Yue Dong

MRI quantitative parameters combined with pathological microvascular characteristics predict lymph node metastasis of cervical squamous cell carcinoma

Background Lymph node metastasis (LNM) greatly affects the prognosis and treatment of cervical squamous cell carcinoma (CSCC). Non-invasive imaging biomarkers that reflect tumor angiogenesis and vascular maturity may help predict LNM. Purpose To determine whether the magnetic resonance imaging (MRI) quantitative parameters combined with microvascular characteristics predict the potential of LNM by reflecting angiogenesis or vascular maturation in CSCC. Material and Methods The clinicopathological characteristics, microvascular characteristics and MRI quantitative parameters of the LNM group (43 cases) and the non-LNM group (42 cases) were analyzed. The correlation between microvascular and MRI quantitative parameters and the ability of combined diagnosis of LNM were analyzed. Results There were significant differences in the FIGO stage and the short diameter of the largest lymph node between patients with or without LNM. There was no significant difference in the apparent diffusion coefficient (ADC) value of the primary tumor between the LNM group and the non-LNM group ( P  >0.05). Spearman’s correlation analysis showed that there was no correlation between ADC and MVD or microvessel pericyte coverage index (MPI) (all P  >0.05). K trans and K ep were significantly higher, and MPI was significantly lower in patients with LNM. There were negative correlations between K trans and MPI, and K ep and MPI. Binary logistic regression analysis showed that a combined prediction model constructed by K trans , K ep , and MPI had the highest diagnostic efficacy. Conclusion K trans and K ep of CSCC can predict LNM by non-invasively reflecting the maturity of tumor vessels, and the combined K trans , K ep , and MPI have promising diagnostic efficiency for LNM.

A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer

This study aimed to improve the accuracy of the diagnosis of lymph node metastasis (LNM) and prediction of patient prognosis in cervical cancer patients using a hybrid model based on MRI and clinical aspects. We retrospectively analyzed routine MR data from 485 patients with pathologically confirmed cervical cancer from January 2014 to June 2021. The data were divided into a training cohort (N = 261), internal cohort (N = 113), and external validation cohort (n = 111). A total of 2194 features were extracted from each ROI from T2WI and CE-T1WI. The clinical model (M1) was built with clinicopathological features including squamous cell carcinoma antigen, MRI-reported LNM, maximal tumor diameter (MTD). The radiomics model (M2) was built with four radiomics features. The hybrid model (M3) was constructed with squamous cell carcinoma antigen, MRI-reported LNM, MTD which consists of M1 and four radiomics features which consist of M2. GBDT algorithms were used to create the scores of M1 (clinical-score, C-score), M2 (radiomic score, R-score), and M3 (hybrid-score, H-score). M3 showed good performance in the training cohort (AUCs, M3 vs. M1 vs. M2, 0.917 vs. 0.830 vs. 0.788), internal validation cohorts (AUCs, M3 vs. M1 vs. M2, 0.872 vs. 0.750 vs. 0.739), and external validation cohort (AUCs, M3 vs. M1 vs. M2, 0.907 vs. 0.811 vs. 0.785). In addition, higher scores were significantly associated with worse disease-free survival (DFS) in the training cohort and the internal validation cohort (C-score, P = 0.001; R-score, P = 0.002; H-score, P = 0.006). Radiomics models can accurately predict LNM status in patients with cervical cancer. The hybrid model, which incorporates clinical and radiomics features, is a novel way to enhance diagnostic performance and predict the prognosis of cervical cancer.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study

Background Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy. Purpose To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer. Material and Methods Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer—including arterial flow (AF), blood volume (BV), and permeability surface (PS)—were measured in two different ways: the region of interest incorporating the “local hot” with the highest enhancement and “cold spot” with the lowest enhancement; and “whole-tumor” measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups. Results There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups ( P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group ( P <0.05), but the AF in whole-tumor regions was not different between the two groups ( P > 0.05). There were no differences in BV and PS between the two groups ( P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion. Conclusion vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

Background Apparent diffusion coefficient (ADC) value is an important quantitative parameter in the research of cervical cancer, affected by some factors. Purpose To investigate the effect of pathological type and menstrual status on the ADC value of cervical cancer. Material and Methods A total of 352 individuals with pathologically confirmed cervical cancer between January 2015 to December 2017 were retrospectively enrolled in this study, including 317 cases with squamous cell carcinomas (SCC) and 35 cases with adenocarcinomas (AC); 177 patients were non-menopausal and 175 were menopausal. All patients underwent a routine 3.0-T magnetic resonance imaging (MRI) scan and diffusion-weighted imaging (DWI) examination using b-values of 0, 800, and 1000 s/mm2. Three parameters including mean ADC (ADCmean), maximum ADC (ADCmax), and minimum ADC (ADCmin) of cervical cancer lesions were measured and retrospectively analyzed. Independent samples t-test was used to compare the difference of ADC values in different menstrual status and pathological types. Results In all menopausal and non-menopausal patients, the ADCmean and ADCmin values of SCC were lower than those of AC ( P<0.05), the ADCmax of two pathological types showed no statistical difference ( P > 0.05). In menopausal patients, the ADCmean, ADCmax, and ADCmin values of SCC were not statistically different compared with those of AC ( P > 0.05). The ADCmean, ADCmax, and ADCmin values of different pathological types cervical cancers in non-menopausal patients were all higher than those in menopausal patients ( P<0.05). Conclusion The ADC values of the cervical cancers were different in different pathological types and were also affected by menstrual status.

Combined dynamic contrast enhanced MRI parameter with clinical factors predict the survival of concurrent chemo-radiotherapy in patients with 2018 FIGO IIICr stage cervical cancer

Combined clinical prognostic factors and magnetic resonance imaging (MRI) parameters on predicting the prognosis after concurrent chemo-radiotherapy (CCRT)in patients with 2018 International Federation of Gynecology and Obstetrics (FIGO) IIICr stage patients. A total of 117 patients with cervical cancer (2018 FIGO stage IIICr) who underwent CCRT were enrolled from Dec.2014 to Jul.2017. 47 patients developed outcome events, including 32 recurrences and 15 deaths. Clinical and MR parameters of primary tumors were analyzed, including apparent diffusion coefficient (ADC) values (ADC The median PFS was 35 months (6-68 month). The 1-year and 3-year PFS rates were was 90.4 %, 74.4 %, respectively. Multivariate analysis showed that 2018 FIGOIIIC2r stage (HR 2.701,95 %CI1.259to. 5.797; p = 0.011), K In conclusion, we have identified IIIC2r stage, K

Multireader diagnostic performance of MRI-based Node-RADS for pelvic lymph node metastasis in endometrial carcinoma.

To evaluate the efficacy of the MRI-based Node Reporting and Data System (MRI-Node-RADS) in diagnosing pelvic lymph node metastasis (PLNM) in patients with endometrial carcinoma (EC). EC patients were retrospectively enrolled from July 2017 to August 2024. Two readers evaluated pelvic lymph nodes (PLNs) using MRI-Node-RADS. Pathological results served as the gold standard for determining the diagnostic accuracy of the scores. The criteria across size-based subregions were compared, focusing on obturator lymph nodes (Ob LNs) and non-obturator lymph nodes (non-Ob LNs). Inter-reader agreement was assessed using the weighted kappa statistic (κ Four hundred seventy-five PLNs were evaluated in 174 EC patients, comprising 85 metastatic and 390 non-metastatic PLNs. The inter-reader agreement was near-perfect at both evaluation levels: patient-level analyses (κ The MRI-Node-RADS effectively diagnoses PLNM, and a score of > 2 may be recommended as the optimal reference value for diagnosing PLNM in EC patients. Question Accurate assessment of PLNM is crucial for patients with EC, yet standardized guidelines for radiological reports are lacking. Findings An MRI-Node-RADS score of > 2 is identified as the optimal cut-off for diagnosing PLNM, with nearly perfect inter-reader agreement. Clinical relevance MRI-Node-RADS demonstrates excellent performance in diagnosing PLN metastases in patients with EC, suggesting that Node-RADS could be used as a reliable tool for clinical staging and personalized therapeutic decision-making.