Implications of the new FIGO staging and the role of imaging in cervical cancer

Survival outcomes of 2018 FIGO stage IIIC versus stages IIIA and IIIB in cervical cancer: A systematic review with meta-analysis

OBJECTIVE

To assess the difference in survival outcomes between stage IIIC and stages IIIA and IIIB in the 2018 FIGO cervical cancer staging system.

METHODS

The PubMed, EMBASE, MEDLINE and Web of Science were searched for articles published from November 1, 2018 to January 31, 2023. Articles published in English were considered. The included studies compared the survival outcomes of patients with cervical cancer in FIGO 2018 stage IIIC with those in stages IIIA and IIIB. Studies focused on rare histopathological types were excluded. The statistical analyses were performed using Stata 17 software. The endpoints were overall survival (OS) and progression-free survival (PFS).

RESULTS

Ten retrospective cohort studies were eligible, involving 2113 (6.2%), 9812 (28.6%), 44 (0.1%), 10 171 (29.7%), 11 677 (34.1%) and 445 (1.3%) patients in stage IIIA, IIIB, IIIA&B, IIIC, IIIC1, and IIIC2, respectively. In the OS group, stage IIIC/C1 was significantly associated with superior survival compared with stage IIIA (hazard risk [HR] 0.62, 95% confidence interval [CI] 0.41-0.93, P = 0.022; I2 = 92.9%) and stage IIIB(A&B) (HR 0.56, 95% CI 0.44-0.71, P < 0.001; I2 = 94.0%). The FIGO 2018 stage IIIC2 was not associated with an increased mortality risk compared with stage IIIA and stage IIIB(A&B). In the PFS group, the outcome of FIGO 2018 stage IIIC/C1 was similar to stage IIIA (HR 0.66, 95% CI 0.27-1.64, P = 0.371; I2 = 65.6%), but better than stage IIIB(A&B) (HR 0.75, 95% CI 0.68-0.83, P < 0.001; I2 = 0.0%). The FIGO 2018 stage IIIC2 has similar PFS outcomes to stage IIIA and stage IIIB(A&B).

CONCLUSION

Our findings demonstrate that survival outcomes of stage IIIC are no worse than those of stage IIIA and stage IIIB in the 2018 FIGO cervical cancer staging system. In cervical cancer, FIGO 2018 stage IIIC1 has significantly better OS outcomes than stage IIIA and stage IIIB.

68Ga-labeled TMTP1 radiotracer for PET imaging of cervical cancer

Molecular imaging enables visualization and characterization of biological processes that influence tumor behavior and response to therapy. The TMTP1 (NVVRQ) peptide has shown remarkable affinity to highly metastatic tumors and and its potential receptor is aminopeptidase P2. In this study, we have designed and synthesized a 68Ga-labeled cyclic TMTP1 radiotracer (68Ga-DOTA-TMTP1), for PET imaging of cervical cancer. The goal of this study was to investigate the properties of this radiotracer and its tumor diagnostic potential. The radiochemical yield of 68Ga-DOTA-TMTP1 was high and the radiochemical purity was greater than 95%. The octanol-water partition coefficient for 68Ga-DOTA-TMTP1 was -2.76 ± 0.08 and 68Ga-DOTA-TMTP1 has showed excellent stability in in vitro studies. The cellular uptake and efflux of 68Ga-DOTA-TMTP1 in paired highly metastatic and lowly metastatic cervical cancer cell line HeLa and C-33A as well as normal cervical epithelial cell line End1 were measured in a γ counter. 68Ga-DOTA-TMTP1 exhibited higher uptake in HeLa cells than in C-33A cells. The binding to HeLa and C-33A cells could be blocked by excess TMTP1. On microPET images, HeLa tumors were clearly visualized within 60 min and the uptake of the radiotracer in HeLa tumors was higher than that of C-33A tumors. After blocking with TMTP1, HeLa tumors uptake was significantly reduced and the specificity 68Ga-DOTA-TMTP1 was thus validated. Overall, we have successfully synthesized 68Ga-DOTA-TMTP1 with high yield and high specific activity and have demonstrated its potential role for highly metastatic tumor-targeted diagnosis.

Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability

Background

The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM.

Methods

A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785).

Conclusion

The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.

An Update on the Role of MRI in Treatment Stratification of Patients with Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide and the most common gynaecological malignancy. The FIGO staging system is the most commonly utilised classification system for cervical cancer worldwide. Prior to the most recent update in the FIGO staging in 2018, the staging was dependent upon clinical assessment alone. Concordance between the surgical and clinical FIGO staging decreases rapidly as the tumour becomes more advanced. MRI now plays a central role in patients diagnosed with cervical cancer and enables accurate staging, which is essential to determining the most appropriate treatment. MRI is the best imaging option for the assessment of tumour size, location, and parametrial and sidewall invasion. Notably, the presence of parametrial invasion precludes surgical options, and the patient will be triaged to chemoradiotherapy. As imaging is intrinsic to the new 2018 FIGO staging system, nodal metastases have been included within the classification as stage IIIC disease. The presence of lymph node metastases within the pelvis or abdomen is associated with a poorer prognosis, which previously could not be included in the staging classification as these could not be reliably detected on clinical examination. MRI findings corresponding to the 2018 revised FIGO staging of cervical cancers and their impact on treatment selection will be described.

Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

PURPOSE

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

PATIENTS AND METHODS

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

RESULTS

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001-1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020-1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013-15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007-1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378-11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

CONCLUSIONS

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

The Role of Multimodal Imaging in Pathological Response Prediction of Locally Advanced Cervical Cancer Patients Treated by Chemoradiation Therapy Followed by Radical Surgery

PURPOSE

This study aimed to develop predictive models for pathological residual disease after neoadjuvant chemoradiation (CRT) in locally advanced cervical cancer (LACC) by integrating parameters derived from transvaginal ultrasound, MRI and PET/CT imaging at different time points and time intervals.

METHODS

Patients with histologically proven LACC, stage IB2-IVA, were prospectively enrolled. For each patient, the three examinations were performed before, 2 and 5 weeks after treatment ("baseline", "early" and "final", respectively). Multivariable logistic regression models to predict complete vs. partial pathological response (pR) were developed and a cost analysis was performed.

RESULTS

Between October 2010 and June 2014, 88 patients were included. Complete or partial pR was found in 45.5% and 54.5% of patients, respectively. The two most clinically useful models in pR prediction were (1) using percentage variation of SUV_max retrieved at PET/CT "baseline" and "final" examination, and (2) including high DWI signal intensity (SI) plus, ADC, and SUV_max collected at "final" evaluation (area under the curve (95% Confidence Interval): 0.80 (0.71-0.90) and 0.81 (0.72-0.90), respectively).

CONCLUSION

The percentage variation in SUV_max in the time interval before and after completing neoadjuvant CRT, as well as DWI SI plus ADC and SUV_max obtained after completing neoadjuvant CRT, could be used to predict residual cervical cancer in LACC patients. From a cost point of view, the use of MRI and PET/CT is preferable.

Risk factors for the recurrence of cervical cancer using MR-based T1 mapping: A pilot study

Objectives

This study aimed to identify risk factors for recurrence in patients with cervical cancer (CC) through quantitative T1 mapping.

Methods

A cohort of 107 patients histopathologically diagnosed with CC at our institution between May 2018 and April 2021 was categorized into surgical and non-surgical groups. Patients in each group were further divided into recurrence and non-recurrence subgroups depending on whether they showed recurrence or metastasis within 3 years of treatment. The longitudinal relaxation time (native T1) and apparent diffusion coefficient (ADC) value of the tumor were calculated. The differences between native T1 and ADC values of the recurrence and non-recurrence subgroups were analyzed, and receiver operating characteristic (ROC) curves were drawn for parameters with statistical differences. Logistic regression was performed for analysis of significant factors affecting CC recurrence. Recurrence-free survival rates were estimated by Kaplan–Meier analysis and compared using the log-rank test.

Results

Thirteen and 10 patients in the surgical and non-surgical groups, respectively, showed recurrence after treatment. There were significant differences in native T1 values between the recurrence and non-recurrence subgroups in the surgical and non-surgical groups (P&lt;0.05); however, there was no difference in ADC values (P&gt;0.05). The areas under the ROC curve of native T1 values for discriminating recurrence of CC after surgical and non-surgical treatment were 0.742 and 0.780, respectively. Logistic regression analysis indicated that native T1 values were risk factors for tumor recurrence in the surgical and non-surgical groups (P=0.004 and 0.040, respectively). Compared with cut-offs, recurrence-free survival curves of patients with higher native T1 values of the two groups were significantly different from those with lower ones (P=0.000 and 0.016, respectively).

Conclusion

Quantitative T1 mapping could help identify CC patients with a high risk of recurrence, supplementing information on tumor prognosis other than clinicopathological features and providing the basis for individualized treatment and follow-up schemes.

The feasibility of reduced field-of-view diffusion-weighted imaging in evaluating bladder invasion of uterine cervical cancer

OBJECTIVES

Uterine cervical cancer with bladder mucosal invasion is classified as FIGO stage IVA with poor prognosis. MRI can rule out the bladder invasion and skipping cystoscopy may be possible; however, high false-positive rate may be problematic. The purpose of this study is to evaluate the diagnostic performance of reduced field-of-view (FOV) diffusion-weighted imaging (DWI) in evaluating bladder mucosal invasion of cervical cancer.

METHODS

3T MRI including T2WI and reduced FOV DWI in 15 women with histologically proven cervical cancer (two stage IIIB, six stage IVA, seven stage IVB) were retrospectively evaluated compared with cystoscopic findings.

RESULTS

Cystoscopy revealed mucosal invasion in 13 of 15 cases. The border between the tumor and the bladder wall was unclear on T2WI and clear on reduced FOV DWI in all 15 cases. The diagnosis of mucosal invasion on reduced FOV DWI had a sensitivity of 100%, specificity of 50%, accuracy of 93%, PPV of 93%, and NPV of 100%.

CONCLUSIONS

Addition of reduced FOV DWI may improve the staging accuracy of MRI for cervical cancer in assessing the bladder mucosal invasion.

ADVANCES IN KNOWLEDGE

Reduced FOV DWI may improve the staging accuracy of cervical cancer in assessing bladder mucosal invasion with high NPV and PPV, which may be helpful for avoiding unnecessary cystoscopy.