Background: Diagnosing the biochemical recurrence (BCR) of prostate cancer (PCa) is a clinical challenge, and early detection of BCR can help patients receive optimal treatment. We conducted a meta-analysis to define the diagnostic accuracy of PET/CT using ¹⁸F-labeled choline, fluciclovine, and prostate-specific membrane antigen (PSMA) in patients with BCR.Methods: Multiple databases were searched until March 30, 2021. We included studies investigating the diagnostic accuracy of ¹⁸F-choline, ¹⁸F-fluciclovine, and ¹⁸F-PSMA PET/CT in patients with BCR. The pooled sensitivity, specificity, and detection rate of ¹⁸F-labeled tracers were calculated with a random-effects model.Results: A total of 46 studies met the included criteria; 17, 16, and 13 studies focused on ¹⁸F-choline, fluciclovine, and PSMA, respectively. The pooled sensitivities of ¹⁸F-choline and ¹⁸F-fluciclovine were 0.93 (95% CI, 0.85–0.98) and 0.80 (95% CI, 0.65–0.897), and the specificities were 0.91 (95% CI, 0.73–0.97) and 0.66 (95% CI, 0.50–0.79), respectively. The pooled detection rates of ¹⁸F-labeled choline, fluciclovine and PSMA were 66, 74, and 83%, respectively. Moreover, the detection rates of ¹⁸F-labeled choline, fluciclovine, and PSMA were 35, 23, and 58% for a PSA level less than 0.5 ng/ml; 41, 46, and 75% for a PSA level of 0.5–0.99 ng/ml; 62, 57, and 86% for a PSA level of 1.0–1.99 ng/ml; 80, 92, and 94% for a PSA level more than 2.0 ng/ml.Conclusion: These three ¹⁸F-labeled tracers are promising for detecting BCR in prostate cancer patients, with ¹⁸F-choline showing superior diagnostic accuracy. In addition, the much higher detection rates of ¹⁸F-PSMA showed its superiority over other tracers, particularly in low PSA levels.Systematic Review Registration: PROSPERO, identifier CRD42020212531.

Background: Although papillary thyroid cancer (PTC) patients are known to have an excellent prognosis, up to 30% of patients experience disease recurrence after initial treatment. Accurately predicting disease prognosis remains a challenge given that the predictive value of several predictors remains controversial. Thus, we investigated whether machine learning (ML) approaches based on comprehensive predictors can predict the risk of structural recurrence for PTC patients. Methods: A total of 2244 patients treated with thyroid surgery and radioiodine were included. Twenty-nine perioperative variables consisting of four dimensions (demographic characteristics and comorbidities, tumor-related variables, lymph node (LN)-related variables, and metabolic and inflammatory markers) were analyzed. We applied five ML algorithms—logistic regression (LR), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and neural network (NN)—to develop the models. The area under the receiver operating characteristic (AUC-ROC) curve, calibration curve, and variable importance were used to evaluate the models’ performance. Results: During a median follow-up of 45.5 months, 179 patients (8.0%) experienced structural recurrence. The non-stimulated thyroglobulin, LN dissection, number of LNs dissected, lymph node metastasis ratio, N stage, comorbidity of hypertension, comorbidity of diabetes, body mass index, and low-density lipoprotein were used to develop the models. All models showed a greater AUC (AUC = 0.738 to 0.767) than did the ATA risk stratification (AUC = 0.620, DeLong test: P < 0.01). The SVM, XGBoost, and RF model showed greater sensitivity (0.568, 0.595, 0.676), specificity (0.903, 0.857, 0.784), accuracy (0.875, 0.835, 0.775), positive predictive value (PPV) (0.344, 0.272, 0.219), negative predictive value (NPV) (0.959, 0.959, 0.964), and F1 score (0.429, 0.373, 0.331) than did the ATA risk stratification (sensitivity = 0.432, specificity = 0.770, accuracy = 0.742, PPV = 0.144, NPV = 0.938, F1 score = 0.216). The RF model had generally consistent calibration compared with the other models. The Tg and the LNR were the top 2 important variables in all the models, the N stage was the top 5 important variables in all the models. Conclusions: The RF model achieved the expected prediction performance with generally good discrimination, calibration and interpretability in this study. This study sheds light on the potential of ML approaches for improving the accuracy of risk stratification for PTC patients. Trial registration: Retrospectively registered at www.chictr.org.cn (trial registration number: ChiCTR2300075574, date of registration: 2023-09-08).

Purpose. To determine whether the radiomic features of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) contribute to prognosis prediction in primary gastric diffuse large B-cell lymphoma (PG-DLBCL) patients. Methods. This retrospective study included 35 PG-DLBCL patients who underwent PET/CT scans at West China Hospital before curative treatment. The volume of interest (VOI) was drawn around the tumor, and radiomic analysis of the PET and CT images, within the same VOI, was conducted. The metabolic and textural features of PET and CT images were evaluated. Correlations of the extracted features with the overall survival (OS) and progression-free survival (PFS) were evaluated. Univariate and multivariate analyses were conducted to assess the prognostic value of the radiomic parameters. Results. In the univariate model, many of the textural features, including kurtosis and volume, extracted from the PET and CT datasets were significantly associated with survival (5 for OS and 7 for PFS (PET); 7 for OS and 14 for PFS (CT)). Multivariate analysis identified kurtosis (hazard ratio (HR): 28.685, 95% confidence interval (CI): 2.067–398.152, p=0.012), metabolic tumor volume (MTV) (HR: 26.152, 95% CI: 2.089–327.392, p=0.011), and gray-level nonuniformity (GLNU) (HR: 14.642, 95% CI: 2.661–80.549, p=0.002) in PET and sphericity (HR: 11.390, 95% CI: 1.360–95.371, p=0.025) and kurtosis (HR: 11.791, 95% CI: 1.583–87.808, p=0.016), gray-level nonuniformity (GLNU) (HR: 6.934, 95% CI: 1.069–44.981, p=0.042), and high gray-level zone emphasis (HGZE) (HR: 9.805, 95% CI: 1.359–70.747, p=0.024) in CT as independent prognostic factors. Conclusion. ¹⁸F-FDG PET/CT radiomic features are potentially useful for survival prediction in PG-DLBCL patients. However, studies with larger cohorts are needed to confirm the clinical prognostication of these parameters.

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 SUV_max 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: SUV_mean, 4.75 versus 2.38 g/ml (P<0.001); SUV_std, 2.04 versus 0.88 g/ml (P=0.001); SUV_max, 10.69 versus 4.76 g/ml (P=0.001); SUV_peak, 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 SUV_max, the AUC for SUV_max 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 SUV_max 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.