What is the impact of corpus uterine invasion on oncologic outcomes in surgically treated cervical cancer?

Construction of refined staging classification systems integrating FIGO/T-categories and corpus uterine invasion for non-metastatic cervical cancer

BACKGROUND

To investigate the prognostic value of corpus uterine invasion (CUI) in cervical cancer (CC), and determine the necessity to incorporate it for staging.

METHODS

A total of 809 cases of biopsy-proven, non-metastatic CC were identified from an academic cancer center. Recursive partitioning analysis (RPA) method was used to develop the refined staging systems with respect to overall survival (OS). Internal validation was performed by using calibration curve with 1000 bootstrap resampling. Performances of the RPA-refined stages were compared against the conventional FIGO 2018 and 9th edition TNM-stage classifications by the receiver operating characteristic curve (ROC) and decision curve analysis (DCA).

RESULTS

We identified that CUI was independently prognostic for death and relapse in our cohort. RPA modeling using a two-tiered stratification by CUI (positive and negative) and FIGO/T-categories divided CC into three risk groupings (FIGO I'-III'/T1'-3'), with 5-year OS of 90.8%, 82.1%, and 68.5% for proposed FIGO stage I'-III', respectively (p ≤ 0.003 for all pairwise comparisons), and 89.7%, 78.8%, and 68.0% for proposed T1'-3', respectively (p < 0.001 for all pairwise comparisons). The RPA-refined staging systems were well validated with RPA-predicted OS rates showed optimal agreement with actual observed survivals. Additionally, the RPA-refined stages outperformed the conventional FIGO/TNM-stage with significantly higher accuracy of survival prediction (AUC: RPA-FIGO vs. FIGO, 0.663 [95% CI 0.629-0.695] vs. 0.638 [0.604-0.671], p = 0.047; RPA-T vs. T, 0.661 [0.627-0.694] vs. 0.627 [0.592-0.660], p = 0.036).

CONCLUSION

CUI affects the survival outcomes in patients with CC. Disease extended to corpus uterine should be classified as stage III/T3.

Nomogram Predicting Lymph Node Metastasis in the Early-Stage Cervical Cancer

Background

Accurately predicting the risk level of lymph node metastasis is essential for the treatment of patients with early cervical cancer. The purpose of this study is to construct a new nomogram based on 2-deoxy-2-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and clinical characteristics to assess early-stage cervical cancer patients’ risk of lymph node metastasis.

Materials and Methods

From January 2019 to November 2020, the records of 234 patients with stage IA-IIA [International Federation of Gynecology and Obstetrics (FIGO) 2018] cervical cancer who had undergone PET/CT examination within 30 days before surgery were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed based on it. The nomogram was developed and validated by internal and external validation. The validation cohorts included 191 cervical cancer patients from December 2020 to October 2021.

Results

Four factors [squamous cell carcinoma associated antigen (SCCA), maximum standardized uptake value of lymph node (nSUVmax), uterine corpus invasion in PET/CT and tumor size in PET/CT] were finally determined as the predictors of the nomogram. At the area under the receiver operating characteristic curve cohort was 0.926 in the primary and was 0.897 in the validation cohort. The calibration curve shows good agreement between the predicted probability and the actual probability. The decision curve analysis showed the clinical utility of the nomogram.

Conclusion

We had established and verified a simple and effective nomogram, which can be used to predict the lymph node metastasis of cervical cancer patients before surgery.

Risk assessment in the patients with uterine cervical cancer harboring intermediate risk factors after radical hysterectomy: a multicenter, retrospective analysis by the Japanese Gynecologic Oncology Group

BACKGROUND

Adjuvant therapy is usually considered for surgically treated patients with uterine cervical cancer harboring intermediate risk (IR) factors such as large tumor diameter, stromal invasion to the outer half, and lymphovascular space invasion (LVSI). However, the indications and types of adjuvant therapy for the IR group remain controversial. This study aimed to analyze the differences in patient outcomes in the IR group to provide novel insights for tailoring adjuvant therapy.

METHODS

Data from 6192 patients with cervical cancer who underwent radical hysterectomy at 116 institutions belonging to the Japanese Gynecologic Oncology Group were reviewed.

RESULTS

In total, 1688 patients were classified into the IR group, of whom 37.3% did not receive adjuvant therapy. Conversely, approximately equal proportions of the remaining patients received adjuvant radiotherapy, concurrent chemoradiotherapy, and chemotherapy. Patients with all three risk factors showed worse overall survival than those with one or two risk factors. In addition to LVSI, non-squamous cell carcinoma histology, and vaginal invasion were identified as independent risk factors for both recurrence and mortality in multivariate analyses. Tumor diameter greater than 40 mm and surgical center volume were identified as independent risk factors for recurrence. Stromal invasion to the outer half and ovarian metastasis were identified as independent risk factors for mortality.

CONCLUSIONS

This study revealed the significant differences in prognosis in the IR group. The indications for adjuvant therapy should be further studied, focusing on conventional risk factors and other pathological findings.

A Research Study to Measure the Efficacy of Terminating Cervical Cancer via Customized Optimum Pathway

Background

To develop a precise prognostic model of overall survival in patients with terminating cervical cancer based on surveillance, epidemiology, and end results (SEER) program.

Methods

The patients were retrieved from SEER data who are diagnosed with terminating cervical cancer from 2004 to 2016. The data were performed using univariate and multivariate analyses and constructed nomograms for predicting survival. Use C-index to validate the model accuracy.

Results

Totally 15839 patients diagnosed with cervical cancer were independently allocated into the training set (n = 11088) and validation set (n = 4751). The multivariate analysis results indicated that age, race, stage_T, stage_M, and stage_N were confirmed as independent risk predictors, and those factors are applied to construct this clinical model. The C-index of overall survival in the training set was 0.6816 (95% confidence intervene (CI), 0.694-0.763) and that in the validation set was 0.6931(95% CI, 0.613-0.779). All calibration curves of various factors were consistent with predicted and actual survival.

Conclusion

The nomogram provides a novel method for predicting the survival of patients with terminating cervical cancer, assisting in accurate therapeutic methods for patients with primary terminating cervical cancer.