Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

BACKGROUND: Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS: CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS: The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION: The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

Magnetic resonance imaging-radiomics evaluation of response to chemotherapy for synchronous liver metastasis of colorectal cancer.

BACKGROUND: Synchronous liver metastasis (SLM) is an indicator of poor prognosis for colorectal cancer (CRC). Nearly 50% of CRC patients develop hepatic metastasis, with 15%-25% of them presenting with SLM. The evaluation of SLM in CRC is crucial for precise and personalized treatment. It is beneficial to detect its response to chemotherapy and choose an optimal treatment method. AIM: To construct prediction models based on magnetic resonance imaging (MRI)-radiomics and clinical parameters to evaluate the chemotherapy response in SLM of CRC. METHODS: A total of 102 CRC patients with 223 SLM lesions were identified and divided into disease response (DR) and disease non-response (non-DR) to chemotherapy. After standardizing the MRI images, the volume of interest was delineated and radiomics features were calculated. The MRI-radiomics logistic model was constructed after methods of variance/Mann-Whitney U test, correlation analysis, and least absolute shrinkage and selection operator in feature selecting. The radiomics score was calculated. The receiver operating characteristics curves by the DeLong test were analyzed with MedCalc software to compare the validity of all models. Additionally, the area under curves (AUCs) of DWI, T2WI, and portal phase of contrast-enhanced sequences radiomics model (Ra-DWI, Ra-T2WI, and Ra-portal phase of contrast-enhanced sequences) were calculated. The radiomics-clinical nomogram was generated by combining radiomics features and clinical characteristics of CA19-9 and clinical N staging. RESULTS: The AUCs of the MRI-radiomics model were 0.733 and 0.753 for the training (156 lesions with 68 non-DR and 88 DR) and the validation (67 lesions with 29 non-DR and 38 DR) set, respectively. Additionally, the AUCs of the training and the validation set of Ra-DWI were higher than those of Ra-T2WI and Ra-portal phase of contrast-enhanced sequences (training set: 0.652 vs 0.628 and 0.633, validation set: 0.661 vs 0.575 and 0.543). After chemotherapy, the top four of twelve delta-radiomics features of Ra-DWI in the DR group belonged to gray-level run-length matrices radiomics parameters. The radiomics-clinical nomogram containing radiomics score, CA19-9, and clinical N staging was built. This radiomics-clinical nomogram can effectively discriminate the patients with DR from non-DR with a higher AUC of 0.809 (95% confidence interval: 0.751-0.858). CONCLUSION: MRI-radiomics is conducive to predict chemotherapeutic response in SLM patients of CRC. The radiomics-clinical nomogram, involving radiomics score, CA19-9, and clinical N staging is more effective in predicting chemotherapeutic response.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.

OBJECTIVE: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T1WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD. RESULTS: Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798. CONCLUSION: The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis.

This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross-sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the top five most powerful texture features of MR images in each sequence quantitatively for detecting the MGMT promoter methylation status. The radiomic signature (Radscore) was generated by a linear combination of the five features and estimates in each sequence. The combined model based on each Radscore was established using multivariate logistic regression analysis. A receiver operating characteristic (ROC) curve, nomogram, calibration, and decision curve analysis (DCA) were used to evaluate the performance of the model. No significant differences were observed in any of the visual radiographic features or clinical factors between different MGMT methylated statuses. The top five most powerful features were selected from a total of 396 texture features of T1, contrast-enhanced T1, T2, and T2 FLAIR. Each sequence's Radscore can distinguish MGMT methylated status. A combined model based on Radscores showed differentiation between methylated MGMT and unmethylated MGMT both in the glioblastoma (GBM) dataset as well as the dataset for all other gliomas. The area under the ROC curve values for the combined model was 0.818, with 90.5% sensitivity and 72.7% specificity, in the GBM dataset, and 0.833, with 70.2% sensitivity and 90.6% specificity, in the overall gliomas dataset. Nomogram, calibration, and DCA also validated the performance of the combined model. The combined model based on texture features could be considered as a noninvasive imaging marker for detecting MGMT methylation status in glioma.

Predicting the progression of Parkinson's disease using conventional MRI and machine learning: An application of radiomic biomarkers in whole-brain white matter.

PURPOSE: This study aimed to develop and validate a radiomics model based on whole-brain white matter and clinical features to predict the progression of Parkinson disease (PD). METHODS: PD patient data from the Parkinson's Progress Markers Initiative (PPMI) database was evaluated. Seventy-two PD patients with disease progression, as measured by the Hoehn-Yahr Scale (HYS) (stage 1-5), and 72 PD patients with stable PD were matched by sex, age, and category of HYS and included in the current study. Each individual's T1 -weighted MRI scans at the baseline timepoint were segmented to isolate whole-brain white matter for radiomics feature extraction. The total dataset was divided into a training and test set according to subject serial number. The size of the training dataset was reduced using the maximum relevance minimum redundancy (mRMR) algorithm to construct a radiomics signature using machine learning. Finally, a joint model was constructed by incorporating the radiomics signature and clinical progression scores. The test data were then used to validate the prediction models, which were evaluated based on discrimination, calibration, and clinical utility. RESULTS: Based on the overall data, the areas under curve (AUCs) of the joint model, signature and Unified Parkinson Disease Rating Scale III PD rating score were 0.836, 0.795, and 0.550, respectively. Furthermore, the sensitivities were 0.805, 0.875, and 0.292, respectively, and the specificities were 0.722, 0.697, and 0.861, respectively. In addition, the predictive accuracy of the model was 0.827, the sensitivity was 0.829 and the specificity was 0.702 for stage-1 PD. For stage-2 PD, the predictive accuracy of the model was 0.854, the sensitivity was 0.960, and the specificity was 0.600. CONCLUSION: Our results provide evidence that conventional structural MRI can predict the progression of PD. This work also supports the use of a simple radiomics signature built from whole-brain white matter features as a useful tool for the assessment and monitoring of PD progression.

A Nomogram Combined Radiomics and Kinetic Curve Pattern as Imaging Biomarker for Detecting Metastatic Axillary Lymph Node in Invasive Breast Cancer.

Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

[Application Values of Gadolinium-ethoxybenzyl Diethylenetriaminepentaacetic Acid Enhanced Magnetic Resonance Imaging-based Radiomics in the Quantitative Assessment of Liver Reserve Function of Patients with Liver Cirrhosis].

Objective To evaluate the correlation between the radiomics signature of hepatobiliary phase imaging of gadolinium-ethoxybenzyl diethylenetriaminepentaacetic acid(Gd-EOB-DTPA)enhanced magnetic resonance imaging(MRI)and Child-Pugh of liver cirrhosis,establish nomogram prediction model,and assess the predictive value of quantitative assessment of liver reserve function of patients with liver cirrhosis. Methods One hundred patients with liver cirrhosis who met the inclusion criteria were divided into 52 patients with Child-Pugh grade A and 48 patients with Child-Pugh grade B+C according to Child-Pugh classification criteria,and were randomly divided into training set and test set at a proportion of 7∶3.The AK software was used to extract the imaging features of the Gd-EOB-DTPA-enhanced MRI hepatobiliary images of the patients in the training set,and the least absolute shrinkage and selection operator feature selection algorithm was used to reduce the dimension of the data,select the features,and construct the radiomics tags.According to the radiomics label Rad-score,a line chart(nomogram)prediction model was established to predict the Child-Pugh B+C level of liver reserve function.The model was applied to the training set and test set respectively,and the diagnostic efficiency was quantitatively evaluated by receiver operating characteristic(ROC)curve. Results After dimension reduction and screening of 396 texture feature parameters extracted by AK software,7 image feature parameters were obtained.According to the above characteristics,the radiomics tag Rad-score was constructed and the nomogram prediction model was created.The differences of Rad-score scores between Child-Pugh A and Child-Pugh B+C groups in training set and test set were statistically analyzed by Wilcoxon rank sum test(P=0.000, P=0.001).The diagnostic efficacy of nomogram prediction model for predicting Child-Pugh B+C grade of liver reserve function in the ROC curve of training set and test set was 0.88 and 0.86 respectively. Conclusions The nomogram prediction model created according to the radiomics tag Rad-score of patients with liver cirrhosis with different liver reserve functions can be used as a more accurate and reliable auxiliary detection tool for liver reserve function.It provides a new means for clinicians to evaluate liver reserve function more accurately.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults.

BACKGROUND: White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. PURPOSE: To develop and validate a radiomics nomogram for the prediction of WMH progression. STUDY TYPE: Retrospective. POPULATION: Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. FIELD STRENGTH/SEQUENCE: T1 -fluid attenuated inversion recovery images were acquired using a 3T scanner. ASSESSMENT: WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. STATISTICAL TESTS: A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed. RESULTS: The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. DATA CONCLUSION: This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer.

BACKGROUND: In pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required. PURPOSE: To develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice. STUDY TYPE: Retrospective. POPULATION: We enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126). FIELD STRENGTH/SEQUENCE: 3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w). ASSESSMENT: ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated. STATISTICAL TESTS: The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit. RESULTS: The AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort). DATA CONCLUSION: The radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.

Whole-tumor perfusion CT using texture analysis in unresectable stage IIIA/B non-small cell lung cancer treated with recombinant human endostatin.

BACKGROUND: To observe the dynamic changes of blood perfusion with whole-tumor computed tomography (CT) perfusion imaging using texture analysis in patients with unresectable stage IIIA/B non-small cell lung cancer (NSCLC) treated with recombinant human endostatin (Endostar). METHODS: This phase II clinical trial recruited 11 patients diagnosed with stage IIIA/B NSCLC. Histological examination prior to treatment revealed squamous cell carcinoma in 4 cases and adenocarcinoma in 7 cases. All patients underwent contrast-enhanced perfusion CT at baseline and a second CT scan 1 week after treatment initiation with Endostar. CT perfusion images including blood flow (BF), blood volume (BV), and permeability (PMB) were imported into OmniKinetics software to quantitatively assess the texture features. Skewness, kurtosis, and entropy were calculated at baseline and after anti-angiogenic therapy. Changes in tumor were analyzed using Wilcoxon signed-rank test. The association of parameters with survival was evaluated using Cox proportional hazards regression model. RESULTS: There were no statistical differences in the mean values of BF, BV, and PMB before and after treatment (P=0.594, 0.477 and 0.328, respectively). The skewness on BF images demonstrated significant differences at baseline and after treatment (0.6±2.7 vs. 1.0±2.6, P=0.010), while skewness of BV and PMB showed no significant variation (P=0.477 and 0.213, respectively). The kurtosis and entropy for BF, BV and PMB showed no significant differences (all P>0.05). In adenocarcinoma, the mean BF showed no significant differences at baseline and after treatment (76.5±25.7 vs. 101.2±46.4, P=0.398), while skewness for BF was significantly higher after treatment than at baseline (-0.19±3.3 vs. 0.59±3.2, P=0.028). No significant associations were found between perfusion CT imaging parameters and progression-free survival. CONCLUSIONS: These results suggested that blood perfusion showed improvement with whole-tumor perfusion CT using texture analysis in patients with stage IIIA/B NSCLC treated by Endostar.

Application evaluation of DCE-MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer.

The present study aimed to investigate the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) combined with quantitative analysis of diffusion weighted imaging (DWI) for the diagnosis of prostate cancer (PCa). A total of 81 patients with prostatic diseases, including PCa (n=44) and benign prostatic hyperplasia (BPH, n=37), were imaged with T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), DWI and DCE-MRI. The blood vessel permeability parameters volume transfer rate constant (Ktrans), back flow rate constant (Kep), extravascular extracellular space volume fraction (Ve), plasma volume fraction (Vp) and apparent diffusion coefficient (ADC) were measured, and compared between the two groups. The efficiency of these tools for the diagnosis of PCa was analyzed by receiver operating characteristic curve analysis. The efficiency of ADC combined with blood vessel permeability parameters in the diagnosis of PCa was analyzed by logistic regression. The correlation between these parameters and the Gleason score was evaluated by Spearman correlation analysis in the PCa group. The results demonstrated that, compared with the BPH group, Ktrans, Kep, Ve and Vp were higher, and ADC was lower in the PCa group (P<0.05). The combination of Kep and ADC offered the highest diagnosis efficiency [area under the curve (AUC=0.939)]. However, the combination of three parameters did not significantly improve the diagnostic efficiency. A subtle improvement in diagnostic efficiency was observed when four parameters (Ktrans + Kep + Ve + ADC) were combined (AUC=0.940), which was significantly higher than with one parameter. The ADC value of the PCa group was negatively correlated with the primary Gleason pattern, secondary Gleason pattern and the total Gleason score in PCa (r=-0.665, -0.456 and -0.714, respectively; P<0.001). The Vp in the PCa group was slightly negatively correlated with the primary Gleason pattern of PCa (r=-0.385; P<0.05); however, no significant correlation was found with secondary Gleason pattern and the total Gleason score. The present study revealed that the combination of DCE-MRI quantitative analysis and DWI was efficient for PCa diagnosis. This may be because DCE-MRI and DWI can noninvasively detect water motility in tumor tissues and alterations in permeability during tumor neovascularization. The present study demonstrated that Kep and ADC values may be used as predictive parameters for PCa diagnosis, which may help differentiate benign from malignant prostate lesions.