Fibronectin-Targeting Dual-Modal MR/NIRF Imaging Contrast Agents for Diagnosis of Gastric Cancer and Peritoneal Metastasis.

The prevalence and fatality rates of gastric cancer (GC) remain elevated, with advanced stages presenting a grim prognosis. Noninvasive diagnosis of GC cancer often proves challenging until the disease has progressed to an advanced stage or metastasized. Initially, the level of fibronectin (FN) in cancer-associated fibroblasts (CAFs) of GC was at least 3.7 times higher than that in normal fibroblasts. Herein, two FN-targeting magnetic resonance/near-infrared fluorescence (MR/NIRF) imaging contrast agents were developed to detect GC and peritoneal metastasis noninvasively. The probes CREKA-Cy7-(Gd-DOTA) and CREKA-Cy7-(Gd-DOTA)3 demonstrated significant FN-targeting capability (with dissociation constants of 1.0 and 2.1 mM) and effective MR imaging performance (with proton relaxivity values of 9.66 and 27.44 mM-1 s-1 at 9.4 T, 37 °C). In vivo imaging revealed a high signal-to-noise ratio and successful visualization of GC metastasis using NIRF imaging as well as successful tumor detection in MR imaging. Therefore, this study highlights the potential of FN-targeting probes for GC diagnosis and aids in the advancement of new diagnostic strategies for the clinical detection of GC.

Bio-orthogonal click chemistry strategy for PD-L1-targeted imaging and pyroptosis-mediated chemo-immunotherapy of triple-negative breast cancer.

BACKGROUND: The combination of programmed cell death ligand-1 (PD-L1) immune checkpoint blockade (ICB) and immunogenic cell death (ICD)-inducing chemotherapy has shown promise in cancer immunotherapy. However, triple-negative breast cancer (TNBC) patients undergoing this treatment often face obstacles such as systemic toxicity and low response rates, primarily attributed to the immunosuppressive tumor microenvironment (TME). METHODS AND RESULTS: In this study, PD-L1-targeted theranostic systems were developed utilizing anti-PD-L1 peptide (APP) conjugated with a bio-orthogonal click chemistry group. Initially, TNBC was treated with azide-modified sugar to introduce azide groups onto tumor cell surfaces through metabolic glycoengineering. A PD-L1-targeted probe was developed to evaluate the PD-L1 status of TNBC using magnetic resonance/near-infrared fluorescence imaging. Subsequently, an acidic pH-responsive prodrug was employed to enhance tumor accumulation via bio-orthogonal click chemistry, which enhances PD-L1-targeted ICB, the pH-responsive DOX release and induction of pyroptosis-mediated ICD of TNBC. Combined PD-L1-targeted chemo-immunotherapy effectively reversed the immune-tolerant TME and elicited robust tumor-specific immune responses, resulting in significant inhibition of tumor progression. CONCLUSIONS: Our study has successfully engineered a bio-orthogonal multifunctional theranostic system, which employs bio-orthogonal click chemistry in conjunction with a PD-L1 targeting strategy. This innovative approach has been demonstrated to exhibit significant promise for both the targeted imaging and therapeutic intervention of TNBC.

MRI Radiomics Signature of Pediatric Medulloblastoma Improves Risk Stratification Beyond Clinical and Conventional MR Imaging Features.

BACKGROUND: Prognostic evaluation is important for personalized treatment in children with medulloblastoma (MB). Limited data are available for risk stratification using a radiomics-based model. PURPOSE: To evaluate the incremental value of an MRI radiomics signature in stratifying the risk of pediatric MB in terms of overall survival (OS). STUDY TYPE: Retrospective. SUBJECTS: A total of 111 children (mean age 5.82 years) with pathologically confirmed MB divided into training and validation cohorts (77 and 34 children, respectively). FIELD STRENGTH/SEQUENCE: A 3 T, contrast-enhanced T1-weighted imaging with inversion recovery. ASSESSMENT: The study endpoint was OS defined as the time between the preoperative MRI study and death or last follow-up. The radiomics signature model and a clinical-MRI model were developed for personalized OS prediction. An integrative model, which combined the radiomics signature and clinical-MRI features, was also built using multivariable Cox regression model. The performance of the three models was evaluated with the C-index. The performance of integrative model was assessed by calibration curve and decision curve analysis (DCA). STATISTICAL TESTS: Independent T-test, Mann-Whitney U test, Fisher's exact tests or chi-square test, logistic regression analysis, Kaplan-Meier survival analysis, C-index, intraclass correlation coefficients (ICC). P < 0.05 was considered statistically significant. RESULTS: The media OS was 2.83 years (3.87 ± 1.85 years). Two clinical and one conventional MR imaging features (remnant, adjuvant treatment, and peritumoral edema) were selected for clinical-MRI model building. The integrative model evaluated OS (C-index 0.823) better than either the radiomics signature (C-index 0.702) or the clinical-MRI model (C-index 0.771). And it also showed good performance in the validation cohort (C-indices: 0.786, 0.756, 0.721), which was validated by the good calibration (P > 0.05) and more benefit. DATA CONCLUSIONS: This study demonstrated that the integrative model, which combined radiomics signature, clinical, and conventional MRI features, showed best performance in OS evaluation for children with MB. The radiomics signature may confer incremental value over clinical-MRI features. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

BACKGROUND: The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). PURPOSE: To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. STUDY TYPE: Retrospective. SUBJECTS: Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). FIELD STRENGTH/SEQUENCE: 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. ASSESSMENT: Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). RESULTS: The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). DATA CONCLUSION: A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging-quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06-12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18-1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00-1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy.

Staging Liver Fibrosis: Comparison of Native T1 Mapping, T2 Mapping, and T1ρ: An Experimental Study in Rats With Bile Duct Ligation and Carbon Tetrachloride at 11.7 T MRI.

BACKGROUND: T1, T2, and T1ρ might be potential biomarkers for assessing liver fibrosis. However, few studies reported the value of them in different animal models. PURPOSE: To investigate and compare the performances of T1, T2, and T1ρ for noninvasively staging liver fibrosis in bile duct ligation (BDL) or carbon tetrachloride (CCl4 ) model. STUDY TYPE: Prospective animal model. SUBJECTS: Liver fibrosis was induced by BDL or injection of CCl4 in 120 rats. FIELD STRENGTH/SEQUENCE: 11.7 T, T1 mapping with 10 repetition times, T2 mapping with 32 echo times, and T1ρ with 10 spin-lock times. ASSESSMENT: T1, T2, and T1ρ were measured and correlated with liver fibrosis stages, as well as the degree of inflammation, steatosis, iron deposition, and the expression of cytokeratin 19. The discriminative performance of T1, T2, and T1ρ for staging liver fibrosis was compared. STATISTICAL TESTS: One-way analysis of variance (ANOVA), Spearman's correlation analysis, factorial design ANOVA, and receiver operating characteristic curves (P < 0.05 was considered statistically significant). RESULTS: T1, T2, and T1ρ (BDL: rho = 0.73, 0.85, 0.68; CCl4 : rho = 0.80, 0.29, 0.61) were significantly correlated with liver fibrosis stages, while there was no significant difference in T2 among stage F0-F4 in the CCl4 model (P = 0.204). The area under the curves (AUCs) range of T1, T2, and T1ρ for predicting ≥F1, ≥F2, ≥F3, and F4 were 0.76-0.95, 0.89-0.98, and 0.80-0.94 in the CCl4 model. For the CCl4 model, the AUCs range of T1, T2, and T1ρ for predicting ≥F1, ≥F2, ≥F3, and F4 were 0.83-0.95, 0.61-0.74, and 0.73-0.89, respectively. T2 had significantly higher AUC in the BDL model than CCl4 model for diagnosing liver fibrosis. DATA CONCLUSION: The most sensitive and accurate method for staging liver fibrosis appeared to be T1 in our animal models followed by T1ρ. T2 may not be suitable for evaluating liver fibrosis. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 2.

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear. PURPOSE: To develop a DL method based on T2-weighted imaging (T2WI) and clinical factors for noninvasively evaluating KRAS mutation in rectal cancer. STUDY TYPE: Retrospective. SUBJECTS: A total of 376 patients (108 women [28.7%]) with histopathology-confirmed rectal adenocarcinoma and KRAS mutation status. FIELD STRENGTH/SEQUENCE: A 3 T, turbo spin echo T2WI and single-shot echo-planar diffusion-weighted imaging (b = 0, 1000 sec/mm2 ). ASSESSMENT: A clinical model was constructed with clinical factors (age, gender, carcinoembryonic antigen level, and carbohydrate antigen 199 level) and MRI features (tumor length, tumor location, tumor stage, lymph node stage, and extramural vascular invasion), and two DL models based on modified MobileNetV2 architecture were evaluated for diagnosing KRAS mutation based on T2WI alone (image model) or both T2WI and clinical factors (combined model). The clinical usefulness of these models was evaluated through calibration analysis and decision curve analysis (DCA). STATISTICAL TESTS: Mann-Whitney U test, Chi-squared test, Fisher's exact test, logistic regression analysis, receiver operating characteristic curve (ROC), Delong's test, Hosmer-Lemeshow test, interclass correlation coefficients, and Fleiss kappa coefficients (P < 0.05 was considered statistically significant). RESULTS: All the nine clinical-MRI characteristics were included for clinical model development. The clinical model, image model, and combined model in the testing cohort demonstrated good calibration and achieved areas under the curve (AUCs) of 0.668, 0.765, and 0.841, respectively. The combined model showed improved performance compared to the clinical model and image model in two cohorts. DCA confirmed the higher net benefit of the combined model than the other two models when the threshold probability is between 0.05 and 0.85. DATA CONCLUSION: The proposed combined DL model incorporating T2WI and clinical factors may show good diagnostic performance. Thus, it could potentially serve as a supplementary approach for noninvasively evaluating KRAS mutation in rectal cancer. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain.

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.

An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions.

OBJECTIVES: To build an artificial intelligence (AI) system to classify benign and malignant non-mass enhancement (NME) lesions using maximum intensity projection (MIP) of early post-contrast subtracted breast MR images. METHODS: This retrospective study collected 965 pure NME lesions (539 benign and 426 malignant) confirmed by histopathology or follow-up in 903 women. The 754 NME lesions acquired by one MR scanner were randomly split into the training set, validation set, and test set A (482/121/151 lesions). The 211 NME lesions acquired by another MR scanner were used as test set B. The AI system was developed using ResNet-50 with the axial and sagittal MIP images. One senior and one junior radiologist reviewed the MIP images of each case independently and rated its Breast Imaging Reporting and Data System category. The performance of the AI system and the radiologists was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The AI system yielded AUCs of 0.859 and 0.816 in the test sets A and B, respectively. The AI system achieved comparable performance as the senior radiologist (p = 0.558, p = 0.041) and outperformed the junior radiologist (p < 0.001, p = 0.009) in both test sets A and B. After AI assistance, the AUC of the junior radiologist increased from 0.740 to 0.862 in test set A (p < 0.001) and from 0.732 to 0.843 in test set B (p < 0.001). CONCLUSION: Our MIP-based AI system yielded good applicability in classifying NME lesions in breast MRI and can assist the junior radiologist achieve better performance. KEY POINTS: • Our MIP-based AI system yielded good applicability in the dataset both from the same and a different MR scanner in predicting malignant NME lesions. • The AI system achieved comparable diagnostic performance with the senior radiologist and outperformed the junior radiologist. • This AI system can assist the junior radiologist achieve better performance in the classification of NME lesions in MRI.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

BACKGROUND: Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. METHODS: This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists using CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. CONCLUSIONS: The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification.

OBJECTIVES: To construct a CT-based radiomics signature and assess its performance in predicting MYCN amplification (MNA) in pediatric patients with neuroblastoma. METHODS: Seventy-eight pediatric patients with neuroblastoma were recruited (55 in training cohort and 23 in test cohort). Radiomics features were extracted automatically from the region of interest (ROI) manually delineated on the three-phase computed tomography (CT) images. Selected radiomics features were retained to construct radiomics signature and a radiomics score (rad-score) was calculated by using the radiomics signature-based formula. A clinical model was established with clinical factors, including clinicopathological data, and CT image features. A combined nomogram was developed with the incorporation of a radiomics signature and clinical factors. The predictive performance was assessed by receiver operating characteristics curve (ROC) analysis and decision curve analysis (DCA). RESULTS: The radiomics signature was constructed using 7 selected radiomics features. The clinical radiomics nomogram, which was based on the radiomics signature and two clinical factors, showed superior predictive performance compared with the clinical model alone (area under the curve (AUC) in the training cohort: 0.95 vs. 0.82, the test cohort: 0.91 vs. 0.70). The clinical utility of clinical radiomics nomogram was confirmed by DCA. CONCLUSIONS: This proposed CT-based radiomics signature was able to predict MNA. Combining the radiomics signature with clinical factors outperformed using clinical model alone for MNA prediction. KEY POINTS: • A CT-based radiomics signature has the ability to predict MYCN amplification (MNA) in neuroblastoma. • Both pre- and post-contrast CT images are valuable in predicting MNA. • Associating the radiomics signature with clinical factors improved the predictive performance of MNA, compared with clinical model alone.

A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.

OBJECTIVES: To investigate the value of full-field digital mammography-based deep learning (DL) in predicting malignancy of Breast Imaging Reporting and Data System (BI-RADS) 4 microcalcifications. METHODS: A total of 384 patients with 414 pathologically confirmed microcalcifications (221 malignant and 193 benign) were randomly allocated into the training, validation, and testing datasets (272/71/71 lesions) in this retrospective study. A combined DL model was developed incorporating mammography and clinical variables. Model performance was evaluated by using areas under the receiver operating characteristic curve (AUC) and compared with the clinical model, stand-alone DL image model, and BI-RADS approach. The predictive performance for malignancy was also compared between the combined model and human readers (2 juniors and 2 seniors). RESULTS: The combined DL model demonstrated favorable AUC, sensitivity, and specificity of 0.910, 85.3%, and 91.9% in predicting BI-RADS 4 malignant microcalcifications in the testing dataset, which outperformed the clinical model, DL image model, and BI-RADS with AUCs of 0.799, 0.841, and 0.804, respectively. The combined model achieved non-inferior performance as senior radiologists (p = 0.860, p = 0.800) and outperformed junior radiologists (p = 0.155, p = 0.029). The diagnostic performance of two junior radiologists was improved after artificial intelligence assistance with AUCs increased to 0.854 and 0.901 from 0.816 (p = 0.556) and 0.773 (p = 0.046), while the interobserver agreement was improved with a kappa value increased to 0.843 from 0.331. CONCLUSIONS: The combined deep learning model can improve the malignancy prediction of BI-RADS 4 microcalcifications in screening mammography and assist junior radiologists to achieve better performance, which can facilitate clinical decision-making. KEY POINTS: • The combined deep learning model demonstrated high diagnostic power, sensitivity, and specificity for predicting malignant BI-RADS 4 mammographic microcalcifications. • The combined model achieved similar performance with senior breast radiologists, while it outperformed junior breast radiologists. • Deep learning could improve the diagnostic performance of junior radiologists and facilitate clinical decision-making.

Clinical-MRI radiomics enables the prediction of preoperative cerebral spinal fluid dissemination in children with medulloblastoma.

BACKGROUND: Medulloblastoma (MB) is the most common pediatric embryonal tumor. Accurate identification of cerebral spinal fluid (CSF) dissemination is important in prognosis prediction. Both MRI of the central nervous system (CNS) and CSF cytology will appear false positive and negative. Our objective was to investigate the added value of preoperative-enhanced T1-weighted image-based radiomic features to clinical characteristics in predicting preoperative CSF dissemination for children with MB. MATERIALS AND METHODS: This retrospective study included 84 children with histopathologically confirmed MB between November 2006 and November 2018 (training cohort, n=60; internal validation cohort, n=24). A set of cases between December 2018 and February 2020 were used for external validation (n=40). The children with normal head and spine magnetic resonance images (MRI) and no subsequent dissemination in 1 year were diagnosed as non-CSF dissemination. The CSF dissemination was manifested as intracranial or intraspinal nodular-enhanced lesions. Clinical features were collected, and conventional MRI features of preoperative head MRI examinations were evaluated. A total of 385 radiomic features were extracted from preoperative-enhanced T1-weighted images. Minimum redundancy, maximum correlation, and least absolute shrinkage and selection operator were performed to select the features with the best performance in predicting preoperative CSF dissemination. A combined clinical-MRI radiomic prediction model was developed using multivariable logistic regression. Receiver operating curve analysis (ROC) was used to validate the predictive performance. Nomogram and decision curve analysis (DCA) were developed to evaluate the clinical utility of the combined model. RESULTS: One clinical and nine radiomic features were selected for predicting preoperative CSF dissemination. The combined model incorporating clinical and radiomic features had the best predictive performance in the training cohort with an AUC of 0.89. This was validated in the internal and external cohorts with AUCs of 0.87 and 0.73. The clinical utility of the model was confirmed by a clinical-MRI radiomic nomogram and DCA. CONCLUSIONS: The combined model incorporating clinical, conventional MRI, and radiomic features could be applied to predict preoperative CSF dissemination for children with MB as a noninvasive biomarker, which could aid in risk evaluation.

Diffusion Metrics for Staging Pancreatic Fibrosis and Correlating With Epithelial-Mesenchymal Transition Markers in a Chronic Pancreatitis Rat Model at 11.7T MRI.

BACKGROUND: Chronic pancreatitis (CP) is characterized by pancreatic fibrosis, in which a epithelial-mesenchymal transition (EMT)-like process is observed. However, few noninvasive approaches have been reported to evaluate pancreatic fibrosis and EMT in an animal model based on diffusion imaging. PURPOSE: To evaluate pancreatic fibrosis in CP by conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) and then explore the correlation between diffusion parameters and the EMT markers in an animal model. STUDY TYPE: Prospective controlled imaging histological correlation. POPULATION: Forty-five rats with CP induced by injecting dibutyltin dichloride solution and 10 normal rats comprised the control group. FIELD STRENGTH/SEQUENCE: 11.7T MR, diffusion imaging with 10 b-values. ASSESSMENT: Apparent diffusion coefficient (ADC), IVIM-associated perfusion fraction (f), pseudodiffusion coefficient (D*), diffusion coefficient (D), DKI-associated mean kurtosis (MK), and mean corrected diffusion coefficient (MD) were quantitatively measured and correlated with pancreatic fibrosis stages as well as the EMT markers E-cadherin and α-smooth muscle actin (α-SMA) expression. The discriminative performance of diffusion parameters for staging fibrosis was compared. STATISTICAL TESTS: Spearman's correlation, Student's t-test, and a receiver operating characteristic curve was conducted for statistical analysis. RESULTS: ADC, D, and MD (r = -0.637, -0.688, and -0.535; P < 0.001) were negatively correlated with pancreatic fibrosis staging, but MK (r = 0.740, P < 0.001) had a positive correlation. ADC, D, MD, and MK were significantly correlated with α-SMA (r = -0.684, -0.728, -0.627, and 0.721, all P < 0.001), while MK was significantly correlated with E-cadherin (r = -0.606, P < 0.001). The area under the curve (AUC) was not significantly different (P > 0.05) among ADC (0.797, 0.816, 0.873), D (0.862, 0.810, 0.895), MD (0.767, 0.772, 0.801), and MK (0.836, 0.893, 0.951) for F1 or greater, F2 or greater, and F3 pancreatic fibrosis separately. DATA CONCLUSION: ADC, D, MD, and MK were helpful for assessing pancreatic fibrosis staging, and these diffusion parameters were also significantly correlated with the expression of EMT markers in pancreatic fibrosis. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:197-206.

Native T1 mapping compared to ultrasound elastography for staging and monitoring liver fibrosis: an animal study of repeatability, reproducibility, and accuracy.

OBJECTIVES: To investigate the repeatability, reproducibility, and staging and monitoring of the performance of native T1 mapping for noninvasively assessing liver fibrosis in comparison with acoustic radiation force impulse (ARFI) elastography. METHODS: The repeatability and reproducibility were explored in 8 male Sprague-Dawley rats with intraclass correlation coefficient (ICC). Different degrees of fibrosis were induced in 52 rats by carbon-tetrachloride (CCl4) insult. Another 16 rats were used to build fibrosis progression and regression models. The native T1 values and shear wave velocity (SWV) were quantified by using native T1 mapping and ARFI elastography, respectively. The METAVIR system (F0-F4) was used for the staging of fibrosis. The area under the receiver operating characteristic curve (AUC) was determined to assess the performance of quantitative parameters for staging and monitoring fibrosis. RESULTS: Native T1 values shared similar good repeatability (ICC = 0.93) and reproducibility (ICC = 0.87) with SWV (ICC = 0.84-0.93). The AUC of native T1 values were 0.84, 0.84, and 0.75 for diagnosing significant fibrosis (≥ F2) and liver cirrhosis (F4) and detecting fibrosis progression, and those of SWV were 0.81, 0.86, and 0.7, respectively. No significant difference in performance was found between the two quantitative parameters (p ≥ 0.496). For detecting fibrosis regression, native T1 values had a better accuracy (AUC = 0.99) than SWV (AUC = 0.56; p = 0.002). CONCLUSION: Native T1 mapping may be a reliable and accurate method for noninvasively assessing liver fibrosis. Compared with ARFI elastography, it provides similar good repeatability and reproducibility, a similar high accuracy for staging fibrosis, and a better accuracy for detecting fibrosis regression. KEY POINTS: • Native T1 mapping is a valuable tool for noninvasively assessing liver fibrosis and can be measured on virtually all clinical MRI machines without additional hardware or gadolinium chelate injection. • Compared with acoustic radiation force impulse elastography, native T1 mapping yields similar good repeatability and reproducibility and a similar high accuracy for staging fibrosis. • Native T1 mapping provides a significantly better performance for detecting fibrosis regression than acoustic radiation force impulse elastography.

Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer.

OBJECTIVE: To develop a T2-weighted (T2W) image-based radiomics signature for the individual prediction of KRAS mutation status in patients with rectal cancer. METHODS: Three hundred four consecutive patients from center I with pathologically diagnosed rectal adenocarcinoma (training dataset, n = 213; internal validation dataset, n = 91) were enrolled in our retrospective study. The patients from center II (n = 86) were selected as an external validation dataset. A total of 960 imaging features were extracted from high-resolution T2W images for each patient. Five steps, mainly univariate statistical tests, were applied for feature selection. Subsequently, three classification methods, i.e., logistic regression (LR), decision tree (DT), and support vector machine (SVM) algorithm, were applied to develop the radiomics signature for KRAS prediction in the training dataset. The predictive performance was evaluated by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). RESULTS: Seven radiomics features were screened as a KRAS-associated radiomics signature of rectal cancer. Our best prediction model was obtained with SVM classifiers with AUC of 0.722 (95%CI, 0.654-0.790) in the training dataset. This was validated in the internal and external validation datasets with good calibration, and the corresponding AUCs were 0.682 (95% CI, 0.569-0.794) and 0.714 (95% CI, 0.602-0.827), respectively. DCA confirmed its clinical usefulness. CONCLUSIONS: The proposed T2WI-based radiomics signature has a moderate performance to predict KRAS status, and may be useful for supplementing genomic analysis to determine KRAS expression in rectal cancer patients. KEY POINTS: • T2WI-based radiomics showed a moderate diagnostic significance for KRAS status. • The best prediction model was obtained with SVM classifier. • The baseline clinical and histopathological characteristics were not associated with KRAS mutation.

MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer.

OBJECTIVES: To investigate the value of MRI radiomics based on T2-weighted (T2W) images in predicting preoperative synchronous distant metastasis (SDM) in patients with rectal cancer. METHODS: This retrospective study enrolled 177 patients with histopathology-confirmed rectal adenocarcinoma (123 patients in the training cohort and 54 in the validation cohort). A total of 385 radiomics features were extracted from pretreatment T2W images. Five steps, including univariate statistical tests and a random forest algorithm, were performed to select the best preforming features for predicting SDM. Multivariate logistic regression analysis was conducted to build the clinical and clinical-radiomics combined models in the training cohort. The predictive performance was validated by receiver operating characteristics curve (ROC) analysis and clinical utility implementing a nomogram and decision curve analysis. RESULTS: Fifty-nine patients (33.3%) were confirmed to have SDM. Six radiomics features and four clinical characteristics were selected for predicting SDM. The clinical-radiomics combined model performed better than the clinical model in both the training and validation datasets. A threshold of 0.44 yielded an area under the ROC (AUC) value of 0.827 (95% confidence interval (CI), 0.6963-0.9580), a sensitivity of 72.2%, a specificity of 94.4%, and an accuracy of 87.0% in the validation cohort for the combined model. A clinical-radiomics nomogram and decision curve analysis confirmed the clinical utility of the combined model. CONCLUSIONS: Our proposed clinical-radiomics combined model could be utilized as a noninvasive biomarker for identifying patients at high risk of SDM, which could aid in tailoring treatment strategies. KEY POINTS: • T2WI-based radiomics analysis helps predict synchronous distant metastasis (SDM) of rectal cancer. • The clinical-radiomics combined model could be utilized as a noninvasive biomarker for predicting SDM. • Personalized treatment can be carried out with greater confidence based on the risk stratification for SDM in rectal cancer.

Combining Clinical Characteristics and Specific Magnetic Resonance Imaging Features to Predict Placenta Accreta.

OBJECTIVE: The aim of this study was to explore the independent clinical and magnetic resonance imaging (MRI) performance risk factors for predicting placenta accreta. METHODS: From January 2012 to December 2015, we retrospectively reviewed the clinical characteristics and MRI features of 97 patients. Of these, 42 were confirmed to be placenta accreta by pathological results or cesarean delivery findings. We tried to identify the independent risk factors by multivariate logistic regression model for significant differences in variables determined by univariate analysis. RESULTS: The multivariate logistic regression model indicated that 2 or more instances of previous cesarean deliveries and/or abortions, placenta previa, and placenta-myometrial interface interruption were independent risk factors for placenta accreta. The odd ratios were 3.79 for patients who had 2 or more instances of previous cesarean deliveries and/or abortions, 0.04 for marginal/partial placenta previa, 0.024 for complete placenta previa, and 6.56 for placenta-myometrial interface interruption. The values of accuracy and positive prediction by combination of a single clinical risk factor and placenta-myometrial interface interruption and of positive prediction by a combination of all 3 risk factors for predicting placenta accreta were raised to 83.5%, 75%, and 92.9%, respectively. We obtained 3 different risk groups by different combinations of all 3 risk factors. CONCLUSIONS: The study suggested that 2 or more instances of previous cesarean deliveries and/or abortion, placenta previa, and placenta-myometrial interface interruption were independent risk factors for placenta accreta. A combination of a single clinical risk factor and an MRI risk factor can improve the diagnosis of placenta accreta, and a combination of all 3 risk factors could help recognize patients with placenta accreta.

18F-labeled magnetic nanoparticles for monitoring anti-angiogenic therapeutic effects in breast cancer xenografts.

PURPOSE: To develop a novel fluorine-18 (18F)-labeled arginine-glycine-aspartic acid (RGD)-coupled ultra-small iron oxide nanoparticle (USPIO) (hereafter, referred to as 18F-RGD@USPIO) and conduct an in-depth investigation to monitor the anti-angiogenic therapeutic effects by using a novel dual-modality PET/MRI probe. METHODS: The RGD peptide and 18F were coupled onto USPIO by click chemistry. In vitro experiments including determination of stability, cytotoxicity, cell binding of the obtained 18F-RGD@USPIO were carried out, and the targeting kinetics and bio-distribution were tested on an MDA-MB-231 tumor model. A total of 20 (n = 10 per group) MDA-MB-231 xenograft-bearing mice were treated with bevacizumab or placebo (intraperitoneal injections of bevacizumab or a volume-equivalent placebo solution at the dose of 5 mg/kg for consecutive 7 days, respectively), and underwent PET/CT and MRI examinations with 18F-RGD@USPIO before and after treatment. Imaging findings were validated by histological analysis with regard to ß3-integrin expression (CD61 expression), microvascular density (CD31 expression), and proliferation (Ki-67 expression). RESULTS: Excellent stability, low toxicity, and good specificity to endothelial of 18F-RGD@USPIO were confirmed. The best time point for MRI scan was 6 h post-injection. No intergroup differences were observed in tumor volume development between baseline and day 7. However, 18F-RGD@USPIO binding was significantly reduced after bevacizumab treatment compared with placebo, both on MRI (P < 0.001) and PET/CT (P = 0.002). Significantly lower microvascular density, tumor cell proliferation, and integrin ß3 expression were noted in the bevacizumab therapy group than the placebo group, which were consistent with the imaging results. CONCLUSION: PET/MRI with the dual-modality nanoprobe, 18F-RGD@USPIO, can be implemented as a noninvasive approach to monitor the therapeutic effects of anti-angiogenesis in breast cancer model in vivo.

Alpha-fetoprotein response following transarterial chemoembolization indicates improved survival for intermediate-stage hepatocellular carcinoma.

BACKGROUND: To investigate the clinical value of the alpha-fetoprotein (AFP) response following transcatheter arterial chemoembolization (TACE) in intermediate-stage hepatocellular carcinoma (HCC). METHODS: Data on patients with Barcelona Clinic Liver Cancer B staging system were analyzed. An AFP response was defined as a decrease in AFP of more than 20% after a TACE session. The association between AFP response and treatment outcome regarding imaging response and overall survival (OS) was explored. Cox proportional hazards models were applied to identify independent risk factors for OS after TACE. RESULTS: Of the enrolled 376 patients with elevated serum AFP >20 ng/mL, 214 (57%) with AFP responses were identified. AFP responders had improved median survival than non-responders (20 vs. 12 months, P = 0.002). AFP response was significantly correlated with imaging response (P < 0.001). The Cox proportional hazards model revealed that AFP response was an independent factor for OS (hazard ratio, 0.59; 95% confidence interval, 0.45-0.78; P < 0.001). In stratified analyses, an AFP response achieved improved survival in patients with tumor diameters ≤5 cm, diameters >5 cm, tumor number ≤3 and without underlying cirrhosis. CONCLUSIONS: The AFP response indicates enhanced survival after TACE in patients with intermediate-stage BCLC.