The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.

OBJECTIVE: To examine whether machine learning (ML) analyses involving clinical and 18F-FDG-PET-based radiomic features are helpful in predicting prognosis in patients with laryngeal cancer. METHODS: This retrospective study included 49 patients with laryngeal cancer who underwent18F-FDG-PET/CT before treatment, and these patients were divided into the training (n = 34) and testing (n = 15) cohorts.Seven clinical (age, sex, tumor size, T stage, N stage, Union for International Cancer Control stage, and treatment) and 40 18F-FDG-PET-based radiomic features were used to predict disease progression and survival. Six ML algorithms (random forest, neural network, k-nearest neighbors, naïve Bayes, logistic regression, and support vector machine) were used for predicting disease progression. Two ML algorithms (cox proportional hazard and random survival forest [RSF] model) considering for time-to-event outcomes were used to assess progression-free survival (PFS), and prediction performance was assessed by the concordance index (C-index). RESULTS: Tumor size, T stage, N stage, GLZLM_ZLNU, and GLCM_Entropy were the five most important features for predicting disease progression.In both cohorts, the naïve Bayes model constructed by these five features was the best performing classifier (training: AUC = 0.805; testing: AUC = 0.842). The RSF model using the five features (tumor size, GLZLM_ZLNU, GLCM_Entropy, GLRLM_LRHGE and GLRLM_SRHGE) exhibited the highest performance in predicting PFS (training: C-index = 0.840; testing: C-index = 0.808). CONCLUSION: ML analyses involving clinical and 18F-FDG-PET-based radiomic features may help predict disease progression and survival in patients with laryngeal cancer. ADVANCES IN KNOWLEDGE: ML approach using clinical and 18F-FDG-PET-based radiomic features has the potential to predict prognosis of laryngeal cancer.

Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer.

PURPOSE: To develop and identify machine learning (ML) models using pretreatment clinical and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-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 [18F]-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 [18F]-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_ZLNU and AJCC stage. CONCLUSIONS: ML analyses using both clinical and [18F]-FDG-PET-based radiomic characteristics may be useful for predicting recurrence in patients with breast cancers who underwent surgery.

Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography.

PURPOSE: This study examined the usefulness of the maximum standardized uptake value (SUVmax) of myocardial [123I]-metaiodobenzylguanidine ([123I]-MIBG) to characterize myocardial function by comparing it with echocardiographic parameters in patients with pheochromocytoma. MATERIALS AND METHODS: This study included 18 patients with pheochromocytoma who underwent both planar and [123I]-MIBG single-photon emission computed tomography/computed tomography scans and echocardiography before surgery. Myocardial [123I]-MIBG visibility and SUVmax were compared with echocardiographic parameters related to systolic and diastolic functions. The Mann-Whitney U test, Fisher exact test, or Spearman rank correlation assessed differences or relationships between two quantitative variables. RESULTS: On visual analysis, 6 patients showed normal myocardial [123I]-MIBG uptake, whereas 12 patients showed decreased myocardial [123I]-MIBG uptake. No patients showed systolic dysfunction. A significant difference was observed in the incidence of diastolic dysfunction between the groups with normal and decreased uptake (p = 0.009), and left ventricular (LV) diastolic dysfunction was observed in 9 (75%) of 12 patients with decreased myocardial uptake. The myocardial SUVmax was significantly lower in 9 patients with LV diastolic dysfunction than in 9 patients with normal cardiac function (1.67 ± 0.37 vs. 3.03 ± 1.38, p = 0.047). Myocardial SUVmax was positively correlated with septal e' (early diastolic velocity of septal mitral annulus) (ρ = 0.51, p = 0.031) and negatively correlated with the septal E/e' ratio (early mitral E-velocity to early diastolic velocity of septal mitral annulus; ρ = - 0.64, p = 0.004), respectively. CONCLUSIONS: LV diastolic dysfunction was inversely related to myocardial [123I]-MIBG uptake. Myocardial [123I]-MIBG SUVmax may be useful for characterizing cardiac function in patients with pheochromocytoma. Second abstract. The semiquantitative analysis using the myocardial SUVmax in 123I-MIBG SPECT/CT was found to be potentially useful for characterizing cardiac function in patients with pheochromocytoma.

The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer.

PURPOSE: To examine whether the machine learning (ML) analyses using clinical and pretreatment 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]-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 [18F]-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 [18F]-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_Entropy), gray level run length matrix run length non-uniformity (GLRLM_RLNU), and T stage. Patients who experienced disease progression displayed significantly higher UICC stage, N stage, GLCM_Entropy, GLRLM_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 [18F]-FDG-PET-based radiomic features, GLCM_Entropy, and GLRLM_RLNU may be the most important predictor of prognosis in patients with hypopharyngeal cancer.

I-131 false-positive uptake in a thymic cyst with expression of the sodium-iodide symporter: A case report.

RATIONALE: I-131 radioiodine false-positive findings in postoperative patients with differentiated thyroid cancer (DTC) should be recognized to avoid unnecessary therapies. PATIENT CONCERNS AND DIAGNOSES: A 50-year-old man underwent I-131 therapy 3 times, including the initial ablative therapy after total thyroidectomy for papillary thyroid cancer. The initial I-131 posttherapeutic whole-body scintigraphy showed 2 cervical and one superior mediastinal focal I-131 positive uptake lesions. The serum thyroglobulin level was negative every time when the radioiodine therapy was performed. Although the 2 cervical positive uptake lesions disappeared after the second therapy, the superior mediastinal I-131 positive uptake persisted even after the third therapy, and this lesion was suspicion of I-131 therapy-resistant node metastasis. INTERVENTIONS AND OUTCOMES: The lesion was resected, and the pathological diagnosis with immune-histochemical analysis was a thymic cyst with thymic epithelial cells having a weak expression of the sodium-iodide symporter (NIS). LESSONS: The false-positive result may be attributed to the NIS expression in the thymic cyst epithelial cells. It is necessary to include a thymic cyst in the differential diagnosis, when I-131 uptake is noted in the superior mediastinal region on I-131 posttherapeutic scans of patients with postoperative DTC. Although the I-131 positive uptake in a thymic cyst may be influenced by the I-131 administered dose and scan timing after I-131 administration, the NIS expression may be essential to the false-positive uptake in a thymic cyst.

The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (18F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs). METHODS: This retrospective study included 79 TET [27 low-risk thymomas (types A, AB and B1), 31 high-risk thymomas (types B2 and B3) and 21 thymic carcinomas] patients who underwent pre-therapeutic 18F-FDG-PET/CT. High-risk TETs (high-risk thymomas and thymic carcinomas) were 52 patients. The 107 PET-based radiomic features, including SUV-related parameters [maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] and 1024 deep-learning features extracted from the convolutional neural network were used to predict the pathological risk subtypes of TETs using six different machine-learning algorithms. The area under the curves (AUCs) were calculated to compare the predictive performances. RESULTS: SUV-related parameters yielded the following AUCs for predicting thymic carcinomas: SUVmax 0.713, MTV 0.442, and TLG 0.479 or high-risk TETs: SUVmax 0.673, MTV 0.533, and TLG 0.539. The best-performing algorithm was the logistic regression model for predicting thymic carcinomas (AUC 0.900, accuracy 81.0%), and the random forest (RF) model for high-risk TETs (AUC 0.744, accuracy 72.2%). The AUC was significantly higher in the logistic regression model than three SUV-related parameters for predicting thymic carcinomas, and in the RF model than MTV and TLG for predicting high-risk TETs (each; p < 0.05). CONCLUSION: 18F-FDG-PET-based radiomic analysis using a machine-learning approach may be useful for predicting the pathological risk subtypes of TETs. ADVANCES IN KNOWLEDGE: Machine-learning approach using 18F-FDG-PET-based radiomic features has the potential to predict the pathological risk subtypes of TETs.

Dual-Energy CT-Derived Electron Density for Diagnosing Metastatic Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: Comparison With Conventional CT and FDG PET/CT Findings.

BACKGROUND. Accurate nodal staging is essential to guide treatment selection in patients with non-small cell lung cancer (NSCLC). To our knowledge, measurement of electron density (ED) using dual-energy CT (DECT) is unexplored for this purpose. OBJECTIVE. The purpose of our study was to assess the utility of ED from DECT in diagnosing metastatic mediastinal lymph nodes in patients with NSCLC in comparison with conventional CT and FDG PET/CT. METHODS. This retrospective study included 57 patients (36 men, 21 women; mean age, 68.4 ± 8.9 [SD] years) with NSCLC and surgically resected mediastinal lymph nodes who underwent preoperative DECT and FDG PET/CT. The patients had a total of 117 resected mediastinal lymph nodes (33 metastatic, 84 nonmetastatic). Two radiologists independently reviewed the morphologic features of nodes on the 120-kVp images and also measured the iodine concentration (IC) and ED of nodes using maps generated from DECT data; consensus was reached for discrepancies. Two different radiologists assessed FDG PET/CT examinations in consensus for positive node uptake. Diagnostic performance was evaluated for individual and pairwise combinations of features. RESULTS. The sensitivity, specificity, and accuracy for nodal metastasis were 15.2%, 98.8%, and 75.2% for the presence of necrosis, respectively; 54.5%, 85.7%, and 76.9% for short-axis diameter greater than 8.5 mm; 63.6%, 73.8%, and 70.9% for long-axis diameter greater than 13.0 mm; 51.5%, 79.8%, and 71.8% for attenuation on 120-kVp images of 95.8 HU or less; 87.9%, 58.3%, and 66.7% for ED of 3.48 × 1023/cm3 or less; and 66.7%, 75.0%, and 72.6% for positive FDG uptake. Among pairwise combinations of features, accuracy was highest for the combination of ED and short-axis diameter (accuracy, 82.9%; sensitivity, 54.5%; specificity, 94.0%) and the combination of ED and positive FDG uptake (accuracy, 82.1%; sensitivity, 60.6%; specificity, 90.5%); these accuracies were greater than those for the individual features (p < .05). The remaining combinations exhibited accuracies ranging from 74.4% to 77.8%. Interobserver agreement analysis showed an intraclass correlation coefficient of 0.90 for ED. IC was not significantly different between metastatic and nonmetastatic nodes (p = .18) and was excluded from the diagnostic performance analysis. CONCLUSION. ED derived from DECT may help diagnose metastatic lymph nodes in NSCLC given decreased ED in metastatic nodes. CLINICAL IMPACT. ED may complement conventional CT findings and FDG uptake on PET/CT in diagnosing metastatic nodes.

Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients.

PURPOSE: To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). METHODS: This retrospective study included 50 cervical cancer patients who underwent 18F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 18F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. RESULTS: The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p < 0.001) and was only the independent factor for PFS in multivariate analysis (HR, 6.89; 95% CI, 1.92-24.69; p = 0.003). CONCLUSION: A machine learning approach based on clinical and pretreatment 18F-FDG PET-based radiomic features may be useful for predicting tumor progression in cervical cancer patients.

Clinical Utility and Limitation of Diagnostic Ability for Different Degrees of Dysplasia of Intraductal Papillary Mucinous Neoplasms of the Pancreas Using 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography.

The diagnostic value of 18F-fluorodeoxyglucose (FDG) uptake in the management of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas remains unclear. This study aimed to assess the role of FDG uptake in the diagnosis of different degrees of dysplasia of IPMNs. We retrospectively analyzed the following three points in 84 patients with IPMNs: (1) risk factors to predict high-grade dysplasia (HGD) and invasive carcinoma (INV); (2) the relationship between FDG uptake and glucose transporter 1 (GLUT-1) expression; and (3) the relationship between FDG uptake and the presence of mural nodules. The histopathological diagnosis was low-grade dysplasia (LGD) in 43 patients, HGD in 16, and INV in 25. The maximum standardized uptake value (SUV-max) was significantly higher in INV than in LGD/HGD (p < 0.0001, p = 0.0136). The sensitivity and specificity to discriminate INV from LGD/HGD were 80.0% and 86.2%, respectively, using the receiver operator characteristic curve, when the optimal cutoff score of SUV-max was set at 4.03. Those values were not different between HGD and LGD. More than half of HGD patients had low GLUT-1 expression. Taken together, FDG-PET/CT is useful in distinguishing between non-invasive and invasive IPMN. Our results offer critical information that may determine surgical treatment strategies.

Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-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 [18F]-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 [18F]-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: [18F]-FDG PET-based radiomic analysis using a machine learning approach may be useful for predicting tumor progression and prognosis in patients with endometrial cancers.

Application of adrenal maximum standardized uptake value to 131I-6ß-iodomethyl-19-norcholesterol SPECT/CT for characterizing unilateral hyperfunctioning adrenocortical masses.

PURPOSE: To evaluate the maximum standardized uptake value (SUVmax) by 131I-6ß-iodomethyl-19-norcholesterol (NP-59) single-photon emission computed tomography (SPECT)/computed tomography (CT) for characterizing unilateral hyperfunctioning adrenocortical masses. METHODS: Ten patients underwent NP-59 SPECT/CT to evaluate the following unilateral adrenocortical hyperfuncting masses: three with Cushing's syndrome (CS), three with subclinical CS, and four with primary aldosteronism (PA). Visual and quantitative or semiquantitative analyses (noncontrast CT HU [Hounsfield units], lesion SUVmax, contralateral SUVmax, and SUVmax ratio [lesion SUVmax/contralateral adrenal SUVmax]) were performed. The Mann-Whitney U test or Chi-squared test was used appropriately to assess differences between quantitative variables or compare categorical data. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: All adrenal tumors were diagnosed as cortical adenomas. On visual analysis, unilateral uptake was noted in three patients with CS and one patient with subclinical CS, whereas bilateral uptake was noted in four patients with PA and two patients with subclinical CS (p = 0.046). No significant difference was observed in CT HU (p = 0.055). The lesion SUVmax and SUVmax ratio were significantly higher and the contralateral SUVmax was significantly lower in six patients with CS than in four patients with PA (each, p < 0.05). The area under the ROC curve and accuracy for differentiating between CS and PA were, respectively, 0.92 and 90.0 % for the lesion SUVmax, 1.00 and 100 % for the contralateral SUVmax, and 0.92 and 90.0 % for the SUVmax ratio. CONCLUSIONS: Quantitative or semiquantitative analysis using the adrenal SUVmax in adrenocortical NP-59 SPECT/CT has potential for characterizing unilateral hyperfunctioning adrenocortical masses.

Correction to: [18F]-FDG-PET/CT and [18F]-FAZA-PET/CT Hypoxia Imaging of Metastatic Thyroid Cancer: Association with Short-Term Progression after Radioiodine Therapy.

This article was update to correct the spelling of Takashi Yoshiura's name; it is correct as displayed here.

[18F]-FDG-PET/CT and [18F]-FAZA-PET/CT Hypoxia Imaging of Metastatic Thyroid Cancer: Association with Short-Term Progression After Radioiodine Therapy.

PURPOSE: To examine the relationships between 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG) and hypoxia tracer [18F]fluoro-azomycinarabinofuranoside ([18F]-FAZA) and between 131I and [18F]-FAZA uptake in patients with metastatic thyroid cancer and to evaluate imaging features associated with short-term progression after 131I therapy. PROCEDURES: The study population was 20 patients (17 women and 3 men; mean age, 67 years) with metastatic thyroid cancer who underwent both [18F]-FDG- and [18F]-FAZA-positron emission tomography (PET)/X-ray computed tomography (CT) examinations before 131I therapy. Short-term response to radioiodine was assessed (mean follow-up, 19 months ± 9). PET parameters including [18F]-FDG-SUVmax, [18F]-FAZA-SUVmax, and [18F]-FAZA-tumor-to-muscle [T/M] were obtained. Mann-Whitney U, Wilcoxon signed-rank, or χ2 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 131I-positive and 33 131I-negative lesions were detected. SUVmax was significantly lower with [18F]-FAZA-PET/CT (1.3 ± 0.6) than with [18F]-FDG-PET/CT (6.4 ± 5.9, p < 0.001). No significant correlation was observed between [18F]-FAZA-PET/CT and 131I imaging concerning visibility (p = 0.36). After 131I therapy, 31 of 57 metastatic lesions displayed short-term progression. Multivariate logistic regression revealed that [18F]-FDG-SUVmax (p = 0.022) and [18F]-FAZA-T/M (p = 0.002) showed significant associations with short-term progression. CONCLUSIONS: Although [18F]-FAZA uptake was low in metastatic thyroid cancers, not only glucose metabolism but also hypoxic conditions may be associated with progression after 131I therapy in patients with metastatic thyroid cancer.

The clinical value of texture analysis of dual-time-point 18F-FDG-PET/CT imaging to differentiate between 18F-FDG-avid benign and malignant pulmonary lesions.

OBJECTIVES: To examine whether the texture analysis of dual-time-point (DTP) F-18-fluorodeoxyglucose (18F-FDG)-PET/CT imaging can differentiate between 18F-FDG-avid benign and malignant pulmonary lesions. METHODS: We compared standardized uptake value (SUV)-related (SUVmax [g/ml] and SUVmean [g/ml]), volumetric (metabolic tumor volume [MTV] [cm3] and total lesion glycolysis [TLG] [g]), and texture (entropy, homogeneity, dissimilarity, intensity variability [IV], size-zone variability [SZV], and zone percentage [ZP]) (MTV ≥ 5.0 cm3 and SUV ≥ 2.5 g/ml) parameters between 13 benign and 46 malignant lesions using the Mann-Whitney U test. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. Stepwise logistic regression analysis was performed to identify and use the independent variables that correctly differentiate between benign and malignant lesions. RESULTS: Malignant pulmonary lesions showed significantly higher SUVmax, SUVmean, MTV, TLG, entropy, dissimilarity, IV, and SZV and significantly lower homogeneity and ZP than benign pulmonary lesions (all p < 0.05) in both early and delayed images. Their areas under the ROC curves (AUCs) ranged between 0.69 and 0.94, and diagnostic accuracies between 64.4% and 93.2%. Entropy-early (p = 0.014), SUVmean-delay (p = 0.039), and dissimilarity-delay (p = 0.027) were independent parameters, and combined use of them yielded the highest AUC (0.98) with 100% sensitivity (46/46), 84.6% specificity (11/13), and 96.7% (57/59) accuracy for distinguishing between benign and malignant lesions. CONCLUSIONS: The individual early and delayed SUV-related, volumetric, and texture parameters showed a wide range of accuracy. Combined use of independent parameters extracted from DTP imaging might yield a high diagnostic accuracy with balanced sensitivity and specificity to differentiate between benign and malignant 18F-FDG-avid pulmonary lesions. KEY POINTS: • Malignant pulmonary lesions showed significantly higher SUV-related (SUVmax and SUVmean) and volumetric (MTV and TLG) parameters than benign pulmonary lesions in both early and delayed images. • Malignant pulmonary lesions showed significantly more heterogeneous18F-FDG uptake than benign pulmonary lesions in both early and delayed images. • Combined use of independent parameters extracted from DTP imaging might yield a high diagnostic accuracy to differentiate between benign and malignant18F-FDG-avid pulmonary lesions.

18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters.

OBJECTIVE:: This retrospective study examined whether the primary tumour 18F-FDG uptake features could predict the high-risk of recurrence in differentiated thyroid cancer (DTC) patients. METHODS:: The enrolled 114 DTC patients underwent preoperative 18F-FDG-PET/CT. SUVmax, SUVmean, metabolic tumour volume (MTV), total lesion glycolysis (TLG) and 6 texture parameters were obtained. Because the texture features can be confounded by the tumour volume effects, 18F-FDG-avid tumour patients were divided into two groups (tumours with MTV ≤ 10.0 cm3 and >10.0 cm3). Diagnostic performance for predicting the high-risk was evaluated by the area under the curve (AUC) by the ROC curve analysis. RESULTS:: Eighty eight 18F-FDG-avid tumours revealed more advanced-risk classification (p = 0.015 → 0.02) than 26 18F-FDG-nonavid tumours, which yielded no high-risk patients. In the 44 MTV > 10.0 cm3 18F-FDG-avid tumour patients, 8 high-risk patients revealed significantly higher SUVmax, SUVmean, MTV, TLG, intensity variability and size-zone variability, and lower zone percentage than 36 non-high-risk patients (p < 0.001-0.016). Their AUC (diagnostic accuracy) ranged between 0.77 (66%) and 0.92 (91%). When each parameter was scored as 0 (negative for high-risk) or 1 (positive for high-risk) according to each threshold criterion, and the 7 parameter summed score ≥5 was defined as high-risk, the accuracy was 93.2% (AUC: 0.98) in the MTV > 10.0 cm3 18F-FDG-avid tumour patients. CONCLUSION:: For primary MTV > 10.0 cm3 18F-FDG-avid DTCs, the combined use of SUV-related, volumetric, and texture parameters may be more useful to identify high-risk patients than the individual parameters. ADVANCES IN KNOWLEDGE:: Combined use of SUV-related, volumetric, and texture parameters may be useful to identify high-risk DTC patients.

A Pilot Study of Texture Analysis of Primary Tumor [18F]FDG Uptake to Predict Recurrence in Surgically Treated Patients with Non-small Cell Lung 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-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET)/X-ray computed tomography (CT). PROCEDURE: This retrospective study included 55 patients with NSCLC who underwent [18F]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 χ2 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.

Response to neoadjuvant chemotherapy for breast cancer judged by PERCIST - multicenter study in Japan.

PURPOSE: The purpose of this study was to evaluate therapeutic response to neoadjuvant chemotherapy (NAC) and predict breast cancer recurrence using Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST). MATERIALS AND METHODS: Fifty-nine breast cancer patients underwent fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) before and after NAC prior to planned surgical resection. Pathological complete response (pCR) of the primary tumor was evaluated using PERCIST, while effects of clinicopathological factors on progression-free survival (PFS) were examined using log-rank and Cox methods. RESULTS: Fifty-six patients and 54 primary tumors were evaluated. Complete metabolic response (CMR), partial metabolic response, stable metabolic disease, and progressive metabolic disease were seen in 45, 7, 3, and 1 patients, respectively, and 43, 7, 3, and 1 primary tumors, respectively. Eighteen (33.3%) of the 54 primary tumors showed pCR. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PERCIST to predict pCR were 100% (18/18), 30.6% (11/36), 41.9% (18/43), 100% (11/11), and 53.7% (29/54), respectively. An optimal percent decrease in peak standardized uptake value for a primary tumor corrected for lean body mass (SULpeak) of 84.3% was found to have a sensitivity of 77.8% (14/18), specificity of 77.8% (28/36), PPV of 63.6% (14/22), NPV of 87.5% (28/32), and accuracy of 77.8% (42/54). Seven (12.5%) of the 56 patients developed recurrent disease (median follow-up 28.1 months, range 11.4-96.4 months). CMR (p = 0.031), pCR (p = 0.024), and early TNM stage (p = 0.033) were significantly associated with longer PFS. CONCLUSION: PERCIST is useful for predicting pathological response and prognosis following NAC in breast cancer patients. However, FDG-PET/CT showed a tendency toward underestimation of the residual tumor, and relatively low specificity and PPV of PERCIST showed that a combination of other imaging modalities would still be needed to predict pCR.

Texture analysis of 18F-FDG PET/CT for grading thymic epithelial tumours: usefulness of combining SUV and texture parameters.

OBJECTIVE: To retrospectively investigate the standardized uptake value (SUV)-related and heterogeneous texture parameters individually and in combination for differentiating between low- and high-risk 18Fluorone-fludeoxyglucose (18F-FDG)-avid thymic epithelial tumours (TETs) with positron emission tomography (PET)/CT. METHODS: SUV-related and 6 texture parameters (entropy, homogeneity, dissimilarity, intensity variability, size-zone variability and zone percentage) were compared between 11 low-risk and 23 high-risk TETs (metabolic tumour volume >10.0 cm3 and SUV ≥2.5). Diagnostic performance was evaluated by receiver operating characteristic analysis. The diagnostic value of combining SUV and texture parameters was examined by a scoring system. RESULTS: High-risk TETs were significantly higher in SUVmax (p = 0.022), entropy (p = 0.038), intensity variability (p = 0.041) and size-zone variability (p = 0.045) than low-risk TETs. Diagnostic accuracies of these 4 parameters, dissimilarity and zone percentage which also showed significance in receiver operating characteristic analysis ranged between 64.7 and 73.5% without significant differences in AUC (range; 0.71 to 0.75) (p ≥ 0.05 each). Each parameter was scored as 0 (negative for high-risk) or 1 (positive for high-risk) according to each threshold criterion, then scores were summed [0 or 1 for low-risk TETs (median; 1); ≥2 for high-risk TETs (median; 4)]. The sensitivity, specificity and accuracy of detecting high-risk TETs were 100, 81.8 and 94.1%, respectively, with an AUC of 0.99. CONCLUSION: The diagnostic performances of individual SUVmax and texture parameters were relatively low. However, combining these parameters can significantly increase diagnostic performance when differentiating between relatively large low- and high-risk 18F-FDG-avid TETs. Advances in knowledge: Combined use of SUVmax and texture parameters can significantly increase the diagnostic performance when differentiating between low- and high-risk TETs.

A small pancreatic hamartoma with an obstruction of the main pancreatic duct and avid FDG uptake mimicking a malignant pancreatic tumor: a systematic case review.

BACKGROUND: Pancreatic hamartomas are extremely rare and may be misdiagnosed as malignant tumors. We report herein a case of a small, solid-type pancreatic hamartoma. CASE PRESENTATION: A 72-year-old female was incidentally detected pancreatic lesion by ultrasonography. Computed tomography and magnetic resonance imaging revealed a 2.0-cm solid lesion. The main pancreatic duct (MPD) was obstructed by the lesion in the head of the pancreas, and the upstream MPD was dilated. 18F-fluorodeoxyglucose (FDG) accumulated avidly in the lesion and increased in FDG intensity from the early to the delayed images. The histopathological studies confirmed the diagnosis of pancreatic hamartoma. Immunohistochemically, the cell membrane of the accessory glands and ducts showed homogeneous expression of glucose transporter type I and hexokinase II. CONCLUSION: Pancreatic hamartomas causing dilatation of the MPD are extremely rare, and this appears to be the first case of a hamartoma to take up FDG avidly. It was a rare occurrence and should be noted that pancreatic hamartomas can cause an obstruction of the MPD and show avid FDG uptake, thereby mimicking malignant pancreatic tumors.

A pilot study for texture analysis of 18F-FDG and 18F-FLT-PET/CT to predict tumor recurrence of patients with colorectal cancer who received surgery.

PURPOSE: This retrospective study was done to examine whether the heterogeneity in primary tumor F-18-fluorodeoxyglucose (18F-FDG) and 18F-3'-fluoro-3'-deoxythymidine (18F-FLT) distribution can predict prognosis of patients with colorectal cancer who received surgery. METHODS: The enrolled 32 patients with colorectal cancer underwent both 18F-FDG- and 18F-FLT-PET/CT studies before surgery. Clinicopathological factors, stage, SUVmax, SUVmean, metabolic tumor volume (SUV ≥ 2.5), total lesion glycolysis, total lesion proliferation and seven texture heterogeneity parameters (coefficient of variation, local parameters: entropy, homogeneity, and dissimilarity; and regional parameters: intensity variability [IV], size-zone variability [SZV], and zone percentage [ZP]) were obtained. Progression free survival (PFS) was calculated by the Kaplan-Meier method. Prognostic significance was assessed by Cox proportional hazards analysis. RESULTS: Eight patients had eventually come to progression, and 24 patients were alive without progression during clinical follow-up [mean follow-up PFS; 55.9 months (range, 1-72)]. High stage (p = 0.004), high 18F-FDG-IV (p = 0.015), high 18F-FDG-SZV (p = 0.013) and high 18F-FLT-entropy (p = 0.015) were significant in predicting poor 5-year PFS. Other parameters did not predict the disease outcome. At bivariate analysis, disease event hazards ratios for 18F-FDG-IV and 18F-FDG-SZV remained significant when adjusted for stage and 18F-FLT-entropy (18F-FDG-IV; p = 0.004 [adjusted for stage], 0.007 [adjusted for 18F-FLT-entropy]; 18F-FDG-SZV; p = 0.028 [adjusted for stage], 0.040 [adjusted for 18F-FLT-entropy]). CONCLUSION: 18F-FDG PET heterogeneity parameters, IV and SZV, have a potential to be strong prognostic factors to predict PFS of patients with surgically resected colorectal cancer and are more useful than 18F-FLT-PET/CT heterogeneity parameters.