The invasion status of lymphovascular space and lymph nodes in cervical cancer assessed by mono-exponential and bi-exponential DWI-related parameters

Radiomics based on MRI in predicting lymphovascular space invasion of cervical cancer: a meta-analysis

Objective

The objective of this meta-analysis is to assess the efficacy of radiomics techniques utilizing magnetic resonance imaging (MRI) for predicting lymphovascular space invasion (LVSI) in patients with cervical cancer (CC).

Methods

A comprehensive literature search was conducted in databases including PubMed, Embase, Cochrane Library, Medline, Scopus, CNKI, and Wanfang, with studies published up to 08/04/2024, being considered for inclusion. The meta-analysis was performed using Stata 15 and Review Manager 5.4. The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score tools. The analysis encompassed the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Summary ROC curves were constructed, and the AUC was calculated. Heterogeneity was investigated using meta-regression. Statistical significance was set at p ≤ 0.05.

Results

There were 13 studies involving a total of 2,245 patients that were included in the meta-analysis. The overall sensitivity and specificity of the MRI-based model in the Training set were 83% (95% CI: 77%-87%) and 72% (95% CI: 74%-88%), respectively. The AUC, DOR, PLR, and NLR of the MRI-based model in the Training set were 0.89 (95% CI: 0.86-0.91), 22 (95% CI: 12-40), 4.6 (95% CI: 3.1-7.0), and 0.21 (95% CI: 0.16-0.29), respectively. Subgroup analysis revealed that the AUC of the model combining radiomics with clinical factors [0.90 (95% CI: 0.87-0.93)] was superior to models based on T2-weighted imaging (T2WI) sequence [0.78 (95% CI: 0.74-0.81)], contrast-enhanced T1-weighted imaging (T1WI-CE) sequence [0.85 (95% CI: 0.82-0.88)], and multiple sequences [0.86 (95% CI: 0.82-0.89)] in the Training set. The pooled sensitivity and specificity of the model integrating radiomics with clinical factors [83% (95% CI: 73%-89%) and 86% (95% CI: 73%-93%)] surpassed those of models based on the T2WI sequence [79% (95% CI: 71%-85%) and 72% (95% CI: 67%-76%)], T1WI-CE sequence [78% (95% CI: 67%-86%) and 78% (95% CI: 68%-86%)], and multiple sequences [78% (95% CI: 67%-87%) and 79% (95% CI: 70%-87%)], respectively. Funnel plot analysis indicated an absence of publication bias (p > 0.05).

Conclusion

MRI-based radiomics demonstrates excellent diagnostic performance in predicting LVSI in CC patients. The diagnostic performance of models combing radiomics and clinical factors is superior to that of models utilizing radiomics alone.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier CRD42024538007.

The value of multiparameter MRI of early cervical cancer combined with SCC-Ag in predicting its pelvic lymph node metastasis

Purpose

To investigate the value of multiparameter MRI of early cervical cancer (ECC) combined with pre-treatment serum squamous cell carcinoma antigen (SCC-Ag) in predicting its pelvic lymph node metastasis (PLNM).

Material and methods

115 patients with pathologically confirmed FIGO IB1~IIA2 cervical cancer were retrospectively included and divided into the PLNM group and the non-PLNM group according to pathological results. Quantitative parameters of the primary tumor include Ktrans, Kep, Ve from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), ADCmean, ADCmin, ADCmax, D, D* and f from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) were measured. Pre-treatment serum SCC-Ag was obtained. The difference of the above parameters between the two groups were compared using the student t-test or Mann-Whitney U test. Multivariate Logistic regression analysis was performed to determine independent risk factors. Receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic efficacy of individual parameters and their combination in predicting PLNM from ECC.

Results

The PLNM group presented higher SCC-Ag [14.25 (6.74,36.75) ng/ml vs.2.13 (1.32,6.00) ng/ml, P<0.001] and lower Ktrans (0.51 ± 0.20 min-1 vs.0.80 ± 0.33 min-1, P < 0.001), ADCmean (0.85 ± 0.09 mm/s2 vs.1.06 ± 0.35 mm/s2, P<0.001), ADCmin [0.67 (0.61,0.75) mm/s2 vs. 0.75 (0.64,0.90) mm/s2, P = 0.012] and f (0.91 ± 0.09 vs. 0.27 ± 0.14, P = 0.001) than the non-LNM group. Multivariate analysis showed that SCC-Ag (OR = 1.154, P = 0.007), Ktrans (OR=0.003, P < 0.001) and f (OR = 0.001, P=0.036) were independent risk factors of PLNM. The combination of SCC-Ag, Ktrans and f possessed the best predicting efficacy for PLNM with an area under curve (AUC) of 0.896, which is higher than any individual parameter: SCC-Ag (0.824), Ktrans (0.797), and f (0.703). The sensitivity and specificity of the combination were 79.1% and 94.0%, respectively.

Conclusions

Quantitative parameters Ktrans and f derived from DCE-MRI and IVIM-DWI of primary tumor and SCC-Ag have great value in predicting PLNM. The diagnostic efficacy of their combination has been further improved.

Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging

PURPOSE

To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer.

PATIENTS AND METHODS

Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b -value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances.

RESULTS

The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency.

CONCLUSIONS

The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

Predicting the status of lymphovascular space invasion using quantitative parameters from synthetic MRI in cervical squamous cell carcinoma without lymphatic metastasis

Purpose

To investigate the value of quantitative longitudinal relaxation time (T1), transverse relaxation time (T2), and proton density (PD) maps derived from synthetic magnetic resonance imaging (MRI) for evaluating the status of lymphovascular space invasion (LVSI) in cervical squamous cell carcinoma (CSCC) without lymph node metastasis (LNM).

Material and methods

Patients with suspected cervical cancer who visited our hospital from May 2020 to March 2023 were collected. All patients underwent preoperative MRI, including routine sequences and synthetic MRI. Patients with pathologically confirmed CSCC without lymphatic metastasis were included in this study. The subjects were divided into negative- and positive-LVSI groups based on the status of LVSI. Quantitative parameters of T1, T2, and PD values derived from synthetic MRI were compared between the two groups using independent samples t-test. Receiver operating characteristic curves were used to determine the diagnostic efficacy of the parameters.

Results

59 patients were enrolled in this study and were classified as positive (n = 32) and negative LVSI groups (n = 27). T1 and T2 values showed significant differences in differentiating negative-LVSI from positive-LVSI CSCC (1307.39 ± 122.02 vs. 1193.03 ± 107.86, P<0.0001; 88.42 ± 7.24 vs. 80.99 ± 5.50, P<0.0001, respectively). The area under the curve (AUC) for T1, T2 values and a combination of T1 and T2 values were 0.756, 0.799, 0.834 respectively, and there is no statistically significant difference in the diagnostic efficacy between individual and combined diagnosis of each parameter.

Conclusions

Quantitative parameters derived from synthetic MRI can be used to evaluate the LVSI status in patients with CSCC without LNM.

Amide proton transfer weighted combined with diffusion kurtosis imaging for predicting lymph node metastasis in cervical cancer

OBJECTIVE

To investigate the value of amide proton transfer weighted (APTw) combined with diffusion kurtosis imaging (DKI) in quantitative prediction of lymph node metastasis (LNM) in cervical carcinoma (CC).

METHODS

Data of 19 LNM(+) and 50 LNM(-) patients with CC were retrospectively analyzed. 3.0 T MRI scan was performed before the operation, including APTw and DKI. After post-processing, quantitative magnetization transfer ratio asymmetric at 3.5 ppm [MTRasym (3.5 ppm)], mean kurtosis (MK), and mean diffusivity (MD) maps were obtained. The MTRasym(3.5 ppm), MK, and MD values were respectively measured by two observers, and intra-class correlation coefficients (ICC) were used to test the consistency of the results. The independent samples t-test or Mann-Whitney U test was used to compare the differences in the values of each parameter. The ROC curve was used to analyze the predictive performance of parameters with significant differences and their combination parameter.

RESULTS

The two observers had good agreement in the measurement of each data (ICC > 0.75). The MTRasym(3.5 ppm) and MK values of the LNM(+) group(3.260 ± 0.538% and 0.531 ± 0.202) were higher than those of the LNM(-) group(2.698 ± 0.597% and 0.401 ± 0.148) (P < 0.05), while there was no significant difference in MD values between the two groups(P > 0.05). The area under the curves (AUCs) of MTRasym(3.5 ppm), MK value, and MTRasym(3.5 ppm) + MK value were 0.763, 0.716, and 0.813, respectively, when predicting LNM status of CC.

CONCLUSION

APTw and DKI can quantitatively predict LNM status of CC, which is of importance in clinical diagnosis and treatment.

Diagnostic performance of ADC values and MRI-based radiomics analysis for detecting lymph node metastasis in patients with cervical cancer: A systematic review and meta-analysis

OBJECTIVE

To evaluate and compare the diagnostic performance of apparent diffusion coefficient (ADC) values and MRI-based radiomics analysis for lymph node metastasis (LNM) detection in patients with cervical cancer (CC).

METHODS

We searched relevant databases for studies on ADC values and MRI-based radiomics analysis for LNM detection in CC between January 2001 and December 2021. Methodological quality assessment of risk of bias using Quality Assessment of Diagnostic Accuracy Studies 2 and radiomics quality score (RQS) of the studies was conducted. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Diagnostic performance was compared between the two quantitative analyses using a two-sample Z-test.

RESULTS

In total, 22 studies including 2314 patients were included. Unclear risk of bias was observed in 4.5-36.4% of the studies. The 8 radiomics studies exhibited a median (interquartile range) RQS of 13.5 (5.5-15.75). The pooled sensitivity, specificity, LR+, LR-, DOR, and AUC of the ADC values vs radiomics analysis were 0.86 vs 0.84, 0.85 vs 0.73, 5.7 vs 3.1, 0.17 vs 0.22, 34 vs 14, and 0.91 vs 0.86, respectively. There was no threshold effect or publication bias, but significant heterogeneity existed among the studies. No significant difference was detected in the diagnostic performance of the two quantitative analyses using the Z-test.

CONCLUSION

ADC values are more clinically promising because they are more easily accessible and widely applied, and exhibit a non-statistically significant trend to outperform radiomics analysis.

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.

Prediction of lymphovascular space invasion in cervical carcinoma using diffusion kurtosis imaging

BACKGROUND

This study aimed to investigate the potential relationship between diffusion kurtosis imaging (DKI)- derived parameters and lymphovascular space invasion (LVSI) in patients with cervical carcinoma.

PATIENTS AND METHODS

This prospective study included 30 patients with cervical carcinoma. The patients underwent MRI, diffusion-weighted imaging (DWI), and DKI prior to surgery. The surgical pathology results were accepted as the reference standard for determining the LVSI status. The DKI-derived parameters, including mean diffusivity (MD) and mean kurtosis (MK), were measured. The apparent diffusion coefficient (ADC) value was also assessed.

RESULTS

The MD value of LVSI positive cervical carcinomas was significantly lower than LVSI negative carcinomas (p-value = 0.01). MK value was significantly higher in LVSI positive tumors (p-value = 0.01). However, the ADC value did not show a significant difference between LVSI positive and LVSI negative tumors (p-value = 0.2). MD and MK parameters showed similar diagnostic accuracy in identifying the LVSI status, with the area under the curve of 0.77 and 0.78, respectively.

CONCLUSION

In this study, DKI-derived parameters were associated with the LVSI status in cervical carcinomas. Further studies with larger sample size are required to confirm these results.

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Background

With the continuous progress of medical imaging technology, evaluation of cervical cancer is increasingly dependent on imaging methods. Diffusion-weighted imaging (DWI) plays an important role, and apparent diffusion coefficient (ADC) value is a unique quantitative parameter in the research of cervical cancer.

Methods

In this prospective study, a total of 273 patients diagnosed with stage IB1 to IIIC1 cervical cancer based on the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging guidelines who underwent pelvic 3.0T magnetic resonance imaging (MRI), including MRI and DWI, were enrolled, and the diagnostic value of preoperative staging of cervical cancer was compared between the MRI and DWI groups. The DWI group was used to explore the potential association of mean ADC (ADC_mean) with different pathological characteristics and receiver operating characteristic (ROC) curves of ADC_mean generated to predict the appropriate postoperative supplementary therapy.

Results

The diagnostic coincidence rate of DWI was higher than that of MRI in preoperative staging of cervical cancer (χ², P<0.05) and determined as stages IB1 + IB2 + IIA1 (90.91%), IB3 + IIA2 (93.48%), and IIIC1p (95.16%). The DWI staging results were consistent with postoperative pathological staging (Kappa value =0.865, P<0.001). We observed significant differences in ADC_mean values in relation to pathological type, histological grade, depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis of cervical cancer (all P<0.05). The area under the ROC curve (AUC) was 0.815, with the best predictive value for postoperative supplementary therapy in cervical cancer (sensitivity 80.0%, specificity 74.0%) at ADC_mean of 0.910×10^-3 mm²/s.

Conclusions

The DWI is a useful tool for preoperative evaluation of cervical cancer. In local cervical lesions, ADC_mean varies in relation to different clinicopathological characteristics and a reference index of <0.910×10^-3 mm²/s can be effectively applied to predict the need for postoperative supplementary therapy.