Role of 18F-FDG PET/CT Radiomics Features in the Differential Diagnosis of Solitary Pulmonary Nodules: Diagnostic Accuracy and Comparison between Two Different PET/CT Scanners.
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ABSTRACT:
SUBMITTER: Albano D
PROVIDER: S-EPMC8584460 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To evaluate the diagnostic accuracy of computed tomography (CT)-guided percutaneous lung biopsy for solitary pulmonary nodules. Three hundred and eleven patients (211 males and 100 females), with a mean age of 59.6 years (range, 19-87 years), who were diagnosed with solitary pulmonary nodules and underwent CT-guided percutaneous transthoracic needle biopsy between January 2008 and January 2014 were reviewed. All patients were confirmed by surgery or the clinical course. The overall diagnostic accuracy and incidence of complications were calculated, and the factors influencing these were statistically evaluated and compared. Specimens were successfully obtained from all 311 patients. A total of 217 and 94 cases were found to be malignant and benign lesions, respectively, by biopsy. Two hundred and twenty-five (72.3%) carcinomas, 78 (25.1%) benign lesions, and 8 (2.6%) inconclusive lesions were confirmed by surgery and the clinical course. The diagnostic accuracy, sensitivity, and specificity of CT-guided percutaneous transthoracic needle biopsy were 92.9%, 95.3%, and 95.7%, respectively. The incidences of pneumothorax and self-limiting bleeding were 17.7% and 11.6%, respectively. Taking account of all evidence, CT-guided percutaneous lung biopsy for solitary pulmonary nodules is an efficient, and safe diagnostic method associated with few complications.
| S-EPMC4482532 | biostudies-literature
Project description:In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4-11.2 pp and 2.2-10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.
| S-EPMC7555302 | biostudies-literature
Project description:Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46-0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.
| S-EPMC8000810 | biostudies-literature
Project description:Purpose Fluorine-18 (18F)-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and gallium-68 (68Ga)-somatostatin analog (SSA) PET/CT imaging have been increasingly used in ectopic adrenocorticotropic hormone syndrome (EAS); however, the diagnostic efficacies of these two methods in patients with EAS remain unclear. Our study aimed to compare the diagnostic efficacies of 18F-FDG PET/CT and 68Ga-DOTANOC PET/CT in EAS. Methods The clinical and imaging data of 68 patients with EAS who underwent 18F-FDG PET/CT and 68Ga-DOTANOC PET/CT examinations from December 2016 to April 2021 were analyzed retrospectively, and the diagnostic efficacies of these methods were compared. Results In 37 cases, imaging was performed to locate the primary tumor lesion (localization group), and in 31 to evaluate tumor load or metastasis (staging group). Primary tumors were detected in 48.65% (18/37) of the localization group patients. According to scan-based analysis, the tumor lesion detection rates and false positive rates of 18F-FDG PET/CT imaging and 68Ga-DOTANOC PET/CT imaging were 18.92% vs. 45.95% (p < 0.05) and 21.62% vs. 2.70% (p < 0.05) respectively. For lesion-based analysis, the tumor lesion detection rates and false positive rates were 24.13% vs. 58.62% (p >0.05) and 31.04% vs. 3.45% (p < 0.05). In 90.32% (28/31) of the staging group patients, 286 of 292 lesions were confirmed as tumor lesions. Based on scan analysis, the detection rates and false positive rates of 18F-FDG PET/CT imaging and 68Ga-DOTANOC PET/CT imaging were 83.87% vs. 67.74% (p > 0.05) and 12.90% vs. 9.68% (p > 0.05) respectively. Based on lesion analysis, the detection rate and false positive rates were 93.84% vs. 54.80% (p < 0.05) and 1.37% vs. 1.03%(p > 0.05). Conclusion 68Ga-DOTANOC PET/CT imaging may be more suitable than 18F-FDG PET/CT for identifying the primary tumor in patients with EAS, while 18F-FDG PET/CT may be more advantageous than 68Ga-DOTANOC PET/CT for patients with suspected metastasis.
| S-EPMC9538925 | biostudies-literature
Project description:PurposeTo evaluate whether quantitative [18F]FDG-PET/CT assessment, including radiomic analysis of [18F]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hürthle cell and Hürthle cell cytology.MethodsProspectively included patients with a Bethesda III or IV thyroid nodule underwent [18F]FDG-PET/CT imaging. Receiver operating characteristic (ROC) curve analysis was performed for standardised uptake values (SUV) and SUV-ratios, including assessment of SUV cut-offs at which a malignant/borderline neoplasm was reliably ruled out (≥ 95% sensitivity). [18F]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [18F]FDG-PET and low-dose CT images. Elastic net regression classifiers were trained in a 20-times repeated random split. Dimensionality reduction was incorporated into the splits. Predictive performance of radiomics was presented as mean area under the ROC curve (AUC) across the test sets.ResultsOf 123 included patients, 84 (68%) index nodules were visually [18F]FDG-positive. The malignant/borderline rate was 27% (33/123). SUV-metrices showed AUCs ranging from 0.705 (95% CI, 0.601-0.810) to 0.729 (0.633-0.824), 0.708 (0.580-0.835) to 0.757 (0.650-0.864), and 0.533 (0.320-0.747) to 0.700 (0.502-0.898) in all (n = 123), non-Hürthle (n = 94), and Hürthle cell (n = 29) nodules, respectively. At SUVmax, SUVpeak, SUVmax-ratio, and SUVpeak-ratio cut-offs of 2.1 g/mL, 1.6 g/mL, 1.2, and 0.9, respectively, sensitivity of [18F]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hürthle cell nodules. In Hürthle cell nodules, cut-offs of 5.2 g/mL, 4.7 g/mL, 3.4, and 2.8, respectively, resulted in 100% sensitivity (95% CI, 66.4-100%). Radiomic analysis of 84 (68%) [18F]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model.ConclusionQuantitative [18F]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hürthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [18F]FDG-positive nodules.Trial registration numberThis trial is registered with ClinicalTrials.gov: NCT02208544 (5 August 2014), https://clinicaltrials.gov/ct2/show/NCT02208544 .
| S-EPMC9165273 | biostudies-literature
Project description:PurposeAs ~25% of cytologically indeterminate thyroid nodules harbour malignancy, diagnostic lobectomy is still performed in many cases. 18FDG PET/CT rules out malignancy in visually negative nodules; however, none of the currently available interpretation criteria differentiates malignant from benign 18FDG-avid nodules. We evaluated the ability of PET metrics and radiomics features (RFs) to predict final diagnosis of 18FDG-avid cytologically indeterminate thyroid nodules.MethodsSeventy-eight patients were retrospectively included. After volumetric segmentation of each thyroid lesion, 4 PET metrics and 107 RFs were extracted. A logistic regression was performed including thyroid stimulating hormone, PET metrics, and RFs to assess their predictive performance. A linear combination of the resulting parameters generated a radiomics score (RS) that was matched with cytology classes (Bethesda III and IV) and compared with final diagnosis.ResultsTwo RFs (shape_Sphericity and glcm_Autocorrelation) differentiated malignant from benign lesions. A predictive model integrating RS and cytology classes effectively stratified the risk of malignancy. The prevalence of thyroid cancer increased from 5 to 37% and 79% in accordance with the number (score 0, 1 or 2, respectively) of positive biomarkers.ConclusionsOur multiparametric model may be useful for reducing the number of diagnostic lobectomies with advantages in terms of costs and quality of life for patients.
| S-EPMC8763930 | biostudies-literature
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.
| S-EPMC9058063 | biostudies-literature
Project description:The aim of this study was to establish and validate the precision of a novel radiomics approach that integrates 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) scan data with clinical information to improve the prognostication of survival rates in patients diagnosed with stage III Non-Small Cell Lung Cancer (NSCLC) who are not candidates for surgery. We evaluated pretreatment 18F-FDG PET-CT scans from 156 individuals diagnosed with stage III inoperable NSCLC at Shandong Cancer Hospital. These individuals were divided into two groups: a training set comprising 110 patients and an internal validation set consisting of 46 patients. By employing random forest classifier and cox proportional hazards model , we identified and utilized relevant features to create predictive models and a nomogram. The effectiveness of these models was assessed through the use of the receiver operating characteristics(ROC) curves, Kaplan-Meier (KM) curves, and the application of the nomogram. Our findings showed that the combined model, which integrates both clinical and radiomic data, outperformed those based solely on clinical or radiomic features in predicting 3-year overall survival(OS). Furthermore, calibration plots revealed a high level of agreement between predicted and actual survival times. The research successfully established a predictive radiomics model that integrates 18F-FDG PET/CT imaging with clinical indicators to enhance survival predictions for patients with stage III inoperable NSCLC.
| S-EPMC11374974 | biostudies-literature
Project description:ObjectiveThe objective of this study was to assess the clinical significance of increased fluorine-18-fluorodeoxyglucose (F-FDG) uptake on PET/CT in joints for evaluation of symptomatic osteoarthritis (OA) and prediction of progression.Patients and methodsIn this prospective study, shoulder, hip, and knee joints were imaged in 65 patients undergoing routine F-FDG PET/CT imaging. Patients completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire to assess joint pain, stiffness, and physical function. Standardized uptake values (SUVs) were measured in hip, knee, acromioclavicular (AC), and glenohumeral (GH) joints. Scout PET/CT images were evaluated for OA using the Kellgren and Lawrence (K/L) system. Patients were followed-up for 5 years to determine the progression of OA on the basis of follow-up imaging or surgical intervention.ResultsSUV of knee (r=0.309, P=0.0003), hip (r=0.260, P=0.0027), AC (r=0.186, P=0.0313), and GH (r=0.191, P=0.0271) joints correlated with WOMAC overall scores. Furthermore, SUV of knee (r=0.410, P<0.0001), hip (r=0.203, P=0.0199), and AC (r=0.364, P<0.0001) joints correlated with K/L scores. The area under the receiver operating characteristic curves for SUV were 0.734 (knee), 0.678 (hip), 0.661 (AC), and 0.544 (GH) for symptomatic OA detection based on WOMAC overall z-score greater or equal to 2. Compared with K/L score [hazard ratio (HR)=0.798, P=0.5324], age (HR=0.992, P=0.8978), and WOMAC overall score (HR=1.089, P=0.1265), only SUV (HR=5.653, P=0.0229) was an independent predictor of OA progression in the knees.ConclusionF-FDG PET/CT may be helpful with localization of painful abnormalities in the inflamed regions of the joints, which could potentially be used to direct individualized treatment in moderate and severe OA. Furthermore, SUV measurement on F-FDG PET/CT could serve as an inflammation activity index in the knees that may be predictive of outcomes and progression rate of OA.
| S-EPMC6002865 | biostudies-literature
Project description:Pleural epithelioid hemangioendothelioma (EHE) is a rare malignancy of vascular-endothelial origin with non-specific symptoms and an unpredictable outcome. Diagnosis of this condition by imaging modalities is challenging, and no standard therapeutic approaches have been established in this regard. In this paper, we described the case of a patient with a low-grade fever, coughing and chest pain who underwent 18F-FDG PET/CT after a positive thorax CT showing multiple bilateral calcified pulmonary nodules and extensive right-sided pleural effusion. Moreover, PET/CT revealed increased tracer uptake on the nodular pleural thickening and one nodule in the upper lobe of the right lung. A diagnostic thoracentesis was performed to obtain the pleural fluid. However, cytology was not diagnostic, and the subsequent thoracotomy with pleural fluid drainage and pleural biopsy was positive for pleural EHE. The study showed also an abundant non-FDG-avid pleural effusion in the collapsed right lung. Despite chest tube insertion and partial drainage of the volume, patient's condition deteriorated, and patient passed away six months after the PET scan.
| S-EPMC5221689 | biostudies-other