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Purpose: To examine the usefulness of semi-quantitative analysis using the standardized uptake value (SUV) of iodine-123 metaiodobenzylguanidine ([ 123 I]-MIBG) for predicting metastatic potential in patients with pheochromocytoma (PHEO) and paraganglioma (PGL)., Procedures: This study included 18 PHEO and 2 PGL patients. [ 123 I]-MIBG visibility and SUV-related parameters (SUVmax, SUVmean, tumor volume of [ 123 I]-MIBG uptake [TV&#95;MIBG], and total lesion [ 123 I]-MIBG uptake) were compared with the pathological grading obtained using the Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) and the Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP), which are used to predict metastatic potential. The PASS scores were categorized as < 4 and ≥ 4. Based on the GAPP scores, PHEOs/PGLs were categorized as follows: well, moderately, and poorly differentiated tumors. The Mann-Whitney U test or Spearman's rank correlation was used to assess differences or associations between two quantitative variables., Results: All PHEOs/PGLs were visualized on [ 123 I]-MIBG scintigraphy. There were 16 PASS < 4 and 4 PASS ≥ 4 tumors. Moreover, 11 and 9 tumors were well and moderately differentiated, respectively. The uptake scores and SUV-related parameters significantly differed between tumors with a PASS score of < 4 and those with a PASS score of ≥ 4 (each, p > 0.05). Moderately differentiated tumors had significantly higher uptake scores and SUV-related parameters except TV&#95;MIBG than well-differentiated tumors (each, p < 0.05). The GAPP score was positively correlated with the uptake scores and SUV-related parameters (each, p < 0.05) except TV&#95;MIBG., Conclusions: The primary tumor [ 123 I]-MIBG uptake assessed using SUV-related parameters can be an imaging tool for predicting metastatic potential in patients with PHEO/PGL., Competing Interests: Declarations. Conflict of Interest: The authors declare that they have no conflict interest. Ethical Approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Informed Consent: Informed consent was waived by the institutional review board for this retrospective study., (© 2024. The Author(s).)

Purpose: To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[ 18 F]fluoro-D-glucose positron emission tomography ([ 18 F]-FDG-PET)-based radiomic characteristics to predict disease recurrences in patients with breast cancers who underwent surgery., Procedures: This retrospective study included 112 patients with 118 breast cancer lesions who underwent [ 18 F]-FDG-PET/ X-ray computed tomography (CT) preoperatively, and these lesions were assigned to training (n=95) and testing (n=23) cohorts. A total of 12 clinical and 40 [ 18 F]-FDG-PET-based radiomic characteristics were used to predict recurrences using 7 different ML algorithms, namely, decision tree, random forest (RF), neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine (SVM) with a 10-fold cross-validation and synthetic minority over-sampling technique. Three different ML models were created using clinical characteristics (clinical ML models), radiomic characteristics (radiomic ML models), and both clinical and radiomic characteristics (combined ML models). Each ML model was constructed using the top ten characteristics ranked by the decrease in Gini impurity. The areas under ROC curves (AUCs) and accuracies were used to compare predictive performances., Results: In training cohorts, all 7 ML algorithms except for logistic regression algorithm in the radiomics ML model (AUC = 0.760) achieved AUC values of >0.80 for predicting recurrences with clinical (range, 0.892-0.999), radiomic (range, 0.809-0.984), and combined (range, 0.897-0.999) ML models. In testing cohorts, the RF algorithm of combined ML model achieved the highest AUC and accuracy (95.7% (22/23)) with similar classification performance between training and testing cohorts (AUC: training cohort, 0.999; testing cohort, 0.992). The important characteristics for modeling process of this RF algorithm were radiomic GLZLM&#95;ZLNU and AJCC stage., Conclusions: ML analyses using both clinical and [ 18 F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery., (© 2023. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Purpose: To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[ 18 F]fluoro-D-glucose positron emission tomography ([ 18 F]-FDG-PET)-based radiomic features were useful for predicting prognosis in patients with hypopharyngeal cancer., Procedures: This retrospective study included 100 patients with hypopharyngeal cancer who underwent [ 18 F]-FDG-PET/X-ray computed tomography (CT) before treatment, and these patients were allocated to the training (n=80) and validation (n=20) cohorts. Eight clinical (age, sex, histology, T stage, N stage, M stage, UICC stage, and treatment) and 40 [ 18 F]-FDG-PET-based radiomic features were used to predict disease progression. A feature reduction procedure based on the decrease of the Gini impurity was applied. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were compared using the area under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis., Results: The five most important features for predicting disease progression were UICC stage, N stage, gray level co-occurrence matrix entropy (GLCM&#95;Entropy), gray level run length matrix run length non-uniformity (GLRLM&#95;RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM&#95;Entropy, GLRLM&#95;RLNU, and T stage than those without progression (each, p<0.001). In both cohorts, the logistic regression model constructed by these 5 features was the best performing classifier (training: AUC=0.860, accuracy=0.800; validation: AUC=0.803, accuracy=0.700). In the logistic regression model, 5-year PFS was significantly higher in patients with predicted non-progression than those with predicted progression (75.8% vs. 8.3%, p<0.001), and this model was only the independent factor for PFS in multivariate analysis (hazard ratio = 3.22; 95% confidence interval = 1.03-10.11; p=0.045)., Conclusions: The logistic regression model constructed by UICC, T and N stages and pretreatment [ 18 F]-FDG-PET-based radiomic features, GLCM&#95;Entropy, and GLRLM&#95;RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer., (© 2022. The Author(s), under exclusive licence to World Molecular Imaging Society.)

Purpose: To examine the prognostic significance of pretreatment 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers., Procedures: Included in this retrospective study were 53 patients with endometrial cancers who underwent [ 18 F]-FDG PET/X-ray computed tomography (CT) before treatment. Since two different PET scanners were used, post-reconstruction harmonization was performed for all PET parameters using the ComBat harmonization method. Four clinical (age, histological type, stage, and treatment method) and 40 [ 18 F]-FDG PET-based radiomic features were ranked, and a subset of useful features was selected based on the decrease in the Gini impurity in terms of associations with disease progression. The machine learning algorithms (random forest, neural network, k-nearest neighbors (kNN), naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC) and validated by the random sampling method. Progression-free survival (PFS) and overall survival (OS) were assessed by the Cox regression analysis., Results: The five best predictors of disease progression were coarseness, gray-level run length nonuniformity, stage, treatment method, and gray-level zone length nonuniformity. The kNN model obtained the best performance classifier for predicting the disease progression (AUC =0.890, accuracy =0.849, F1 score =0.848, precision =0.857, and recall =0.849). Coarseness which was the first ranked radiomic feature was selected for survival analyses, and only coarseness remained as a significant and independent factor for both PFS (hazard ratios (HR), 0.65; 95 % confidence interval [CI], 0.49-0.86; p=0.003) and OS (HR, 0.52; 95 % CI, 0.36-0.76; p<0.001) at multivariate Cox regression analysis., Conclusions: [ 18 F]-FDG PET-based radiomic analysis using a machine learning approach may be useful for predicting tumor progression and prognosis in patients with endometrial cancers., (© 2021. World Molecular Imaging Society.)

Purpose: To examine the relationships between 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]-FDG) and hypoxia tracer [ 18 F]fluoro-azomycinarabinofuranoside ([ 18 F]-FAZA) and between 131 I and [ 18 F]-FAZA uptake in patients with metastatic thyroid cancer and to evaluate imaging features associated with short-term progression after 131 I therapy., Procedures: The study population was 20 patients (17 women and 3 men; mean age, 67 years) with metastatic thyroid cancer who underwent both [ 18 F]-FDG- and [ 18 F]-FAZA-positron emission tomography (PET)/X-ray computed tomography (CT) examinations before 131 I therapy. Short-term response to radioiodine was assessed (mean follow-up, 19 months ± 9). PET parameters including [ 18 F]-FDG-SUVmax, [ 18 F]-FAZA-SUVmax, and [ 18 F]-FAZA-tumor-to-muscle [T/M] were obtained. Mann-Whitney U, Wilcoxon signed-rank, or χ 2 tests were used to assess differences between two quantitative variables or compare categorical data. Predictive factors for short-term progression were investigated with logistic regression analysis., Results: Eleven lymph node metastatic lesions were identified in 9 patients and 46 distant metastatic lesions (lung, 19; bone, 17; and liver, 10) in 14 patients. A total of 24 131 I-positive and 33 131 I-negative lesions were detected. SUVmax was significantly lower with [ 18 F]-FAZA-PET/CT (1.3 ± 0.6) than with [ 18 F]-FDG-PET/CT (6.4 ± 5.9, p < 0.001). No significant correlation was observed between [ 18 F]-FAZA-PET/CT and 131 I imaging concerning visibility (p = 0.36). After 131 I therapy, 31 of 57 metastatic lesions displayed short-term progression. Multivariate logistic regression revealed that [ 18 F]-FDG-SUVmax (p = 0.022) and [ 18 F]-FAZA-T/M (p = 0.002) showed significant associations with short-term progression., Conclusions: Although [ 18 F]-FAZA uptake was low in metastatic thyroid cancers, not only glucose metabolism but also hypoxic conditions may be associated with progression after 131 I therapy in patients with metastatic thyroid cancer.

Purpose: To examine whether the heterogeneous texture parameters in primary tumor can predict prognosis of patients with non-small cell lung cancer (NSCLC) received surgery after 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET)/X-ray computed tomography (CT)., Procedure: This retrospective study included 55 patients with NSCLC who underwent [ 18 F]FDG-PET/CT before surgery from January 2011 and December 2015. SUV-related (SUVmax and SUVmean), volumetric (metabolic tumor volume [SUV ≥ 2.5], and total lesion glycolysis) and texture parameters (local parameters; entropy, homogeneity, and dissimilarity and regional parameters; intensity variability [IV], size-zone variability [SZV], and zone percentage [ZP]) were obtained. Tumor size, TNM stage, SUV-related, volumetric, and texture parameters were compared between the patients with progression and without progression using Mann-Whitney's U or χ 2 test and progression-free survival (PFS) and prognostic significance were assessed by Kaplan-Meier method and Cox regression analysis, respectively., Results: Nineteen patients eventually showed progression, and 36 patients were alive without progression during clinical follow-up (median follow-up PFS; 23 months [range, 1-71]). The patients with progression showed significantly larger tumor size (p < 0.001), higher IV (p = 0.010), and higher SZV (p = 0.007) than those without progression. PFS was significantly shorter in patients with large tumor size (p = 0.008), high T stage (p = 0.009), high stage (p = 0.013), high IV (p = 0.012), and high SZV (p = 0.015) at univariate analysis. At multivariate analysis, stage (hazard ratio [HR] 1.62, p = 0.035) and IV (hazard ratio 6.19, p = 0.048) were only remained independent predictors for PFS., Conclusions: The regional heterogeneity texture parameters IV and SZV can predict tumor progression, and IV has the potential to predict prognosis of surgically treated NSCLC patients.