Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 6 de 6
Mais filtros

Base de dados
Tipo de estudo
Intervalo de ano de publicação
Eur J Radiol ; 114: 38-44, 2019 May.
Artigo em Inglês | MEDLINE | ID: mdl-31005174


PURPOSE: To investigate the efficiency of radiomics signature in discriminating between benign and malignant prostate lesions with similar biparametric magnetic resonance imaging (bp-MRI) findings. EXPERIMENTAL DESIGN: Our study consisted of 331 patients underwent bp-MRI before pathological examination from January 2013 to November 2016. Radiomics features were extracted from peripheral zone (PZ), transition zone (TZ), and lesion areas segmented on images obtained by T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and its derivative apparent-diffusion coefficient (ADC) imaging. The individual prediction model, built using the clinical data and biparametric MRI features (Bp signature), was prepared using data of 232 patients and validated using data of 99 patients. The predictive performance was calculated and demonstrated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. RESULTS: The Bp signature, based on the six selected radiomics features of bp-MRI, showed better discrimination in the validation cohort (area under the curve [AUC], 0.92) than on each subcategory images (AUC, 0.81 on T2WI; AUC, 0.77 on DWI; AUC, 0.89 on ADC). The differential diagnostic efficiency was poorer with the clinical model (AUC, 0.73), built using the selected independent clinical risk factors with statistical significance (P < 0.05), than with the Bp signature. Discrimination efficiency improved when including the Bp signature and clinical factors [i.e., the individual prediction model (AUC, 0.93)]. CONCLUSION: The Bp signature, based on bp-MRI, performed better than each single imaging modality. The individual prediction model including the radiomics signatures and clinical factors showed better preoperative diagnostic performance, which could contribute to clinical individualized treatment.

Neoplasias da Próstata/diagnóstico , Adulto , Idoso , Área Sob a Curva , Estudos de Coortes , Diagnóstico Diferencial , Imagem de Difusão por Ressonância Magnética/métodos , Humanos , Imagem por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Curva ROC , Estudos Retrospectivos
Eur Radiol ; 29(9): 4670-4677, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30770971


OBJECTIVE: To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). METHODS: We retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts. RESULTS: Nine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort). CONCLUSIONS: A validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD. KEY POINTS: • Radiomic features of spinal cord lesions in MS and NMOSD were different. • Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.

J Biomed Opt ; 24(1): 1-8, 2019 01.
Artigo em Inglês | MEDLINE | ID: mdl-30701723


The objective of our study is to develop a multimodality approach by combining magnetic resonance imaging (MRI) and optical imaging methods to assess acute murine colitis at the macro- and microscopic level. In vivo MRI is used to measure the cross-sectional areas of colons at the macroscopic level. Dual-color confocal laser endomicroscopy (CLE) allows in vivo examination of the fluorescently labeled epithelial cells and microvessels in the mucosa with a spatial resolution of ∼1.4 µm during ongoing endoscopy. To further validate the structural changes of the colons in three-dimensions, ex vivo light-sheet fluorescence microscopy (LSFM) is applied for in-toto imaging of cleared colon sections. MRI, LSFM, and CLE findings are significantly correlated with histological scoring (p < 0.01) and the inflammation-associated activity index (p < 0.01). Our multimodality imaging technique permits visualization of mucosa in colitis at different scales, which can enhance our understanding of the pathogenesis of inflammatory bowel diseases.

Eur Radiol ; 2018 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-30519931


OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.

J Magn Reson Imaging ; 2018 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-30102438


BACKGROUND: Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. PURPOSE: To evaluate a radiomic signature of LN involvement based on sagittal T1 contrast-enhanced (CE) and T2 MRI sequences. STUDY TYPE: Retrospective. POPULATION: In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. FIELD STRENGTH/SEQUENCE: T1 CE and T2 MRI sequences at 3T. ASSESSMENT: The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. STATISTICAL TESTS: A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. RESULTS: The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. DATA CONCLUSIONS: A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.

Transl Oncol ; 11(1): 94-101, 2018 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-29216508


OBJECTIVES: To predict epidermal growth factor receptor (EGFR) mutation status using quantitative radiomic biomarkers and representative clinical variables. METHODS: The study included 180 patients diagnosed as of non-small cell lung cancer (NSCLC) with their pre-therapy computed tomography (CT) scans. Using a radiomic method, 485 features that reflect the heterogeneity and phenotype of tumors were extracted. Afterwards, these radiomic features were used for predicting epidermal growth factor receptor (EGFR) mutation status by a least absolute shrinkage and selection operator (LASSO) based on multivariable logistic regression. As a result, we found that radiomic features have prognostic ability in EGFR mutation status prediction. In addition, we used radiomic nomogram and calibration curve to test the performance of the model. RESULTS: Multivariate analysis revealed that the radiomic features had the potential to build a prediction model for EGFR mutation. The area under the receiver operating characteristic curve (AUC) for the training cohort was 0.8618, and the AUC for the validation cohort was 0.8725, which were superior to prediction model that used clinical variables alone. CONCLUSION: Radiomic features are better predictors of EGFR mutation status than conventional semantic CT image features or clinical variables to help doctors to decide who need EGFR tyrosine kinase inhibitor (TKI) treatment.