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.

Preoperative CT Radiomics Nomogram for Predicting Microvascular Invasion in Stage I Non-Small Cell Lung Cancer

RATIONALE AND OBJECTIVES: This study aims to develop and validate a nomogram integrating clinical-CT and radiomic features for preoperative prediction of microvascular invasion (MVI) in patients with stage I non‑small cell lung cancer (NSCLC).

MATERIALS AND METHODS

This retrospective study analyzed 188 cases of stage I NSCLC (63 MVI positives and 125 negatives), which were randomly assigned to training (n = 133) and validation cohorts (n = 55) at a ratio of 7:3. Preoperative non-contrast and contrast-enhanced CT (CECT) images were used to analyze computed tomography (CT) features and extract radiomics features. The student's t-test, the Mann-Whitney-U test, the Pearson correlation, the least absolute shrinkage and selection operator, and multivariable logistic analysis were used to select the significant CT and radiomics features. Multivariable logistic regression analysis was performed to build the clinical-CT, radiomics, and integrated models. The predictive performances were evaluated through the receiver operating characteristic curve and compared with the DeLong test. The integrated nomogram was analyzed regarding discrimination, calibration, and clinical significance.

RESULTS

The rad-score was developed with one shape and four textural features. The integrated nomogram incorporating radiomics score, spiculation, and the number of tumor-related vessels (TVN) demonstrated better predictive efficacy than the radiomics and clinical-CT models in the training cohort (area under the curve [AUC], 0.893 vs 0.853 and 0.828, and p = 0.043 and 0.027, respectively) and validation cohort (AUC, 0.887 vs 0.878 and 0.786, and p = 0.761 and 0.043, respectively). The nomogram also demonstrated good calibration and clinical usefulness.

CONCLUSION

The radiomics nomogram integrating the radiomics with clinical-CT features demonstrated good performance in predicting MVI status in stage I NSCLC. The nomogram may be a useful tool for physicians in improving personalized management of stage I NSCLC.

MRI Texture Analysis for Preoperative Prediction of Lymph Node Metastasis in Patients with Nonsquamous Cell Cervical Carcinoma

RATIONALE AND OBJECTIVES

To preoperatively predict lymph node metastasis (LNM) in patients with cervical nonsquamous cell carcinoma (non-SCC) based on magnetic resonance imaging (MRI) texture analysis.

MATERIALS AND METHODS

This retrospective study included 104 consecutive patients (mean age of 47.2 ± 11.3 years) with stage IB-IIA cervical non-SCC. According to the ratio of 7:3, 72, and 32 patients were randomly divided into the training and testing cohorts. A total of 272 original features were extracted. In the process of feature selection, features with intraclass correlation coefficients (ICCs) less than 0.8 were eliminated. The Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were applied to reduce redundancy, overfitting, and selection biases. Further, a support vector machine (SVM) with linear kernel function was applied to select the optimal feature set with a high discrimination power.

RESULTS

The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI (LN status on MRI)-based SVM models yielded an AUC and accuracy of 0.78 and 0.79; 0.79 and 0.69; 0.79 and 0.81 for predicting LNM in the training cohort, and 0.82 and 0.78; 0.82 and 0.69; 0.79 and 0.72 in the testing cohort. The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI + DWI + LNS-MRI-based SVM models performed better than morphologic criteria of LNS-MRI and yield similar discrimination abilities in predicting LNM in the training and testing cohorts (all p-value > 0.05). In addition, the T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the AC and ASC subgroups (all p-value > 0.05).

CONCLUSION

The T2WI + DWI-based, T2WI + DWI + CE-T1WI-based and T2WI+DWI+LNS-MRI-based SVM models showed similar good discrimination ability and performed better than the morphologic criteria of LNS-MRI in predicting LNM in patients with cervical non-SCC. The inclusion of the CE-T1WI sequence and morphologic criteria of LNS-MRI did not significantly improve the performance of the T2WI + DWI-based model. The T2WI + DWI-based and T2WI + DWI + LNS-MRI-based SVM models showed robust performance in the subgroup analysis.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03292-z.

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.

Prediction of minimal hepatic encephalopathy by using an radiomics nomogram in chronic hepatic schistosomiasis patients

Objective

To construct an MR-radiomics nomogram to predict minimal hepatic encephalopathy (MHE) in patients with chronic hepatic schistosomiasis (CHS).

Methods

From July 2017 to July 2020, 236 CHS patients with non-HE (n = 140) and MHE (n = 96) were retrospective collected and randomly divided into training group and testing group. Radiomics features were extracted from substantia nigra-striatum system of a brain diffusion weighted images (DWI) and combined with clinical predictors to build a radiomics nomogram for predicting MHE in CHS patients. The ROC curve was used to evaluate the predicting performance in training group and testing group. The clinical decisive curve (CDC) was used to assess the clinical net benefit of using radiomics nomogram in predicting MHE.

Results

Low seralbumin (P < 0.05), low platelet count (P < 0.05) and high plasma ammonia (P < 0.05) was the significant clinical predictors for MHE in CHS patients. The AUC, specificity and sensitivity of the radiomics nomogram were 0.89, 0.90 and 0.86 in the training group, and were 0.83, 0.85 and 0.75 in the training group. The CDC analysis showed clinical net benefits for the radiomics nomogram in predicting MHE.

Conclusions

The radiomics nomogram combining DWI radiomics features and clinical predictors could be useful tool to predict MHE in CHS patients.

Minimal hepatic encephalopathy (MHE) is usually neglected clinically in chronic hepatic schistosomiasis (CHS) patients. The diffusion change in substantia nigra-striatum system of MHE patients has been reported. We hypothesized that the change could be better detected by DWI-based radiomics. A radiomics nomogram combining radiomics and clinical predictors of MHE was built to predict MHE in CHS patients. The results demonstrate that the radiomics nomogram would be useful for predicting MHE in CHS patients.

MRI-Based Radiomics Nomogram for Selecting Ovarian Preservation Treatment in Patients With Early-Stage Endometrial Cancer

Background

Ovarian preservation treatment (OPT) was recommended in young women with early-stage endometrial cancer [superficial myometrial invasion (MI) and grades (G) 1/2-endometrioid adenocarcinoma (EEC)]. A radiomics nomogram was developed to assist radiologists in assessing the depth of MI and in selecting eligible patients for OPT.

Methods

From February 2014 to May 2021, 209 G 1/2-EEC patients younger than 45 years (mean 39 ± 4.3 years) were included. Of them, 104 retrospective patients were enrolled in the primary group, and 105 prospective patients were enrolled in the validation group. The radiomics features were extracted based on multi-parametric magnetic resonance imaging, and the least absolute shrinkage and selection operator algorithm was applied to reduce the dimensionality of the data and select the radiomics features that correlated with the depth of MI in G 1/2-EEC patients. A radiomics nomogram for evaluating the depth of MI was developed by combing the selected radiomics features with the cancer antigen 125 and tumor size. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the radiomics nomogram and of radiologists without and with the aid of the radiomics nomogram. The net reclassification index (NRI) and total integrated discrimination index (IDI) based on the total included patients to assess the clinical benefit of radiologists with the radiomics nomogram were calculated.

Results

In the primary group, for evaluating the depth of MI, the AUCs were 0.96 for the radiomics nomogram; 0.80 and 0.86 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.98 and 0.98 for radiologists 1 and 2 with the aid of the nomogram, respectively. In the validation group, the AUCs were 0.88 for the radiomics nomogram; 0.82 and 0.83 for radiologists 1 and 2 without the aid of the nomogram, respectively; and 0.94 and 0.94 for radiologists 1 and 2 with the aid of the nomogram, respectively. The yielded NRI and IDI values were 0.29 and 0.43 for radiologist 1 and 0.23 and 0.37 for radiologist 2, respectively.

Conclusions

The radiomics nomogram outperformed radiologists and could help radiologists in assessing the depth of MI and selecting eligible OPTs in G 1/2-EEC patients.

Treatment of Upper Gastrointestinal Bleeding by Percutaneous Transsplenic Varices Embolization in Chronic Hepatic Schistosomiasis Japonicum Patients

To evaluate percutaneous transsplenic varices embolization (PTSVE) in the treatment of upper gastrointestinal bleeding (UGIB) in patients with chronic hepatic schistosomiasis japonicum (CHS), 29 CHS patients (20 males and 9 females) complicated with UGIB were selected as the investigation subjects. The patients were treated by PTSVE under the guidance of X-ray fluoroscopy. The success rate of PTSVE and the rate of complications were observed. In addition, the degrees of varices before and after PTSVE were evaluated by abdominal computed tomography (CT). Results showed that 26 CHS patients (89.6%) were successfully treated with PTSVE. Three cases (10.3%) failed, and two experienced intraperitoneal bleeding within 1 week after PTSVE. The abdominal CT showed a significant decrease of the varices stage in coronary (P < 0.001), esophageal (P = 0.006), and paraesophageal (P = 0.013) varices, but slightly increased perisplenic varices within 1 month of the intervention (P = 0.014). PTSVE may be a safe and effective procedure for the treatment of UGIB in CHS patients, particularly suitable for those with a widened hepatic fissure and exposed hepatic portal vein trunk and an enlarged spleen.

CT diagnosis and prognosis prediction of tracheal adenoid cystic carcinoma

PURPOSE

To evaluate computed tomography (CT) features and establish a predictive model for the clinical diagnosis and prognosis of tracheal adenoid cystic carcinoma (ACC).

METHOD

From January 2010 to December 2018, 82 patients with tracheal tumors, including 46 patients with ACC confirmed by surgery and histopathology, were enrolled in this study. These patients' clinicopathologic information, CT features and survival outcomes were recorded and analyzed. Independent predictors of diagnosis and prognosis of tracheal ACC were determined by both univariate and multivariate analyses.

RESULTS

Compared with tracheal non-ACC patients, univariate analysis showed that ACC patients were more likely to have extensive longitudinal length (p < 0.001) and to appear as annular wall thickening (p = 0.001), transmural growth (p = 0.036), poorly defined border (p = 0.003) and mild enhancement (p = 0.001). Multivariate logistic analysis showed that longitudinal length and enhancement degree were independent predictors of tracheal ACC. The 3-year and 5-year disease-free survival (DFS) were 75.7 % and 64.5 %, respectively. Longitudinal length (≥ 34 mm), transverse length (≥ 20 mm) and transmural growth were associated with poor DFS in univariate analysis. After multivariate adjustment, only transverse length (≥ 20 mm) was an adverse prognostic factor for DFS (hazard ratio = 4.594, 95 % confidence interval = 1.240-17.017; p = 0.022).

CONCLUSIONS

CT longitudinal length and enhancement degree of tumors showed satisfactory discrimination for tracheal ACC. Excessive CT transverse length might be an unfavorable indicator for ACC recurrence and could be helpful for predicting the survival outcomes of ACC at the initial diagnosis.

Optimization of 7,12-dimethylbenz(a)anthracene-induced rat epithelial ovarian tumors

Ovarian carcinoma is the second most common malignant tumor of the female reproductive system and an notable cause of cancer death. The detection and diagnosis of early ovarian carcinomas are still clinical challenges, which calls for imaging studies using early ovarian carcinoma animal models. The present study aimed to optimize the 7,12-dimethylbenz(a)anthracene (DMBA)-induced model of rat ovarian tumors by investigating the delivery methods, induction dose and time of DMBA exposure, and explored the morphological features of tumors using MRI. Three schemes were performed. In scheme one the ovary was covered with absorbable hemostatic gauze loaded with a high concentration of liquid DMBA. For this scheme, 150 Sprague-Dawley rats were divided into three groups depending on the DMBA dose (1.0, 2.0 and 3.0 mg). In scheme two DMBA solution was injected under the ovarian capsule. For this scheme, 159 rats were divided into 0.5, 1.0 and 1.5 mg DMBA groups. In scheme three the ovary was covered with absorbable gauze loaded with a high concentration of solid DMBA. For this scheme 161 rats were divided into 1.0, 2.0 and 3.0 mg DMBA groups. Each group of the three schemes was further subdivided into 60-, 90-, 120-, 150- and 180-day groups. In scheme two, the tumor formation rate was 75.6% (99/131), which was the highest in the 1.5 mg group (86.4%, 38/44) and reached 100% (10/10) on day 120. The induced tumors were serous in 93.9% (93/99) of tumors. Borderline ovarian tumors accounted for 19.2% (19/99) of all tumors, and ovarian cancer accounted for 46.5% (46/99). The mean maximum diameter (MMD) of borderline ovarian tumors was 10.29±3.41 mm, and that of ovarian cancer was 15.19±7.10 mm. MMD of the solid components increased with increasing malignancy. Cystic, cystic-solid and solid tumors were observed. The ovarian subcapsular injection of 1.5 mg DMBA was the best scheme for the rat ovarian tumor model. The present model is ideal for investigating the occurrence, development and imaging of ovarian tumors.

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 K^trans, k_ep, and v_e 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 K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (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 K^trans 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.

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.

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.

Apparent diffusion coefficients measured using different regions of interest in differentiating borderline from malignant ovarian tumors

Background

Apparent diffusion coefficients (ADCs) measured using different regions of interest (ROIs) are widely used in differentiating ovarian tumors.

Purpose

To evaluate the diagnostic performance of ADCs with different ROIs in differentiating borderline ovarian tumors (BOTs) from malignant ovarian tumors (MOTs).

Material and Methods

Thirty-five BOTs and 54 MOTs who underwent diffusion-weighted magnetic resonance imaging (MRI) were evaluated retrospectively. ADC values were independently measured using five ROI methods: round; rectangle; hot-spot; edge drawing; and five sample ROIs. The inter- and intraclass correlation coefficients (ICCs), one-way analysis of variance, receiver operating characteristic curve analysis, and unpaired t-tests were used to perform the statistical analyses.

Results

The measurement reproducibility of the minimum ADC and mean ADC values were good or excellent for BOTs and MOTs (ICC = 0.70–0.95). The minimum and mean ADC value by the edge drawing ROI were significantly higher than those of the other ROI methods (both P < 0.05). The area under the curve (AUC) of the minimum ADC value was less than that of the mean ADC value from the five ROI methods, whereas the AUCs of the mean ADC values from the round ROI and five sample ROIs were significantly larger than those of the other ROI methods ( P < 0.05). The minimum and mean ADC values from the five ROI methods showed significant differences between BOTs and MOTs (all P < 0.05).

Conclusion

The ROI shape influences the diagnostic performance of ADC value for differentiating BOTs from MOTs. The mean ADC values from the round ROI and five sample ROIs have better diagnostic efficiency.

The diagnostic performance of ADC value for tumor grade, deep myometrial invasion and lymphovascular space invasion in endometrial cancer: a meta-analysis

BACKGROUND

Disputes exist regarding whether the apparent diffusion coefficient (ADC) can differentiate the tumor grade, deep myometrial invasion and lymphovascular space invasion (LVSI) in endometrial cancer. The aim of this review was to assess the diagnostic performance of the ADC value in endometrial cancer.

MATERIAL AND METHODS

The PubMed, Web of Science, Embase and Cochrane Library databases were searched for studies that used the ADC value to assess tumor grade, deep myometrial invasion and LVSI in endometrial cancer. We used forest plots to analyze the heterogeneity and generate the pooled sensitivity (SEN) and specificity (SPE). We used summary receiver operating characteristic (SROC) curves to work out the area under the SROC curve (AUC). Likelihood ratios (LRs) were also obtained.

RESULTS

Of the 460 identified studies, 11 studies met our inclusion criteria and were included. Overall, nine studies (491 patients) aimed at differentiating high tumor grade had a pooled SEN, SPE and AUC of 77%, 73% and 81%, respectively; three studies (181 patients) for differentiating deep myometrial invasion had a pooled SEN, SPE and AUC of 71%, 67% and 77%, respectively; and two studies (106 patients) for differentiating LVSI had a pooled SEN and SPE of 66% and 74%, respectively. The positive and negative LRs were 2.77 and 0.35 for the tumor grade, 2.08 and 0.45 for deep myometrial invasion, and 2.48 and 0.45 for LVSI.

CONCLUSION

This meta-analysis showed that the ADC value had a moderate diagnostic performance for the tumor grade, deep myometrial invasion and LVSI in endometrial cancer.

The value of MR-detectable ipsilateral ovaries in characterizing the origin and malignancy of an adnexal tumors

OBJECTIVE

To investigate the magnetic resonance (MR) imaging morphological relationship between adnexal tumors and the ipsilateral ovaries to characterize the origin and malignancy of tumors.

MATERIAL AND METHODS

Clinical and MR imaging data of 496 adnexal tumors confirmed by histology (ovary tumors, n = 400, non-ovarian tumors, n = 96; benign tumors, n = 183, borderline tumors, n = 120, and malignant tumors, n = 193) were retrospectively analyzed. The presence and shape of the ipsilateral ovaries within the context of adnexal tumors of different origins, malignancies and configurations were evaluated. The relationships between the presence of the ipsilateral ovary and patient age, menstrual status and tumor size were also analyzed.

RESULT

The ipsilateral ovary was detected on MRI in 23% (90/400) of ovarian tumors and in 45% (43/96) of non-ovarian tumors (p < 0.001). A normal ovoid morphology of the ipsilateral ovary was found in only 7% (26/400) of ovarian tumors and in 26% (25/96) of non-ovarian tumors (p < 0.001). The ipsilateral ovary was detectable in 38% (69/183) of benign tumors, 35% (42/120) of borderline tumors, and 11% (22/193) of malignant tumors (p <  0.001); in 24% (24/101) of cystic tumors, 27% (49/179) of mixed cystic-solid tumors and 28% (60/216) of solid tumors (p = 0.737); and in 40% (120/303) of adnexal tumors in premenopausal patients and 7% (13/193) of adnexal tumors in postmenopausal patients (p < 0.001).

CONCLUSION

Detection of the ipsilateral ovary contributes to the localization and characterization of adnexal tumors. The ipsilateral ovary can be detected more frequently in non-ovarian tumors and in benign or borderline ovarian tumors.

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.

The value of DCE-MRI in assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas

Background

To investigate dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas (EOCs).

Methods

7,12-dimethylbenz[A]anthracene (DMBA) was applied to induce EOCs in situ in 46 SD rats. Conventional MRI and DCE-MRI were performed to evaluate the morphology and perfusion features of the tumors, including the time-signal intensity curve (TIC), volume transfer constant (K^trans), rate constant (K_ep), extravascular extracellular space volume ratio (V_e) and initial area under the curve (IAUC). DCE-MRI parameters were correlated with histological grade, microvascular density (MVD), vascular endothelial growth factor (VEGF) and fraction of Ki67-positive cells and the serum level of cancer antigen 125 (CA125).

Results

Thirty-five of the 46 rats developed EOCs. DCE-MRI showed type III TIC more frequently than type II (29/35 vs. 6/35, p < 0.001) in EOCs. The two types of TIC of tumors had significant differences in the histological grade, MVD, expression of VEGF and Ki67, and the serum level of CA125 (all p < 0.01). K^trans, K_ep and IAUC values showed significant differences in different histological grades in overall and pairwise comparisons except for IAUC in grade 2 vs. grade 3 (all p < 0.01). There was no significant difference in V_e values among the three grade groups (p > 0.05). K^trans, K_ep and IAUC values were positively correlated with MVD, VEGF and Ki67 expression (all p < 0.01). V_e was not significantly correlated with MVD, VEGF expression, Ki67 expression and the CA125 level (all p > 0.05).

Conclusions

TIC types and perfusion parameters of DCE-MRI can reflect tumor grade, angiogenesis and cell proliferation to some extent, thereby helping treatment planning and predicting prognosis.

Early Lung Adenocarcinoma in Mice: Micro-Computed Tomography Manifestations and Correlation with Pathology

Lung cancer is the most common fatal malignancy for both men and women and adenocarcinoma is the most common histologic type. Early diagnosis of lung cancer can significantly improve the survival rate of patients. This study aimed to investigate the micro-computed tomography (micro-CT) manifestations of early lung adenocarcinoma (LAC) in mice and to provide a new perspective for early clinical diagnosis. Early LAC models in 10 mice were established by subcutaneously injecting 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) solution. Micro-CT scan and multiple planar reconstruction (MPR) were used for mouse lungs. Micro-CT features of early LAC, especially the relationships between tumor and bronchus, were analyzed and correlated with pathology. Micro-CT findings of early LAC were divided into three types: non-solid (n = 8, 6%), partly solid (n = 85, 64%) and totally solid (n = 39, 30%). Tumor-bronchus relationships, which could be observed in 110 of 132(83%) LAC, were classified into four patterns: type I (n = 16, 15%), bronchus was truncated at the margin of the tumor; type II (n = 33, 30%), bronchus penetrated into the tumor with tapered narrowing and interruption; type III (n = 38, 35%), bronchus penetrated into the tumor with a patent and intact lumen; type IV (n = 99, 90%), bronchus ran at the border of the tumor with an intact or compressed lumen. Micro-CT manifestations of early LAC correlated well with pathological findings. Micro-CT can clearly demonstrate the features of mouse early LAC and bronchus-tumor relationships, and can also provide a new tool and perspective for the study of early LAC.

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.

Using air bronchograms on multi-detector CT to predict the invasiveness of small lung adenocarcinoma

PURPOSE

To investigate the prevalence of multidetector CT (MDCT) air bronchograms and their value in predicting the invasiveness of lung adenocarcinomas.

METHODS

MDCT scans of 606 nodules in 582 patients with a lung adenocarcinoma less than 2cm in diameter confirmed by surgery and pathology were reviewed. Air bronchograms were classified into three patterns: type I, bronchus with intact lumen; type II, bronchus with dilated or tortuous lumen; and type III, bronchus with obstructed lumen.

RESULTS

Air bronchograms were demonstrated on MDCT in 210 of 606 (34.7%) lung adenocarcinomas with 16.6% (35/211) preinvasive lesions (PL), 30.5% (50/164) minimally invasive adenocarcinoma (MIA), and 54.1% (125/231) invasive adenocarcinoma (IAC) (P=0.000); 18.3% (44/240) pure ground-glass nodules (GGNs), 44.2% (137/310) mixed GGNs, and 51.8% (29/56) solid nodules (P=0.000). Type I was slightly more common in MIA (36/164, 22.0%) than IAC (40/231, 17.3%) and PL (30/211, 14.2%) but without differences among them (P=0.147). Type II (PL: 5/211, 2.4%; MIA: 13/164, 7.9%; IAC: 53/231, 22.9%) and type III (PL: 0/211; MIA: 1/164, 0.6%; IAC: 32/231, 13.9%) were observed more frequently with increasing lung adenocarcinoma invasiveness (both P=0.000).

CONCLUSIONS

The prevalence and patterns of air bronchograms on MDCT can predict the invasiveness of small lung adenocarcinomas.

HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules

OBJECTIVE

To investigate the high-resolution computed tomography (HRCT) features that distinguish lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) from invasive adenocarcinomas (IACs) appearing as ground-glass nodules (GGNs), and to select candidates for sublobar resection.

METHODS

Two hundred and twenty-nine patients with 237 GGNs of less than 2 cm (139 AIS-MIA nodules and 98 IAC nodules) confirmed by surgery and pathology were retrospectively reviewed. The HRCT features of the AIS-MIAs and IACs were analysed and compared. Receiver operating characteristic (ROC) analyses were conducted to determine the cutoff values for the qualitative variables and their diagnostic performances.

RESULTS

Significant differences were found in the density, nodule and solid component diameters, CT values of the ground-glass and solid components, lobulated shape, spiculated margin, abnormal pulmonary vein and artery, air bronchogram, and pleural indentation of the GGNs between the two groups. Multivariate and ROC analyses revealed that larger diameter of nodules (≥12.2 mm) and solid components (≥6.7 mm), and higher CT values of the solid components (≥ -192 HU) in the GGNs with air bronchogram were significantly associated with IACs.

CONCLUSIONS

HRCT can identify distinguishing morphological features between AIS-MIAs and IACs, and is helpful for selecting candidates for sublobar resection.

KEY POINTS

• IACs appearing as GGNs were often ≥ 12.2 mm in diameter. • IACs were often ≥ 6.7 mm in solid component diameter. • The solid components of the IACs often exhibited ≥ -192 HU. • IACs exhibited air bronchogram more frequently than AIS-MIAs.

MRI for differentiating ovarian endometrioid adenocarcinoma from high-grade serous adenocarcinoma

PURPOSE

To investigate magnetic resonance imaging (MRI) features for differentiating ovarian endometrioid adenocarcinoma (OEC) from high-grade serous adenocarcinoma (HGSC).

MATERIALS AND METHODS

Twenty-three patients with 25 OECs and 93 patients with 139 HGSCs confirmed by surgery and pathology underwent conventional MRI and diffusion-weighted imaging (DWI). The MRI features of the tumors, including laterality, size, shape, configuration, signal intensity, ADC value of solid component, enhancement, ascites, synchronous primary cancer (SPC) of the ovary and endometrium, and clinical stage, were evaluated and compared between two groups.

RESULTS

The following characteristics were significantly more common for OECs than HGSCs: unilateral (91.3% vs 50.5%, P < 0.001), larger mass (80.0% vs 48.2%, P = 0.005), round or oval shape (64.0% vs 17.3%, P < 0.001), mainly cystic with mural nodules or papillary projections (72.0% vs 18.7%, P < 0.001), cystic component with homogeneous iso- or hyperintensity on T1WI (82.6% vs 4.3%, P < 0.001), moderate enhancement (52.0% vs 26.6%, P = 0.011), no or mild ascites (91.3% vs 57.0%, P = 0.002), and SPC (43.5% vs 4.3%, P < 0.001). The ADC value of the solid component was higher in OECs (0.979 ± 0.197 × 10(-3) mm(2)/s) than in HGSCs (0.820 ± 0.112 × 10(-3) mm(2)/s) (P = 0.002). When a mainly cystic mass with mural nodules or papillary projections was associated with any one of homogeneously iso- or hyperintense cystic component on TIWI, a relatively higher ADC value and SPC, the sensitivity, specificity, accuracy, and positive and negative predictive values for characterizing OEC were 87.0%, 93.5%, 92.2%, 76.9%, and 96.7%, respectively.

CONCLUSIONS

Conventional MRI combining DWI is helpful for differentiating OECs from HGSCs.

Comparison of multislice computed tomography and clinical scores for diagnosing acute appendicitis

Objective

To compare Raja Isteri Pengiran Anak Saleha Appendicitis (RIPASA) and Alvarado scores with multislice computed tomography (MSCT) for diagnosing acute appendicitis (AA).

Methods

This retrospective study included patients with abdominal pain who had undergone MSCT, and whose medical notes included RIPASA and Alvarado score parameters. MSCT was compared with RIPASA and Alvarado scores for diagnosing AA.

Results

Of 297 patients included, sensitivity, specificity and accuracy for diagnosing AA were 95.2%, 73.6% and 87.2% for RIPASA score (cutoff value 7.5) and 63.1%, 80.9% and 69.7% for Alvarado score (cutoff value 7). Sensitivity, specificity and accuracy of MSCT for diagnosing AA were 98.9%, 96.4% and 98.0%, respectively. In terms of accuracy, statistically significant differences were observed between RIPASA and Alvarado scores, and between MSCT and RIPASA scores. The mean RIPASA score was significantly different in the simple AA group (9.7 ± 2.2) compared with other AA groups (10.5 ± 1.7). No statistically significant difference was observed in RIPASA score between nonperforated and perforated AA. MSCT sensitivity, specificity and accuracy for diagnosing simple AA were 94.1%, 96.4% and 95.8%, respectively; for differentiating perforated and nonperforated AA, scores were 90.2%, 95.2% and 94.1%, respectively.

Conclusion

MSCT is the optimum diagnostic tool for AA, followed by RIPASA score and Alvarado score, particularly in diagnosing simple and perforated AA.

Diffusion-weighted MR imaging for differentiating borderline from malignant epithelial tumours of the ovary: pathological correlation

OBJECTIVES

To investigate diffusion-weighted (DW) magnetic resonance (MR) imaging for differentiating borderline from malignant epithelial tumours of the ovary.

METHODS

This retrospective study included 60 borderline epithelial ovarian tumours (BEOTs) in 48 patients and 65 malignant epithelial ovarian tumours (MEOTs) in 54 patients. DW imaging as well as conventional MR imaging was performed. Signal intensity on DW imaging was assessed and apparent diffusion coefficient (ADC) value was measured. The results were correlated with histopathology and cell density.

RESULTS

The majority of MEOTs showed high signal intensity on DW imaging, whereas most BEOTs showed low or moderate signal intensity (P = 0.000). The mean ADC value of the solid components in BEOTs (1.562 ± 0.346 × 10(-3) mm(2)/s) was significantly higher than in MEOTs (0.841 ± 0.209 × 10(-3) mm(2)/s). A threshold value of 1.039 × 10(-3) mm(2)/s permitted the distinction with a sensitivity of 97.0%, a specificity of 92.2% and an accuracy of 96.4%. There was an inverse correlation between ADC value and cell density (r = -0.609; P = 0.0000) which was significantly lower in BEOTs than in MEOTs.

CONCLUSIONS

DW imaging is useful for differentiating borderline from malignant epithelial tumours of the ovary.

KEY POINTS

DW MR imaging is useful for differentiating BEOTs from MEOTs. Patients with BEOTs are treated differently from patients with MEOTs. Conservative fertility-sparing laparoscopic surgery can be performed in patients with BEOTs. BEOTs often affect young women of childbearing age.

MRI appearances of mucinous borderline ovarian tumors: pathological correlation

PURPOSE

The purpose of this study is to evaluate the MRI features of mucinous borderline ovarian tumors (MBOT).

MATERIALS AND METHODS

MRI morphology of 30 MBOT proven MBOT by surgery and pathology was retrospectively studied and correlated with the histopathological findings. On MRI, tumors were classified into three morphological categories: (i) unilocular cyst in five (17%) tumors. (ii) multilocular cyst in 23 (76%) tumors. (iii) solid mass in 2 (7%) tumors. MRI features of tumors were identified including the multilocularity (23/30, 77%), honeycomb loculi (15/30, 50%), signal discrepancy (different signal intensity on T1WI and T2WI) (19/30, 63%), thickened wall or septa (>3 mm) (16/30, 53%).

RESULTS

Intestinal type and endocervical type of MBOT, two distinctly histologic subtypes, were found in 20 (67%) and 10 (33%) tumors respectively. There were a higher prevalence of multilocularity (P = 0.026), honeycomb loculi (P = 0.025), and signal discrepancy (P = 0.024) in intestinal type than endocervical type of MBOT.

CONCLUSION

Typical MRI features of MBOT are large multilocular tumors with honeycomb loculi, heterogeneous signal intensity of the loculi, and thickened wall or septa.

MRI appearances of ovarian serous borderline tumor: pathological correlation

PURPOSE

To investigate the spectrum of MRI appearances of ovarian serous borderline tumor (SBT).

MATERIALS AND METHODS

Following ethics approval, 31 patients with 51 histologically proven ovarian SBTs underwent preoperative MRI. Images were evaluated, by two observers for the location, shape, size, internal architecture, signal intensity, and extent or stage of the tumors. The MRI findings were correlated with pathological findings.

RESULTS

Twenty of 31 patients (65%) demonstrated bilateral ovarian SBTs on MRI. Three MRI morphological patterns of ovarian SBT were identified: (i) Mainly cystic mass with multiple intracystic papillary projections from the wall and septations was observed in 24 (47%) tumors. (ii) Solid mass with hierarchical branching papillary and fibrous stalk architecture was observed in 8 (16%) tumors. The branching papillary projections were hyperintensity on T2WI, intermediate intense on DWI, and enhanced intensely after the administration of Gd-DTPA. The internal branching fibrous stalks were hypointensity on T2WI and enhanced slightly. (iii) Mixed cystic-solid mass was observed in 19 (37%) tumors. The cystic and solid components had the architecture and signal intensity similar to those of cystic and solid SBTs. Papillary projections were the common architecture of all three types of tumors.

CONCLUSION

On MRI, the ovarian SBT has some morphological distinguishing features. The solid papillary architecture with internal branching fibrous stalk is a somewhat more characteristic MRI appearance.

MRI in differentiating ovarian borderline from benign mucinous cystadenoma: pathological correlation

PURPOSE

To investigate MRI in differentiating borderline mucinous cystadenoma (MC) from benign MC of the ovary.

MATERIALS AND METHODS

We studied MR images of 26 benign MCs and 24 borderline MCs of the ovary. The following MRI features of tumors were evaluated and compared between two groups: laterality, shape, size, loculation, signal intensity of the fluid, thickness of the septation and the wall, and vegetations. The results of the MRI were then compared with the pathological findings.

RESULTS

Honeycomb loculi, high signal intensity on T1WI, and low signal intensity on T2WI of the intracystic content, thickened septation or wall (≥5 mm), and vegetations (≥5 mm) were significantly more common in borderline MC than in benign MC with the sensitivity and specificity of identifying borderline MC of 50.0% and 80.8%, 41.7% and 96.2%, 45.8% and 96.2%, and 62.5% and 96.2%, respectively. The presence of any one of the following features-honeycomb loculi with a low signal intensity on T2WI, thickened septation or wall (≥5 mm), and vegetations (≥5 mm)-yielded the sensitivity, specificity, and accuracy of identifying borderline MC of 91.7%, 92.3%, and 92.0%, respectively.

CONCLUSION

MRI has the ability to accurately demonstrate the morphological characteristics of ovarian MC and reliably differentiate borderline MC from benign MC.

Acute mesenteric ischemia induced by ligation of porcine superior mesenteric vein: multidetector CT evaluations

RATIONALE AND OBJECTIVES

To evaluate multidetector computed tomography (MDCT) for detecting the early changes and dynamic evolution of acute mesenteric ischemia (AMI) induced by the ligation of superior mesenteric vein (SMV) in an experimental porcine model.

MATERIALS AND METHODS

Twelve pigs were randomly assigned to three experimental groups, and one control group with three pigs in each group. After laparotomy, the SMV was separated and ligated in nine pigs and separated without ligation in three controls. MDCT pre- and postcontrast with arterial, venous, and delayed phase scans, and CT angiography reconstructions of mesenteric vessels were carried out at preoperation, 6 hours, 12 hours, and 18 hours after ligation. The findings of mesenteric vessels, bowel, abdominal cavity at pre- and postoperation, and dynamic evolution were correlated with pathology.

RESULTS

AMI-induced pathological changes were identified in all nine experimental pigs. MDCT angiography clearly delineated main trunk of the SMV, peripheral major and minor tributaries up to brushy vasa recta, and the location and shape of ligations. The early ischemic findings were bowel wall thickening, mesenteric edema, ascites, and pronounced bowel enhancement. Superior mesenteric artery and its major branches appeared spasm with poor filling and delayed and prolonged visualization. SMV and its tributaries were poorly delineated with delayed opacification. We also saw thinning of bowel wall, dilatating bowel with fluid, aggravating mesenteric edema and ascites, and poor enhanced bowel over time.

CONCLUSION

MDCT detects early changes of mesenteric ischemia and its evolution after ligation of porcine SMV, and may find application in early diagnosis of human venous occlusive AMI.

The relationship between solitary pulmonary nodules and bronchi: multi-slice CT-pathological correlation

AIM

To investigate the relationship between solitary pulmonary nodules (SPN) and bronchi and its value in predicting the nature of the SPN.

MATERIALS AND METHODS

We performed volumetric targeted scans of 0.5 mm collimation with multi-slice computed tomography (MSCT), reconstructing multiplanar reconstructions (MPR), curved multiplanar reconstructions (CMPR) and surface-shaded display (SSD) images of bronchi in 78 consecutive patients with SPN (53 malignant and 25 benign) and correlated the findings with those of macroscopic and microscopic specimens.

RESULTS

With this CT protocol, the third to seventh-order bronchi were shown continuously and very clearly in all patients. CT findings were consistent with those of specimens. CT demonstrated the relationship between the SPN and bronchi in 46 (86.8%) malignant and 18 (75.0%) benign nodules. Five types of tumour-bronchus relationships were identified with MSCT. Type I: the bronchus was obstructed abruptly by the SPN; type II: the bronchus penetrated into the SPN with tapered narrowing and interruption; type III: the bronchial lumen shown within the SPN was patent and intact; type IV: the bronchus ran around the periphery of the SPN with intact lumen; type V: the bronchus was displaced, compressed and narrowed by the SPN. Malignant nodules were most commonly of type I (58.5%), secondly of type IV (26.4%) and rarely of type V (1.9%). Benign nodules were most often of type V (36.0%), followed by type III (20.0%), type I (16.0%), and there were no type II. Types I, II and IV were more common in malignant nodules, whereas type V was seen more frequently seen in benign nodules (p<0.05). There was no statistically significant difference between the two groups regarding type III.

CONCLUSION

Ultra-thin section with MSCT and MPR, CMPR and SSD reconstruction can improve the demonstration of the patterns of tumour-bronchus relationships, which can reflect the pathological changes of the nodules to some extent and help differentiate malignant from benign tumours.