Purpose: Our study assessed the ability 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics to differentiate breast carcinoma from breast lymphoma using machine-learning approach. Methods: Sixty-five breast nodules from 44 patients diagnosed as breast carcinoma or breast lymphoma were included. Standardized uptake value (SUV) and radiomic features from CT and PET images were extracted using local image features extraction software. Six discriminative models including PETa (based on clinical, SUV and radiomic features from PET images), PETb (SUV and radiomic features from PET images), PETc (radiomic features only from PET images), CTa (clinical and radiomic features from CT images), CTb (radiomic features only from CT images), and SUV model were generated using least absolute shrinkage and selection operator method and linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were calculated to evaluate the discriminative ability of these models. Results: PETa and CTa models showed the best ability to differentiation in training and validation group (AUCs of 0.867 and 0.806 for PETa model, AUCs of 0.891 and 0.759 for CTa model, respectively). Conclusion: Models based on clinical, SUV, and radiomic features of 18 F-FDG PET/ CT images could accurately discriminate breast carcinoma from breast lymphoma. K E Y W O R D S breast lymphoma, diagnosis, linear discriminant analysis, machine-learning, radiomic

Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p≤0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P<0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P<0.001); TLG, 42.24 versus 9.90 (P<0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.

Transarterial radioembolization with radionuclide‐labeled microspheres is successfully used in hepatocellular carcinoma (HCC) treatment, but the non‐biodegradability and rapid settlement of the microsphere material are associated with unsatisfied distribution and unable for multiple administrations. In this study, a novel biodegradable chitosan–collagen composite microsphere (CCM) with ideal settlement rate is prepared. The Fourier‐transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) results indicate CCMs have desirable shapes with diameters around 10 µm, and considerable biodegradability within 12 weeks. These CCMs are successfully radiolabeled with 131I and processed efficiency of 70.4 MBq mg−1 of microspheres as well as favorable stability in vitro. Then, 131I‐CCMs are injected into rats with orthotopic HCC via the hepatic artery which effectively improves the median overall survival from 19 to 44 days (p < 0.05). Single photon emission computed tomography (SPECT/CT) imaging and immunohistochemical analysis indicate well‐localized biodistribution and consistent stability of 131I‐CCMs in the liver over 28 days. Magnetic resonance imaging (MRI) and gross specimens monitoring confirm the inhibited tumor growth after 131I‐CCMs treatment. In conclusion, these biodegradable 131I‐CCMs exhibit optimal radiolabeling efficiency, stability, and favorably radioembolization effect for orthotopic HCC in a rodent model, suggesting potential for interventional cancer therapy.

Background Positron emission tomography (PET) imaging using the radiotracers 18F-fluorodeoxyglucose (FDG) or 18F-fluorothymidine (FLT) has been proposed as imaging biomarkers of cell proliferation. Purpose To explore the correlations of FDG and FLT uptake with the Ki-67 labeling index in patients with lung cancer. Material and Methods Major databases were systematically searched for all relevant literature published in English. The correlation coefficient (rho) and its 95% confidence interval (CI) of individual studies were meta-analyzed using a random-effects model. The sources of heterogeneity were explored by subgroup analyses. Results Twenty-seven articles involving 1213 patients were included in this meta-analysis, comprising 22 studies for FDG uptake/Ki-67 expression correlation and eight for FLT uptake/Ki-67 expression correlation. The pooled rho values for 18F-FDG/Ki-67 correlation and 18F-FLT/Ki-67 correlation were 0.45 (95% CI, 0.41-0.50) and 0.65 (95% CI, 0.56-0.73), respectively, which indicated a moderate correlation for the former and a significant one for the latter. Although the subgroup analyses based on study design, scanner, sample method, and Ki-67 labeling method did not significantly explain the heterogeneity, these factors were potential sources of heterogeneity. In lung cancer, the pooled SUVmax of FDG uptake was significantly higher than that of FLT uptake (7.59 versus 3.86, P < 0.05). In addition, compared to FDG, FLT showed higher specificity yet lower sensitivity for the diagnosis of pulmonary lesions. Conclusion Both 18F-FDG and 18F-FLT correlate significantly with the Ki-67 labeling index in pulmonary lesions, and the latter, with a stronger correlation, may be more reliable for assessing tumor cell proliferation in lung cancer.

Monoacylglycerol lipase (MAGL) is a 33 kDa serine protease primarily responsible for hydrolyzing 2-arachidonoylglycerol into the proinflammatory eicosanoid precursor arachidonic acid in the central nervous system. Inhibition of MAGL constitutes an attractive therapeutic concept for treating psychiatric disorders and neurodegenerative diseases. Herein, we present the design and synthesis of multiple reversible MAGL inhibitor candidates based on a piperazinyl azetidine scaffold. Compounds 10 and 15 were identified as the best-performing reversible MAGL inhibitors by pharmacological evaluations, thus channeling their radiolabeling with fluorine-18 in high radiochemical yields and favorable molar activity. Furthermore, evaluation of [18F]10 and [18F]15 ([18F]­MAGL-2102) by autoradiography and positron emission tomography (PET) imaging in rodents and nonhuman primates demonstrated favorable brain uptakes, heterogeneous radioactivity distribution, good specific binding, and adequate brain kinetics, and [18F]15 demonstrated a better performance. In conclusion, [18F]15 was found to be a suitable PET radioligand for the visualization of MAGL, harboring potential for the successful translation into humans.