Dataset Information

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

ABSTRACT: This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature selection methods and five classification algorithms. The model set with the highest average AUC value was selected, in which some models were selected and divided into Groups A, B, and C. Individual and joint voting predictions were performed in each group for the entire data. The model set based on MRI combined with 18F-FDG-PET had the highest average AUC compared with isolated MRI or 18F-FDG-PET. Joint voting prediction showed better performance than the individual prediction when all models reached an agreement. In conclusion, radiomics derived from MRI and 18F-FDG-PET could help differentiate GBM from SBM preoperatively. The combined application of multiple models can provide greater benefits.

SUBMITTER: Cao X

PROVIDER: S-EPMC8986767 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

Cao Xu X Tan Duo D Liu Zhi Z Liao Meng M Kan Yubo Y Yao Rui R Zhang Liqiang L Nie Lisha L Liao Ruikun R Chen Shanxiong S Xie Mingguo M

Scientific reports 20220406 1

This study aimed to explore the ability of radiomics derived from both MRI and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) images to differentiate glioblastoma (GBM) from solitary brain metastases (SBM) and to investigate the combined application of multiple models. The imaging data of 100 patients with brain tumours (50 GBMs and 50 SBMs) were retrospectively analysed. Three model sets were built on MRI, 18F-FDG-PET, and MRI combined with 18F-FDG-PET using five feature sele ...[more]

PMID: 35388124

Similar Datasets

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.

Project description:Simple Summary PET/CT is an important staging modality in the baseline assessment of locally advanced esophageal squamous cell carcinoma. Accurate staging and response prediction in these patients is essential for management. The aim of this retrospective study was to assess the usefulness of 18F-FDG PET/CT radiomics features in predicting outcomes such as tumor and nodal categories, PET-based response to induction chemotherapy, progression-free survival, and overall survival. In a final cohort of 74 patients, we found that the developed radiomics models can predict these clinical and prognostic outcomes with reasonable accuracy, similar or better than those derived from conventional imaging. Future studies with a larger cohort would be helpful in establishing the significance of these models. Abstract This study aimed to assess the usefulness of radiomics features of 18F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category—70.4%, 70.4%, and 81.5%, respectively; cN category—69.0%, 86.2%, and 86.2%, respectively; PET response—60.0%, 66.7%, and 70.0%, respectively; PFS—60.7%, 75.0%, and 75.0%, respectively; and OS—51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

Project description:Early detection of gastrointestinal stromal tumor (GIST) liver metastases is crucial for the management and prognosis. In our experience, GIST liver metastases can display hypermetabolism on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and marked enhancement on magnetic resonance imaging (MRI), which are uncommon in other tumors before treatment. Most literature focus on the imaging evaluation, prognosis after treatment and less is known about imaging features on both imaging methods before treatment. This study analyzes the imaging features of newly diagnosed GIST liver metastases on 18F-FDG PET/CT and MRI, with goal of improving diagnostic accuracy. This retrospective study included 55 patients with pathological or radiographical confirmed GIST liver metastases who underwent PET/CT (n=29), MRI (n=22), or both methods (n=4). PET/CT and MRI interpretation including lesion's morphologic features, number, density or signal intensity, hemorrhage, cystic changes or necrosis, maximum standardized uptake value (SUVmax) of liver metastases and liver background on PET imaging, degree and pattern of enhancement on MRI were obtained by two experienced nuclear medicine physicians and two radiologists respectively. Data are presented as numbers, percentages, means ± standard deviations or median (interquartile range). The correlation between diameter and SUVmax of metastases, and primary tumor SUVmax and synchronous liver metastases SUVmax were analyzed by Spearman's rank test. On PET/CT visual analysis, 38.9%, 23.9%, and 37.2% of lesions showed significant hypermetabolism, slightly higher metabolism, and equal or lower metabolism than liver, respectively. There was a weak correlation between the diameter and SUVmax of liver metastases (rs =0.370, P<0.001), and a moderate correlation between SUVmax of synchronous liver metastases and the primary tumors (rs =0.492, P<0.001). On contrast-enhanced MRI, 90.8% of lesions showed heterogeneous enhancement in the arterial phase with the variable presentation, and 74.3% had different enhancement patterns between margins and intratumoral parenchyma. Liver lesions in GIST displaying significant, slight hypermetabolism on 18F-FDG PET/CT, marked or heterogeneous gradual enhancement within the intratumoral parenchyma with ring-like enhancement on MRI may denote the diagnosis of liver metastasis. However, GIST liver metastases may also display equal or lower metabolism than liver parenchyma on PET, making small lesions more difficult to diagnose.

Project description:BackgroundSpinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma.Materials and methodsA total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves.ResultsA clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p < 0.001) sets. The AUC values of the radiomics nomogram model were higher than that of radiologist in training, validation and external test sets (all p < 0.05). Moreover, no significant difference in AUC values of radiomics nomogram model was found between the validation set and external test set (p = 0.212).ConclusionThe radiomics nomogram can differentiate spinal metastasis and multiple myeloma with a moderate to good performance, and may be as a valuable method to assist in the clinical diagnosis and preoperative decision-making.

Dataset Information

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

Publications

Differentiating solitary brain metastases from glioblastoma by radiomics features derived from MRI and 18F-FDG-PET and the combined application of multiple models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets