High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4.

BACKGROUND: BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS: This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS: Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.

Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.

OBJECTIVES: To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model. METHODS: We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative. Radiomics features were extracted from T2-weighted imaging, T1-weighted imaging, diffusion-weighted imaging, and enhanced T1-weighted imaging of rectal cancer, followed by the dimension reduction of the features. Logistic regression, support vector machine, Bayes, K-nearest neighbor, and random forests algorithms were trained to obtain the radiomics signatures. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each radiomics signature. The best radiomics signature was selected and combined with clinical and radiological characteristics to construct a joint model for predicting EMVI. Finally, the predictive performance of the joint model was assessed. RESULTS: The Bayes-based radiomics signature performed well in both the training set and the test set, with the AUCs of 0.744 and 0.738, sensitivities of 0.754 and 0.728, and specificities of 0.887 and 0.918, respectively. The joint model performed best in both the training set and the test set, with the AUCs of 0.839 and 0.835, sensitivities of 0.633 and 0.714, and specificities of 0.901 and 0.885, respectively. CONCLUSIONS: The joint model demonstrated the best diagnostic performance for the preoperative prediction of EMVI in patients with rectal cancer. Hence, it can be used as a key tool for clinical individualized EMVI prediction. KEY POINTS: • Radiomics features from magnetic resonance imaging can be used to predict extramural venous invasion (EMVI) in rectal cancer. • Machine learning can improve the accuracy of predicting EMVI in rectal cancer. • Radiomics can serve as a noninvasive biomarker to monitor the status of EMVI.

MRI-based radiomics nomogram for predicting temporal lobe injury after radiotherapy in nasopharyngeal carcinoma.

OBJECTIVES: To develop and validate a magnetic resonance imaging (MRI)-based radiomics nomogram model combining radiomic features and clinical factors for the prediction of radiotherapy-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC). METHODS: From 203 NPC cases receiving radiotherapy, 128 RTLI-positive and 278 RTLI-negative lobes were retrospectively analyzed. They were randomly divided into training (n = 285) and validation (n = 121) sets. Three hundred ninety-six texture features based on T2WI images were extracted from each temporal lobe. The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to reduce the dimension of the features and establish a radiomics signature model. Clinical risk factors and the radiomics signature were combined by multivariable logistic regression analysis to construct a radiomics nomogram model. We assessed the performance of the radiomics nomogram on discrimination, calibration, and clinical utility. RESULTS: The radiomics signature consisted of 14 selected features that were significantly associated with RTLI. In the training set, the radiomics nomogram model demonstrated a better predictive performance (AUC, 0.87; 95% CI, 0.82-0.91) than the radiomics model (AUC, 0.71; 95% CI, 0.65-0.78) and clinical model (AUC, 0.73; 95% CI, 0.67-0.79). These results were confirmed in the validation set. The radiomics nomogram model demonstrated good calibration and was clinically useful by decision curve analysis. CONCLUSION: The radiomics nomogram model combining radiomics signatures and clinical factors is an effective method for the noninvasive prediction of RTLI in NPC patients after radiotherapy. KEY POINTS: • The radiomics model based on T2WI images at the end of intensity-modulated radiotherapy can predict radiotherapy-induced temporal lobe injury in patients with NPC. • Dosimetric factors can improve the prediction performance of the radiomics model in predicting radiotherapy-induced temporal lobe injury. • An MRI-based radiomics nomogram combining radiomics signatures and clinical factors had better prediction performance than both radiomics and clinical model for the prediction of radiotherapy-induced temporal lobe injury in patients with NPC.

CT-based radiomics for differentiating intracranial contrast extravasation from intraparenchymal haemorrhage after mechanical thrombectomy.

OBJECTIVE: To develop a nonenhanced CT-based radiomic signature for the differentiation of iodinated contrast extravasation from intraparenchymal haemorrhage (IPH) following mechanical thrombectomy. METHODS: Patients diagnosed with acute ischaemic stroke who underwent mechanical thrombectomy in 4 institutions from December 2017 to June 2020 were included in this retrospective study. The study population was divided into a training cohort and a validation cohort. The nonenhanced CT images taken after mechanical thrombectomy were used to extract radiomic features. The maximum relevance minimum redundancy (mRMR) algorithm was used to eliminate confounding variables. Afterwards, least absolute shrinkage and selection operator (LASSO) logistic regression was used to generate the radiomic signature. The diagnostic performance of the radiomic signature was evaluated by the area under the curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: A total of 166 intraparenchymal areas of hyperattenuation from 101 patients were used. The areas of hyperattenuation were randomly allocated to the training and validation cohorts at a ratio of 7:3. The AUC of the radiomic signature was 0.848 (95% confidence interval (CI) 0.780-0.917) in the training cohort and 0.826 (95% CI 0.705-0.948) in the validation cohort. The accuracy of the radiomic signature was 77.6%, with a sensitivity of 76.7%, a specificity of 78.9%, a PPV of 85.2%, and a NPV of 68.2% in the validation cohort. CONCLUSIONS: The radiomic signature constructed based on initial post-operative nonenhanced CT after mechanical thrombectomy can effectively differentiate IPH from iodinated contrast extravasation. KEY POINTS: • Radiomic features were extracted from intraparenchymal areas of hyperattenuation on initial post-operative CT scans after mechanical thrombectomy. • The nonenhanced CT-based radiomic signature can differentiate IPH from iodinated contrast extravasation early. • The radiomic signature may help prevent unnecessary rescanning after mechanical thrombectomy, especially in cases where contrast extravasation is highly suggestive.

CT imaging findings of renal epithelioid lipid-poor angiomyolipoma.

OBJECTIVES: To identify specific imaging and clinicopathological features of a rare potentially malignant epithelioid variant of renal lipid-poor angiomyolipoma (E-lpAML). METHODS: A total of 20 patients with E-lpAML and 43 patients with other lpAML were retrospectively included. Multiphase computed tomography (CT) imaging features and clinicopathological findings were recorded. Independent predictors for E-lpAML were identified using multivariate logistic regression and were used to construct a diagnostic score for differentiation of E-lpAML from other lpAML. RESULTS: The E-lpAML group consisted of 6 men and 14 women (age median ± SD: 39.45 ± 15.70, range: 16.0-68.0 years). E-lpAML tended to appear as hyperdense mass lesions located at the renal sinus (n = 8, 40%) or at the renal cortex (n = 12, 60%), with a "fast-in and slow-out" enhancement pattern (n = 20, 100%), cystic degeneration (n = 18, 90%), "eyeball" sign (n = 11, 55%), and tumor neo-vasculature (n = 15, 75%) on CT. Multivariate logistic regression analysis showed that the independent predictors for diagnosing E-lpAML were cystic degeneration on CT imaging and CT value of the tumor in corticomedullary phase of enhancement. A predictive model was built with the two predictors, achieving an area under the curve (AUC) of 93.5% (95% confidence interval (95%CI): 84.3-98.2%) with a sensitivity of 95.0% (95%CI: 75.1-99.9%) and a specificity of 83.72% (95%CI: 69.3-93.2%). CONCLUSION: We identified specific CT imaging features and predictors that could contribute to the correct diagnosis of E-lpAML. Our findings should be helpful for clinical management of E-lpAML which could potentially be malignant and may require nephron-sparing surgery while other lpAML tumors which are benign require no intervention. KEY POINTS: • It is important to differentiate renal epithelioid lipid-poor angiomyolipoma (E-lpAML) from other lpAML because of differences in clinical management. • E-lpAML tumors tend to be large hyperdense tumors in the renal sinus with cystic degeneration and "fast-in and slow-out" pattern of enhancement. • Our CT imaging-based predictive model was robust in its performance for predicting E-lpAML from other lpAML tumors.

Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer.

OBJECTIVES: Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC. METHODS: A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves. RESULTS: The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value. CONCLUSIONS: We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC. KEY POINTS: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.

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.

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.

The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.

BACKGROUND: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need. PURPOSE: To construct a radiomics nomogram to stratify GBM patients into long- vs. short-term survival. STUDY TYPE: Retrospective. POPULATION: One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T MRI Scanners, T1 WI, T2 WI, T2 FLAIR, and contrast-enhanced T1 WI sequences ASSESSMENT: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test. RESULTS: The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively. DATA CONCLUSION: Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long- vs. short-term survival. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

The value of various peritumoral radiomic features in differentiating the invasiveness of adenocarcinoma manifesting as ground-glass nodules.

OBJECTIVES: To evaluate the ability of CT radiomic features extracted from peritumoral parenchyma of 2 mm and 5 mm distinguishing invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). METHODS: For this retrospective study, 121 lung adenocarcinomas appearing as ground-glass nodules on thin-section CT were evaluated. Quantitative radiomic features were extracted from the peritumoral parenchymal region of 2 mm and 5 mm on CT imaging, and the radiomic models of External2 and External5 were constructed. The ROC curves were used to evaluate the performance of different models. Differences between the AUCs were evaluated using DeLong's method. RESULTS: The radiomic scores of IAC were statistically higher than those of MIA/AIS in both the External2 and External5 models. The AUCs of the External2 and External5 models were 0.882, 0.778 in the training cohort and 0.888, 0.804 in the validation cohort, respectively. The AUC of the External2 model was not statistically different from the External5 model both in the training cohort (p = 0.116) and validation cohort (p = 0.423). CONCLUSIONS: The radiomic features extracted from the peritumoral region of 2 mm and 5 mm at thin-section CT showed good predictive values to differentiate the IAC from AIS/MIA. The radiomic features from the peritumoral region of 5 mm provide no additional benefit in distinguishing IAC from MIA/AIS than that of the 2 mm region. KEY POINTS: • The radiomic models from various peritumoral lung parenchyma were developed and validated to predict invasiveness of adenocarcinoma. • The peritumoral parenchyma of lung adenocarcinoma may contain useful information. • Radiomics from peritumoral lung parenchyma of 5 mm provides no added efficiency of the prediction for invasiveness of lung adenocarcinoma.

Non-contrast CT-based radiomic signature for screening thoracic aortic dissections: a multicenter study.

OBJECTIVE: To develop a non-contrast CT-based radiomic signature to effectively screen for thoracic aortic dissections (ADs). METHODS: We retrospectively enrolled 378 patients who underwent non-contrast chest CT scans along with CT angiography or MRI from 4 medical centers. The training and validation sets were from 3 centers, while the external test set was from a 4th center. Radiomic features were extracted from non-contrast CT images. The radiomic signature was created on the basis of selected features by a logistic regression algorithm. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were conducted to assess the predictive ability of radiomic signature. RESULTS: The radiomic signature demonstrated AUCs of 0.91 (95% confidence interval [CI], 0.86-0.95) in the training set, 0.92 (95% CI, 0.86-0.98) in the validation set, and 0.90 (95% CI, 0.82-0.98) in the external test set. The predicted diagnosis was in good agreement with the probability of thoracic AD. In the external test group, the diagnostic accuracy, sensitivity, specificity, PPV, and NPV were 90.5%, 85.7%, 91.7%, 70.6%, and 96.5%, respectively. CONCLUSIONS: A radiomic signature based on non-contrast CT images can effectively predict thoracic ADs. This method may serve as a potential screening tool for thoracic ADs. KEY POINTS: • The non-contrast CT-based radiomic signature can effectively predict the thoracic aortic dissections. • This radiomic signature shows better predictive performance compared to the current clinical model. • This prediction method may be a potential tool for screening thoracic aortic dissections.

Risk stratification of thymic epithelial tumors by using a nomogram combined with radiomic features and TNM staging.

OBJECTIVES: To construct and validate a nomogram model that integrated the CT radiomic features and the TNM staging for risk stratification of thymic epithelial tumors (TETs). METHODS: A total of 136 patients with pathology-confirmed TETs who underwent CT examination were collected from two institutions. According to the WHO pathological classification criteria, patients were classified into low-risk and high-risk groups. The TNM staging was determined in terms of the 8th edition AJCC/UICC staging criteria. LASSO regression was performed to extract the optimal features correlated to risk stratification among the 704 radiomic features calculated. A nomogram model was constructed by combining the Radscore and the TNM staging. The clinical performance was evaluated by ROC analysis, calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was employed for survival analysis. RESULTS: Five optimal features identified by LASSO regression were employed to calculate the Radscore correlated to risk stratification. The nomogram model showed a better performance in both training cohort (AUC = 0.84, 95%CI 0.75-0.91) and external validation cohort (AUC = 0.79, 95%CI 0.69-0.88). The calibration curve and DCA analysis indicated a better accuracy of the nomogram model for risk stratification than either Radscore or the TNM staging alone. The KM analysis showed a significant difference between the two groups stratified by the nomogram model (p = 0.02). CONCLUSIONS: A nomogram model that integrated the radiomic signatures and the TNM staging could serve as a reliable model of risk stratification in predicting the prognosis of patients with TETs. KEY POINTS: • The radiomic features could be associated with the TET pathophysiology. • TNM staging and Radscore could independently stratify the risk of TETs. • The nomogram model is more objective and more comprehensive than previous methods.

No staghorn calculi and none/mild hydronephrosis may be risk factors for severe bleeding complications after percutaneous nephrolithotomy.

BACKGROUND: To explore the risk factors for severe bleeding complications after percutaneous nephrolithotomy (PCNL) according to the modified Clavien scoring system. METHODS: We retrospectively analysed 2981 patients who received percutaneous nephrolithotomies from January 2014 to December 2020. Study inclusion criteria were PCNL and postoperative mild or severe renal haemorrhage in accordance with the modified Clavien scoring system. Mild bleeding complications included Clavien 2, while severe bleeding complications were greater than Clavien 3a. It has a good prognosis and is more likely to be underestimated and ignored in retrospective studies in bleeding complications classified by Clavien 1, so no analysis about these was conducted in this study. Clinical features, medical comorbidities and perioperative characteristics were analysed. Chi-square, independent t tests, Pearson's correlation, Fisher exact tests, Mann-Whitney and multivariate logistic regression were used as appropriate. RESULTS: Of the 2981 patients 70 (2.3%), met study inclusion criteria, consisting of 51 men and 19 women, 48 patients had severe bleeding complications. The remaining 22 patients had mild bleeding. Patients with postoperative severe bleeding complications were more likely to have no or slight degree of hydronephrosis and have no staghorn calculi on univariate analysis (p < 0.05). Staghorn calculi (OR, 95% CI, p value 0.218, 0.068-0.700, 0.010) and hydronephrosis (OR, 95% CI, p value 0.271, 0.083-0.887, 0.031) were independent predictors for severe bleeding via multivariate logistic regression analysis. Other factors, such as history of PCNL, multiple kidney stones, site of puncture calyx and mean corrected intraoperative haemoglobin drop were not related to postoperative severe bleedings. CONCLUSIONS: The absence of staghorn calculi and a no or mild hydronephrosis were related to an increased risk of post-percutaneous nephrolithotomy severe bleeding complications.

Pretreatment MR-Based Radiomics Signature as Potential Imaging Biomarker for Assessing the Expression of Topoisomerase II alpha (TOPO-IIα) in Rectal Cancer.

BACKGROUND: Rectal cancer (RC) is one of the most common cancers throughout the world. Chemotherapy or neoadjuvant chemotherapy play an important role in the treatment of advanced RC. Whether to add topoisomerase inhibitor to individualized chemotherapy is a puzzling question for clinicians. PURPOSE: To investigate whether pretreatment MR-based radiomics signature can assess the expression of topoisomerase II alpha (TOPO-IIα) in RC. STUDY TYPE: Retrospective. POPULATION: In all, 122 patients with RC. Field Strength/Sequence: Pretreatment 3.0T; T2 WI turbo spin echo (TSE) sequence. ASSESSMENT: A training group (n = 85) and a test group (n = 37) with pathologically confirmed RC. Patients underwent TOPO-IIα expression. A total of 180 radiomics features were extracted from oblique axial T2 WI TSE images of the entire primary tumor. The least absolute shrinkage and selection operator (LASSO) regression model was used to reduce the dimension of the data and select the features. STATISTICAL TESTS: The assessment models were established by multivariable logistic regression analysis. The performance of the model was assessed by the receiver operating characteristic (ROC) curve, nomogram, and calibration. RESULTS: The radiomics signature, which consisted of 10 selected optimal features, was significantly associated with TOPO-IIα expression (P < 0.01 for both training and test groups). The area under the curve (AUC), the sensitivity, and the specificity for assessing TOPO-IIα expression, were 0.859, 0.872, and 0.739, respectively, in the training group, while they were 0.762, 0.941, and 0.600 in the test group. The nomogram model of the radiomics signature (Rad-score) had good calibration. Calibration curves were plotted to assess the calibration of the radiomics nomogram that was accompanied with the Hosmer-Lemeshow test (P = 0.52). DATA CONCLUSION: The proposed pretreatment MR-based radiomics signature was associated with TOPO-IIα expression. A radiomics nomogram might be helpful in the individualized assessment of TOPO-IIα expression in patients with RC. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1881-1889.

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.

Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors.

OBJECTIVE: The progression of white matter hyperintensities (WMH) varies considerably in adults. In this study, we aimed to predict the progression and related risk factors of WMH based on the radiomics of whole-brain white matter (WBWM). METHODS: A retrospective analysis was conducted on 141 patients with WMH who underwent two consecutive brain magnetic resonance (MR) imaging sessions from March 2014 to May 2018. The WBWM was segmented to extract and score the radiomics features at baseline. Follow-up images were evaluated using the modified Fazekas scale, with progression indicated by scores ≥ 1. Patients were divided into progressive (n = 65) and non-progressive (n = 76) groups. The progressive group was subdivided into any WMH (AWMH), periventricular WMH (PWMH), and deep WMH (DWMH). Independent risk factors were identified using logistic regression. RESULTS: The area under the curve (AUC) values for the radiomics signatures of the training sets were 0.758, 0.749, and 0.775 for AWMH, PWMH, and DWMH, respectively. The AUC values of the validation set were 0.714, 0.697, and 0.717, respectively. Age and hyperlipidemia were independent predictors of progression for AWMH. Age and body mass index (BMI) were independent predictors of progression for DWMH, while hyperlipidemia was an independent predictor of progression for PWMH. After combining clinical factors and radiomics signatures, the AUC values were 0.848, 0.863, and 0.861, respectively, for the training set, and 0.824, 0.818, and 0.833, respectively, for the validation set. CONCLUSIONS: MRI-based radiomics of WBWM, along with specific risk factors, may allow physicians to predict the progression of WMH. KEY POINTS: • Radiomics features detected by magnetic resonance imaging may be used to predict the progression of white matter hyperintensities. • Radiomics may be used to identify risk factors associated with the progression of white matter hyperintensities. • Radiomics may serve as non-invasive biomarkers to monitor white matter status.

Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?

BACKGROUND: Anterior mediastinal cysts (AMC) are often misdiagnosed as thymomas and undergo surgical resection, which caused unnecessary treatment and medical resource waste. The purpose of this study is to explore potential possibility of computed tomography (CT)-based radiomics for the diagnosis of AMC and type B1 and B2 thymomas. METHODS: A group of 188 patients with pathologically confirmed AMC (106 cases misdiagnosed as thymomas in CT) and thymomas (82 cases) and underwent routine chest CT from January 2010 to December 2018 were retrospectively analyzed. The lesions were manually delineated using ITK-SNAP software, and radiomics features were performed using the artificial intelligence kit (AK) software. A total of 180 tumour texture features were extracted from enhanced CT and unenhanced CT, respectively. The general test, correlation analysis, and LASSO were used to features selection and then the radiomics signature (radscore) was obtained. The combined model including radscore and independent clinical factors was developed. The model performances were evaluated on discrimination, calibration curve. RESULTS: Two radscore models were constructed from the unenhanced and enhanced phases based on the selected four and three features, respectively. The AUC, sensitivity, and specificity of the enhanced radscore model were 0.928, 89.3%, and 83.8% in the training dataset and 0.899, 84.6%, and 87.5% in the test dataset (higher than the unenhanced radscore model). The combined model of enhanced CT including radiomics features and independent clinical factors yielded an AUC, sensitivity and specificity of 0.941, 82.1%, and 94.6% in the training dataset and 0.938, 92.3%, and 87.5% in the test dataset (higher than the unenhanced combined model and enhanced radscore model). CONCLUSIONS: The study suggested the possibility that the combined model in enhanced CT provided a potential tool to facilitate the differential diagnosis of AMC and type B1 and B2 thymomas.

Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative.

BACKGROUND: Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge. OBJECTIVE: To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice. METHODS: A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated. RESULTS: A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60]. CONCLUSION: The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.