Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early-Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study.

BACKGROUND: Deep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate optimal therapy decision. PURPOSE: To develop a nomogram to identify DSI in cervical AC/ASC. STUDY TYPE: Retrospective. POPULATION: Six hundred and fifty patients (mean age of 48.2 years) were collected from center 1 (primary cohort, 536), centers 2 and 3 (external validation cohorts 1 and 2, 62 and 52). FIELD STRENGTH/SEQUENCE: 5-T, T2-weighted imaging (T2WI, SE/FSE), diffusion-weighted imaging (DWI, EPI), and contrast-enhanced T1-weighted imaging (CE-T1WI, VIBE/LAVA). ASSESSMENT: The DSI was defined as the outer 1/3 stromal invasion on pathology. The region of interest (ROI) contained the tumor and 3 mm peritumoral area. The ROIs of T2WI, DWI, and CE-T1WI were separately imported into Resnet18 to calculate the DL scores (TDS, DDS, and CDS). The clinical characteristics were retrieved from medical records or MRI data assessment. The clinical model and nomogram were constructed by integrating clinical independent risk factors only and further combining DL scores based on primary cohort and were validated in two external validation cohorts. STATISTICAL TESTS: Student's t-test, Mann-Whitney U test, or Chi-squared test were used to compare differences in continuous or categorical variables between DSI-positive and DSI-negative groups. DeLong test was used to compare AU-ROC values of DL scores, clinical model, and nomogram. RESULTS: The nomogram integrating menopause, disruption of cervical stromal ring (DCSRMR), DDS, and TDS achieved AU-ROCs of 0.933, 0.807, and 0.817 in evaluating DSI in primary and external validation cohorts. The nomogram had superior diagnostic ability to clinical model and DL scores in primary cohort (all P < 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2. DATA CONCLUSION: The nomogram achieved good performance for evaluating DSI in cervical AC/ASC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma.

OBJECTIVE: To develop a comprehensive nomogram based on MRI intra- and peritumoral radiomics signatures and independent risk factors for predicting parametrial invasion (PMI) in patients with early-stage cervical adenocarcinoma (AC) and adenosquamous carcinoma (ASC). METHODS: A total of 460 patients with IB to IIB cervical AC and ASC who underwent preoperative MRI examination and radical trachelectomy/hysterectomy were retrospectively enrolled and divided into primary, internal validation, and external validation cohorts. The original (Ori) and wavelet (Wav)-transform features were extracted from the volumetric region of interest of the tumour (ROI-T) and 3mm- and 5mm-peritumoral rings (ROI-3 and ROI-5), respectively. Then the Ori and Ori-Wav feature-based radiomics signatures from the tumour (RST) and 3 mm- and 5 mm-peritumoral regions (RS3 and RS5) were independently built and their diagnostic performances were compared to select the optimal ones. Finally, the nomogram was developed by integrating optimal intra- and peritumoral signatures and clinical independent risk factors based on multivariable logistic regression analysis. RESULTS: FIGO stage, disruption of the cervical stromal ring on MRI (DCSRMR), parametrial invasion on MRI (PMIMR), and serum CA-125 were identified as independent risk factors. The nomogram constructed by integrating independent risk factors, Ori-Wav feature-based RST, and RS5 yielded AUCs of 0.874 (0.810-0.922), 0.885 (0.834-0.924), and 0.966 (0.887-0.995) for predicting PMI in the primary, internal and external validation cohorts, respectively. Furthermore, the nomogram was superior to radiomics signatures and clinical model for predicting PMI in three cohorts. CONCLUSION: The nomogram can preoperatively, accurately, and noninvasively predict PMI in patients with early-stage cervical AC and ASC. CLINICAL RELEVANCE STATEMENT: The nomogram can preoperatively, accurately, and noninvasively predict PMI and facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy in patients with early-stage cervical AC and ASC. KEY POINTS: The accurate preoperative prediction of PMI in early-stage cervical AC and ASC can facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy. The nomogram integrating independent risk factors, Ori-Wav feature-based RST, and RS5 can preoperatively, accurately, and noninvasively predict PMI in early-stage cervical AC and ASC. The nomogram was superior to radiomics signatures and clinical model for predicting PMI in early-stage cervical AC and ASC.

Magnetic resonance spectroscopy and liquid chromatography-mass spectrometry metabolomics study may differentiate pre-eclampsia from gestational hypertension.

OBJECTIVE: To investigate the findings of magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and serum metabolomics for differentiating pre-eclampsia (PE) from gestational hypertension (GH). METHODS: This prospective study enrolled 176 subjects including a primary cohort with healthy non-pregnant women (HN, n = 35), healthy pregnant women (HP, n = 20), GH (n = 27), and PE (n = 39) and a validation cohort with HP (n = 22), GH (n = 22), and PE (n = 11). T1 signal intensity index (T1SI), apparent diffusion coefficient (ADC) value, and the metabolites on MRS were compared. The differentiating performances of single and combined MRI and MRS parameters for PE were evaluated. Serum liquid chromatography-mass spectrometry (LC-MS) metabolomics was investigated by sparse projection to latent structures discriminant analysis. RESULTS: Increased T1SI, lactate/creatine (Lac/Cr), and glutamine and glutamate (Glx)/Cr and decreased ADC value and myo-inositol (mI)/Cr in basal ganglia were found in PE patients. T1SI, ADC, Lac/Cr, Glx/Cr, and mI/Cr yielded an area under the curves (AUC) of 0.90, 0.80, 0.94, 0.96, and 0.94 in the primary cohort, and of 0.87, 0.81, 0.91, 0.84, and 0.83 in the validation cohort, respectively. A combination of Lac/Cr, Glx/Cr, and mI/Cr yielded the highest AUC of 0.98 in the primary cohort and 0.97 in the validation cohort. Serum metabolomics analysis showed 12 differential metabolites, which are involved in pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism. CONCLUSIONS: MRS is expected to be a noninvasive and effective tool for monitoring GH patients to avoid the development of PE. KEY POINTS: • Increased T1SI and decreased ADC value in the basal ganglia were found in PE patients than in GH patients. • Increased Lac/Cr and Glx/Cr, and decreased mI/Cr in the basal ganglia were found in PE patients than in GH patients. • LC-MS metabolomics showed that the major differential metabolic pathways between PE and GH were pyruvate metabolism, alanine metabolism, glycolysis, gluconeogenesis, and glutamate metabolism.

Ferrous sulfate reverses cerebral metabolic abnormality induced by minimal hepatic encephalopathy.

Orally administered ferrous iron was previously reported to significantly improve the cognition and locomotion of patients with minimal hepatic encephalopathy (MHE). However, the metabolic mechanisms of the therapeutic effect of ferrous iron are unknown. In this study, MHE was induced in rats by partial portal vein ligation (PPVL), and was treated with ferrous sulfate. The Morris water maze was used to evaluate the cognitive condition of the rats. The metabolites observed by NMR and validated by liquid chromatography-mass spectrometry were defined as the key affected metabolites. The enzyme activities and trace element contents in the rat brains were also investigated. The Mn content was found to be increased but the ferrous iron content decreased in the cortex and striatum in MHE. Decreased oxoglutarate dehydrogenase activity and increased glutamine synthetase (GS) and pyruvate carboxylase (PC) activity were observed in the cortex of MHE rats. Decreased pyruvate dehydrogenase activity and increased GS and PC activity were observed in the striatum of MHE rats. The levels of BCAAs and taurine were significantly decreased, and the contents of GABA, lactate, arginine, aspartate, carnosine, citrulline, cysteine, glutamate, glutamine, glycine, methionine, ornithine, proline, threonine and tyrosine were significantly increased. These metabolic abnormalities described above were restored after treatment with ferrous sulfate. Pathway enrichment analysis suggested that urea cycle, aspartate metabolism, arginine and proline metabolism, glycine and serine metabolism, and glutamate metabolism were the major metabolic abnormalities in MHE rats, but these processes could be restored and cognitive impairment could be improved by ferrous sulfate administration.

In vivo detection of dysregulated choline metabolism in paclitaxel-resistant ovarian cancers with proton magnetic resonance spectroscopy.

BACKGROUND: Chemoresistance gradually develops during treatment of epithelial ovarian cancer (EOC). Metabolic alterations, especially in vivo easily detectable metabolites in paclitaxel (PTX)-resistant EOC remain unclear. METHODS: Xenograft models of the PTX-sensitive and PTX-resistant EOCs were built. Using a combination of in vivo proton-magnetic resonance spectroscopy (1H-MRS), metabolomics and proteomics, we investigated the in vivo metabolites and dysregulated metabolic pathways in the PTX-resistant EOC. Furthermore, we analyzed the RNA expression to validate the key enzymes in the dysregulated metabolic pathway. RESULTS: On in vivo 1H-MRS, the ratio of (glycerophosphocholine + phosphocholine) to (creatine + phosphocreatine) ((GPC + PC) to (Cr + PCr))(i.e. Cho/Cr) in the PTX-resistant tumors (1.64 [0.69, 4.18]) was significantly higher than that in the PTX-sensitive tumors (0.33 [0.10, 1.13]) (P = 0.04). Forty-five ex vivo metabolites were identified to be significantly different between the PTX-sensitive and PTX-resistant tumors, with the majority involved of lipids and lipid-like molecules. Spearman's correlation coefficient analysis indicated in vivo and ex vivo metabolic characteristics were highly consistent, exhibiting the highest positive correlation between in vivo GPC + PC and ex vivo GPC (r = 0.885, P < 0.001). These metabolic data suggested that abnormal choline concentrations were the results from the dysregulated glycerophospholipid metabolism, especially choline metabolism. The proteomics data indicated that the expressions of key enzymes glycerophosphocholine phosphodiesterase 1 (GPCPD1) and glycerophosphodiester phosphodiesterase 1 (GDE1) were significantly lower in the PTX-resistant tumors compared to the PTX-sensitive tumors (both P < 0.01). Decreased expressions of GPCPD1 and GDE1 in choline metabolism led to an increased GPC levels in the PTX-resistant EOCs, which was observed as an elevated total choline (tCho) on in vivo 1H-MRS. CONCLUSIONS: These findings suggested that dysregulated choline metabolism was associated with PTX-resistance in EOCs and the elevated tCho on in vivo 1H-MRS could be as an indicator for the PTX-resistance in EOCs.

A predictive nomogram for two-year growth of CT-indeterminate small pulmonary nodules.

OBJECTIVES: Lung cancer is the most common cancer and the leading cause of cancer-related death worldwide. The optimal management of computed tomography (CT)-indeterminate pulmonary nodules is important. To optimize individualized follow-up strategies, we developed a radiomics nomogram for predicting 2-year growth in case of indeterminate small pulmonary nodules. METHODS: A total of 215 histopathology-confirmed small pulmonary nodules (21 benign and 194 malignant) in 205 patients with ultra-high-resolution CT (U-HRCT) were divided into growth and nongrowth nodules and were randomly allocated to the primary (n = 151) or validation (n = 64) group. The least absolute shrinkage and selection operator (LASSO) method was used for radiomics feature selection and radiomics signature determination. Multivariable logistic regression analysis was used to develop a radiomics nomogram that integrated the radiomics signature with significant clinical parameters (sex and nodule type). The area under the curve (AUC) was applied to assess the predictive performance of the radiomics nomogram. The net benefit of the radiomics nomogram was assessed using a clinical decision curve. RESULTS: The radiomics signature and nomogram yielded AUCs of 0.892 (95% confidence interval [CI]: 0.843-0.940) and 0.911 (95% CI: 0.867-0.955), respectively, in the primary group and 0.826 (95% CI: 0.727-0.926) and 0.843 (95% CI: 0.749-0.937), respectively, in the validation group. The clinical usefulness of the nomogram was demonstrated by decision curve analysis. CONCLUSIONS: A radiomics nomogram was developed by integrating the radiomics signature with clinical parameters and was easily used for the individualized prediction of two-year growth in case of CT-indeterminate small pulmonary nodules. KEY POINTS: • A radiomics nomogram was developed for predicting the two-year growth of CT-indeterminate small pulmonary nodules. • The nomogram integrated a CT-based radiomics signature with clinical parameters and was valuable in developing an individualized follow-up strategy for patients with indeterminate small pulmonary nodules.

Radiologic Imaging Modalities for Colorectal Cancer.

BACKGROUND: Studies reported various diagnostic value of radiologic imaging modalities for diagnosis and management of colorectal cancer (CRC). AIMS: To summary the diagnosis and management of CRC using computed tomography colonography (CTC), magnetic resonance colonography (MRC), and positron emission tomography (PET)/computed tomography (CT). METHODS: Comprehensive literature searches were conducted in PubMed, EmBase, and the Cochrane library for studies published before April 2021. The diagnostic performance of CTC, MRC, and PET/CT for CRC was summarized. RESULTS: A total of 54 studies (17 studies for CTC, 8 studies for MRC, and 29 studies for PET/CT) were selected for final analysis. The sensitivity and specificity for CTC ranged from 27 to 100%, 88 to 100%, respectively, and the pooled sensitivity and specificity for CTC were 0.97 (95% CI 0.88-0.99) and 0.99 (95% CI 0.99-1.00). The sensitivity and specificity for MRC ranged from 48 to 100%, 60 to 100%, respectively, and the pooled sensitivity and specificity for MRC were 0.98 (95% C: 0.77-1.00) and 0.94 (95% CI 0.84-0.98). The sensitivity and specificity for PET/CT ranged from 84 to 100%, 33 to 100%, respectively, and the pooled sensitivity and specificity for PET/CT were 0.94 (95% CI 0.92-0.96) and 0.94 (95% CI 0.90-0.97). The area under the receiver operating characteristic curve for CTC, MRC, and PET/CT was 1.00 (95% CI 0.99-1.00), 0.99 (95% CI 0.98-1.00), and 0.97 (0.95% CI 0.95-0.98), respectively. CONCLUSIONS: This study suggested both CTC and MRC with relative higher diagnostic value for diagnosing CRC, while PET/CT with higher diagnostic value in detecting local recurrence for patients with CRC.

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study.

OBJECTIVE: To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively. METHODS: During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model. RESULTS: The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone. CONCLUSIONS: The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC. KEY POINTS: • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

OBJECTIVE. The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS. This retrospective study enrolled 117 patients with HGSOC, including 90 patients with recurrence and 27 without recurrence; 1046 radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images using a manual segmentation method. L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select features, and the synthetic minority oversampling technique (SMOTE) was used to balance our dataset. A support vector machine (SVM) classifier was used to build the classification model. To validate the performance of the proposed models, we applied a leave-one-out cross-validation method to train and test the classifier. Cox proportional hazards regression, Harrell concordance index (C-index), and Kaplan-Meier plots analysis were used to evaluate the associations between radiomics signatures and RFS. RESULTS. The fusion radiomics-based model yielded a significantly higher AUC value of 0.85 in evaluating RFS than the model using contrast-enhanced T1-weighted imaging features alone or T2-weighted imaging features alone (AUC = 0.79 and 0.74 and p = .02 and .01, respectively). Kaplan-Meier survival curves showed significant differences between high and low recurrence risk in patients with HGSOC by different models. The fusion model combining radiomics features and clinical information showed higher performance than the clinical model (C-index = 0.62 and 0.60, respectively). CONCLUSION. The proposed MRI-based radiomics signatures may provide a potential way to develop a prediction model and can help identify patients with advanced HGSOC who have a high risk of recurrence.

Perfusion-based functional magnetic resonance imaging for differentiating serous borderline ovarian tumors from early serous ovarian cancers in a rat model.

BACKGROUND: Differentiation of borderline tumors from early ovarian cancer has recently received increasing attention, since borderline tumors often affect young women of childbearing age who desire to preserve fertility. However, previous studies have demonstrated that non-enhanced magnetic resonance imaging (MRI) sequences cannot sufficiently differentiate these tumors. PURPOSE: To investigate the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating serous borderline ovarian tumors (SBOT) from early serous ovarian cancers (eSOCA). MATERIAL AND METHODS: Twenty SBOT and 20 eSOCA rat models were performed with DCE-MRI and IVIM-DWI at 3.0-T MR scanner. Qualitative and quantitative parameters of DCE-MRI were acquired and compared between two groups and correlated with the microvessel density (MVD). The receiver operating characteristic (ROC) curve analyses were conducted to determine their differentiating performances. RESULTS: SBOTs presented significantly lower values of the initial area under the enhancement curve (iAUC), volume transfer constant (Ktrans), and extracellular extravascular volume fraction (ve) (P < 0.05) and a significantly higher value of true diffusion (D) (P = 0.001) compared with eSOCAs. The diagnostic effectiveness of ve combined with D was significantly better than that of ve or Ktrans alone (P ≤ 0.039). CONCLUSION: DCE-MRI may represent a promising tool for differentiating SBOTs from eSOCAs and may not be replaced by IVIM-DWI. Combining DCE-MRI with DWI may improve the diagnostic performance of ovarian tumors.

Preoperative Assessment for High-Risk Endometrial Cancer by Developing an MRI- and Clinical-Based Radiomics Nomogram: A Multicenter Study.

BACKGROUND: High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. PURPOSE: To develop a radiomics nomogram for high-risk EC prediction preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). FIELD STRENGTH/SEQUENCE: 1.5/3T scanners; T2 -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. ASSESSMENT: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). STATISTICAL TESTS: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. RESULTS: The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. DATA CONCLUSION: The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI.

BACKGROUND: Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers. PURPOSE: To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC. MATERIAL AND METHODS: We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis. RESULTS: All histogram parameters of Ktrans, kep, and ve were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of Ktrans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and kep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of Ktrans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively. CONCLUSION: Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Role of proton MR spectroscopy in the differentiation of borderline from malignant epithelial ovarian tumors: A preliminary study.

BACKGROUND: Due to the overlapping imaging appearances between borderline and malignant epithelial ovarian tumors (EOTs), borderline EOTs often represent a diagnostic challenge on conventional MRI. Proton magnetic resonance spectroscopy (1 H-MRS) might have potential to differentiate borderline from malignant tumors. PURPOSE: To investigate the ability of 1 H-MRS to differentiate borderline from malignant EOTs. STUDY TYPE: Prospective. POPULATION: In all, 278 patients with adnexal masses. FIELD STRENGTH/SEQUENCE: 1.5 T Siemens Avanto MRI system and 1 H-MRS using a point-resolved spectroscopy sequence (PRESS). ASSESSMENT: Resonance peak integrals of the most common metabolites were analyzed and compared between the two groups. STATISTICAL TESTS: The ratios of metabolites between borderline and malignant EOTs were compared with the Mann-Whitney U-test. A receiver operating characteristic (ROC) curve was used to determine their differential diagnosis performances. RESULTS: In the solid components of borderline and malignant EOTs, the mean Cho/Cr, NAA/Cr, and NAA/Cho ratios were 4.4 ± 1.1 and 9.9 ± 2.8; 10.4 ± 3.0 and 2.2 ± 1.0; and 2.4 ± 0.7 and 0.3 ± 0.1, respectively (all P < 0.001). The sensitivity, specificity, and area under the curve (AUC) were 91%, 100%, and 0.98 for the Cho/Cr ratio; 100%, 98%, and 0.99 for the NAA/Cr ratio; and 100%, 100%, and 1.00 for the NAA/Cho ratio, respectively. In the cystic components, the mean Cho/Cr, NAA/Cr, and NAA/Cho ratios were 3.2 ± 0.8 and 5.1 ± 1.2; 9.1 ± 3.4 and 2.3 ± 1.4; and 2.9 ± 1.2 and 0.5 ± 0.4, respectively (all P < 0.001). The sensitivity, specificity, and AUC were 84%, 82%, and 0.89 for the Cho/Cr ratio; 94%, 97%, and 0.99 for the NAA/Cr ratio; and 94%, 97%, and 0.99 for the NAA/Cho ratio, respectively. DATA CONCLUSION: The NAA/Cho ratio is a reliable biomarker for differentiating borderline from malignant EOTs. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1684-1693.

Diffusion-weighted imaging and diffusion kurtosis imaging for early evaluation of the response to docetaxel in rat epithelial ovarian cancer.

BACKGROUND: To investigate diffusion-weighted magnetic imaging (DWI) and diffusion kurtosis magnetic imaging (DKI) for the early detection of the response to docetaxel (DTX) chemotherapy in rat epithelial ovarian cancer (EOC). METHODS: 7,12-Dimethylbenz[A]anthracene was applied to induce orthotopic EOC in Sprague-Dawley rats. Rats with EOC were treated with DTX on day 0 (treatment group) or were left untreated (control group). DWI and DKI were performed on days 0, 3, 7, 14 and 21 after treatment. On day 21, the tumors were categorized into the sensitive and insensitive groups according to the size change. The cutoff values of the DWI and DKI parameters for the early response were determined. The experiment was repeated, and the treatment group was divided into the sensitive and insensitive groups according to the initially obtained cutoff values. The DWI and DKI parameters were correlated with tumor size, proliferation, apoptosis and tumor necrosis. RESULTS: In the sensitive vs. insensitive or control group, significant differences were found in the Δ% of the DWI and DKI parameters (ADC, D and K) from day 3 and in tumor size from day 14. Early on day 7, the Δ% of K had an AUC of 1 and sensitivity and specificity values of 100% and 100%, respectively, to detect the response to DTX using a cutoff value of 19.03% reduction in K. From day 7, significant differences were found in the Δ% of Ki-67 and CA125 in the sensitive vs. control group and from day 14 in the sensitive vs. insensitive group. From day 14, there were significant differences in the Δ% of Bcl-2, apoptosis and tumor necrosis in the sensitive vs. control or insensitive group. The Δ% values of ADC and D were negatively correlated with the Δ% values of tumor size, Ki-67, CA125 and Bcl-2 and were positively correlated with the Δ% values of apoptosis and tumor necrosis. The Δ% of K was positively correlated with the Δ% values of tumor size, Ki-67, CA125 and Bcl-2 and was negatively correlated with the Δ% values of apoptosis and tumor necrosis. CONCLUSIONS: DWI and DKI parameters, especially K, are superior for imaging tumor size for the early detection of the response to DTX chemotherapy in induced rat EOC.

Multi-parametric MRI in cervical cancer: early prediction of response to concurrent chemoradiotherapy in combination with clinical prognostic factors.

OBJECTIVE: To investigate the prediction of response to concurrent chemoradiotherapy (CCRT) through a combination of pretreatment multi-parametric magnetic resonance imaging (MRI) with clinical prognostic factors (CPF) in cervical cancer patients. METHODS: Sixty-five patients underwent conventional MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI) before CCRT. The patients were divided into non- and residual tumour groups according to post-treatment MRI. Pretreatment MRI parameters and CPF between the two groups were compared and prognostic factors, optimal thresholds, and predictive performance for post-treatment residual tumour occurrence were estimated. RESULTS: The residual group showed a lower maximum slope of increase (MSIL) and signal enhancement ratio (SERL) in low-perfusion subregions, a higher apparent diffusion coefficient (ADC) value, and a higher stage than the non-residual tumour group (p < 0.001, p = 0.003, p < 0.001, and p < 0.001, respectively). MSIL and ADC were independent prognostic factors. The combination of both measures improved the diagnostic performance compared with individual MRI parameters. A further combination of these two factors with CPF exhibited the highest predictive performance. CONCLUSIONS: Pretreatment MSIL and ADC were independent prognostic factors for cervical cancer. The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. The combination of multi-parametric MRI with CPF further improved the predictive performance. KEY POINTS: • Pretreatment MSI L and ADC were independent prognostic factors for post-treatment residual tumours. • The residual groups showed lower MSI L , higher ADC and higher stage. • The predictive capacity of multi-parametric MRI was superior to individual MRI parameters. • The combination of multi-parametric MRI with CPF exhibited the highest predictive performance.

Utility of DWI with quantitative ADC values in ovarian tumors: a meta-analysis of diagnostic test performance.

Background Diffusion-weighted imaging (DWI) and quantitative apparent diffusion coefficient (ADC) values are widely used in the differential diagnosis of ovarian tumors. Purpose To assess the diagnostic performance of quantitative ADC values in ovarian tumors. Material and Methods PubMed, Embase, the Cochrane Library, and local databases were searched for studies assessing ovarian tumors using quantitative ADC values. We quantitatively analyzed the diagnostic performances for two clinical problems: benign vs. malignant tumors and borderline vs. malignant tumors. We evaluated diagnostic performances by the pooled sensitivity and specificity values and by summary receiver operating characteristic (SROC) curves. Subgroup analyses were used to analyze study heterogeneity. Results From the 742 studies identified in the search results, 16 studies met our inclusion criteria. A total of ten studies evaluated malignant vs. benign ovarian tumors and six studies assessed malignant vs. borderline ovarian tumors. Regarding the diagnostic accuracy of quantitative ADC values for distinguishing between malignant and benign ovarian tumors, the pooled sensitivity and specificity values were 0.91 and 0.91, respectively. The area under the SROC curve (AUC) was 0.96. For differentiating borderline from malignant tumors, the pooled sensitivity and specificity values were 0.89 and 0.79, and the AUC was 0.91. The methodological quality of the included studies was moderate. Conclusion Quantitative ADC values could serve as useful preoperative markers for predicting the nature of ovarian tumors. Nevertheless, prospective trials focused on standardized imaging parameters are needed to evaluate the clinical value of quantitative ADC values in ovarian tumors.

Diffusion kurtosis imaging for differentiating between the benign and malignant sinonasal lesions.

PURPOSE: The study aimed to evaluate diffusion kurtosis imaging (DKI) in the differentiation between benign and malignant sinonasal lesions, and to compare the diagnostic performance of DKI with diffusion weighted imaging (DWI). MATERIALS AND METHODS: Eight-one patients with solid sinonasal lesions confirmed by surgery and pathology (46 malignant and 35 benign) underwent conventional MRI, DWI, and DKI. DKI was performed employing a 13 extended b-value ranging from 0 to 2500 s/mm2 . Apparent diffusion coefficient (ADC) from DWI, kurtosis (K), and diffusion coefficient (D) from DKI were measured and compared between two groups. RESULTS: ADC and D values were significantly lower in the malignant sinonasal lesions than in the benign sinonasal lesions (1.11 ± 0.41 versus 1.58 ± 0.50 × 10-3 mm2 /s and 1.45 ± 0.36 versus 2.03 ± 0.49 × 10-3 mm2 /s, respectively, both P < 0001). K value was significantly higher in the malignant lesions than in the benign lesions (0.91 ± 0.23 versus 0.57 ± 0.24, P < 0001). The receiver operating characteristic curve analyses yielded a cutoff ADC value of 1.27 × 10-3 mm2 /s for differentiating between benign and malignant lesions, with a sensitivity of 69.6%, a specificity of 77.1% and an accuracy of 74.0%; a cutoff D value of 1.75 × 10-3 mm2 /s, with a sensitivity of 82.6%, a specificity of 77.1% and an accuracy of 80.2%; a cutoff K value of 0.63 with a sensitivity of 95.7%, a specificity of 77.1% and an accuracy of 87.7%. The area under the curve of K value was significantly larger than that of ADC value (0.875 versus 0.762; P < 0.05). CONCLUSION: K value of DKI demonstrates significantly higher accuracy compared with ADC value for the differentiation between benign and malignant sinonasal lesions. DKI may be a noninvasive method to evaluate the sinonasal lesions. LEVEL OF EVIDENCE: 1 J. MAGN. RESON. IMAGING 2017;45:1446-1454.

Minimum apparent diffusion coefficient for predicting lymphovascular invasion in invasive cervical cancer.

PURPOSE: To investigate the diagnostic performance of minimum apparent diffusion coefficient (mini-ADC) for predicting lymphovascular invasion (LVI) in invasive cervical cancer. MATERIALS AND METHODS: Ninety-six patients with pathologically confirmed invasive cervical cancer (CC) underwent conventional preoperative magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) on a 3.0T MRI system. Tumor ADC, mini-ADC and mini-ADC ratio (mini-ADC value / tumor ADC value) were obtained and compared between LVI-positive and LVI-negative invasive CC, and correlation between LVI status and Ki-67, p16, p63, and clinical prognostic factors were analyzed. ADC thresholds and diagnostic performance were determined by receiver operating characteristic (ROC) analysis. RESULTS: Tumor ADC showed no significant difference (P = 0.300) between LVI-positive invasive CC (n = 27) and LVI-negative invasive CC (n = 69); the mini-ADC and mini-ADC ratio were significantly lower in LVI-positive invasive CC than in LVI-negative invasive CC ([0.712 ± 0.078 × 10-3 mm2 /s] vs. [0.867 ± 0.099 × 10-3 mm2 /s], P < 0.001; and [0.772 ± 0.062] vs. [0.917 ± 0.052], P < 0.001, respectively). ROC curve analysis yielded a cutoff mini-ADC value of 0.837 in the differentiation of LVI-positive and LVI-negative invasive CC, with a sensitivity of 65%, specificity of 100%, and area under the curve (AUC) of 0.885; a cutoff mini-ADC ratio of 0.875 with a sensitivity of 78%, specificity of 100%, AUC of 0.970, positive predictive value of 100%, and negative predictive value of 64%. There was a positive correlation between LVI status and Ki-67 (r = 0.241, P = 0.014) and a negative correlation between mini-ADC and LVI status (r = -0.582, P < 0.001); mini-ADC and Ki-67 (r = -0.587, P < 0.001). CONCLUSION: Mini-ADC value appears to be a simple and effective tool for the prediction of LVI status in invasive CC, and the mini-ADC ratio may be the best parameter in discriminating between LVI-positive and LVI-negative invasive CC. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 4 J. MAGN. RESON. IMAGING 2017;45:1771-1779.

Diffusion kurtosis imaging for differentiating borderline from malignant epithelial ovarian tumors: A correlation with Ki-67 expression.

PURPOSE: To investigate the use of diffusion kurtosis imaging (DKI) in differentiating borderline from malignant epithelial ovarian tumors (MEOTs) and to correlate DKI parameters with Ki-67 expression. MATERIALS AND METHODS: Fifty-two consecutive patients with epithelial ovarian tumors (17 borderline epithelial ovarian tumors, BEOTs; 35 MEOTs) were prospectively evaluated using DKI with b values of 0, 500, 1000, 1500, 2000, and 2500 s/mm2 and standard diffusion-weighted imaging (DWI) with b values of 0 and 1000 s/mm2 using a 1.5T magnetic resonance imaging (MRI) unit. The kurtosis (K) and diffusion coefficient (D) from DKI and apparent diffusion coefficient (ADC) from standard DWI were measured, compared, and correlated with Ki-67 expression between the two groups. Statistical analyses were performed using the Mann-Whitney U-test, receiver operating characteristic (ROC) curves, and Spearman's correlation. RESULTS: The K value was significantly lower in BEOTs than in MEOTs (0.55 ± 0.09 vs. 0.9 ± 0.2), while the D and ADC values were significantly higher in BEOTs than in MEOTs (2.27 ± 0.35 vs. 1.39 ± 0.37 and 1.72 ± 0.36 vs. 1.1 ± 0.25, respectively) (P < 0.001). For differentiating between BEOTs and MEOTs, the sensitivity, specificity, and accuracy were 88.2%, 94.3%, and 92.3% for K value; 88.2%, 91.4%, and 90.4% for D value; and 88.2%, 88.6%, and 88.5% for ADC value, respectively. However, there were no differences in the diagnostic performances among the three parameters above (K vs. ADC, P = 0.203; D vs. ADC, P = 0.148; K vs. D, P = 0.904). The K value was positively correlated with Ki-67 expression (r = 0.699), while the D and ADC values were negatively correlated with Ki-67 expression (r = -0.680, -0.665, respectively). CONCLUSION: Preliminary findings demonstrate that DKI is an alternative tool for differentiating BEOTs from MEOTs, and is correlated with Ki-67 expression. However, no added value is found for DKI compared with standard DWI. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1499-1506.

Endometrial Stromal Sarcoma of the Uterus: Magnetic Resonance Imaging Findings Including Apparent Diffusion Coefficient Value and Its Correlation With Ki-67 Expression.

OBJECTIVE: This study aimed to investigate the conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) features of endometrial stromal sarcoma (ESS) including a preliminary investigation of the correlation between the apparent diffusion coefficient (ADC) value and Ki-67 expression. METHODS: The clinical and MRI data of 15 patients with ESS confirmed by surgery and pathology were analyzed retrospectively. The conventional MR morphological features, signal intensity on DWI, ADC value (n = 14), and clinicopathological marker Ki-67 (n = 13) were evaluated. RESULTS: Of 15 patients with ESS, 13 tumors were low-grade ESS (LGESS), and the remaining 2 were high-grade ESS (HGESS); 9 tumors were located in the myometrium, 5 were located in the endometrium and/or cervical canal, and 1 was located in extrauterine. Thirteen (87%) of 15 tumors showed a homo- or heterogeneous isointensity on T1-weighted imaging and a heterogeneous hyperintensity on T2-weighted imaging. The hypointense bands were observed in 11 tumors (73%) on T2-weighted imaging. The degenerations (cystic/necrosis/hemorrhage) were observed in 7 LGESS tumors and 2 HGESS tumors. The DWI hyperintensity was observed in 13 tumors (93%) and isointensity in remaining 1. The mean ADC value of the solid components in 14 ESSs was (1.05 ± 0.20) × 10mm/s. The contrast-enhanced MRI showed an obvious enhancement in 14 tumors (93%) (heterogeneous in 7 LGESSs and 2 HGESSs; homogeneous in 5 LGESSs). The ADC value was inversely correlated with the Ki-67 expression (r = -0.613, P = 0.026). CONCLUSIONS: Patients with ESS showed some characteristics on conventional MRI and DWI, and there was an inverse correlation between the ADC value and Ki-67 expression.