Reliability study of MRI in detecting lymph node metastasis of cervical cancer: a multi-center retrospective study result analysis

Introduction

Cervical cancer (CC) stands as a prevalent malignant neoplasm within the female reproductive tract (1). Radical hysterectomy and pelvic lymph node dissection (PLND) provide satisfactory survival rates for patients with stage IB1–IIA2 CC (2). However, only a modest percentage, approximately 8–26%, of these patients demonstrate pathological lymph node metastasis (p-LNM) (2). Conversely, the adoption of radical surgery in conjunction with adjuvant chemotherapy and radiation may engender severe complications and adversely affect the patient overall (3,4).

In recent years, research has demonstrated a lack of significant disparity in overall survival (OS) between the sentinel lymph node biopsy (SLNB) group and PLND groups. From three clinical studies involving a total of 1,952 patients, the 4.5-year OS rates were 90.8%, 95.2%, and 100.0% in the SLNB group, and 97.2%, 96.0%, and 97.6% in the PLND group, respectively. The intergroup P values were 0.22, 0.97, and 0.051, respectively, indicating no statistically significant differences between the groups (5). Within this framework, preoperative evaluation of lymph node (LN) status assumes paramount significance, as it serves as a pivotal determinant in selecting the optimal treatment approach and exerts a considerable influence on prognosis. Present Dutch guidelines advocate for the use of magnetic resonance imaging (MRI) in the clinical staging of early-stage CC (6). However, due to a lack of consensus, the utilization of imaging modalities in clinical practice varies considerably. Pelvic MRI has been established as the preferred imaging modality for local staging of CC due to its superior soft tissue contrast, high spatial resolution, and absence of ionizing radiation (1). In evaluating LNM, radiologists primarily rely on visual analysis of LN morphological features—such as short-axis diameter ≥10 mm, round contour, irregular borders, and abnormal enhancement patterns (7,8). However, this approach has significant limitations. First, overlapping imaging features caused by inflammatory reactions may lead to high false-positive rates, with postoperative pathology confirming metastases in only 50% of suspicious nodes (9). Second, there is a notable risk of missing micrometastases. Histological studies reveal that 54.4% of metastatic LNs exhibit a short-axis diameter <10 mm (10,11), a size threshold that conventional MRI struggles to detect. Additionally, radiologists’ diagnostic performance varies with experience: In gynecologic oncology imaging, the sensitivity for detecting LNM can reach 70% among physicians with 7 years of experience but drops to 50% for those with 4 years of experience (12). Synthesizing multiple systematic reviews, although MRI demonstrates high specificity (75–91.6%) and negative predictive value (NPV, 71–98.4%) for excluding LNM (13,14), its sensitivity (52–75%) and positive predictive value (PPV, 52–75%) remain suboptimal (14). Recent studies further confirm that MRI’s sensitivity for LNM (60%) is significantly lower than that of positron emission tomography (PET) (74.7%), with limited capability to detect micrometastases (<1 cm) and early-stage metastases (15).

Therefore, although several previous studies have discussed the feasibility of MRI in detecting LNM in CC, the results remain controversial. Thus, we conducted a multicenter retrospective study, aiming to assess the reliability of MRI in differentiating between metastatic and non-metastatic LNs in CC through a larger-scale analysis. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2204/rc).

Methods

Patients

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective study received approval from the Institutional Review Board of The First Affiliated Hospital of Ningbo University (Ethical Approval Number: 2024-052A), and all participating institutions were also informed of and agreed to the study, with the requirement for informed consent waived. The participating institutions comprised The First Affiliated Hospital of Ningbo University (n=137, January 2021 to December 2023), The First Affiliated Hospital of Wenzhou Medical University (n=788, January 2015 to December 2020), and Lishui Central Hospital (n=102, January 2021 to December 2023), collectively encompassing 1,027 patients. All of these patients met the following inclusion criteria: (I) histologically confirmed primary CC; (II) underwent radical hysterectomy and PLND; (III) underwent pelvic MRI examination within 2 weeks before surgery; (IV) no concurrent cancer; (V) no preoperative treatment; and (VI) complete clinical pathological data, such as age, International Federation of Gynecology and Obstetrics (FIGO) stage, histological characteristics, LNM, and MRI LN status. The exclusion criteria included preoperative adjuvant chemotherapy or radiotherapy, metastatic cancer, and image quality affected by imaging artifacts.

MRI examinations

All patients underwent pelvic MRI in the supine position using the same 3.0-T MRI scanner equipped with a 16-channel phased-array coil. Prior to the examination, patients fasted for at least 4 hours and maintained a moderately filled bladder. The imaging protocol included axial and sagittal T1-weighted imaging (T1WI), axial T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI). The contrast agent used was Gd-DTPA (0.1 mmol/kg, 2 mL/s, Hengrui Medicine, Shanghai, China). All images were stored in Digital Imaging and Communications in Medicine (DICOM) format.

Study design

The imaging features of LNs were independently reviewed by two gynecologists (Y.G. and X.Y.) with over a decade of experience in gynecologic oncology, and subsequently verified by a radiologist (Y.P.) with more than 10 years of expertise in radiology. These findings were further validated through re-examination and consensus discussion by Qingqing Lu from the Department of Radiology with 10 years of experience at The First Affiliated Hospital of Ningbo University and Kun Tang, a physician with over 20 years of experience in the Department of Radiology at The First Affiliated Hospital of Wenzhou Medical University. If the short-axis diameter of the largest LN detected on MRI equaled or exceeded 1 cm, the MRI-LN status was classified as positive (16). The histopathology of LNs served as the definitive reference standard, which could be evaluated through LN dissection or tumor reduction surgery, with reports furnished by the pathology department. Subsequently, the MRI findings were juxtaposed with the pathological results for comparative analysis.

Construction of clinical feature models and nomograms

In order to improve the accuracy of MRI in assessing LNM, we used the “rms” package to construct clinical feature models and nomograms, and drew receiver operating characteristic (ROC) curves and calibration curves to evaluate the accuracy of each model.

Statistical analysis

A confusion matrix was used to calculate the sensitivity, specificity, PPV, and NPV of MRI in evaluating LNM. The Wilson method was used to calculate the confidence interval (CI) of the binomial distribution. Logistic regression was used to assess the association between various clinical factors and LNM. Student’s t-test was used for all comparisons of continuous variables. Categorical variables were compared using the chi-square test. All statistical analyses were performed throughout with the use of R software, version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Accuracy of MRI in assessing LNM

The patient enrolment flowchart is shown in Figure 1. During the study reference period, a total of 1,058 CC patients were admitted to the hospital. Of these, 1,027 patients met the criteria for inclusion in the study. All patients underwent radical hysterectomy and PLND, with preoperative LN evaluation conducted via MRI. Figure 2 shows an MRI image of a typical metastatic LN in a patient. The cross-tabulation of MRI-positive (MRI⁺) and MRI-negative (MRI⁻) results with pathological verification results is shown in Table 1. As per the table, the prevalence of confirmed LNM through final pathology stood at 19.4% (199/1,027) among all patients assessed. Among them, only 27.6% (55/199) of patients had preoperative MRI indicating positive LNs. Among the total evaluated patients, 10.9% (112/1,027) exhibited radiologically positive LNs, of which 49.1% (55/112) were validated as LNM via pathology. In the MRI⁻ patient group, around 15.7% (144/915) were identified to harbor LNM based on final pathology. According to this table, a chi-square test was performed. The chi-square value for MRI⁺ assessment of pathological positives was 64.1, which is much greater than the critical value of 3.841. We conclude that there is a significant difference between MRI⁺ assessment and actual pathological results (positive). Similarly, the chi-square value for MRI⁻ assessment of pathological negatives was 7.8, also far exceeding the critical value of 3.841, indicating a significant difference between MRI⁻ assessment and actual pathological results (negative). The sensitivity, specificity, PPV, and NPV of MRI in assessing LNM were 27.6%, 93.0%, 49.1%, and 84.3%, respectively (Table 2). When the results from the three centers were analyzed separately (Tables S1-S4), no statistically significant differences were observed in sensitivity, PPV, or NPV, indicating overall consistency in core diagnostic metrics across all centers. Although specificity showed significant inter-center variation (P=0.034), pairwise comparisons revealed significant differences only between Wenzhou and Lishui (P=0.011), with no significant disparities in Wenzhou versus Ningbo (P=0.303) or Ningbo versus Lishui (P=0.063). This discrepancy may be attributed to Lishui’s smaller sample size compared to the other two centers. These findings further corroborate our earlier hypothesis.

Figure 1 Patient selection flowchart.

Figure 2 MRI image of a typical metastatic lymph node in one of these patients. (A) Nodular hyperintensity (shown by blue-green arrow) with a long diameter of 19.5 mm was seen in the right pelvic area on T2 fat-suppression images. (B) The lesion showed ring-like enhancement after enhancement; (C) the signal intensity was reduced on ADC; and (D) the diffusion was limited on DWI. ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; MRI, magnetic resonance imaging.

The influence of clinical features on the accuracy of MRI in assessing pathological results

In order to evaluate the influence of various clinical features on the accuracy of MRI assessment of pathological results, the distribution of various clinical features among each group was analyzed, as shown in Table 3. Among patients with MRI⁺ or MRI⁻, no significant differences in age, occupation, education, menopausal status, and pathological type were observed between the two groups when comparing patients with LNM⁻ and LNM⁺.

Logistic regression analysis of risk factors for CC LNM

Our logistic regression analysis revealed associations between various clinical factors and overall CC LNM, including age, occupation, education level, parity, squamous cell carcinoma antigen (SCCA), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), body mass index (BMI), menopausal status, and pathological type. Our results showed that LNM in CC patients was related to MRI results, SCCA, CA125, and CEA (P<0.05), but not to age, occupation, and education level. Notably, MRI⁺ accounted for the highest weight coefficient (coefficient =1.276104, P=8.43e−08) (Table 4).

Accuracy of MRI outcome in assessing LNM

Since MRI positivity accounted for the highest weight coefficient in the assessment of LNM, we first evaluated the accuracy of MRI alone for the assessment of LNM. In order to show the accuracy of MRI in predicting LN positivity more intuitively, we plotted the ROC curve, and the results showed an area under the curve (AUC) =0.601 (Figure S1A). In order to improve the accuracy of MRI in assessing LNM, we attempted to construct a clinical characteristics model using the statistically significant variables in logistic regression (MRI outcome, age, occupation), and drew ROC curves to evaluate the stability of the model. The results showed that after adding SCCA, CEA, and CA125, the AUC of the ROC curve was 0.741, and the accuracy of the model was slightly improved compared with that using MRI only (Figure S1B).

Nomogram was constructed to increase the accuracy of the model

Therefore, we went a step further and constructed a nomogram in the expectation of a reliable clinical model that could be used to assess LNM. According to the results of C-index, MRI still accounted for the decisive factor (Table S5). The nomogram and calibration curve are shown in Figure 3 with a concordance index (C-index) of 0.741.

Figure 3 Clinical nomogram for predicting lymph node metastasis in cervical cancer and model validation. (A) Nomogram. (B) Calibration curve of the nomogram. CA125, carbohydrate antigen 125; CEA, carcinoembryonic antigen; MRI, magnetic resonance imaging; SCCA, squamous cell carcinoma antigen.

Influencing factors of MRI misclassification

In both models, MRI plays a decisive role, but the sensitivity and specificity of MRI were not high. We therefore performed an analysis of clinical factors that might lead to misclassification on MRI. We found that the SCCA level of the misclassified group (including MRI⁺/LNM⁻ and MRI⁻/LNM⁺) was significantly higher than that of the accurate group, whereas there were no differences in age, CA125, and CEA levels between the two groups. The misclassification rate of SCCA was higher than that of other pathological types, and there was no significant difference between different cultures, occupations, and menopause. However, since the misclassification group included both MRI⁺/LNM⁻ and MRI⁻/LNM⁺, we found that there was no difference in SCCA level between MRI⁺/LNM⁻ and MRI⁻/LNM⁺ in the misclassification group (Figure 4).

Figure 4 Results of (A) age, (B,C) SCCA, (D) CEA, and (E) CA125 T tests between the accurate and misjudged groups. The misjudgment rates among different (F) education levels, (G) occupations, (H) menstruation, and (I) pathological types. CA125, carbohydrate antigen 125; CEA, carcinoembryonic antigen; MRI, magnetic resonance imaging; SCCA, squamous cell carcinoma antigen.

The influence of human papillomavirus (HPV) status and tumor size on pathological LNM

To identify additional features associated with pathologically positive LNs, we reanalyzed clinical factors (including HPV status and tumor size) in the patient cohort from The First Affiliated Hospital of Wenzhou Medical University (Tables S6,S7). The results revealed that patients with HPV-16 and HPV-18 infections exhibited significantly higher LN pathological positivity rates (19.0% and 17.0%, respectively), particularly HPV-16. The HPV-16 group demonstrated a substantially increased risk of pathological positivity [odds ratio (OR) =4.57, P<0.001] and was identified as the primary high-risk subtype. The HPV-18 group also showed significant risk but lower than HPV-16 (OR =3.25, P=0.016), whereas the non-16/18 HPV group displayed no significant difference compared to the HPV-negative group (P=0.562). Concurrently, larger tumor size correlated with higher pathological positivity rates (≤2 vs. >4 cm: 6.1% vs. 20.6%). The >4 cm group had a 3.4-fold higher positivity rate than the ≤2 cm group. Tumor size >4 cm significantly increased the risk (OR =2.27, P<0.001), with each 1 cm increase in tumor diameter elevating positivity risk by 127%, indicating a strong positive correlation between tumor size and pathological positivity.

Discussion

LNM is the most prevalent form of CC metastasis, closely associated with prognosis. Liu et al. pointed out that the OS rate for pelvic LNM-negative cohorts is 91%, contrasting with only 67% for LNM-positive cohorts (17-20). In 2018, the FIGO staging system incorporated LNM in stage IIIC and recommended the use of imaging results (IIICr) and pathological (IIICp) analysis to identify LNM. This means that after LNM is diagnosed by imaging in patients with CC, LN status determines whether radical hysterectomy or (chemotherapy) radiotherapy is recommended in early-stage CC according to current guidelines and updated clinical practice (21). Concurrent chemoradiotherapy (CCRT) is the preferred treatment for IIICr CC (22). Treatment adjustments guided by imaging findings are noted in roughly 13% of early-stage CC patients (6).

In clinical practice, MRI has emerged as the preferred method for preoperative staging assessment of CC due to its superior soft tissue resolution and increased sensitivity in detecting parametrial invasion (53%) compared to clinical assessment (29%) (23). European guidelines clearly stipulate that MRI examination is essential for the initial evaluation and local staging of CC (24). Currently, there is an ongoing debate regarding the consensus on the imaging evaluation of metastatic LNs in tumors. Computed tomography (CT) and MRI are commonly employed for assessing metastatic LNs in CC patients, relying on the measurement of LN size. However, the sensitivity of these imaging techniques in detecting LNM in CC ranges from 30% to 73%, and specificity ranges from 44% to 93%, showing significant variations. PET-CT is acknowledged as a valuable technique for detecting tumor LNM, offering higher sensitivity and specificity, notably due to its capacity to provide comprehensive whole-body diagnostic information. However, its clinical utilization has been limited to date due to its elevated cost (25). Consequently, MRI remains one of the most frequently employed modalities for preoperative assessment of LNM in CC. However, conventional imaging may miss occult disease, which could lead to the persistent presence of LN and/or distant metastases, ultimately resulting in disease progression and potentially fatal consequences.

Research suggests that the prevalence of LN involvement in early-stage CC patients is estimated to range between 15% and 20% (26). In our study, the percentage of patients with pathologically confirmed LNM was 19.4% (199/1,027). Nonetheless, the percentage of patients with preoperative MRI indicating LN positivity and subsequently confirmed by postoperative pathology was merely 5.4% (55/1,027). The sensitivity of MRI in assessing LNM was recorded at 0.28. Other studies have reported that even with the utilization of highly precise and costly PET/CT or DWI-MRI, their sensitivities remain at 0.66/0.87 (27). Moreover, a substantial number of patients exhibit preoperative LNs indicating negativity, yet postoperative pathology reveals positivity. This implies that a notable portion of patients might be inaccurately evaluated for the severity of their disease solely through imaging evaluation, leading to the selection of unfavorable treatment modalities.

Some researchers have long used imaging features and clinical features to build models to improve the diagnostic efficiency of MRI (28,29). However, this method cannot be widely promoted due to the complex imaging features and the differences in different hospital models. Therefore, we expect to construct a relatively reliable model to diagnose LNM by combining simple and easily accessible clinical features with MRI findings. The results showed that the addition of clinical variables such as SCCA resulted in a modest but very limited improvement in AUC. After the nomogram was constructed, the C-index value also showed that the accuracy of the nomogram was not as good as we expected. MRI outcomes played a decisive role in both models. In the subsequent analysis of clinical factors that might lead to MRI misclassification, we found that the SCCA level in the misclassification group (including MRI⁺/LNM⁻ and MRI⁻/LNM⁺) was significantly higher than that in the accurate group, whereas there were no significant differences in other clinical factors between the two groups. There was no difference in SCCA level between the two misclassification groups (MRI⁺/LNM⁻ and MRI⁻/LNM⁺). Previous studies have shown that SCCA can be used as a predictive marker for pelvic LNM before initial treatment of early-stage squamous cell carcinoma (30). This leads to a very contradictory conclusion. Logistic regression analysis showed that SCCA was associated with a higher risk of LNM, which was consistent with previous studies, but the t-test showed that higher SCCA was associated with a higher risk of MRI misjudgment. That is to say, the higher the SCCA, the higher the risk of LNM. However, under the premise of positive MRI, these patients are prone to misjudgment, that is, pathological LN negativity. We infer that MRI itself has a low value in the diagnosis of LNM, whereas MRI has the highest weight in the diagnostic model, so there is such a contradictory outcome.

In addition, in our study, only 49.1% (55/112) of patients who were classified as IIICr due to LNM being detected on MRI were pathologically confirmed to have LNM. Therefore, the accuracy of MRI assessment alone in diagnosing LNM is questionable. If patients were found to have LNM by preoperative imaging examination, according to the standard treatment of IIICr, 50.9% of patients misclassified as stage IIIC would have missed the opportunity for surgery due to chemoradiotherapy. Furthermore, previous studies have indicated that staging patients solely based on radiological LN status is overly simplistic. Grouping all women with radiologically positive LNs into a single stage results in significant heterogeneity within the patient population and large variations in survival rates. This renders this clinical feature insufficiently precise for determining prognosis and lacking clinical significance. In Wright et al.’s study, based on the 2018 FIGO staging system, 11,089 women were found to have LN positivity and were reclassified as stage IIIC based on imaging results. The 5-year survival rate for stage IIIC1 patients (60.8%) was higher than that for stage II patients (56.1%), indicating an inconsistency where a higher FIGO stage did not correspond to a poorer 5-year survival rate. However, when grouped according to actual pathological LN status, within each corresponding stage, patients with LN-negative disease had higher survival rates than those with LN-positive disease (31). Data from Chinese studies further demonstrate that the 5-year OS for stage IIICr CC was comparable to that of stages I and II. The 5-year OS rates were: 98.8% for stages IA–IB, 97.7% for stages IIA–IIB, 79.4% for stage IIIC-p, 96.9% for stage IIICr, and 76.7% for the IIIC r + p group. In contrast, there was no statistically significant difference in 5-year OS between the stage IIICr cohort and stages IA–IB and IIA–IIB (P=0.21). This discrepancy may be attributed to the high false-positive rate associated with stage IIICr, leading to the misclassification of patients who should have been assigned to lower stage (32). Other studies have pointed out that the survival outcomes for stage IIIC1 patients are influenced by local tumor factors such as lymphovascular space invasion and tumor histology (33). Combining our analysis results, we should also focus on HPV16/18 status and tumor size. Research shows that scholars stratified stage IIIC1 patients into low-risk (no prognostic factors), intermediate-risk (1 prognostic factor), and high-risk (≥2 prognostic factors) groups based on factors like tumor size, pelvic sidewall invasion, bilateral LNM, and the number of pelvic lymph node metastasis (PLNM). The 3-year disease-free survival (DFS) rates for these groups were 92.1%, 70.0%, and 51.1%, respectively (P<0.001). Tumor size and the number of PLNM significantly impacted DFS (34). The finding that the number of PLNM is an independent prognostic factor for reduced DFS and OS in stage IIIC CC has also been confirmed in other literature (35). Crucially, these factors require pathological confirmation via surgery. This highlights the value of surgery for patients diagnosed with LNM by imaging (stage IIICp). Additionally, surgery holds advantages: (I) It enables the removal of the primary tumor and surrounding affected tissues, thereby reducing tumor burden; (II) Surgeons can acquire precise pathological data, evaluate LN status accurately, and guide postoperative chemotherapy/immunotherapy based on advancements in genetic sequencing technology (36). Furthermore, multicenter prospective studies have shown that pelvic and para-aortic LN dissection before CCRT can improve prognosis (37).

Therefore, even for IIICr patients, surgery retains significant value. It not only furnishes crucial information for subsequent radiation fields and adjuvant treatment plans but also enhances overall prognosis by excising visible metastatic LNs, thereby augmenting the efficacy of chemoradiotherapy. According to our multicenter statistical findings, MRI exhibits low sensitivity and accuracy in distinguishing metastatic from non-metastatic LNs in CC. Consequently, classifying patients as stage IIIC solely based on MRI suggesting LN positivity and thus opting for CCRT while abandoning surgical treatment is questionable.

Conclusions

Findings from this study reveal that the sensitivity and accuracy of preoperative MRI in diagnosing LNM are suboptimal. Although imaging is essential for guiding surgery, relying solely on imaging evidence to advocate for the exclusion of surgical treatment for stage IIIC CC patients requires further deliberation.

Acknowledgments

We extend our gratitude to Kun Tang from the Department of Radiology at Wenzhou Medical University, and Qingqing Lu from the Department of Radiology at The First Affiliated Hospital of Ningbo University, for their rigorous secondary verification of the MRI image results.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2204/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-24-2204/dss

Funding: This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant (No. Q22H168103), National Natural Science Foundation of China (No. 82102752), and Medical Health Science and Technology Project of Zhejiang Provincial Health (No. 2022489512).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-2204/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective study received approval from the Institutional Review Board of The First Affiliated Hospital of Ningbo University (Ethical Approval Number: 2024-052A), and all participating institutions were also informed and agreed to the study, with the requirement for informed consent waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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