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A new diagnostic marker for differentiating multicentric gliomas from multiple intracranial diffuse large B-cell lymphomas on 18F-FDG PET images.

ABSTRACT: Intracranial gliomas and lymphomas may share similar radiological manifestations, while the treatment strategies for them are different. The aim of the study was to investigate the diagnostic value of fluorine-18-fluoro-2-deoxy-D-glucose (F-FDG) positron emission computed tomography (PET) for differentiation of multicentric gliomas and intracranial multiple diffuse large B-cell lymphomas (DLBCLs) as a study of diagnostic accuracy.A total of 32 patients with multiple intracranial tumors visualized on contrast-enhanced magnetic resonance imaging (MRI) were retrospectively evaluated. Histopathological findings confirmed multicentric gliomas and multiple DLBCLs in 17 and 15 patients, respectively. All patients underwent F-FDG PET with or without C-methionine PET. Maximum standardized uptake values (SUVmax) and tumor-to-normal tissue (T/N) ratios were compared between the 2 tumors. The diagnostic value of F-FDG PET for differentiating multicentric gliomas from multiple DLBCLs was evaluated by receiver operating characteristic (ROC) analysis.The SUVmax of multiple DLBCLs was significantly higher than that of multicentric gliomas (P = .009). However, the percentage of maximum difference-value of SUVmax (or T/N ratio) of multiple DLBCLs was significant lower than that of multicentric gliomas (P < .001). The ROC curve demonstrated that the percentage of maximum difference-value of SUVmax (or T/N ratio) on F-FDG PET images could effectively differentiate multicentric gliomas from multiple DLBCLs, with a cut-off value of 44.4%, sensitivity of 64.7%, and specificity of 100% (P < .001).Percentage of maximum difference-value of SUVmax (or T/N ratio) on F-FDG PET images might be a potential indicator for distinguishing multicentric gliomas from intracranial multiple DLBCLs, which might help determine the treatment strategy.

SUBMITTER: Wang K

PROVIDER: S-EPMC5556232 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Project description:We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Project description:PurposeThe purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods.MethodsThis retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively.ResultsA total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively).ConclusionThe radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.

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A new diagnostic marker for differentiating multicentric gliomas from multiple intracranial diffuse large B-cell lymphomas on 18F-FDG PET images.

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