Impact of partial-volume correction in oncological PET studies:A systematic review and meta-analysis

Purpose: Positron-emission tomography can be useful in oncology for diagnosis, (re)staging, determining prognosis, and response assessment. However, partial-volume effects hamper accurate quantification of lesions &lt;2–3× the PET system’s spatial resolution, and the clinical impact of this is not evident. This systematic review provides an up-to-date overview of studies investigating the impact of partial-volume correction (PVC) in oncological PET studies.Methods: We searched in PubMed and Embase databases according to the PRISMA statement, including studies from inception till May 9, 2016. Two reviewers independently screened all abstracts and eligible full-text articles and performed quality assessment according to QUADAS-2 and QUIPS criteria. For a set of similar diagnostic studies, we statistically pooled the results using bivariate meta-regression.Results: Thirty-one studies were eligible for inclusion. Overall, study quality was good. For diagnosis and nodal staging, PVC yielded a strong trend of increased sensitivity at expense of specificity. Meta-analysis of six studies investigating diagnosis of pulmonary nodules (679 lesions) showed no significant change in diagnostic accuracy after PVC (p = 0.222). Prognostication was not improved for non-small cell lung cancer and esophageal cancer, whereas it did improve for head and neck cancer. Response assessment was not improved by PVC for (locally advanced) breast cancer or rectal cancer, and it worsened in metastatic colorectal cancer.Conclusions: The accumulated evidence to date does not support routine application of PVC in standard clinical PET practice. Consensus on the preferred PVC methodology in oncological PET should be reached. Partial-volume-corrected data should be used as adjuncts to, but not yet replacement for, uncorrected data.</p

Targeting PSMA Revolutionizes the Role of Nuclear Medicine in Diagnosis and Treatment of Prostate Cancer

Targeting the prostate-specific membrane antigen (PSMA) protein has become of great clinical value in prostate cancer (PCa) care. PSMA positron emission tomography/computed tomography (PET/CT) is increasingly used in initial staging and restaging at biochemical recurrence in patients with PCa, where it has shown superior detection rates compared to previous imaging modalities. Apart from targeting PSMA for diagnostic purposes, there is a growing interest in developing ligands to target the PSMA-protein for radioligand therapy (RLT). PSMA-based RLT is a novel treatment that couples a PSMA-antibody to (alpha or beta-emitting) radionuclide, such as Lutetium-177 (177Lu), to deliver high radiation doses to tumor cells locally. Treatment with 177Lu-PSMA RLT has demonstrated a superior overall survival rate within randomized clinical trials as compared to routine clinical care in patients with metastatic castration-resistant prostate cancer (mCRPC). The current review provides an overview of the literature regarding recent developments in nuclear medicine related to PSMA-targeted PET imaging and Theranostics

Optimization and validation of 18F-DCFPyL PET radiomics-based machine learning models in intermediate- to high-risk primary prostate cancer.

IntroductionRadiomics extracted from prostate-specific membrane antigen (PSMA)-PET modeled with machine learning (ML) may be used for prediction of disease risk. However, validation of previously proposed approaches is lacking. We aimed to optimize and validate ML models based on 18F-DCFPyL-PET radiomics for the prediction of lymph-node involvement (LNI), extracapsular extension (ECE), and postoperative Gleason score (GS) in primary prostate cancer (PCa) patients.MethodsPatients with intermediate- to high-risk PCa who underwent 18F-DCFPyL-PET/CT before radical prostatectomy with pelvic lymph-node dissection were evaluated. The training dataset included 72 patients, the internal validation dataset 24 patients, and the external validation dataset 27 patients. PSMA-avid intra-prostatic lesions were delineated semi-automatically on PET and 480 radiomics features were extracted. Conventional PET-metrics were derived for comparative analysis. Segmentation, preprocessing, and ML methods were optimized in repeated 5-fold cross-validation (CV) on the training dataset. The trained models were tested on the combined validation dataset. Combat harmonization was applied to external radiomics data. Model performance was assessed using the receiver-operating-characteristics curve (AUC).ResultsThe CV-AUCs in the training dataset were 0.88, 0.79 and 0.84 for LNI, ECE, and GS, respectively. In the combined validation dataset, the ML models could significantly predict GS with an AUC of 0.78 (p0.05) and ECE (0.66, p>0.05), but a lower AUC for GS (0.73, pConclusionIn internal and external validation, 18F-DCFPyL-PET radiomics-based ML models predicted high postoperative GS but not LNI or ECE in intermediate- to high-risk PCa. Therefore, the clinical benefit seems to be limited. These results underline the need for external and/or multicenter validation of PET radiomics-based ML model analyses to assess their generalizability

Accuracy and Precision of Partial-Volume Correction in Oncological PET/CT Studies

Accurate quantification of tracer uptake in small tumors using PET is hampered by the partial-volume effect as well as by the method of volume-of-interest (VOI) delineation. This study aimed to investigate the effect of partial-volume correction (PVC) combined with several VOI methods on the accuracy and precision of quantitative PET. Methods: Four image-based PVC methods and resolution modeling (applied as PVC) were used in combination with several common VOI methods. Performance was evaluated using simulations, phantom experiments, and clinical repeatability studies. Simulations were based on a whole-body F-18-FDG PET scan in which differently sized spheres were placed in lung and mediastinum. A National Electrical Manufacturers Association NU2 quality phantom was used for the experiments. Repeatability data consisted of an F-18-FDG PET/CT study on 11 patients with advanced non-small cell lung cancer and an F-18-fluoromethylcholine PET/CT study on 12 patients with metastatic prostate cancer. Results: Phantom data demonstrated that most PVC methods were strongly affected by the applied resolution kernel, with accuracy differing by about 20%-50% between full-width-at half-maximum settings of 5.0 and 7.5 mm. For all PVC methods, large differences in accuracy were seen among all VOI methods. Additionally, the image-based PVC methods were observed to have variable sensitivity to the accuracy of the VOI methods. For most PVC methods, accuracy was strongly affected by more than a 2.5-mm misalignment of true (simulated) VOI. When the optimal VOI method for each PVC method was used, high accuracy could be achieved. For example, resolution modeling for mediastinal lesions and iterative deconvolution for lung lesions were 99% +/- 1.5% and 99% +/- 0.9% accurate, respectively, for spheres 15-40 mm in diameter. Precision worsened slightly for resolution modeling and to a larger extent for some image-based PVC methods. Uncertainties in delineation propagated into uncertainties in PVC performance, as confirmed by the clinical data. Conclusion: The accuracy and precision of the tested PVC methods depended strongly on VOI method, resolution settings, contrast, and spatial alignment of the VOI. PVC has the potential to substantially improve the accuracy of tracer uptake assessment, provided that robust and accurate VOI methods become available. Commonly used delineation methods may not be adequate for this purpose

Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network

Purpose: Visual reading of 18F-florbetapir positron emission tomography (PET) scans is used in the diagnostic process of patients with cognitive disorders for assessment of amyloid-ß (Aß) depositions. However, this can be time-consuming, and difficult in case of borderline amyloid pathology. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop, train, validate and test a convolutional neural network (CNN) for discriminating between Aß negative and positive 18F-florbetapir PET scans in patients with subjective cognitive decline (SCD). Methods: 18F-florbetapir PET images were acquired and visually assessed. The SCD cohort consisted of 133 patients from the SCIENCe cohort and 22 patients from the ADNI database. From the SCIENCe cohort, standardized uptake value ratio (SUVR) images were computed. From the ADNI database, SUVR images were extracted. 2D CNNs (axial, coronal and sagittal) were built to capture features of the scans. The SCIENCe scans were randomly divided into training and validation set (5-fold cross-validation), and the ADNI scans were used as test set. Performance was evaluated based on average accuracy, sensitivity and specificity from the cross-validation. Next, the best performing CNN was evaluated on the test set. Results: The sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity. Conclusion: The 2D-CNN algorithm can classify Aß negative and positive 18F-florbetapir PET scans with high performance in SCD patients

Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features

Purpose Biomarkers that can accurately predict outcome in DLBCL patients are urgently needed. Radiomics features extracted from baseline [F-18]-FDG PET/CT scans have shown promising results. This study aims to investigate which lesionand feature-selection approaches/methods resulted in the best prediction of progression after 2 years. Methods A total of 296 patients were included. 485 radiomics features (n= 5 conventional PET, n= 22 morphology, n= 50 intensity, n = 408 texture) were extracted for all individual lesions and at patient level, where all lesions were aggregated into one VOL 18 features quantifying dissemination were extracted at patient level. Several lesion selection approaches were tested (largest or hottest lesion, patient level [all with/without dissemination], maximum or median of all lesions) and compared to the predictive value of our previously published model. Several data reduction methods were applied (principal component analysis, recursive feature elimination (RFE), factor analysis, and univariate selection). The predictive value of all models was tested using a fivefold cross-validation approach with 50 repeats with and without oversampling, yielding the mean cross-validated AUC (CV-AUC). Additionally, the relative importance of individual radiomics features was determined. Results Models with conventional PET and dissemination features showed the highest predictive value (CV-AUC: 0.720.75). Dissemination features had the highest relative importance in these models. No lesion selection approach showed significantly higher predictive value compared to our previous model. Oversampling combined with RFE resulted in highest CV-AUCs. Conclusion Regardless of the applied lesion selection or feature selection approach and feature reduction methods, patient level conventional PET features and dissemination features have the highest predictive value

Preoperative characteristics of all included patients per dataset.

Preoperative characteristics of all included patients per dataset.</p

The AUC, sensitivity and specificity of the radiomics-based models per outcome.

The AUC, sensitivity and specificity of the radiomics-based models per outcome.</p

Final histopathological results of all included patients who underwent radical prostatectomy and extended pelvic lymph node dissection per dataset.

Final histopathological results of all included patients who underwent radical prostatectomy and extended pelvic lymph node dissection per dataset.</p