Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer.

BACKGROUND: It remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC). PURPOSE: To evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC. STUDY TYPE: Retrospective. POPULATION: 219 patients aged from 15 to 79 years were enrolled. FIELD STRENGTH/SEQUENCE: 3.0 or 1.5T, axial fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), and contrast enhanced T1-weighted imaging (CE-T1WI). ASSESSMENT: MRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics-clinical model (RCM). STATISTICAL TESTS: Mann-Whitney U test, chi-squared test, DeLong test, log-rank test. P < 0.05 indicated a significant difference. RESULTS: All the RSs constructed on WPTV exhibited higher AUCs (0.720-0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261). DATA CONCLUSION: The radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut-off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

The fusion rates at different times of cortical iliac crest autograft or allograft compared with cages after anterior cervical discectomy and fusion: a meta-analysis.

OBJECTIVE: The cortical iliac crest autograft (CICA)/structural allograft (SA) has still been recognized as the gold standard for the ACDF technique for its high degree of histocompatibility and osteoinduction ability though the flourishing and evolving cage development. However, there was no further indication for using CICA/SA in ACDF based on basic information of inpatients. Our operative experience implied that applying CICA/SA has an advantage on faster fusion but not the long-term fusion rate. Therefore, our study aimed to compare the fusion rates between CICA and cage, between SA and cage, and between CICA/CA and cage. METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), a comprehensive literature search of electronic databases including PubMed, Embase, Cochrane Library and Web of Science was conducted to identify these clinical trials that investigated the postoperative 3, 6, 12 and 24 months fusion rates of CICA/structural SA versus cage. Assessment of risk of bias, data extraction and statistical analysis were then carried out by two independent authors with the resolve-by-consensus method. The primary outcome was fusion rate at 3, 6, 12 and 24 months postoperatively. The secondary outcomes were also meta-analyzed such as hardware complications, operative duration and hospitalization time. Our meta-analysis was registered with PROSPERO (Identifier: CRD42022345247). RESULT: A total of 3451 segments (2398 patients) derived from 34 studies were included after the screening of 3366 articles. The segmental fusion rates of CICA were higher than cages at 3 (P = 0.184, I2 = 40.9%) and 6 (P = 0.147, I2 = 38.8%) months postoperatively, but not 12 (P = 0.988, I2 = 0.0%) and 24 (P = 0.055, I2 = 65.6%) months postoperatively. And there was no significant difference in segmental fusion rates between SA and cage at none of 3 (P = 0.047, I2 = 62.2%), 6 (P = 0.179, I2 = 41.9%) and 12 (P = 0.049, I2 = 58.0%) months after operations. As for secondary outcomes, the CICA was inferior to cages in terms of hardware complications, operative time, blood loss, hospitalization time, interbody height, disk height and Odom rating. The hardware complication of using SA was significantly higher than the cage, but not the hospitalization time, disk height, NDI and Odom rating. CONCLUSION: Applying CICA has an advantage on faster fusion than using a cage but not the long-term fusion rate in ACDF. Future high-quality RCTs regarding the hardware complications between CICA and cage in younger patients are warranted for the deduced indication.

Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images.

Background: Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter- and intra-observer variability. This study aims to assess the feasibility of deep learning methods in the automatic segmentation of EOC on T2-weighted magnetic resonance images. Methods: A total of 339 EOC patients from eight different clinical centers were enrolled and divided into 4 groups: training set (n=154), validation set (n=25), internal test set (n=50) and external test set (n=110). Six common-used algorithms, including convolutional neural networks (CNNs) (U-Net, DeepLabv3, U-Net++ and PSPNet) and transformers (TransUnet and Swin-Unet), were used to conduct automatic segmentations. The performances of these automatic segmentation methods were evaluated by means of dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), precision and recall. Results: All the results look promising, which demonstrates the feasibility of using deep learning for EOC segmentation. Overall, CNNs and transformers showed similar performances in both internal and external test sets. Among all the models, U-Net++ performed best with a DSC, HD, ASSD, precision and recall of 0.851, 25.3, 1.75, 0.838, 0.882 and 0.740, 42.5, 4.21, 0.825, 0.725 in internal and external test sets, respectively. Conclusions: Fully automated segmentation of EOC is possible with deep learning. The segmentation performance is related to the International Federation of Gynecology and Obstetrics (FIGO) stages and histological types of EOC.

Magnetic Resonance Imaging and Diffusion Weighted Imaging-Based Histogram in Predicting Mesenchymal Transition High-Grade Serous Ovarian Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) findings in predicting mesenchymal transition (MT) high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: Patients with HGSOC were enrolled from May 2017 to December 2020, who underwent pelvic MRI including DWI (b = 0,1000 s/mm2) before surgery, and were assigned to the MT HGSOC or non-MT HGSOC group according to histopathology results. Clinical characteristics and MRI features including DWI-based histogram metrics were assessed and compared between the two groups. Univariate and multivariate analyses were performed to identify the significant variables associated with MT HGSOC - these variables were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: A total of 81 consecutive patients were recruited for pelvic MRI before surgery, including 37 (45.7%) MT patients and 44 (54.3%) non-MT patients. At univariate analysis, the features significantly related to MT HGSOC were identified as absence of discrete primary ovarian mass, pouch of Douglas implants, ovarian mass size, tumor volume, mean, SD, median, and 95th percentile apparent diffusion coefficient (ADC) values (all p < 0.05). At multivariate analysis, the absence of discrete primary ovarian mass {odds ratio (OR): 46.477; p = 0.025}, mean ADC value ≤ 1.105 (OR: 1.023; p = 0.009), and median ADC value ≤ 1.038 (OR: 0.982; p = 0.034) were found to be independent risk factors associated with MT HGSOC. The combination of all independent criteria yielded the largest AUC of 0.82 with a sensitivity of 83.87% and specificity of 66.67%, superior to any of the single predictor alone (p ≤ 0.012). The predictive C-index nomogram performance of the combination was 0.82. CONCLUSION: The combination of absence of discrete primary ovarian mass, lower mean ADC value, and median ADC value may be helpful for preoperatively predicting MT HGSOC.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma.

PURPOSE: To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). METHODS: Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. RESULTS: A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. CONCLUSION: Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Pan-cancer analysis reveals interleukin-17 family members as biomarkers in the prediction for immune checkpoint inhibitor curative effect.

Background: The interleukin-17 (IL-17) family contains six homologous genes, IL-17A to IL-17F. Growing evidence indicates that dysregulated IL-17 family members act as major pathogenic factors in the early and late stages of cancer development and progression. However, the prevalence and predictive value of IL-17 for immune checkpoint inhibitor (ICI) therapeutic effectiveness in multiple tumor types remain largely unknown, and the associations between its expression levels and immunotherapy-associated signatures also need to be explored. Methods: The pan-cancer dataset in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). The immunotherapeutic cohorts included IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study. Other datasets, namely, the GEO dataset and PRECOG, GEO, and METABRIC databases, were also included. In 33 TCGA tumor types, a pan-cancer analysis was carried out including their expression map, clinical risk assessment, and immune subtype analysis, along with their association with the stemness indices, tumor microenvironment (TME) in pan-cancer, immune infiltration analysis, ICI-related immune indicators, and drug sensitivity. RT-PCR was also carried out to verify the gene expression levels among MCF-10A and MCF-7 cell lines. Results: The expression of the IL-17 family is different between tumor and normal tissue in most cancers, and consistency has been observed between gene activity and gene expression. RT-PCR results show that the expression differences in the IL-17 family of human cell (MCF-10A and MCF-7) are consistent with the bioinformatics differential expression analysis. Moreover, the expression of the IL-17 family can be a sign of patients' survival prognosis in some tumors and varies in different immune subtypes. Moreover, the expression of the IL-17 family presents a robust correlation with immune cell infiltration, ICI-related immune indicators, and drug sensitivity. High expression of the IL-17 family is significantly related to immune-relevant pathways, and the low expression of IL-17B means a better immunotherapeutic response in BLCA. Conclusion: Collectively, IL-17 family members may act as biomarkers in predicting the prognosis of the tumor and the therapeutic effects of ICIs, which provides new guidance for cancer treatment.

MRI-Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors.

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management. PURPOSE: To develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists. STUDY TYPE: Retrospective study of eight clinical centers. SUBJECTS: Between January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159). FIELD STRENGTH/SEQUENCE: Three axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2-weighted imaging with fat saturation (T2WI FS), echo planar diffusion-weighted imaging, and 2D volumetric interpolated breath-hold examination of contrast-enhanced T1-weighted imaging (CE-T1WI) with FS. ASSESSMENT: Three monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE-T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience. STATISTICAL TESTS: We used DeLong test, chi-square test, Mann-Whitney U-test, and t-test, with significance level of 0.05. RESULTS: Both EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795-0.915) and 0.884 (95% CI, 0.831-0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797). DATA CONCLUSION: The developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Whole-tumor histogram analysis of apparent diffusion coefficient for differentiating adenosquamous carcinoma and adenocarcinoma from squamous cell carcinoma in patients with cervical cancer.

BACKGROUND: Differentiating adenosquamous carcinoma (ASC) and adenocarcinoma (AC) from squamous cell carcinoma (SCC) precisely is crucial for treatment strategy and prognosis prediction in patients with cervical cancer (CC). PURPOSE: To differentiate ASC and AC from SCC in patients with CC using the apparent diffusion coefficient (ADC) histogram analysis. MATERIAL AND METHODS: A total of 118 patients with histologically diagnosed ASC, AC, and SCC were included. The ADC histogram parameters were extracted from ADC maps. Receiver operating characteristic analysis was performed to evaluate the diagnostic performance of each ADC histogram parameter in differentiating the subtypes of CC. The predictors for histologic subtypes were further selected using univariate and multivariate logistic regression analyses. RESULTS: The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of the ASC were significantly lower than those of the AC; and ADCkurtosis and ADCskewness of the ASC were lower than those of the SCC. The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of AC were significantly higher than those of the SCC. The ADCP10 and ADCP10 + diameter yielded the AUCs of 0.753 and 0.778 in differentiating ASC from AC. The ADCmedian and ADCmedian + diameter yielded the AUCs of 0.807 and 0.838 in differentiating AC from SCC. The ADCskewness yielded the AUC of 0.713 in differentiating ASC from SCC. CONCLUSION: The ADCP10 and ADCP10 + diameter, ADCmedian, and ADCmedian + diameter performed well in differentiating ASC from AC and AC from SCC, respectively. However, ADCskewness exhibited a limited ability in differentiating ASC from SCC.

Multiple instance convolutional neural network with modality-based attention and contextual multi-instance learning pooling layer for effective differentiation between borderline and malignant epithelial ovarian tumors.

Malignant epithelial ovarian tumors (MEOTs) are the most lethal gynecologic malignancies, accounting for 90% of ovarian cancer cases. By contrast, borderline epithelial ovarian tumors (BEOTs) have low malignant potential and are generally associated with a good prognosis. Accurate preoperative differentiation between BEOTs and MEOTs is crucial for determining the appropriate surgical strategies and improving the postoperative quality of life. Multimodal magnetic resonance imaging (MRI) is an essential diagnostic tool. Although state-of-the-art artificial intelligence technologies such as convolutional neural networks can be used for automated diagnoses, their application have been limited owing to their high demand for graphics processing unit memory and hardware resources when dealing with large 3D volumetric data. In this study, we used multimodal MRI with a multiple instance learning (MIL) method to differentiate between BEOT and MEOT. We proposed the use of MAC-Net, a multiple instance convolutional neural network (MICNN) with modality-based attention (MA) and contextual MIL pooling layer (C-MPL). The MA module can learn from the decision-making patterns of clinicians to automatically perceive the importance of different MRI modalities and achieve multimodal MRI feature fusion based on their importance. The C-MPL module uses strong prior knowledge of tumor distribution as an important reference and assesses contextual information between adjacent images, thus achieving a more accurate prediction. The performance of MAC-Net is superior, with an area under the receiver operating characteristic curve of 0.878, surpassing that of several known MICNN approaches. Therefore, it can be used to assist clinical differentiation between BEOTs and MEOTs.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram.

OBJECTIVES: To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). METHODS: In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. RESULTS: In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). CONCLUSIONS: The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. KEY POINTS: • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

Predicting Disease-Free Survival With Multiparametric MRI-Derived Radiomic Signature in Cervical Cancer Patients Underwent CCRT.

Prognostic biomarkers that can reliably predict the disease-free survival (DFS) of locally advanced cervical cancer (LACC) are needed for identifying those patients at high risk for progression, who may benefit from a more aggressive treatment. In the present study, we aimed to construct a multiparametric MRI-derived radiomic signature for predicting DFS of LACC patients who underwent concurrent chemoradiotherapy (CCRT). METHODS: This multicenter retrospective study recruited 263 patients with International Federation of Gynecology and Obetrics (FIGO) stage IB-IVA treated with CCRT for whom pretreatment MRI scans were performed. They were randomly divided into two groups: primary cohort (n = 178) and validation cohort (n = 85). The LASSO regression and Cox proportional hazard regression were conducted to construct the radiomic signature (RS). According to the cutoff of the RS value, patients were dichotomized into low- and high-risk groups. Pearson's correlation and Kaplan-Meier analysis were conducted to evaluate the association between the RS and DFS. The RS, the clinical model incorporating FIGO stage and lymph node metastasis by the multivariate Cox proportional hazard model, and a combined model incorporating RS and clinical model were constructed to estimate DFS individually. RESULTS: The final radiomic signature consisted of four radiomic features: T2W_wavelet-LH_ glszm_Size Zone NonUniformity, ADC_wavelet-HL-first order_ Median, ADC_wavelet-HH-glrlm_Long Run Low Gray Level Emphasis, and ADC_wavelet _LL_gldm_Large Dependence High Gray Emphasis. Higher RS was significantly associated with worse DFS in the primary and validation cohorts (both p<0.001). The RS demonstrated better prognostic performance in predicting DFS than the clinical model in both cohorts (C-index, 0.736-0.758 for RS, and 0.603-0.649 for clinical model). However, the combined model showed no significant improvement (C-index, 0.648, 95% CI, 0.571-0.685). CONCLUSIONS: The present study indicated that the multiparametric MRI-derived radiomic signature could be used as a non-invasive prognostic tool for predicting DFS in LACC patients.

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.

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

OBJECTIVES: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

Pathologic characteristics of spinal tuberculosis: analysis of 181 cases.

OBJECTIVE: This study aimed to provide a basis for the diagnosis of spinal TB by analyzing its pathologic characteristics. METHODS: The data of 181 patients with spinal TB who underwent surgery from January 2013 to January 2019 at the General Hospital of Ningxia Medical University were retrospectively analyzed. The participants comprised 80 men and 101 women with an average age of 45.1 ± 16.5 (range: 14-78) years. Based on the assessment of tissue samples, five patients had cervical TB, 49 had thoracic TB, 86 had lumbar TB, 22 had thoracolumbar TB, and 19 had lumbosacral TB. Tuberculous granulation tissue, sclerotic bone, sequestrum, and intervertebral disc tissue were collected for hematoxylin and eosin staining. The proportion of patients with atypical and typical pathologic characteristics was identified and compared for statistical analysis. RESULTS: The typical pathologic characteristics included tubercles, granulomas, caseous necrosis, multinuclear giant cells, infiltration of acute inflammatory cells, sequestration, and fibroblastic proliferation. A total of 119 patients had caseous necrosis, 95 had multinuclear giant cells, 68 had granulomatous inflammation, and 21 had tubercles. Moreover, 46 (25.4%) patients had at least three pathologic characteristics and only 12 (6.6%) exhibited all the pathologic characteristics. Of the 35 (19.3%) patients with atypical pathologic characteristics, 17 had lymphocyte infiltration, 10 had fibroblastic proliferation, 2 had hyaline changes, 1 had local hemorrhage, 1 chronic inflammatory change, 2 had sequestration, 1 had dilated and congested vessels, and 1 had acute suppurative inflammation. CONCLUSIONS: The most common pathologic characteristics were caseous necrosis, multinuclear giant cells, granulomatous inflammation, and tubercles. Moreover, multiple pathologic characteristics were observed in patients with spinal TB and one type of these characteristics was dominant. However, atypical pathologic characteristics were also noted. Thus, both pathologic examination and clinical analysis must be performed to improve the diagnostic rate of spinal TB.

Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer.

BACKGROUND: Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer. PURPOSE: To establish a multiparametric MRI (mpMRI)-based radiomics nomogram for preoperatively predicting LNM status. STUDY TYPE: Retrospective. POPULATION: Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort. FIELD STRENGTH: Radiomic features were extracted from a 1.5T mpMRI scan (T1 -weighted imaging [T1 WI], fat-saturated T2 -weighted imaging [FS-T2 WI], contrast-enhanced [CE], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps). ASSESSMENT: The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated. STATISTICAL TESTS: The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively. RESULTS: The radiomic signature allowed a good discrimination between the LNM and non-LNM groups, with a C-index of 0.856 (95% confidence interval [CI], 0.794-0.918) in the primary cohort and 0.883 (95% CI, 0.809-0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C-index, 0.882 [95% CI, 0.827-0.937], C-index, 0.893 [95% CI, 0.822-0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. DATA CONCLUSION: A radiomics nomogram was developed by incorporating the radiomics signature with the MRI-reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early-stage cervical cancer. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.

Histogram Analysis Comparison of Monoexponential, Advanced Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors.

BACKGROUND: The accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear. PURPOSE: To compare the whole solid-tumor volume histogram analysis of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE-MRI. STUDY TYPE: Retrospective. POPULATION: Twenty patients with BEOTs and 42 patients with MEOTs. FIELD STRENGTH/SEQUENCE: 1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE-MRI. ASSESSMENT: Histogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (Ktrans ), rate constant (kep ), and extravascular extracellular volume fraction (ve ). STATISTICAL TESTS: The Mann-Whitney U-test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE-MRI parameters. RESULTS: ADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, Ktrans , kep , and ve were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with Ktrans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and Ktrans (r = -0.320, P = 0.025) and between D and kep (r = -0.267, P = 0.037). DATA CONCLUSION: The 10th percentile of Dk was the most valuable metric and Dk combined with Ktrans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion-related parameters derived from IVIM and DCE-MRI. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:257-268.

A rat model of serous borderline ovarian tumors induced by 7,12-dimethylbenz[a]anthracene.

Serous borderline ovarian tumors (SBOTs) behave between benign cystadenomas and carcinomas, and the effective detection and clinical management of SBOTs remain clinical challenges. Because it is difficult to isolate and enrich borderline tumor cells, a borderline animal model is in need. 7,12-dimethylbenz[a]anthracene (DMBA) is capable of inducing the initiation, promotion, and progression of serous ovarian tumors. This study aims to investigate the proper dosage and induction time of DMBA for rat models of SBOTs, and explore their morphological features demonstrated by magnetic resonance (MR) imaging and molecular genetic characteristics. Rats were randomly divided into six groups (1 mg/70 D, 2 mg/70 D, 3 mg/70 D, 2 mg/50 D, 2 mg/90 D, and 2 mg/110 D). The 3 mg/70 D group induced the most SBOTs (50.0%, 12/24). The micropapillary projections were shown on MR imaging, which was the characteristic of SBOTs. The Cyclin D1 characterizing an early pathogenetic event strongly expressed in induced serous benign tumors (SBTs). The immunoreactivity staining scores of P53 expression significantly increased from SBTs, SBOTs to serous ovarian carcinomas (SCAs), which elucidate that P53 might be a promising biomarker to grade serous ovarian tumors. Based on morphological and molecular genetic similarities, this rodent SBOT model was suitable for investigating the pathogenesis of serous ovarian tumors and developing an early detection strategy.

Quantitative dynamic contrast-enhanced MR imaging for differentiating benign, borderline, and malignant ovarian tumors.

PURPOSE: This study aimed to investigate the diagnostic performance of quantitative DCE-MRI for characterizing ovarian tumors. METHODS: We prospectively assessed the differences of quantitative DCE-MRI parameters (Ktrans, kep, and ve) among 15 benign, 28 borderline, and 66 malignant ovarian tumors; and between type I (n = 28) and type II (n = 29) of epithelial ovarian carcinomas (EOCs). DCE-MRI data were analyzed using whole solid tumor volume region of interest (ROI) method, and quantitative parameters were calculated based on a modified Tofts model. The non-parametric Kruskal-Wallis test, Mann-Whitney U test, Pearson's chi-square test, intraclass correlation coefficient (ICC), variance test, and receiver operating characteristic curves (ROC) were used for statistical analysis. RESULTS: The largest Ktrans and kep values were observed in ovarian malignant tumors, followed by borderline and benign tumors (all P < 0.001). Kep was the better parameter for differentiating benign tumors from borderline and malignant tumors, with a sensitivity of 89.3% and 95.5%, a specificity of 86.7% and 100%, an accuracy of 88.4% and 96.3%, and an area under the curve (AUC) of 0.94 and 0.992, respectively, whereas Ktrans was better for differentiating borderline from malignant tumors with a sensitivity of 60.7%, a specificity of 78.8%, an accuracy of 73.4%, and an AUC of 0.743. In addition, a combination with kep could further improve the sensitivity to 78.9%. The median Ktrans and kep values were significantly higher in type II than in type I EOCs. CONCLUSION: DCE-MRI with volume quantification is a technically feasible method, and can be used for the differentiation of ovarian tumors and for discriminating between type I and type II EOCs.