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.

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Mucin-producing tumors of the ovary--preoperative differentiation between metastatic ovarian mucinous carcinoma and primary mucinous malignant tumors.

OBJECTIVE: To investigate the clinical and magnetic resonance imaging (MRI) features for preoperatively discriminating  primary ovarian mucinous malignant tumors (POMTs) and metastatic mucinous carcinomas involving the ovary (MOMCs). METHODS: This retrospective multicenter study enrolled 61 patients with 22 POMTs and 49 MOMCs, which were pathologically proved between November 2014 to Jane 2023. The clinical and MRI features were evaluated and compared between POMTs and MOMCs. Univariate and multivariate analyses were performed to identify the significant variables between the two groups, which were then incorporated into a predictive nomogram, and ROC curve analysis was subsequently carried out to evaluate diagnostic performance. RESULTS: 35.9% patients with MOMCs were discovered synchronously with the primary carcinomas; 25.6% patients with MOMCs were bilateral, and all of the patients with POMTs were unilateral. The biomarker CEA was significantly different between the two groups (p = 0.002). There were significant differences in the following MRI features: tumor size, configuration, enhanced pattern, the number of cysts, honeycomb sign, stained-glass appearance, ascites, size diversity ratio, signal diversity ratio. The locular size diversity ratio (p = 0.005, OR = 1.31), and signal intensity diversity ratio (p = 0.10, OR = 4.01) were independent predictors for MOMCs. The combination of above independent criteria yielded the largest area under curve of 0.922 with a sensitivity of 82.3% and specificity of 88.9%. CONCLUSIONS: Patients with MOMCs were more commonly bilaterally and having higher levels of CEA, but did not always had a malignant tumor history. For ovarian mucin-producing tumors, the uniform locular sizes and signal intensities were more predict MOMCs.

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma.

OBJECTIVE: To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. METHODS: Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. RESULTS: In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). CONCLUSIONS: The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Whole-tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma.

OBJECTIVE: To investigate the application of whole-tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA). METHODS: Preoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion-weighted imaging scans. Risk stratification was based on ESMO-ESTRO-ESP guidelines: low-, intermediate-, high-intermediate-, and high-risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation. RESULTS: A decreasing tendency in multiple ADC values was observed from the low- to high-intermediate-risk EEAs. The (low + intermediate)-risk EEAs and low-risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high-intermediate + high)-risk EEAs and non-low-risk EEAs (all P < 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)- and the low-risk EEAs from the other EEAs, respectively. CONCLUSION: Whole-tumor ADC histogram metrics might be helpful for preoperatively identifying low- and (low + intermediate)-risk EEAs, facilitating personalized therapeutic planning.

The prevalence rate, mortality, and 5-year overall survival of Schistosoma japonicum patients with human malignancy.

Background: Only a few studies have focused on the association between Schistosoma japonicum and human malignancy. The aim of this study was to update the prevalence rate, mortality, and 5-year overall survival of S. japonicum patients with human malignancy. Methods: From January 20, 2018, to January 31, 2021, 5,866 inpatients were included in the study. A total of 656 S. japonicum patients with malignancy were identified. Cases were stratified by gender and age groups. The cancer sites, prevalence rate, mortality, and 5-year overall survival of the patients were reported. The S. japonicum patients with malignancy were further divided into a non-digestive system tumor group (n = 309) and a digestive system tumor group (n = 347), including those with cancer in the esophagus, stomach, colon, rectum, liver, gallbladder, bile duct, or pancreas. Chi-squared test and odds ratio with confidence intervals were performed between these two groups. Results: Lung cancer was found the most common malignancy, accounting for 18.6% of all malignancies, followed by colorectal, stomach, liver, and gallbladder cancers. These five leading malignancies accounted for approximately 61.8% of all cases. Colorectal cancer was the leading cause of malignancy death, followed by lung, stomach, gallbladder, and liver cancers. These five leading causes of death accounted for approximately 55.6% of all death cases. Statistical significance was found in the prevalence rate between S. japonicum and non-S. japonicum patients with/without digestive system tumor (p < 0.001). The odds ratio of S. japonicum patients with digestive system tumors was 1.6 (95%CI: 1.4-1.9). Conclusion: S. japonicum contributes to a significant prevalence and mortality in digestive system tumors, including colorectal, stomach, liver, and gallbladder cancers.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer.

OBJECTIVES: Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. METHODS: The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. RESULTS: Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. CONCLUSIONS: The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.

The interaction of ammonia and manganese in abnormal metabolism of minimal hepatic encephalopathy: A comparison metabolomics study.

This study was to investigate the effects of ammonia and manganese in the metabolism of minimal hepatic encephalopathy (MHE). A total of 32 Sprague-Dawley rats were divided into four subgroups: chronic hyperammonemia (CHA), chronic hypermanganese (CHM), MHE and control group (CON). 1H-NMR-based metabolomics was used to detect the metabolic changes. Sparse projection to latent structures discriminant analysis was used for identifying and comparing the key metabolites. Significant elevated blood ammonia were shown in the CHA, CHM, and MHE rats. Significant elevated brain manganese (Mn) were shown in the CHM, and MHE rats, but not in the CHA rats. The concentrations of γ-amino butyric acid (GABA), lactate, alanine, glutamate, glutamine, threonine, and phosphocholine were significantly increased, and that of myo-inositol, taurine, leucine, isoleucine, arginine, and citrulline were significantly decreased in the MHE rats. Of all these 13 key metabolites, 10 of them were affected by ammonia (including lactate, alanine, glutamate, glutamine, myo-inositol, taurine, leucine, isoleucine, arginine, and citrulline) and 5 of them were affected by manganese (including GABA, lactate, myo-inositol, taurine, and leucine). Enrichment analysis indicated that abnormal metabolism of glutamine and TCA circle in MHE might be affected by the ammonia, and abnormal metabolism of GABA might be affected by the Mn, and abnormal metabolism of glycolysis and branched chain amino acids metabolism might be affected by both ammonia and Mn. Both ammonia and Mn play roles in the abnormal metabolism of MHE. Chronic hypermanganese could lead to elevated blood ammonia. However, chronic hyperammonemia could not lead to brain Mn deposition.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

OBJECTIVES: To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS: A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS: The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS: The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT: An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS: • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.

OBJECTIVE: This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS: In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS: Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS: The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS: • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

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

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

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

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

Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer.

Background: Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC. Methods: A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA). Results: Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model. Conclusions: The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.

Development of a prediction model for gross residual in high-grade serous ovarian cancer by combining preoperative assessments of abdominal and pelvic metastases and multiparametric MRI.

RATIONALE AND OBJECTIVES: To preoperatively predict residual tumor (RT) in patients with high-grade serous ovarian carcinoma (HGSOC) via a radiomic-clinical nomogram. METHODS: A total of 128 patients with advanced HGSOC were enrolled (training cohort: n=106; validation cohort: n=22). Serum cancer antigen-125 (CA125), serum human epididymis protein 4 (HE-4) level, and neutrophil-to-lymphocyte ratio (NLR) were obtained from the medical records. Metastases in abdomen and pelvis (MAP) of HGSOC patients was evaluated and scored based on preoperative abdominal and pelvic enhanced CT, MRI and/or PET-CT. A volume of interest (VOI) of each tumor was manually contoured along the boundary slice-by-slice. Radiomic features were extracted from the T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images. Univariate and multivariate analyses were used to determine the independent predictors of RT status. Least absolute shrinkage and selection operator (LASSO) logistic regression was performed to select optimal features and construct radiomic models. A radiomic-clinical nomogram incorporating radiomic signature and clinical parameters was developed and evaluated in training and validation cohorts. RESULTS: MAP score (p = 0.002), HE-4 level (p = 0.001) and NLR (p = 0.008) were independent predictors of RT status. The final radiomic-clinical nomogram showed satisfactory prediction performance in training (AUC = 0.936), cross validation (AUC = 0.906) and separate validation cohorts (AUC = 0.900), and fitted well in calibration curves (p > 0.05). Decision curve further confirmed the clinical application value of the nomogram. CONCLUSION: The proposed MRI-based radiomic-clinical nomogram achieved excellent preoperative prediction of the RT status in HGSOC.

Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram.

PURPOSE: To develop and validate an MRI-based radiomics nomogram for the preoperative prediction of miliary changes in the small bowel mesentery (MCSBM) in advanced high-grade serous ovarian cancer (HGSOC). MATERIALS AND METHODS: One hundred and twenty-eight patients with pathologically proved  advanced HGSOC (training cohort: n = 91; validation cohort: n = 37) were retrospectively included. All patients were initially evaluated as MCSBM-negative by preoperative imaging modalities but were finally confirmed by surgery and histopathology (MCSBM-positive: n = 53; MCSBM-negative: n = 75). Five radiomics signatures were built based on the features from multisequence magnetic resonance images. Independent clinicoradiological factors and radiomics-fusion signature were further integrated to construct a radiomics nomogram. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves and clinical utility. RESULTS: Radiomics signatures, ascites, and tumor size were independent predictors of MCSBM. A nomogram integrating radiomics features and clinicoradiological factors demonstrated satisfactory predictive performance with areas under the curves (AUCs) of 0.871 (95% CI 0.801-0.941) and 0.858 (95% CI 0.739-0.976) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) revealed that the nomogram had a significantly improved ability compared with the clinical model in the training cohort (NRI = 0.343, p = 0.002; IDI = 0.299, p < 0.001) and validation cohort (NRI = 0.409, p = 0.015; IDI = 0.283, p = 0.001). CONCLUSION: Our proposed nomogram has the potential to serve as a noninvasive tool for the prediction of MCSBM, which is helpful for the individualized assessment of advanced HGSOC patients.

Network Analysis and Nomogram in the Novel Classification and Prognosis Prediction of Advanced Schistosomiasis Japonica.

Clinical classification of advanced schistosomiasis japonica is important for treatment options and prognosis prediction. Network analysis was used to solve the problem of complexity and co-occurrence complications in classification of advanced schistosomiasis. A total of 4,125 retrospective patients were enrolled and divided randomly into a training cohort (n = 2,888) and a validation cohort (n = 1,237). Network analysis was used to cluster the isolated complications of advanced schistosomiasis. The accuracy of the network was evaluated. Nomograms based on the clustered complications were built to predict 1- to 5-year survival rates in advanced schistosomiasis. The predictive performance of the nomogram was also evaluated and validated. Fifteen isolated complications were identified: metabolic syndromes, minimal hepatic encephalopathy, hepatic encephalopathy, chronic obstructive pulmonary disease, pulmonary hypertension, respiratory failure, right heart failure, gastroesophageal variceal bleeding, gastrointestinal ulcer bleeding, splenomegaly, fibrosis, chronic kidney disease, ascites, colorectal polyp, and colorectal cancer. Through network analysis, three major clustered complications were achieved-namely, schistosomal abnormal metabolic syndromes (related to chronic metabolic abnormalities), schistosomal abnormal hemodynamics syndromes (related to severe portal hypertension and portosystemic shunting), and schistosomal inflammatory granulomatous syndromes (related to granulomatous inflammation). The nomograms showed a good performance in prognosis prediction of advanced schistosomiasis. The novel classification-based nomogram was useful in predicting the survival rate in advanced schistosomiasis japonica.

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.

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.

Diffusion-Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High-Grade Serous Ovarian Carcinoma.

BACKGROUND: Preoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded. PURPOSE: To investigate the value of diffusion-weighted MRI (DW-MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients. STUDY TYPE: Prospective. SUBJECTS: A total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years). FIELDS STRENGTH/SEQUENCE: A 3.0 T, readout-segmented echo-planar DWI. ASSESSMENT: The MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI-PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI-PCI alone; and a fusion model combining the clinical-morphological information and DWI-PCI. STATISTICAL TESTS: Multivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant. RESULTS: Sixty-seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI-PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA-125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI-PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI. DATA CONCLUSIONS: The combination of two clinical factors, MRI morphological characteristics and DWI-PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.