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1.
J Xray Sci Technol ; 2022 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-35527621

RESUMO

PURPOSE: To establish a machine-learning (ML) model based on coronary computed tomography angiography (CTA) images for evaluating myocardial ischemia in patients diagnosed with coronary atherosclerosis. METHODS: This retrospective analysis includes CTA images acquired from 110 patients. Among them, 58 have myocardial ischemia and 52 have normal myocardial blood supply. The patients are divided into training and test datasets with a ratio 7 : 3. Deep learning model-based CQK software is used to automatically segment myocardium on CTA images and extract texture features. Then, seven ML models are constructed to classify between myocardial ischemia and normal myocardial blood supply cases. Predictive performance and stability of the classifiers are determined by receiver operating characteristic curve with cross validation. The optimal ML model is then validated using an independent test dataset. RESULTS: Accuracy and areas under ROC curves (AUC) obtained from the support vector machine with extreme gradient boosting linear method are 0.821 and 0.777, respectively, while accuracy and AUC achieved by the neural network (NN) method are 0.818 and 0.757, respectively. The naive Bayes model yields the highest sensitivity (0.942), and the random forest model yields the highest specificity (0.85). The k-nearest neighbors model yields the lowest accuracy (0.74). Additionally, NN model demonstrates the lowest relative standard deviations (0.16 for accuracy and 0.08 for AUC) indicating the high stability of this model, and its AUC applying to the independent test dataset is 0.72. CONCLUSION: The NN model demonstrates the best performance in predicting myocardial ischemia using radiomics features computed from CTA images, which suggests that this ML model has promising potential in guiding clinical decision-making.

2.
BMC Musculoskelet Disord ; 23(1): 424, 2022 May 06.
Artigo em Inglês | MEDLINE | ID: mdl-35524240

RESUMO

BACKGROUND: Osteoporosis is associated with a decrease of bone mineralized component as well as a increase of bone marrow fat. At present, there are few studies using radiomics nomogram based fat-water material decomposition (MD) images of dual-energy spectral CT as an evaluation method of abnormally low Bone Mineral Density (BMD). This study aims to establish and validate a radiomics nomogram based the fat-water imaging of dual-energy spectral CT in diagnosing low BMD. METHODS: Ninety-five patients who underwent dual-energy spectral CT included T11-L2 and dual x-ray absorptiometry (DXA) were collected. The patients were divided into two groups according to T-score, normal BMD(T ≥ -1) and abnormally low BMD (T < -1). Radiomic features were selected from fat-water imaging of the dual-energy spectral CT. Radscore was calculated by summing the selected features weighted by their coefficients. A nomogram combining the radiomics signature and significant clinical variables was built. The ROC curve was performed to evaluate the performance of the model. Finally, we used decision curve analysis (DCA) to evaluate the clinical usefulness of the model. RESULTS: Five radiomic features based on fat-water imaging of dual-energy spectral CT were constructed to distinguish abnormally low BMD from normal BMD, and its differential performance was high with an area under the curve (AUC) of 0.95 (95% CI, 0.89-1.00) in the training cohort and 0.97 (95% CI, 0.91-1.00) in the test cohort. The radiomics nomogram showed excellent differential ability with AUC of 0.96 (95%CI, 0.91-1.00) in the training cohort and 0.98 (95%CI, 0.93-1.00) in the test cohort, which performed better than the radiomics model and clinics model only. The DCA showed that the radiomics nomogram had a higher benefit in differentiating abnormally low BMD from normal BMD than the clinical model alone. CONCLUSION: The radiomics nomogram incorporated radiomics features and clinical factor based the fat-water imaging of dual-energy spectral CT may serve as an efficient tool to identify abnormally low BMD from normal BMD well.


Assuntos
Nomogramas , Osteoporose , Humanos , Osteoporose/diagnóstico por imagem , Estudos Retrospectivos , Tomografia Computadorizada por Raios X/métodos , Água
3.
Front Oncol ; 12: 824509, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35530350

RESUMO

Objective: We aimed to establish an MRI radiomics model and a Delta radiomics model to predict tumor retraction after induction chemotherapy (IC) combined with concurrent chemoradiotherapy (CCRT) for primary nasopharyngeal carcinoma (NPC) in non-endemic areas and to validate its efficacy. Methods: A total of 272 patients (155 in the training set, 66 in the internal validation set, and 51 in the external validation set) with biopsy pathologically confirmed primary NPC who were screened for pretreatment MRI were retrospectively collected. The NPC tumor was delineated as a region of interest in the two sequenced images of MRI before treatment and after IC, followed by radiomics feature extraction. With the use of maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms, logistic regression was performed to establish pretreatment MRI radiomics and pre- and post-IC Delta radiomics models. The optimal Youden's index was taken; the receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drawn to evaluate the predictive efficacy of different models. Results: Seven optimal feature subsets were selected from the pretreatment MRI radiomics model, and twelve optimal subsets were selected from the Delta radiomics model. The area under the ROC curve, accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of the MRI radiomics model were 0.865, 0.827, 0.837, 0.813, 0.776, and 0.865, respectively; the corresponding indicators of the Delta radiomics model were 0.941, 0.883, 0.793, 0.968, 0.833, and 0.958, respectively. Conclusion: The pretreatment MRI radiomics model and pre- and post-IC Delta radiomics models could predict the IC-CCRT response of NPC in non-epidemic areas.

4.
Cancer Imaging ; 22(1): 22, 2022 May 12.
Artigo em Inglês | MEDLINE | ID: mdl-35550658

RESUMO

BACKGROUND: Radiomics plays an important role in the field of oncology. Few studies have focused on the identification of factors that may influence the classification performance of radiomics models. The goal of this study was to use contrast-enhanced mammography (CEM) images to identify factors that may potentially influence the performance of radiomics models in diagnosing breast lesions. METHODS: A total of 157 women with 161 breast lesions were included. Least absolute shrinkage and selection operator (LASSO) regression and the random forest (RF) algorithm were employed to construct radiomics models. The classification result for each lesion was obtained by using 100 rounds of five-fold cross-validation. The image features interpreted by the radiologists were used in the exploratory factor analyses. Univariate and multivariate analyses were performed to determine the association between the image features and misclassification. Additional exploratory analyses were performed to examine the findings. RESULTS: Among the lesions misclassified by both LASSO and RF ≥ 20% of the iterations in the cross-validation and those misclassified by both algorithms ≤5% of the iterations, univariate analysis showed that larger lesion size and the presence of rim artifacts and/or ripple artifacts were associated with more misclassifications among benign lesions, and smaller lesion size was associated with more misclassifications among malignant lesions (all p <  0.050). Multivariate analysis showed that smaller lesion size (odds ratio [OR] = 0.699, p = 0.002) and the presence of air trapping artifacts (OR = 35.568, p = 0.025) were factors that may lead to misclassification among malignant lesions. Additional exploratory analyses showed that benign lesions with rim artifacts and small malignant lesions (< 20 mm) with air trapping artifacts were misclassified by approximately 50% more in rate compared with benign and malignant lesions without these factors. CONCLUSIONS: Lesion size and artifacts in CEM images may affect the diagnostic performance of radiomics models. The classification results for lesions presenting with certain factors may be less reliable.

5.
EJNMMI Res ; 12(1): 23, 2022 Apr 21.
Artigo em Inglês | MEDLINE | ID: mdl-35445899

RESUMO

BACKGROUND: To investigate the value of 18F-FDG PET/CT molecular radiomics combined with a clinical model in predicting thoracic lymph node metastasis (LNM) in invasive lung adenocarcinoma (≤ 3 cm). METHODS: A total of 528 lung adenocarcinoma patients were enrolled in this retrospective study. Five models were developed for the prediction of thoracic LNM, including PET radiomics, CT radiomics, PET/CT radiomics, clinical and integrated PET/CT radiomics-clinical models. Ten PET/CT radiomics features and two clinical characteristics were selected for the construction of the integrated PET/CT radiomics-clinical model. The predictive performance of all models was examined by receiver operating characteristic (ROC) curve analysis, and clinical utility was validated by nomogram analysis and decision curve analysis (DCA). RESULTS: According to ROC curve analysis, the integrated PET/CT molecular radiomics-clinical model outperformed the clinical model and the three other radiomics models, and the area under the curve (AUC) values of the integrated model were 0.95 (95% CI: 0.93-0.97) in the training group and 0.94 (95% CI: 0.89-0.97) in the test group. The nomogram analysis and DCA confirmed the clinical application value of this integrated model in predicting thoracic LNM. CONCLUSIONS: The integrated PET/CT molecular radiomics-clinical model proposed in this study can ensure a higher level of accuracy in predicting the thoracic LNM of clinical invasive lung adenocarcinoma (≤ 3 cm) compared with the radiomics model or clinical model alone.

6.
J Comput Assist Tomogr ; 46(3): 447-454, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35405690

RESUMO

OBJECTIVE: The aim of this study was to explore the clinical utility of spinal magnetic resonance imaging-based radiomics to predict treatment response (TR) in patients with multiple myeloma (MM). METHODS: A total of 123 MM patients (85 in the training cohort and 38 in the test cohort) with complete response (CR) (n = 40) or non-CR (n = 83) were retrospectively enrolled in the study. Key feature selection and data dimension reduction were performed using the least absolute shrinkage and selection operator regression. A nomogram was built by combining radiomic signatures and independent clinical risk factors. The prediction performance of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Treatment response was assessed by determining the serum and urinary levels of M-proteins, serum-free light chain ratio, and the percentage of bone marrow plasma cells. RESULTS: Thirteen features were selected to build a radiomic signature. The International Staging System (ISS) stage was selected as an independent clinical factor. The radiomic signature and nomogram showed better calibration and higher discriminatory capacity (AUC of 0.929 and 0.917 for the radiomics and nomogram in the training cohort, respectively, and 0.862 and 0.874 for the radiomics and nomogram in the test cohort, respectively) than the clinical model (AUC of 0.661 and 0.674 in the training and test cohort, respectively). Decision curve analysis confirmed the clinical utility of the radiomics model. CONCLUSIONS: Nomograms incorporating a magnetic resonance imaging-based radiomic signature and ISS stage help predict the response to chemotherapy for MM and can be useful in clinical decision-making.

7.
Front Aging Neurosci ; 14: 782036, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35309889

RESUMO

Neuroimaging biomarkers that predict the edema after acute stroke may help clinicians provide targeted therapies and minimize the risk of secondary injury. In this study, we applied pretherapy MRI radiomics features from infarction and cerebrospinal fluid (CSF) to predict edema after acute ischemic stroke. MRI data were obtained from a prospective, endovascular thrombectomy (EVT) cohort that included 389 patients with acute stroke from two centers (dataset 1, n = 292; dataset 2, n = 97), respectively. Patients were divided into edema group (brain swelling and midline shift) and non-edema group according to CT within 36 h after therapy. We extracted the imaging features of infarct area on diffusion weighted imaging (DWI) (abbreviated as DWI), CSF on fluid-attenuated inversion recovery (FLAIR) (CSFFLAIR) and CSF on DWI (CSFDWI), and selected the optimum features associated with edema for developing models in two forms of feature sets (DWI + CSFFLAIR and DWI + CSFDWI) respectively. We developed seven ML models based on dataset 1 and identified the most stable model. External validations (dataset 2) of the developed stable model were performed. Prediction model performance was assessed using the area under the receiver operating characteristic curve (AUC). The Bayes model based on DWI + CSFFLAIR and the RF model based on DWI + CSFDWI had the best performances (DWI + CSFFLAIR: AUC, 0.86; accuracy, 0.85; recall, 0.88; DWI + CSFDWI: AUC, 0.86; accuracy, 0.84; recall, 0.84) and the most stability (RSD% in DWI + CSFFLAIR AUC: 0.07, RSD% in DWI + CSFDWI AUC: 0.09), respectively. External validation showed that the AUC of the Bayes model based on DWI + CSFFLAIR was 0.84 with accuracy of 0.77 and area under precision-recall curve (auPRC) of 0.75, and the AUC of the RF model based on DWI + CSFDWI was 0.83 with accuracy of 0.81 and the auPRC of 0.76. The MRI radiomics features from infarction and CSF may offer an effective imaging biomarker for predicting edema.

8.
Acad Radiol ; 2022 Mar 25.
Artigo em Inglês | MEDLINE | ID: mdl-35346564

RESUMO

RATIONALE AND OBJECTIVES: This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors. MATERIALS AND METHODS: A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups. RESULTS: The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group. CONCLUSION: The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.

9.
Br J Radiol ; : 20211014, 2022 Mar 24.
Artigo em Inglês | MEDLINE | ID: mdl-35312376

RESUMO

OBJECTIVE: This study aimed to develop a model to predict KRAS mutations in colorectal cancer according to radiomic signatures based on CT and clinical risk factors. METHODS: This retrospective study included 172 patients with colorectal cancer. All patients were randomized at a 7:3 ratio into a training cohort (n = 121, 38.8% positive for KRAS mutation) and a validation cohort (n = 51, 39.2% positive for KRAS mutation). Radiomics features were extracted from single-slice and full-volume regions of interest on the portal-venous CT images. The least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, and logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS: 1018 radiomics features were extracted from single-slice and full-volume ROIs. Eight features were retained to construct 2D (two-dimensional, 2D) radiomics model. Similarly, eight features were retained to construct 3D (three-dimensional, 3D) radiomics model. The area under the curve (AUC) values of the test cohort were 0.75 and 0.84, respectively. Delong test showed that the integrated nomogram (AUC = 0.92 in the test cohort) had better clinical predictive efficiency than 2D radiomics (p-value < 0.05) model and 3D radiomics model (p-value < 0.05). CONCLUSION: The 2D and 3D radiomics models can both predict KRAS mutations. And, the integrated nomogram can be better applied to predict KRAS mutation status in colorectal cancer. ADVANCES IN KNOWLEDGE: CT-based radiomics showed satisfactory diagnostic significance for the KRAS status in colorectal cancer, the clinical-combined model may be applied in the individual pre-operative prediction of KRAS mutation.

10.
Artigo em Inglês | MEDLINE | ID: mdl-35344062

RESUMO

PURPOSE: Tumor heterogeneity, which is associated with poor outcomes, has not been exhibited in the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade and Necrosis (SSIGN) scores. Radiomics allows an in-depth characterization of heterogeneity across the tumor, but its incremental value to the existing prognostic models for clear cell renal cell carcinoma (ccRCC) outcome is unknown. The purpose of this study was to evaluate the association between the radiomics-based tumor heterogeneity and postoperative risk of recurrence in localized ccRCC, and to assess its incremental value to UISS and SSIGN. METHODS: A multicenter 866 ccRCC patients derived from 12 Chinese hospitals were studied. The endpoint was recurrence-free survival (RFS). A CT-based radiomics signature (RS) was developed and assessed in the whole cohort and in the subgroups stratified by UISS and SSIGN. Two combined nomograms, the R-UISS (combining RS and UISS) and R-SSIGN (combining RS and SSIGN), were developed. The incremental value of RS to UISS and SSIGN in RFS prediction was evaluated. R statistical software was used for statistics. RESULTS: Patients with low radiomics scores were 4.44 times more likely to experience recurrence than those with high radiomics scores (P<0.001). Stratified analysis suggested the association is significant among low- and intermediate-risk patients identified by UISS and SSIGN. The R-UISS and R-SSIGN showed better predictive capability than UISS and SSIGN did with higher C-indices (R-UISS vs. UISS, 0.74 vs. 0.64; R-SSIGN vs. SSIGN, 0.78 vs. 0.76) and higher clinical net benefit. CONCLUSIONS: The radiomics-based tumor heterogeneity can predict outcome and add incremental value to the existing prognostic models in localized ccRCC patients. Incorporating radiomics-based tumor heterogeneity in ccRCC prognostic models may provide the opportunity to better surveillance and adjuvant clinical trial design.

11.
Front Oncol ; 12: 772770, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35186727

RESUMO

OBJECTIVES: EGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy. MATERIALS AND METHODS: We retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of EGFR gene testing. Then manually segmented VOI, captured the clinicopathological features, analyzed traditional radiology features, and extracted radiomic, and deep learning features. The cases were randomly divided into training and test set. We carried out feature screening; then applied the light GBM algorithm, Resnet-101 algorithm, logistic regression to develop sole models, and fused models to predict EGFR mutation conditions. The efficiency of models was evaluated by ROC and PRC curves. RESULTS: We successfully established Modelclinical, Modelradiomic, ModelCNN (based on clinical-radiology, radiomic and deep learning features respectively), Modelradiomic+clinical (combining clinical-radiology and radiomic features), and ModelCNN+radiomic+clinical (combining clinical-radiology, radiomic, and deep learning features). Among the prediction models, ModelCNN+radiomic+clinical showed the highest performance, followed by ModelCNN, and then Modelradiomic+clinical. All three models were able to accurately predict EGFR mutation with AUC values of 0.751, 0.738, and 0.684, respectively. There was no significant difference in the AUC values between ModelCNN+radiomic+clinical and ModelCNN. Further analysis showed that ModelCNN+radiomic+clinical effectively improved the efficacy of Modelradiomic+clinical and showed better efficacy than ModelCNN. The inclusion of clinical-radiology features did not effectively improve the efficacy of Modelradiomic. CONCLUSIONS: Either deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods.

12.
Br J Radiol ; 95(1133): 20210986, 2022 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-35143254

RESUMO

OBJECTIVES: To determine the diagnostic ability of cervical mucosa radiomics signature of sagittal T2WI and T1 contrast-enhanced (CE) imaging in detecting early-stage cervical cancers with negative MRI. METHODS: Preoperative images of postoperative pathology confirmed early-stage cervical cancer patients and normal cervix patients admitted to our hospital between January 2013 and December 2020 were retrospectively reviewed. Patients with cancer signals on T2WI, T1CE and DWI were deleted. Regions of interests (ROIs) were delineated on cervical mucosa (from cervical canal to cervical dome) with 5 mm width on sagittal T2WI and T1CE. The maximum-relevance and minimumredundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the calculation of radiomics signature scores. Diagnostic performance was assessed and compared between radiomics prediction models (model 1: T1CE; model 2: T2WI; model 3: model one combined with model 2). Differential diagnostic ability of radiomics signature in detecting lymphatic vascular space invasion (LVSI) was further explored. RESULTS: Diagnostic performance of model three was higher than model 1 and model 2 both in primary (model 3 0.874, model 1 0.857, model 2 0.816) and validation (model 3 0.853, model 1 0.847, model 2 0.634) cohorts. Model 3 showed statistical diagnostic difference compared with model 2 (primary p = 0.008, validation p = 0.000). However, the diagnostic improvement ability of model 3 showed no statistical difference compared with model 1 (primary p = 0.351, validation p = 0.739). Diagnostic efficiency of model 3 in detecting LVSI was not apparent (AUC 0.64). CONCLUSIONS: Radiomics analysis of cervical mucosa combining T1CE and T2WI is promising for predicting MRI invisible early-stage cervical cancers, however further ability in detecting LVSI was not apparent. ADVANCES IN KNOWLEDGE: Conventional MRI was originally defined as meaningless in very early-stage cervical cancers. However, whether MRI radiomics analysis of cervical mucosa can detecting tiny changes of invisible early stage cervical cancers has not been researched yet.


Assuntos
Colo do Útero , Neoplasias do Colo do Útero , Colo do Útero/diagnóstico por imagem , Colo do Útero/patologia , Feminino , Humanos , Imageamento por Ressonância Magnética/métodos , Nomogramas , Estudos Retrospectivos , Neoplasias do Colo do Útero/diagnóstico por imagem , Neoplasias do Colo do Útero/patologia
13.
Eur J Radiol ; 149: 110197, 2022 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-35149339

RESUMO

PURPOSE: This paper aims to use radiomics-clinical analysis based on CT imaging to distinguish between acute and chronic osteoporotic vertebral fractures. METHOD: A total of 147 patients who underwent both dual-energy X-ray absorptiometry (DEXA), CT and MRI of the spine were analyzed retrospectively. The patients were assigned to either a training cohort (n = 103) or a validation cohort (n = 44). The radiomics model and combined nomogram model were established by multivariate logistic regression analysis. The predictive performance was assessed with receiver operating characteristic (ROC) curve. RESULTS: Fourteen radiomic features based on spine CT images were constructed to distinguish acute versus chronic osteoporotic vertebral fractures, and its differentialperformance was good with an area under the curve (AUC) of 0.90 (95% CI, 0.84-0.95) in the training cohort and 0.82 (95% CI, 0.69-0.94) in the validation cohort. Based on the radiomic signature and clinical fracture line feature, a combined nomogram was developed and showed excellent differential ability with highest AUC of 0.93 (95 %CI,0.88-0.98) in the training cohort and 0.86 (95 %CI,0.73-0.98) in the validation cohort, which performed better than the clinical model significantly only. CONCLUSIONS: A quantitative nomogram based on clinical fracture line feature and radiomic features of CT images can be used to distinguish acute and chronic osteoporotic vertebral fractures with excellent predictive ability, which can be served as a potential decision support tool to assist clinicians in evaluating the phase of vertebral fractures timely, especially in situation where spine MRI was not available for patient.


Assuntos
Fraturas por Osteoporose , Fraturas da Coluna Vertebral , Humanos , Fraturas por Osteoporose/diagnóstico por imagem , Estudos Retrospectivos , Fraturas da Coluna Vertebral/diagnóstico por imagem , Coluna Vertebral/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos
14.
J Ovarian Res ; 15(1): 22, 2022 Feb 03.
Artigo em Inglês | MEDLINE | ID: mdl-35115022

RESUMO

BACKGROUND: Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery. PURPOSE: To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods. METHODS: A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing. RESULTS: The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively. CONCLUSIONS: Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.


Assuntos
Neoplasias Ovarianas/diagnóstico por imagem , Adulto , Feminino , Humanos , Aprendizado de Máquina , Imageamento por Ressonância Magnética , Pessoa de Meia-Idade , Neoplasias Ovarianas/classificação , Neoplasias Ovarianas/patologia , Período Pré-Operatório , Curva ROC , Estudos Retrospectivos , Adulto Jovem
15.
Nat Commun ; 13(1): 963, 2022 Feb 18.
Artigo em Inglês | MEDLINE | ID: mdl-35181649

RESUMO

In low-dimensional systems with strong electronic correlations, the application of an ultrashort laser pulse often yields novel phases that are otherwise inaccessible. The central challenge in understanding such phenomena is to determine how dimensionality and many-body correlations together govern the pathway of a non-adiabatic transition. To this end, we examine a layered compound, 1T-TiSe2, whose three-dimensional charge-density-wave (3D CDW) state also features exciton condensation due to strong electron-hole interactions. We find that photoexcitation suppresses the equilibrium 3D CDW while creating a nonequilibrium 2D CDW. Remarkably, the dimension reduction does not occur unless bound electron-hole pairs are broken. This relation suggests that excitonic correlations maintain the out-of-plane CDW coherence, settling a long-standing debate over their role in the CDW transition. Our findings demonstrate how optical manipulation of electronic interaction enables one to control the dimensionality of a broken-symmetry order, paving the way for realizing other emergent states in strongly correlated systems.

16.
Acad Radiol ; 2022 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-35033450

RESUMO

RATIONALE AND OBJECTIVES: This study aimed to develop a clinically practical model to predict V-raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation in colorectal cancer according to radiomic signatures based on computed tomography (CT) and clinical risk factors, and to determine the model's diagnostic accuracy for BRAF mutation status. MATERIALS AND METHODS: This retrospective study included 140 patients with colorectal cancer. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator algorithm was adopted to construct a radiomics signature according to imaging features of the tumor lesion, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model. The predictive performance was evaluated by receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis. RESULTS: The radscore, generated by 5 selected radiomics features, demonstrated a favorable ability to predict BRAF mutation in both the training (area under the receiver operating characteristic curve [AUC] 0.93) and validation (AUC 0.87) cohorts. Subsequently, integrating two independent predictors (including the radscore and clinical risk factors) into a nomogram exhibited more favorable discriminatory performance, with the AUC improved to 0.95 and 0.88 in both cohorts. Moreover, the accuracy for predicting BRAF mutations was higher than that of the clinical model, ranging from 0.70 to 0.89. CONCLUSION: The proposed CT-based radiomics signature is associated with BRAF mutations. The present study also proposes a combined model can potentially be applied in the individual preoperative prediction of BRAF mutation status in colorectal cancer. ADVANCES IN KNOWLEDGE: CT-based radiomics showed satisfactory diagnostic significance for the BRAF status in colorectal cancer, the clinical-combined model may be applied in the individual preoperative prediction of BRAF mutation.

17.
Curr Med Sci ; 42(1): 217-225, 2022 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-35089491

RESUMO

OBJECTIVE: The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS: In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION: The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.


Assuntos
Carcinoma Adenoescamoso/diagnóstico por imagem , Carcinoma Ductal Pancreático/diagnóstico por imagem , Neoplasias Pancreáticas/diagnóstico por imagem , Tomografia Computadorizada por Raios X/normas , Adulto , Idoso , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X/métodos
18.
Eur Radiol ; 2022 Jan 17.
Artigo em Inglês | MEDLINE | ID: mdl-35037969

RESUMO

OBJECTIVES: To develop and externally validate a machine learning (ML) model based on diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) to identify the onset time of wake-up stroke from MRI. METHODS: DWI and FLAIR images of stroke patients within 24 h of clear symptom onset in our hospital (dataset 1, n = 410) and another hospital (dataset 2, n = 177) were included. Seven ML models based on dataset 1 were developed to estimate the stroke onset time for binary classification (≤ 4.5 h or > 4.5 h): Random Forest (RF), support vector machine with kernel (svmLinear) or radial basis function kernel (svmRadial), Bayesian (Bayes), K-nearest neighbor (KNN), adaptive boosting (AdaBoost), and neural network (NNET). ROC analysis and RSD were performed to evaluate the performance and stability of the ML models, respectively, and dataset 2 was externally validated to evaluate the model generalization ability using ROC analysis. RESULTS: svmRadial achieved the best performance with the highest AUC and accuracy (AUC: 0.896, accuracy: 0.878), and was the most stable (RSD% of AUC: 0.08, RSD% of accuracy: 0.06). The svmRadial model was then selected as the final model, and the AUC of the svmRadial model for predicting the onset time external validation was 0.895, with 0.825 accuracy. CONCLUSIONS: The svmRadial model using DWI + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset. KEY POINTS: • Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset. • A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time. • External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.

19.
Catheter Cardiovasc Interv ; 99(1): E12-E21, 2022 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-34652068

RESUMO

OBJECTIVE: To evaluate the prognostic implications of atherosclerosis functional pattern on ischemia-causing vessels received percutaneous coronary intervention (PCI) or conservative treatment. BACKGROUND: Quantitative flow ratio (QFR)-derived pullback pressure gradient (PPG) index is recently proposed to characterize atherosclerosis functional pattern, but its prognostic value remains unclear. METHODS: QFR-derived PPG index was retrospectively calculated in patients from the PANDA III trial. Vessels with low or high PPG treated by PCI or not were compared for the risk of 2-year vessel-oriented composite outcome (VOCO), which was a composite of vessel-related ischemia-driven revascularization, vessel-related myocardial infarction, or cardiac death. RESULTS: A total of 1444 vessels were included while 94 (6.5%) VOCOs occurred within 2 years. Among physiologically ischemic vessels (QFR ≤ 0.80) treated by PCI, those with low PPG acquired higher VOCO risk than those with high PPG (8.4% vs. 3.8%; adjusted hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.18 to 3.86), and a similar VOCO risk (8.4% vs. 7.8%; adjusted HR 1.11, 95%CI 0.70-1.78) compared to those treated by conservatively. After multiple adjustment, PPG index was an independent predictor for VOCO (HR 1.30, 95% CI 1.05-1.62). The addition of PPG to the model of clinical risk factors substantially improved the predictions of VOCO (C-index 0.67 vs. 0.62, net reclassification index 0.42). CONCLUSIONS: PCI treatment was associated with improved outcomes in vessels with high PPG, but not for those with low PPG, which acquired similar risk of VOCO compared to vessels treated conservatively. QFR-derived PPG might assist the treatment strategy selection in ischemia-causing vessels.


Assuntos
Doença da Artéria Coronariana , Reserva Fracionada de Fluxo Miocárdico , Intervenção Coronária Percutânea , Angiografia Coronária , Doença da Artéria Coronariana/diagnóstico por imagem , Doença da Artéria Coronariana/terapia , Vasos Coronários/diagnóstico por imagem , Vasos Coronários/cirurgia , Humanos , Valor Preditivo dos Testes , Estudos Retrospectivos , Fatores de Risco , Fatores de Tempo , Resultado do Tratamento
20.
Eur Radiol ; 32(3): 1813-1822, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-34655310

RESUMO

OBJECTIVE: To develop a nomogram based on MRI radiomics and clinical features for preoperatively predicting H3K27M mutation in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. METHODS: The institutional database was reviewed to identify patients with pHGGs with a midline location of the brain who underwent tumor biopsy with preoperative MRI scans between June 2016 and June 2021. A total of 107 patients with pHGGs, including 79 patients with H3K27M mutation, were consecutively included and randomly divided into training and test sets. Radiomics features were extracted from fluid-attenuated inversion recovery (FLAIR), diffusion-weighted (DW) and post-contrast T1-weighted images, and apparent diffusion coefficient (ADC) maps. The minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were performed for radiomics signature construction. Clinical and radiological features were analyzed to select clinical predictors. A nomogram was then developed by incorporating the radiomics signature and selected clinical predictors. RESULTS: Nine radiomics features were selected to construct the radiomics signature, which showed a favorable discriminatory ability in training and test sets with an area under the curve (AUC) of 0.95 and 0.92, respectively. Ring enhancement was identified as an independent clinical predictor (p < 0.01). The nomogram, constructed with radiomics signature and ring enhancement, showed good calibration and discrimination in training and testing sets (AUC: 0.95 and 0.90 respectively). CONCLUSIONS: The nomogram which combined radiomics signature and ring enhancement had a satisfactory ability to predict H3K27M mutation in pHGGs with a midline of the brain. KEY POINTS: • Conventional MRI features were not powerful enough to predict H3K27M mutation status in pediatric high-grade gliomas (pHGGs) with a midline location of the brain. • An MRI-based radiomics signature showed satisfactory ability to predict H3K27M mutation status of pHGGs located in the midline of the brain. • Associating the radiomics signature with clinical factors improved predictive performance.


Assuntos
Glioma , Encéfalo , Criança , Glioma/diagnóstico por imagem , Glioma/genética , Humanos , Imageamento por Ressonância Magnética , Mutação , Nomogramas , Estudos Retrospectivos
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