MRI combined with clinical features to differentiate ovarian thecoma-fibroma with cystic degeneration from ovary adenofibroma.

OBJECTIVE: To explore the value of magnetic resonance imaging (MRI) and clinical features in identifying ovarian thecoma-fibroma (OTF) with cystic degeneration and ovary adenofibroma (OAF). METHODS: A total of 40 patients with OTF (OTF group) and 28 patients with OAF (OAF group) were included in this retrospective study. Univariable and multivariable analyses were performed on clinical features and MRI between the two groups, and the receiver operating characteristic (ROC) curve was plotted to estimate the optimal threshold and predictive performance. RESULTS: The OTF group had smaller cyst degeneration degree (P < .001), fewer black sponge sign (20% vs. 53.6%, P = .004), lower minimum apparent diffusion coefficient value (ADCmin) (0.986 (0.152) vs. 1.255 (0.370), P < .001), higher age (57.4 ± 14.2 vs. 44.1 ± 15.9, P = .001) and more postmenopausal women (72.5% vs. 28.6%, P < .001) than OAF. The area under the curve of MRI, clinical features and MRI combined with clinical features was 0.870, 0.841, and 0.954, respectively, and MRI combined with clinical features was significantly higher than the other two (P < .05). CONCLUSION: The cyst degeneration degree, black sponge sign, ADCmin, age and menopause were independent factors in identifying OTF with cystic degeneration and OAF. The combination of MRI and clinical features has a good effect on the identification of the two. ADVANCES IN KNOWLEDGE: This is the first time to distinguish OTF with cystic degeneration from OAF by combining MRI and clinical features. It shows the diagnostic performance of MRI, clinical features, and combination of the two. This will facilitate the discriminability and awareness of these two diseases among radiologists and gynaecologists.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

OBJECTIVE: To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). METHODS: Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. RESULTS: Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CONCLUSION: CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Development of a nomogram based on body composition analysis of quantitative computed tomography combined with clinical prognostic factors to predict disease-free survival after surgery and adjuvant chemotherapy in patients with gastric cancer.

Background: Patients with gastric cancer (GC) have a high recurrence rate after surgery. To predict disease-free survival (DFS), we investigated the value of body composition changes (BCCs) measured by quantitative computed tomography (QCT) in assessing the prognosis of patients with GC undergoing resection combined with adjuvant chemotherapy and to construct a nomogram model in combination with clinical prognostic factors (CPFs). Methods: A retrospective study of 60 patients with GC between February 2015 and June 2019 was conducted. Pre- and posttreatment CT images of patients was used to measure bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA), and the rate of BCC was calculated. CPFs such as maximum tumor diameter (MTD), human epidermal growth factor receptor-2 (HER2), and Ki-67 were derived from postoperative pathological findings. Independent prognostic factors affecting DFS in GC were screened via univariate and multivariate Cox regression analysis. The Kaplan-Meier method and log-rank test were used to plot survival curves and compare the curves between groups, respectively. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves to evaluate the efficacy of the nomogram. Results: The results of multivariate Cox regression analysis showed that ΔBMD [hazard ratio (HR): 4.577; 95% confidence interval (CI): 1.483-14.132; P=0.008], ΔPMA (HR: 5.784; 95% CI: 1.251-26.740; P=0.025), HER2 (HR: 4.819; 95% CI: 2.201-10.549; P<0.001), and maximal tumor diameter (HR: 3.973; 95% CI: 1.893-8.337; P<0.001) were independent factors influencing DFS. ΔBMD, ΔSFA, ΔVFA, ΔTFA, and ΔPMA were -3.86%, -23.44%, -19.57%, -22.45%, and -5.94%, respectively. The prognostic model of BCCs combined with CPFs had the highest predictive performance. Decision curve analysis (DCA) indicated good clinical benefit for the prognostic nomogram. The concordance index of the prognostic nomogram was 0.814, and the area under the curve (AUC) of predicting 2- and 3-year DFS were 0.879 and 0.928, respectively. The calibration curve showed that the nomogram-predicted DFS aligned well with the actual DFS. Conclusions: The prognostic nomogram combining BCCs and CPFs was able to reliably predict the DFS of patients with GC.

Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer.

PURPOSES: To investigate the value of nomograms based on clinical prognostic factors (CPF), intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and MRI-derived radiomics in predicting recurrence and disease-free survival (DFS) after concurrent chemoradiotherapy (CCRT) for locally advanced cervical cancer (LACC). METHODS: Retrospective analysis of data from 115 patients with ⅠB-ⅣA cervical cancer who underwent CCRT and had been followed up consistently. All patients were randomized 2:1 into training and validation groups. Pre-treatment IVIM-DWI parameters (ADC-value, D-value, D*-value and f-value) and pre- and post-treatment three-dimensional radiomics parameters (from axial T2WI) of primary lesions were measured. The LASSO algorithm and Logistic regression analysis were used to filter texture features and calculate radiomics score (Rad-score). Multivariate Logistic and Cox regression analysis was used to construct nomograms to predict recurrence and DFS for patients with LACC after CCRT respectively, with internal and external validation. RESULTS: External beam radiotherapy dose, f-value, pre-treatment and post-treatment Rad-score were independent prognostic factors for recurrence and DFS in patients with cervical cancer, forming Model1 and Model2, with OR values of 0.480, 1.318, 3.071, 3.200 and HR values of 0.322, 3.372, 5.138, 7.204. The area under the curve (AUC) of Model1 for predicting recurrence of cervical cancer was 0.977, with internal and external validation C-indexes of 0.977 and 0.962. The AUC for Model2 predicting disease-free survival (DFS) at 1, 3, and 5 years was 0.895, 0.888 and 0.916 respectively, with internal and external C-indexes of 0.860 and 0.892. The decision curves analysis and clinical impact curves further indicate the high predictive efficiency and stability of nomograms. CONCLUSION: The nomograms based on clinical, IVIM-DWI and radiomics parameters have high clinical value in predicting recurrence and DFS of patients with LACC after CCRT and can provide a reference for prognostic assessment and individualized treatment of cervical cancer patients.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters.

Objective: To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods: A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results: The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion: A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma.

Objective: To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs). Methods: In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC. Results: The AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002). Conclusion: The nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.

A Nomogram Combining MRI Multisequence Radiomics and Clinical Factors for Predicting Recurrence of High-Grade Serous Ovarian Carcinoma.

Objective: To develop a combined nomogram based on preoperative multimodal magnetic resonance imaging (mMRI) and clinical information for predicting recurrence in patients with high-grade serous ovarian carcinoma (HGSOC). Methods: This retrospective study enrolled 141 patients with clinicopathologically confirmed HGSOC, including 65 patients with recurrence and 76 without recurrence. Radiomics features were extracted from the mMRI images (FS-T2WI, DWI, and T1WI+C). L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select radiomics features. A multivariate logistic regression analysis was used to build the classification models. A nomogram was established by incorporating clinical risk factors and radiomics Radscores. The area under the curve (AUC) of receiver operating characteristics, accuracy, and calibration curves were assessed to evaluate the performance of classification models and nomograms in discriminating recurrence. Kaplan-Meier survival analysis was used to evaluate the associations between the Radscore or clinical factors and disease-free survival (DFS). Results: One clinical factor and seven radiomics signatures were ultimately selected to establish the predictive model for this study. The AUCs for identifying recurrence in the training and validation cohorts were 0.76 (0.68, 0.84) and 0.67 (0.53, 0.81) with the clinical model, 0.78 (0.71, 0.86) and 0.74 (0.61, 0.86) with the multiradiomics model, and 0.83 (0.77, 0.90) and 0.78 (0.65, 0.90) with the combined nomogram, respectively. The DFS was significantly shorter in the high-risk group than in the low-risk group. Conclusion: By incorporating radiomics Radscores and clinical factors, we created a radiomics nomogram to preoperatively identify patients with HGSOC who have a high risk of recurrence, which may serve as a potential tool to guide personalized treatment.

IVIM-DWI and MRI-based radiomics in cervical cancer: Prediction of concurrent chemoradiotherapy sensitivity in combination with clinical prognostic factors.

PURPOSE: To identify the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and magnetic resonance imaging (MRI)-based radiomics combined with clinical prognostic factors (CPF) in predicting concurrent chemoradiotherapy (CCRT) sensitivity of locally advanced cervical cancer (LACC). METHODS: A retrospective analysis of 163 patients (assigned to training or test groups) who underwent conventional MRI and IVIM-DWI before CCRT were divided into sensitive and resistant groups according to their efficacy at 6 months after CCRT. Per-treatment IVIM-DWI parameters (ADC, D, D⁎ and f value), 3D texture features (from axial T2WI) and CPF were measured, analyzed and screened. The prediction model and its nomogram were developed by combining screened parameters and then validated internally and externally. RESULTS: Clinical stage, f value, D value, InverseVariance, SizeZoneNonUniformity, and Minimum were selected to construct prediction model. All parameters except D value showed independent diagnostic value in multivariate Logistic regression analysis and composed prediction model, with AUCs of 0.987 and 0.984 for training and test groups, respectively. The calibration curve (Brier score of 0.042, C-index of 0.987), decision curve and clinical impact curve further demonstrated the reliability and clinical value of prediction model. CONCLUSION: IVIM-DWI, MRI-based radiomics and CPF showed high clinical value in predicting CCRT sensitivity for LACC with better predictive performance when combined.

A Preliminary Exploration Using Imaging Methods to Predict the Possibility of the Recurrence of Serous Ovarian Cancer in Patients Undergoing Total Resection.

Background: The purpose of our research was to explore the value of preoperative CT and MRI examinations and clinical indicators in the prediction of recurrence of ovarian serous carcinoma in patients who underwent satisfactory staging surgery. Procedure: Detailed inclusion and exclusion criteria were installed to screen all patients collected and the eligible patients were divided into two groups. The CT and MRI features and some clinical characteristics of two groups were analyzed, in addition, the apparent diffusion coefficient (ADC) value in tumor solid region was measured. Univariate analysis was used in this study. Results: There were 78 patients with histologically proven ovarian serous carcinoma. According to the strict inclusion and exclusion criteria, we retained 29 patients (recurrence group: 11 patients, no recurrence group: 18 patients). For the peritoneal implantation metastasis in CT or MRI images and Ki67 proliferation index (Ki67 PI), the differences between two cohorts were statistically significant (P < 0.05). The rate of peritoneal metastasis in the recurrence cohort (10/11, 91%) was higher than that in the no recurrence cohort (7/18, 39%). Patients with high Ki67 PI expression had lower recurrence risk than those with low Ki67 PI expression, HR=0.172 (95%CI: 0.050-0.589, P=0.005), and patients without peritoneal planting had lower recurrence risk than those with it, HR=9.373 (95%CI: 1.194-73.551, P=0.033). For FIGO III patients, ipsilateral fallopian tube involvement was statistically significant between the two groups (P < 0.05). The differences in the other preoperative imaging characteristics of ovarian serous cancer, including the volume; capsule of the mass; main components; ADC value; cystic change; bleeding; degree of enhancement of the mainly solid region in 3 periods; and range of tumor involvement in the ovary, uterus, bladder, bowel, and pelvic wall, were not statistically significant. In addition, the differences in the other clinical indicators (i.e., age, FIGO stage) between the two cohorts were not statistically significant. Conclusions: In CT and MRI examinations before surgery, peritoneal implantation metastasis was suggestive of the possibility of the recurrence of serous ovarian carcinoma in the near future. In addition to that, ipsilateral fallopian tube involvement and Ki67 PI may also indicate the possibility of recurrence (the former was only applicable to FIGO III patients).

A Combination Analysis of IVIM-DWI Biomarkers and T2WI-Based Texture Features for Tumor Differentiation Grade of Cervical Squamous Cell Carcinoma.

Purpose: To explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and texture analysis on T2-weighted imaging (T2WI) for evaluating pathological differentiation of cervical squamous cell carcinoma. Method: This retrospective study included a total of 138 patients with pathologically confirmed poor/moderate/well-differentiated (71/49/18) who underwent conventional MRI and IVIM-DWI scans. The values of ADC, D, D ∗ , and f and 58 T2WI-based texture features (18 histogram features, 24 gray-level co-occurrence matrix features, and 16 gray-level run length matrix features) were obtained. Multiple comparison, correlation, and regression analyses were used. Results: For IVIM-DWI, the ADC, D, D ∗ , and f were significantly different among the three groups (p < 0.05). ADC, D, and D ∗ were positively correlated with pathological differentiation (r = 0.262, 0.401, 0.401; p < 0.05), while the correlation was negative for f (r = -0.221; p < 0.05). The comparison of 52 parameters of texture analysis on T2WI reached statistically significant levels (p < 0.05). Multivariate logistic regression analysis incorporated significant IVIM-DWI, and texture features on T2WI showed good diagnostic performance both in the four differentiation groups (poorly vs. moderately, area under the curve(AUC) = 0.797; moderately vs. well, AUC = 0.954; poorly vs. moderately and well, AUC = 0.795; and well vs. moderately and poorly, AUC = 0.952). The AUCs of each parameters alone were smaller than that of each regression model (0.503∼0.684, 0.547∼0.805, 0.511∼0.712, and 0.636∼0.792, respectively; pairwise comparison of ROC curves between regression model and individual variables, p < 0.05). Conclusions: IVIM-DWI biomarkers and T2WI-based texture features had potential to evaluate the pathological differentiation of cervical squamous cell carcinoma. The combination of IVIM-DWI with texture analysis improved the predictive performance.

The predictive value of conventional MRI combined with radiomics in the immediate ablation rate of HIFU treatment for uterine fibroids.

OBJECTIVE: This study aimed to assess the predictive value of conventional magnetic resonance imaging (MRI) combined with radiomics in determining the nonperfused volume ratio (NPVR) following high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. METHODS AND MATERIALS: A total of 216 symptomatic uterine fibroids in 216 women were subjected to HIFU ablation from October 2015 to March 2020. Baseline clinical and MR parameters acquired before and after HIFU ablation were analyzed, and the NPVR was calculated accordingly. Radiomics features were extracted using A.K. software on T2-weighted imaging (T2WI). The minimum redundancy and maximum relevancy (mRMR) method were used to refine the selected radiomics features. Then, multiple linear regression models, the Wilcoxon signed-rank test, and Spearman's rank correlation and Bland-Altman analyses were conducted. RESULTS: Conventional MRI combined with radiomics revealed the signal intensity on T2WI (X9), enhancement degree on T1-weighted imaging (T1WI) (X11), uterine fibroid location (X4), wavelet_glszm_SizeZoneNonUniformity first order (X12) and wavelet_HHH_firstorder_Skewness (X13) negatively affected the NPVR. The resulting regression equation was NPVR = 104.030 - 11.886 × X9 - 5.459 × X11 - 2.776 × X4 - 0.20 × X12 - 16.913 × X13. The adjusted R2 values of the conventional MRI model and combined model were 0.385 and 0.408, respectively, and the two fitted models were statistically significant (p < 0.05). No significant differences were observed between the predicted NPVR value [81 (71, 91) %] of the combined model and the actual NPVR value [89 (77, 97) %] (p > 0.05). In addition, the predicted NPVR was correlated with the actual NPVR (r = 0.655, p < 0.001). CONCLUSIONS: The efficiency of the combined model was better than that of the conventional MRI model in predicting the NPVR following HIFU ablation for uterine fibroids. Radiomics is an important supplemental modality to conventional MRI.

The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma.

Purpose: This study aims to evaluate the value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) for evaluating extramural vascular invasion (EMVI) of rectal adenocarcinoma. Methods: Ninety-six patients with pathologically confirmed rectal adenocarcinoma after surgical resections were collected. Patients were divided into the EMVI positive group (n=39) and the EMVI negative group (n=57). We measured the IVIM-DWI parameters and TA parameters of rectal adenocarcinoma. We compare the differences of the above parameters between the two groups and establish a prediction model through multivariate logistic regression analysis. the ROC curve was performed for parameters with each individual and in combination. Results: ADC, D, D* value between the two groups were statistically significant (P= 0.015,0.031,0). Six groups of texture parameters were statistically significant between the two groups (P=0.007,0.037,0.011,0.005,0.007,0.002). Logistic regression prediction model shows that GLCM entropy_ALL DIRECTION_offset7_SD and D* are important independent predictors, and the AUC of the regression prediction model was 0.821, the sensitivity was 92.98%, the specificity was 61.54%, and the Yoden index was 0.5452. The AUC was significantly higher than that of other single parameters. Conclusion: 3.0T MRI IVIM-DWI parameters combined with texture analysis can provide valuable information for EMVI evaluation of rectal adenocarcinoma before the operation.

A Nomogram of Combining IVIM-DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively.

BACKGROUND: Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists. PURPOSE: To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively. STUDY TYPE: Retrospective. POPULATION: A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma. FIELD STRENGTH/SEQUENCE: A 3.0 Tesla (T); T2 -weighted imaging (T2 WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence. ASSESSMENT: Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LN-) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T2 WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed. STATISTICAL TESTS: General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA). RESULTS: The LN+ group had a significantly lower D* value ([13.20 ± 13.66 vs. 23.25 ± 18.71] × 10-3  mm2 /sec) and a higher f value (0.43 ± 0.12 vs. 0.34 ± 0.10) than the LN- group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort. DATE CONCLUSION: The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Nomogram of Combining CT-Based Body Composition Analyses and Prognostic Inflammation Score: Prediction of Survival in Advanced Epithelial Ovarian Cancer Patients.

PURPOSE: To investigate the value of body composition changes measured by quantitative computer tomography (QCT) in evaluating the prognosis of advanced epithelial ovarian cancer (AEOC) patients who underwent primary debulking surgery (PDS) and adjuvant platinum-based chemotherapy, and constructed a nomogram model for predicting survival in combination with prognostic inflammation score (PIS). METHOD: Fifty-seven patients with AEOC between 2012 and 2016 were retrospectively enrolled. Pre- and post-treatment CT images were used to analyze the body composition biomarkers. The subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), cross-sectional area of paraspinal skeletal muscle area (PMA), skeletal muscle density (SMD), body mineral density (BMD) were measured from two sets of CT images. RESULTS: In multivariate analyses, VFA gain, PMA loss, BMD loss, and PIS were independent risk factors of overall survival (OS) (HR = 3.7, 3.0, 2.8, 1.9, respectively, all p < 0.05). Receiver operating characteristic (ROC) curves showed that the prognostic model combining body composition changes (BCC) and PIS had the highest predictive performance (area under the curve = 0.890). The concordance index (C-index) of the prognostic nomogram was 0.779 (95% CI, 0.673-0.886). Decision curve analysis (DCA) demonstrated the prognostic nomogram had a great distinguishing performance. CONCLUSION: CT-based body composition analyses and PIS were associated with poor OS for AEOC patients who underwent PDS and adjuvant platinum-based chemotherapy. The prognostic nomogram with a combination of BCC and PIS was dependable in predicting survival for AEOC patients during treatment.

Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer.

RATIONALE AND OBJECTIVES: To investigate the feasibility and value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) and texture parameters of primary lesions and lymph nodes for predicting pelvic lymph node metastasis in patients with cervical cancer. MATERIALS AND METHODS: A total of 143 patients with cervical cancer confirmed by surgical pathology were analyzed retrospectively and 125 patients were enrolled in primary lesions study, 83 patients and 134 lymph nodes were enrolled in lymph nodes study. Patients and lymph nodes were randomly divided into training group and test group at a ratio of 2: 1. The IVIM-DWI parameters and 3D texture features of primary lesions and lymph nodes of all patients were measured. The least absolute shrinkage and selection operator algorithm, spearman's correlation analysis, independent two-sample t-test and Mann-Whitney U-test were used to select texture parameters. Multivariate Logistic regression analysis and receiver operating characteristic curves were used to model and evaluate diagnostic performances. RESULTS: In primary lesions study, model 1 was constructed by combining f value, original_shape_Sphericity and original_firstorder_Mean of primary lesions. In lymph nodes study, model 2 was constructed by combining short diameter, circular enhancement and rough margin of lymph nodes. Model 3 was constructed by combining ADC, f value and original_glszm_Small Area Emphasis of lymph nodes. The areas under curve of model 1, 2 and 3 in training group and test group were 0.882, 0.798, 0.907 and 0.862, 0.771, 0.937 respectively. CONCLUSION: Models based on IVIM-DWI and texture parameters of primary lesions and lymph nodes both performed well in diagnosing pelvic lymph node metastasis of cervical cancer and were superior to morphological features of lymph nodes. Especially, parameters of lymph nodes showed higher diagnostic efficiency and clinical significance.

N6-Methyladenosine-Sculpted Regulatory Landscape of Noncoding RNA.

The exploration of dynamic N6-methyladenosine (m6A) RNA modification in mammalian cells has attracted great interest in recent years. M6A modification plays pivotal roles in multiple biological and pathological processes, including cellular reprogramming, fertility, senescence, and tumorigenesis. In comparison with growing research unraveling the effects of m6A modifications on eukaryotic messenger RNAs, reports of the association between noncoding RNAs and m6A modification are relatively limited. Noncoding RNAs that undergo m6A modification are capable of regulating gene expression and also play an important role in epigenetic regulation. Moreover, the homeostasis of m6A modification can be affected by noncoding RNAs across a broad spectrum of biological activities. Importantly, fine-tuning and interaction between these processes are responsible for cell development, as well as the initiation and progression of the disease. Hence, in this review, we provide an account of recent developments, revealing biological interactions between noncoding RNAs and m6A modification, and discuss the potential clinical applications of interfering with m6A modification.

The Value of MRI Findings Combined With Texture Analysis in the Differential Diagnosis of Primary Ovarian Granulosa Cell Tumors and Ovarian Thecoma-Fibrothecoma.

OBJECTIVE: This study aims to explore the value of magnetic resonance imaging (MRI) and texture analysis (TA) in the differential diagnosis of ovarian granulosa cell tumors (OGCTs) and thecoma-fibrothecoma (OTCA-FTCA). METHODS: The preoperative MRI data of 32 patients with OTCA-FTCA and 14 patients with OGCTs, confirmed by pathological examination between June 2013 and August 2020, were retrospectively analyzed. The texture data of three-dimensional MRI scans based on T2-weighted imaging and clinical and conventional MRI features were analyzed and compared between tumor types. The Mann-Whitney U-test, χ 2 test/Fisher exact test, and multivariate logistic regression analysis were used to identify differences between the OTCA-FTCA and OGCTs groups. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic curve analysis was carried out to evaluate diagnostic efficiency. RESULTS: A multivariate analysis of the imaging-based features combined with TA revealed that intratumoral hemorrhage (OR = 0.037), log-sigma-20mm-3D_glszm_SmallAreaEmphasis (OR = 4.40), and log-sigma-2-0mm-3D_glszm_SmallAreaHighGrayLevelEmphasis (OR = 1.034) were independent features for discriminating between OGCTs and OTCA-FTCA (P < 0.05). An imaging-based diagnosis model, TA-based model, and combination model were established. The areas under the curve of the three models in predicting OGCTs and OTCA-FTCA were 0.935, 0.944, and 0.969, respectively; the sensitivities were 93.75, 93.75, and 96.87%, respectively; and the specificities were 85.71, 92.86, and 92.86%, respectively. The DeLong test indicated that the combination model had the highest predictive efficiency (P < 0.05), with no significant difference among the three models in differentiating between OGCTs and OTCA-FTCA (P > 0.05). CONCLUSIONS: Compared with OTCA-FTCA, intratumoral hemorrhage may be characteristic MR imaging features with OGCTs. Texture features can reflect the microheterogeneity of OGCTs and OTCA-FTCA. MRI signs and texture features can help differentiate between OGCTs and OTCA-FTCA and provide a more comprehensive and accurate basis for clinical treatment.

MRI-based radiomics and ADC values are related to recurrence of endometrial carcinoma: a preliminary analysis.

BACKGROUND: To identify predictive value of apparent diffusion coefficient (ADC) values and magnetic resonance imaging (MRI)-based radiomics for all recurrences in patients with endometrial carcinoma (EC). METHODS: One hundred and seventy-four EC patients who were treated with operation and followed up in our institution were retrospectively reviewed, and the patients were divided into training and test group. Baseline clinicopathological features and mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) were analyzed. Radiomic parameters were extracted on T2 weighted images and screened by logistic regression, and then a radiomics signature was developed to calculate the radiomic score (radscore). In training group, Kaplan-Meier analysis was performed and a Cox regression model was used to evaluate the correlation between clinicopathological features, ADC values and radscore with recurrence, and verified in the test group. RESULTS: ADCmean showed inverse correlation with recurrence, while radscore was positively associated with recurrence. In univariate analyses, FIGO stage, pathological types, myometrial invasion, ADCmean, ADCmin and radscore were associated with recurrence. In the training group, multivariate Cox analysis showed that pathological types, ADCmean and radscore were independent risk factors for recurrence, which were verified in the test group. CONCLUSIONS: ADCmean value and radscore were independent predictors of recurrence of EC, which can supplement prognostic information in addition to clinicopathological information and provide basis for individualized treatment and follow-up plan.

Body Composition Changes in Hepatocellular Carcinoma: Prediction of Survival to Transcatheter Arterial Chemoembolization in Combination With Clinical Prognostic Factors.

Treatment-related toxicities and decreased levels of patient performance during cancer therapy might contribute to body composition changes (BCC) and thereby impact outcomes. This study investigated the association between BCC during transcatheter arterial chemoembolization (TACE) and outcome in patients with hepatocellular carcinoma (HCC), and developed a nomogram for predicting survival in combination with clinical prognostic factors (CPF). Pretreatment and posttreatment computed tomography (CT) images of 75 patients with HCC who were treated between 2015 and 2018 were analyzed. The bone mineral density (BMD), cross-sectional area of paraspinal muscles (CSAmuscle), subcutaneous fat area (SFA), and visceral fat area (VFA) were measured from two sets of CT images. Count the changes in body composition during treatment and sort out the CPF of patients. Using cox regression models, CSAmuscle change, SFA change, VFA change, child-push class, and portal vein thrombosis were independent prognostic factors for overall survival (OS) (HR=5.932, 2.384, 3.140, 1.744, 1.794, respectively. P < 0.05). Receiver operating characteristic curves (ROCs) showed the prediction model combination of BCC and CPF exhibited the highest predictive performance (AUC=0.937). Independent prognostic factors were all contained into the prognostic nomogram, the concordance index (C-index) of prognostic nomogram was 0.787 (95% CI, 0.675-0.887). Decision curve analysis (DCA) demonstrated that the prognostic nomogram was clinically useful. Nomogram-based risk classification systems were also constructed to facilitate risk stratification in HCC for optimization of clinical management. In conclusion, we identified CSAmuscle change, SFA change, VFA change, Child-Pugh class, and portal vein thrombosis were independent prognostic factors for HCC. The prognostic nomogram with a combination of BCC and CPF that can be applied in the individualized prediction of survival in patients with HCC after TACE.

Nomograms of Combining Apparent Diffusion Coefficient Value and Radiomics for Preoperative Risk Evaluation in Endometrial Carcinoma.

OBJECTIVES: To evaluate the value of nomogram models combining apparent diffusion coefficient (ADC) value and radiomic features on magnetic resonance imaging (MRI) in predicting the type, grade, deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) of endometrial carcinoma (EC) preoperatively. METHODS: This study included 210 EC patients. ADC value was calculated, and radiomic features were measured on T2-weighted images. The univariate and multivariate logistic regressions and cross-validations were performed to reduce valueless features, then radiomics signatures were developed. Nomogram models using ADC combined with radiomic features were developed in the training cohort. The receiver operating characteristic (ROC) curve was performed to estimate the diagnostic efficiency of nomogram models by the area under the curve (AUC) in the training and validation cohorts. RESULTS: The ADC value was significantly different between each subgroup. Radiomic features were ultimately limited to four features for type, six features for grade, six features for DMI, four features for LVSI, and eight features for LNM for the nomogram models. The AUC of the nomogram model combining ADC value and radiomic features in the training and validation cohorts was 0.851 and 0.867 for type, 0.959 and 0.880 for grade, 0.839 and 0.766 for DMI, 0.816 and 0.746 for LVSI, and 0.910 and 0.897 for LNM. CONCLUSIONS: The nomogram models of ADC value combined with radiomic features were associated with the type, grade, DMI, LVSI, and LNM of EC, and provide an effective, non-invasive method to evaluate preoperative risk stratification for EC.