Stacking Ensemble Learning-Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma.

This study aimed to develop an analytic approach based on [18F]FDG PET radiomics using stacking ensemble learning to improve the outcome prediction in diffuse large B-cell lymphoma (DLBCL). Methods: In total, 240 DLBCL patients from 2 medical centers were divided into the training set (n = 141), internal testing set (n = 61), and external testing set (n = 38). Radiomics features were extracted from pretreatment [18F]FDG PET scans at the patient level using 4 semiautomatic segmentation methods (SUV threshold of 2.5, SUV threshold of 4.0 [SUV4.0], 41% of SUVmax, and SUV threshold of mean liver uptake [PERCIST]). All extracted features were harmonized with the ComBat method. The intraclass correlation coefficient was used to evaluate the reliability of radiomics features extracted by different segmentation methods. Features from the most reliable segmentation method were selected by Pearson correlation coefficient analysis and the LASSO (least absolute shrinkage and selection operator) algorithm. A stacking ensemble learning approach was applied to build radiomics-only and combined clinical-radiomics models for prediction of 2-y progression-free survival and overall survival based on 4 machine learning classifiers (support vector machine, random forests, gradient boosting decision tree, and adaptive boosting). Confusion matrix, receiver-operating-characteristic curve analysis, and survival analysis were used to evaluate the model performance. Results: Among 4 semiautomatic segmentation methods, SUV4.0 segmentation yielded the highest interobserver reliability, with 830 (66.7%) selected radiomics features. The combined model constructed by the stacking method achieved the best discrimination performance. For progression-free survival prediction in the external testing set, the areas under the receiver-operating-characteristic curve and accuracy of the stacking-based combined model were 0.771 and 0.789, respectively. For overall survival prediction, the stacking-based combined model achieved an area under the curve of 0.725 and an accuracy of 0.763 in the external testing set. The combined model also demonstrated a more distinct risk stratification than the International Prognostic Index in all sets (log-rank test, all P < 0.05). Conclusion: The combined model that incorporates [18F]FDG PET radiomics and clinical characteristics based on stacking ensemble learning could enable improved risk stratification in DLBCL.

Positron Emission Tomography Molecular Imaging for Phenotyping and Management of Lymphoma.

Positron emission tomography (PET) represents molecular imaging for non-invasive phenotyping of physiological and biochemical processes in various oncological diseases. PET imaging with 18F-fluorodeoxyglucose (18F-FDG) for glucose metabolism evaluation is the standard imaging modality for the clinical management of lymphoma. One of the 18F-FDG PET applications is the detection and pre-treatment staging of lymphoma, which is highly sensitive. 18F-FDG PET is also applied during treatment to evaluate the individual chemo-sensitivity and accordingly guide the response-adapted therapy. At the end of the therapy regiment, a negative PET scan is indicative of a good prognosis in patients with advanced Hodgkin's lymphoma and diffuse large B-cell lymphoma. Thus, adjuvant radiotherapy may be alleviated. Future PET studies using non-18F-FDG radiotracers, such as 68Ga-labeled pentixafor (a cyclic pentapeptide that enables sensitive and high-contrast imaging of C-X-C motif chemokine receptor 4), 68Ga-labeled fibroblast activation protein inhibitor (FAPI) that reflects the tumor microenvironment, and 89Zr-labeled atezolizumab that targets the programmed cell death-ligand 1 (PD-L1), may complement 18F-FDG and offer essential tools to decode lymphoma phenotypes further and identify the mechanisms of lymphoma therapy.

High-resolution pediatric age-specific 18F-FDG PET template: a pilot study in epileptogenic focus localization.

BACKGROUND: PET imaging has been widely used in diagnosis of neurological disorders; however, its application to pediatric population is limited due to lacking pediatric age-specific PET template. This study aims to develop a pediatric age-specific PET template (PAPT) and conduct a pilot study of epileptogenic focus localization in pediatric epilepsy. METHODS: We recruited 130 pediatric patients with epilepsy and 102 age-matched controls who underwent 18F-FDG PET examination. High-resolution PAPT was developed by an iterative nonlinear registration-averaging optimization approach for two age ranges: 6-10 years (n = 17) and 11-18 years (n = 50), respectively. Spatial normalization to the PAPT was evaluated by registration similarities of 35 validation controls, followed by estimation of potential registration biases. In a pilot study, epileptogenic focus was localized by PAPT-based voxel-wise statistical analysis, compared with multi-disciplinary team (MDT) diagnosis, and validated by follow-up of patients who underwent epilepsy surgery. Furthermore, epileptogenic focus localization results were compared among three templates (PAPT, conventional adult template, and a previously reported pediatric linear template). RESULTS: Spatial normalization to the PAPT significantly improved registration similarities (P < 0.001), and nearly eliminated regions of potential biases (< 2% of whole brain volume). The PAPT-based epileptogenic focus localization achieved a substantial agreement with MDT diagnosis (Kappa = 0.757), significantly outperforming localization based on the adult template (Kappa = 0.496) and linear template (Kappa = 0.569) (P < 0.05). The PAPT-based localization achieved the highest detection rate (89.2%) and accuracy (80.0%). In postsurgical seizure-free patients (n = 40), the PAPT-based localization also achieved a substantial agreement with resection areas (Kappa = 0.743), and the highest detection rate (95%) and accuracy (80.0%). CONCLUSION: The PAPT can significantly improve spatial normalization and epileptogenic focus localization in pediatric epilepsy. Future pediatric neuroimaging studies can also benefit from the unbiased spatial normalization by PAPT. TRIAL REGISTRATION: NCT04725162: https://clinicaltrials.gov/ct2/show/NCT04725162.