Semi-automated segmentation methods of SSTR PET for dosimetry prediction in refractory meningioma patients treated by SSTR-targeted peptide receptor radionuclide therapy.

OBJECTIVES: Tumor dosimetry with somatostatin receptor-targeted peptide receptor radionuclide therapy (SSTR-targeted PRRT) by 177Lu-DOTATATE may contribute to improved treatment monitoring of refractory meningioma. Accurate dosimetry requires reliable and reproducible pretherapeutic PET tumor segmentation which is not currently available. This study aims to propose semi-automated segmentation methods to determine metabolic tumor volume with pretherapeutic 68Ga-DOTATOC PET and evaluate SUVmean-derived values as predictive factors for tumor-absorbed dose. METHODS: Thirty-nine meningioma lesions from twenty patients were analyzed. The ground truth PET and SPECT volumes (VolGT-PET and VolGT-SPECT) were computed from manual segmentations by five experienced nuclear physicians. SUV-related indexes were extracted from VolGT-PET and the semi-automated PET volumes providing the best Dice index with VolGT-PET (Volopt) across several methods: SUV absolute-value (2.3)-threshold, adaptative methods (Jentzen, Otsu, Contrast-based method), advanced gradient-based technique, and multiple relative thresholds (% of tumor SUVmax, hypophysis SUVmean, and meninges SUVpeak) with optimal threshold optimized. Tumor-absorbed doses were obtained from the VolGT-SPECT, corrected for partial volume effect, performed on a 360° whole-body CZT-camera at 24, 96, and 168 h after administration of 177Lu-DOTATATE. RESULTS: Volopt was obtained from 1.7-fold meninges SUVpeak (Dice index 0.85 ± 0.07). SUVmean and total lesion uptake (SUVmeanxlesion volume) showed better correlations with tumor-absorbed doses than SUVmax when determined with the VolGT (respective Pearson correlation coefficients of 0.78, 0.67, and 0.56) or Volopt (0.64, 0.66, and 0.56). CONCLUSION: Accurate definition of pretherapeutic PET volumes is justified since SUVmean-derived values provide the best tumor-absorbed dose predictions in refractory meningioma patients treated by 177Lu-DOTATATE. This study provides a semi-automated segmentation method of pretherapeutic 68Ga-DOTATOC PET volumes to achieve good reproducibility between physicians. CLINICAL RELEVANCE STATEMENT: SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET are predictive of tumor-absorbed doses in refractory meningiomas treated by 177Lu-DOTATATE, justifying to accurately define pretherapeutic PET volumes. This study provides a semi-automated segmentation of 68Ga-DOTATOC PET images easily applicable in routine. KEY POINTS: • SUVmean-derived values from pretherapeutic 68Ga-DOTATOC PET images provide the best predictive factors of tumor-absorbed doses related to 177Lu-DOTATATE PRRT in refractory meningioma. • A 1.7-fold meninges SUVpeak segmentation method used to determine metabolic tumor volume on pretherapeutic 68Ga-DOTATOC PET images of refractory meningioma treated by 177Lu-DOTATATE is as efficient as the currently routine manual segmentation method and limits inter- and intra-observer variabilities. • This semi-automated method for segmentation of refractory meningioma is easily applicable to routine practice and transferrable across PET centers.

Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study.

Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[18F]-fluoro-phenyl-alanine (18F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic 18F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic 18F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.

Effects of Carbidopa Premedication on 18F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis.

PURPOSE: This study aimed to determine the impact of carbidopa premedication on static, dynamic and radiomics parameters of 18F-FDOPA PET in brain tumors. METHODS: The study included 54 patients, 18 of whom received carbidopa, who underwent 18F-FDOPA PET for newly diagnosed gliomas. SUV-derived, 105 radiomics features and TTP dynamic parameters were extracted from volumes of interest in healthy brains and tumors. Simulation of the effects of carbidopa on time-activity curves were generated. RESULTS: All static and TTP dynamic parameters were significantly higher in healthy brain regions of premedicated patients (ΔSUVmean = +53%, ΔTTP = +48%, p < 0.001). Furthermore, carbidopa impacted 81% of radiomics features, of which 92% correlated with SUVmean (absolute correlation coefficient ≥ 0.4). In tumors, premedication with carbidopa was an independent predictor of SUVmean (ΔSUVmean = +52%, p < 0.001) and TTP (ΔTTP = +24%, p = 0.025). All parameters were no longer significantly modified by carbidopa premedication when using ratios to healthy brain. Simulated data confirmed that carbidopa leads to higher tumor TTP values, corrected by the ratios. CONCLUSION: In 18F-FDOPA PET, carbidopa induces similarly higher SUV and TTP dynamic parameters and similarly impacts SUV-dependent radiomics in healthy brain and tumor regions, which is compensated for by correcting for the tumor-to-healthy-brain ratio. This is a significant advantage for multicentric study harmonization.