues. Additionally, the effect of tumor burden on organ uptake remains unclear. Therefore, the aim of this study was to describe and compare organ and tumor distribution of [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE using a physiologically based pharmacokinetic (PBPK) model and to identify factors that might cause biodistribution and tumor uptake differences between both peptides. In addition, the effect of tumor burden on peptide biodistribution in gastroenteropancreatic (GEP) neuroendocrine tumor (NET) patients was assessed.

METHODS

A PBPK model was developed for [⁶⁸Ga]Ga-(HA-)DOTATATE in GEP-NET patients. Three tumor compartments were added, representing primary tumor, liver metastases and other metastases. Furthermore, reactions describing receptor binding, internalization and recycling, renal clearance and intracellular degradation were added to the model. Scan data from GEP-NET patients were used for evaluation of model predictions. Simulations with increasing tumor volumes were performed to assess the tumor sink effect.

RESULTS

Data of 39 and 59 patients receiving [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE, respectively, were included. Evaluations showed that the model adequately described image-based patient data and that different receptor affinities caused organ uptake dissimilarities between both peptides. Sensitivity analysis indicated that tumor blood flow and blood volume impacted tumor distribution most. Tumor sink predictions showed a decrease in spleen uptake with increasing tumor volume, which seemed clinically relevant for patients with total tumor volumes higher than ~ 550 mL.

CONCLUSION

The developed PBPK model adequately predicted tumor and organ uptake for this GEP-NET population. Relevant organ uptake differences between [⁶⁸Ga]Ga-DOTATATE and [⁶⁸Ga]Ga-HA-DOTATATE were caused by different affinity profiles, while tumor uptake was mainly affected by tumor blood flow and blood volume. Furthermore, tumor sink predictions showed that for the majority of patients a tumor sink effect is not expected to be clinically relevant.

Predicting subacute hematological toxicity of 177Lu-DOTATATE therapy using healthy organs' uptake on post-treatment quantitative SPECT: A pilot study

The aim is to investigate the usefulness of 177Lu-DOTA-0-Tyr3-Octreotate (DOTATATE) healthy organs' (spleen, kidneys, bone marrow) standard uptake value for the prediction of subacute hematological toxicity in patients undergoing 177Lu-DOTATATE treatment. All patients referred from January 2021 to May 2022 for 177Lu-DOTATATE treatment were retrospectively screened. For each treatment session, baseline clinical data including age, sex, weight, delay between 177Lu-DOTATATE treatment and last cold somatostatin analogue intake were collected. Mean standardized uptake value (SUVmean) of healthy organs was measured and analyzed by generalized linear mixed effect models. Outcomes (significant decrease of platelets, hemoglobin levels and neutrophils) were assessed 1 month later, considering their within-subject biological coefficient of variation, published by the European Federation of Clinical Chemistry and Laboratory Medicine. A total of 9 patients (33 treatment sessions) were included. No predictive factors were identified for platelet and neutrophil decrease. Splenic SUVmean was found to be a significant predictor of hemoglobin levels decrease. Using an optimal threshold of ≥6.22, derived sensitivity and specificity to predict hemoglobin decrease were 85.7% [46.4; 99.0] and 76.9% [57.5; 89.2] respectively with an accuracy of 82.4%. Although not significantly predictive of hematological toxicity, bone marrow SUVmean and renal SUVmean were correlated with splenic SUVmean. Quantitative single photon emission computed tomography and healthy organs analysis might help to foresee hematological subacute toxicity in patients undergoing 177Lu-DOTATATE treatment and improve patient management.

Differences in tumor-to-normal organ SUV ratios measured with 68 Ga-DOTATATE PET compared with 177 Lu-DOTATATE SPECT in patients with neuroendocrine tumors

BACKGROUND

Our goal is to quantitatively compare radiotracer biodistributions within tumors and major normal organs on pretherapy 68 Ga-DOTATATE PET to post-therapy 177 Lu-DOTATATE single-photon emission computed tomography (SPECT) in patients receiving peptide receptor radionuclide therapy (PRRT).

METHODS

PET/CT at ~ 60 min postinjection of Ga-68 DOTATATE and research 177 Lu-SPECT/CT imaging ~ at 4 h (SPECT1) and ~ 24 h (SPECT2) post-cycle#1 were available. Manual contours of lesions on baseline CT or MRI were applied to co-registered SPECT/CT and PET/CT followed by deep learning-based CT auto-segmentation of organs. Tumor-to-normal organ ratios (TNR) were calculated from standardized uptake values (SUV) mean and SUV peak for tumor, and SUV mean for non-tumoral liver (nliver), spleen and kidney.

RESULTS

There were 90 lesons in 24 patients with progressive metastatic neuroendocrine tumor. The correlation between PET and SPECT SUV TNRs were poor/moderate: PET versus SPECT1 R 2  = 0.19, 0.21, 0.29; PET versus SPECT2 R 2  = 0.06, 0.16, 0.33 for TNR nliver ,TNR spleen ,TNR kidney , respectively. Across all patients, the average value of the TNR measured on PET was significantly lower than on SPECT at both time points ( P  < 0.001). Using SUV mean for tumor, average TNR values and 95% confidence intervals (CI) were PET: TNR nliver  = 3.5 [CI: 3.0-3.9], TNR spleen  = 1.3 [CI, 1.2-1.5], TNR kidney  = 1.7 [CI: 1.6-1.9]; SPECT1: TNR nliver  = 10 [CI: 8.2-11.7], TNR spleen  = 2.9 [CI: 2.5-3.4], TNR kidney  = 2.8 [CI: 2.3-3.3]; SPECT2: TNR nliver  = 16.9 [CI: 14-19.9], TNR spleen  = 3.6 [CI: 3-4.2], TNR kidney  = 3.6 [CI: 3.0-4.2]. Comparison of PET and SPECT results in a sphere phantom study demonstrated that these differences are not attributed to imaging modality.

CONCLUSIONS

Differences in TNR exist for the theranostic pair, with significantly higher SUV TNR on 177 Lu SPECT compared with 68 Ga PET. We postulate this phenomenon is due to temporal differences in DOTATATE uptake and internalization in tumor as compared to normal organs.

Prognostic value of whole-body PET volumetric parameters extracted from 68Ga-DOTATOC-PET/CT in well-differentiated neuroendocrine tumors

Aim: To evaluate the prognostic value of somatostatin receptor tumor burden (SRTB) at ⁶⁸Ga-DOTATOC positron emission tomography/computed tomography (PET/CT) in patients with well-differentiated neuroendocrine tumors (WD-NETs). Methods: We retrospectively analyzed ⁶⁸Ga-DOTATOC-PET/CT of 84 patients with histologically confirmed WD-NETs (51 G1, 30 G2 and 3 G3). For each PET/CT, all DOTATOC-avid lesions were independently segmented by 2 operators using a customized threshold based on the healthy liver maximum standardized uptake value (SUV_max) using LIFEx 5.1. Somatostatin receptor expressing tumor volume (SRETV) and total lesion somatostatin receptor expression (TLSRE=SRETV*SUVmean) were extracted for each lesion and then whole-body SRETV and TLSRE (SRETVwb and TLSREwb) were defined as the sum of SRETV and TLSRE of all segmented lesions in each patient, respectively. Time to progression (TTP) was defined as the combination of disease-free-survival in patients undergoing curative surgery (n = 10) and progression-free survival for patients with unresectable/metastatic disease (n = 74). TTP and overall survival (OS) were calculated by Kaplan-Meier analysis, log-rank test, and Cox's proportional hazard model. Results: After a median follow-up period of 15.5 months disease progression was confirmed in 35 patients (41.7%) and 14 patients died. Higher SRETVwb (>39.1ml) and TLSREwb (>306.8g) were significantly correlated with shorter median TTP (TTP = 12months vs not reached; p<0.001). In multivariate analysis, SRETVwb (P = 0.005) was the only independent predictor of TTP regardless of histopathologic grade and TNM staging. Conclusion: According to our results, SRETVwb and TLSREwb extracted from ⁶⁸Ga-DOTATOC-PET/CT could predict TTP/OS and might have an important clinical utility in the management of in patients with WD-NETs.

Prospective study of dynamic whole-body 68Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors

To present the feasibility of a dynamic whole-body (DWB) ⁶⁸Ga-DOTATOC-PET/CT acquisition in patients with well-differentiated neuroendocrine tumors (WD-NETs). Sixty-one patients who underwent a DWB ⁶⁸Ga-DOTATOC-PET/CT for a histologically proven/highly suspected WD-NET were prospectively included. The acquisition consisted in single-bed dynamic acquisition centered on the heart, followed by the DWB and static acquisitions. For liver, spleen and tumor (1–5/patient), Ki values (in ml/min/100 ml) were calculated according to Patlak's analysis and tumor-to-liver (TLR-Ki) and tumor-to-spleen ratios (TSR-Ki) were recorded. Ki-based parameters were compared to static parameters (SUVmax/SUVmean, TLR/TSRmean, according to liver/spleen SUVmean), in the whole-cohort and according to the PET system (analog/digital). A correlation analysis between SUVmean/Ki was performed using linear and non-linear regressions. Ki-liver was not influenced by the PET system used, unlike SUVmax/SUVmean. The regression analysis showed a non-linear relation between Ki/SUVmean (R² = 0.55,0.68 and 0.71 for liver, spleen and tumor uptake, respectively) and a linear relation between TLRmean/TLR-Ki (R² = 0.75). These results were not affected by the PET system, on the contrary of the relation between TSRmean/TSR-Ki (R² = 0.94 and 0.73 using linear and non-linear regressions in digital and analog systems, respectively). Our study is the first showing the feasibility of a DWB ⁶⁸Ga-DOTATOC-PET/CT acquisition in WD-NETs.