Parametrial involvement in women with low-risk, early-stage cervical cancer

Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients

OBJECTIVE

To explore the factors affecting the prognosis of cervical cancer (CC), and to construct and evaluate predictive nomograms to guide individualized clinical treatment.

METHODS

The clinicopathological and follow-up data of CC patients from June 2013 to December 2019 in Sun Yat-sen Memorial Hospital of Sun Yat-sen University were retrospectively analyzed. Log-rank test was used for univariate survival analysis, and Cox multivariate regression was used to identify independent prognostic factors, based on which nomogram models were established and evaluated in multiple aspects.

RESULTS

Patients were randomly assigned into the training (n = 746) and validation sets (n = 329). Survival analysis of the training set identified cervical myometrial invasion, parametrial involvement, and malignant tumor history as prognosticators of postoperative DFS and pathological type, cervical myometrial invasion, and history of STD for OS. C-index was 0.799 and 0.839 for the nomograms for DFS and OS, respectively. Calibration curves and Brier scores also indicated high performance. Importantly, decision curve analysis suggested great clinical applicability of these nomograms.

CONCLUSIONS

In this study, we analyzed a cohort of 1075 CC patients and identified DFS- or OS-associated clinicohistologic characteristics. Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. These models may facilitate individualized treatment and patient selection for clinical trials. Future investigations with larger cohorts and prospective designs are warranted for validating these prognostic models.

Fertility-sparing surgery for early-stage cervical cancer: A case series study on the efficacy and feasibility of cervical conization followed by pelvic lymphadenectomy

OBJECTIVES

To evaluate the oncologic and obstetric outcomes of cervical conization followed by pelvic lymphadenectomy, which is used as a fertility-sparing procedure, in reproductive-aged patients with early-stage cervical cancer.

METHODS

We performed a retrospective study of patients with stage IA1-IB1 cervical cancer who underwent cervical conization followed by pelvic lymphadenectomy from 2011 to 2020 at Kumamoto University Hospital.

RESULTS

In total, eight patients underwent conization followed by pelvic lymphadenectomy. The median age of the patients was 33 (range: 28-36) years. Four (50.0%) patients were nulliparous. Seven (87.5%) patients were diagnosed with squamous cell carcinoma (87.5%) and one (12.5%) with adenocarcinoma. Five (62.5%), two (25.0%), and one (12.5%) presented with stage IA1, IA2, and IB1 disease, respectively. Five (62.5%) patients had lymphovascular space invasion (LVSI) based on the assessment of specimens obtained via conization. However, none had lymph node metastasis based on pelvic lymphadenectomy. Regarding long-term oncologic outcomes, recurrence was not observed at a median follow-up of 60 (range: 8-107) months. In addition, obstetric outcomes were consistently favorable in terms of achieving pregnancy, preterm delivery, and live birth. During the study period, two patients who actively attempted to conceive had four pregnancies, resulting in full-term deliveries, and one was on her first trimester of pregnancy.

CONCLUSION

Cervical conization combined with pelvic lymphadenectomy represents a feasible conservative management for histologically well-selected patients with early-stage cervical cancer. Furthermore, an optimal histopathological evaluation of conization specimens will contribute to decision-making regarding the use of this fertility-sparing procedure.

Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer

OBJECTIVE

To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC).

METHODS

A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOI^entire, VOI^+5 mm, and VOI^-5 mm) to build 3 rad-scores using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Logistic regression was applied to build combined model incorporating rad-scores with clinical risk factors and compared with clinical model alone. Kaplan-Meier analysis was used to further validate prognostic value of selected clinical and radiomics characteristics.

RESULTS

Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI^+5 mm) performed best with a C-index of 0.750 in the training set and 0.839 in the validation set. Combined model was constructed by incorporating age categorized by 55, Federation of Gynecology and Obstetrics (Figo) stage, and lymphovascular space invasion with rad-score (VOI^+5 mm). Combined model performed better than clinical model in DFS prediction in both the training set (C-index 0.815 vs 0.709; p = 0.024) and the validation set (C-index 0.866 vs 0.719; p = 0.001).

CONCLUSION

Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making.

KEY POINTS

• Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI^+5 mm) performed best with favorable C-index values. • Combined model incorporating multiparametric MRI-based radiomics with clinical risk factors performed significantly better in DFS prediction than the clinical model. • Combined model presented as a nomogram can be easily used to predict survival, thereby facilitating clinical decision-making.

Pre-operative surgical algorithm: sentinel lymph node biopsy as predictor of parametrial involvement in early-stage cervical cancer

Introduction

Radical hysterectomy is the gold standard in the management of early-stage cervical cancer. Parametrectomy aims to remove occult disease but is associated with significant surgical morbidity. Avoiding unnecessary parametrectomy in a subset of patients at low risk of parametrial involvement may decrease the incidence of such morbidity. The purpose of this study was to identify patients at low risk of parametrial involvement in early-stage cervical cancer potentially eligible for less radical surgery based on pre-operative criteria and sentinel lymph node (SLN) status.

Methods

We performed an ancillary analysis of data from two prospective trials on sentinel node biopsy for cervical cancer (SENTICOL I and II). Patients with International Federation of Gynecology and Obstetrics (FIGO) IA–IIA cervical cancer who underwent primary radical surgery and bilateral SLN mapping were identified between 2005 and 2012 from 25 French oncologic centers. Patients who underwent pre-operative brachytherapy or did not undergo radical surgery (simple trachelectomy, simple hysterectomy, or lymph node staging only) were excluded.

Results

Of 174 patients who fullfiled the inclusion criteria, 9 patients (5.2%) had parametrial involvement and 24 patients (13.8%) had positive SLN. Most patients had 2018 FIGO stage IB1 disease (86.1%) and squamous cell carcinomas (68.9%). Parametrial involvement was significantly associated with tumor size ≥20 mm on pelvic magnetic resonance imaging (MRI) (adjusted odds ratio (ORa) 9.30, 95% CI 1.71 to 50.57, p=0.01) and micrometastic or macrometastatic SLN (ORa 8.98, 95% CI 1.59 to 50.84, p=0.01). Of 114 patients with tumors <20 mm on pre-operative MRI and negative SLN after ultrastaging, only one patient had parametrial involvement (0.9%). By triaging patients with both of these criteria in a two-step surgical procedure, unjustified and contra-indicated radical hysterectomy would have been avoided in 65.5% and 8.6% of cases, respectively.

Conclusions

Less radical surgery may be an option for patients with bilateral negative SLN after ultrastaging and tumors <20 mm. SLN status should be integrated into the decision-making process for tailored surgery in early-stage cervical cancer.

Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram

PURPOSE

To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC).

MATERIALS AND METHODS

All 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated.

RESULTS

For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement.

CONCLUSIONS

The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.

KEY POINTS

• No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.