Region-specific Risk Factors for Pelvic Lymph Node Metastasis in Patients with Stage IB1 Cervical Cancer

Predictors of Non-Sentinel Lymph Node Metastasis in Patients with Positive Sentinel Lymph Node in Early-Stage Cervical Cancer: A SENTICOL GROUP Study

BACKGROUND

The goal of this study was to identify the risk factors for metastasis in the remaining non-sentinel lymph nodes (SLN) in the case of positive SLN in early-stage cervical cancer.

METHODS

An ancillary analysis of two prospective multicentric databases on SLN biopsy for cervical cancer (SENTICOL I and II) was performed. Patients with early-stage cervical cancer (FIGO 2018 IA to IIA1), with bilateral SLN detection and at least one positive SLN after ultrastaging, were included.

RESULTS

405 patients were included in SENTICOL I and Il. Fifty-two patients had bilateral SLN detection and were found to have SLN metastasis. After pelvic lymphadenectomy, metastatic involvement of non-SLN was diagnosed in 7 patients (13.5%). Patients with metastatic non-SLN were older (51.9 vs. 40.8 years, p = 0.01), had more often lympho-vascular space invasion (LVSI) (85.7% vs. 35.6%, p = 0.03), and had more often parametrial involvement (42.9% vs. 6.7%, p = 0.003). Multivariate analysis retained age (OR = 1.16, 95% IC = [1.01-1.32], p = 0.03) and LVSI (OR = 25.97, 95% IC = [1.16-582.1], p = 0.04) as independently associated with non-SLN involvement.

CONCLUSIONS

Age and LVSI seemed to be predictive of non-SLN metastasis in patients with SLN metastasis in early-stage cervical cancer. Larger cohorts are needed to confirm the results and clinical usefulness of such findings.

Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis

BACKGROUND

Radiomics-based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI-invisible EC to date.

PURPOSE

To develop and validate radiomics models based on sagittal T2-weighted images (T2WI) and T1-weighted contrast-enhanced images (T1CE) for the preoperative assessment of MRI-invisible early-stage EC and myometrial invasion (MI).

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-eight consecutive patients (mean age 50.7 years) with MRI-invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3.

FIELD STRENGTH/SEQUENCE

3-T, T2WI, and T1CE sequences, turbo spin echo.

ASSESSMENT

Two radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad-score 1 (RS1) and rad-score 2 (RS2) for evaluating MRI-invisible EC and MI.

STATISTICAL TESTS

The least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann-Whitney U tests and Chi-square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

Model 1 had good performance for preoperative detecting of MRI-invisible early-stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI-invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834).

DATA CONCLUSION

MRI-based radiomics models may provide good performance for detecting MRI-invisible EC and MI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer

OBJECTIVE

To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer.

METHODS

A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort (n = 129) and internal validation cohort (n = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and T2 weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models.

RESULTS

T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773).

CONCLUSION

The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up.

ADVANCES IN KNOWLEDGE

A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.

Risk Factor Assessment of Lymph Node Metastasis in Patients With FIGO Stage IB1 Cervical Cancer

Objectives

To assess the risk factors of lymph node metastasis (LNM) in patients with FIGO stage (2009) IB1 cervical cancer (CC).

Methods

Patients with FIGO stage IB1 CC who underwent radical resection between 2012 and 2018 were recruited. The risk factors for LNM were analysed. A recursive partitioning analysis (RPA) was used to divide the patients into risk groups and assess their risk of LNM.

Results

The 5-year overall survival rate was 91.72%, while 80.0% and 93.5% for patients with or without LNM (P<0.05). Multivariable logistic regression analysis showed that lymphovascular invasion (LVI), depth of invasion (DI), tumour size (TS), squamous cell carcinoma (SCC) antigen level were independent risk factors (all P<0.05). Patients were divided into low-risk (no LVI, DI <1/2, TS <2 cm), intermediate-risk (no LVI, DI <1/2, TS ≥2 cm; no LVI, DI ≥1/2, normal SCC level; LVI, DI <1/2, TS <2 cm), and high-risk (no LVI, DI ≥1/2, SCC level ≥1.5 ng/ml; LVI, TS <2 cm, DI ≥1/2; LVI, TS ≥2 cm) groups by RPA according to these four factors. The incidence of LNM among the three groups was 0.00%, 4.40%, and 24.10%, respectively (all P<0.001). The 5-year overall survival rates differed among the groups (98.2%, 92.7%, 83.0%, respectively, P=0.001).

Conclusions

LNM affects the prognosis of patients with FIGO stage IB1 CC. Lymphadenectomy may be avoided for patients in the low-risk group and recommended for those in the high-risk group. Whether dissection is performed in the intermediate-risk group depends on the lymph node biopsy results.

Data from small cell neuroendocrine carcinoma of the cervix: FIGO 2018 staging is more accurate than FIGO 2009

OBJECTIVE

To compare the prognostic value of International Federation of Gynecology and Obstetrics (FIGO) 2009 and 2018 staging systems in surgical patients with small cell neuroendocrine carcinoma of the cervix (SCNEC).

METHODS

We re-staged 64 surgical IB-IIA (FIGO 2009) SCNEC patients according to the FIGO 2018 system and refined stage IIIC of FIGO 2018 based on tumor local invasion. The prognostic factors were analyzed, and the advantages of FIGO 2018 were compared with 2009.

RESULTS

The 5-year overall survival rate (OS) was 78.5% for stage I and 22.2% for stage II (FIGO 2009). In FIGO 2018, there was no difference between stage I and II, and the 5-year OS was 74.1%, 60.2%, and 0% for stage I/II, IIIC1, and IIIC2. After combining stage IIIC with the local invasion stage (T1 was limited to the cervix and vagina; T2 involved the parametrium; T3 involved the pelvic or abdominal cavity), the 5-year OS for stage IIICT1, IIICT2, and IIICT3 was 83.3%, 30.0%, and 0%, respectively.

CONCLUSIONS

For stage II SCNEC patients, FIGO 2009 underestimated the prognosis, while FIGO 2018 was more accurate. For stage IIIC, FIGO 2018 might be more individualized and accurate after combining stage IIIC with tumor local invasion.

Can Conization Specimens Predict Sentinel Lymph Node Status in Early-Stage Cervical Cancer? A SENTICOL Group Study

Simple Summary

Lymph node involvement is one of the major prognosis factors for early-stage cervical cancer. Improvement in preoperative identification of node-positive patients may lead to a more accurate triage to primary chemoradiation for these patients instead of radical surgery followed by adjuvant radiotherapy, given the increased morbidity of combined treatment. Several studies have well established risk factors for node involvement, but they are based on final pathologic examination of radical hysterectomy specimens and are usually extrapolated for preoperative risk assessment. Among these risk factors, tumor size, lymphovascular space invasion (LVSI) and depth of stromal invasion might be assessed in conization specimens. Our findings suggest that patients with depth of stromal invasion lower than 10 mm and no LVSI in conization specimens had lower risk of micro- and macrometastatic SLN. In this subpopulation, full node dissection may be questionable in case of SLN unilateral detection.

Abstract

Background: The prognosis of patients with cervical cancer is significantly worsened in case of lymph node involvement. The goal of this study was to determine whether pathologic features in conization specimens can predict the sentinel lymph node (SLN) status in early-stage cervical cancer. Methods: An ancillary analysis of two prospective multicentric database on SLN biopsy for cervical cancer (SENTICOL I and II) was carried out. Patients with IA to IB2 2018 FIGO stage, who underwent preoperative conization before SLN biopsy were included. Results: Between January 2005 and July 2012, 161 patients from 25 French centers fulfilled the inclusion criteria. Macrometastases, micrometastases and Isolated tumor cells (ITCs) were found in 4 (2.5%), 6 (3.7%) and 5 (3.1%) patients respectively. Compared to negative SLN patients, patients with micrometastatic and macrometastatic SLN were more likely to have lymphovascular space invasion (LVSI) (60% vs. 29.5%, p = 0.04) and deep stromal invasion (DSI) ≥ 10 mm (50% vs. 17.8%, p = 0.04). Among the 93 patients with DSI < 10 mm and absence of LVSI on conization specimens, three patients (3.2%) had ITCs and only one (1.1%) had micrometastases. Conclusions: Patients with DSI < 10 mm and no LVSI in conization specimens had lower risk of micro- and macrometastatic SLN. In this subpopulation, full node dissection may be questionable in case of SLN unilateral detection.

FABP4 is an independent risk factor for lymph node metastasis and poor prognosis in patients with cervical cancer

Background

Pelvic lymph node metastasis (LNM) is a crucial independent prognostic factor in cervical cancer (CCa) and serves as an indicator for radiation therapy as the primary or an adjuvant treatment option. However, preoperative diagnosis of LNM remains challenging. Thus, we aimed to identify biomarkers of LNM in patients with presumed early-stage CCa.

Methods

The differentially expressed genes (DEGs) between tumours with different lymph node statuses were identified by using The Cancer Genome Atlas database. Then, univariate Cox regression analysis and Kaplan–Meier analyses were utilized to screen overall survival (OS)-associated genes. Multivariate Cox analysis and logistical analysis were utilized to evaluate independent risk factors for OS and LNM, respectively. Subsequently, the protein level of fatty acid binding protein 4 (FABP4) was detected in normal cervical and CCa tissues by immunohistochemistry assays. EdU assays were performed to determine whether FABP4 altered the proliferation of cervical cancer cells. Wound healing and Transwell assays were conducted to explore the effects of FABP4 depletion on migratory and invasive abilities of cervical cancer cells. F-actin fluorescence staining were performed to investigate morphological change and Western blotting analyses were performed to determine epithelial mesenchymal transition-related marker expression and downstream signalling pathways.

Results

A total of 243 DEGs, including 55 upregulated and 188 downregulated DEGs, were found in CCa patients with LNM versus those without LNM. Among these, FABP4 was found to be closely associated with poor OS. Multivariate analysis uncovered that FABP4 was an independent risk factor for OS and LNM in patients with CCa. The immunohistochemical results verified dramatically increased FABP4 expression in CCa tissues compared to normal cervical epithelia and its association with poor OS and LNM. In vitro, The proliferation, migration and invasion of cervical cancer cells were significantly inhibited after knocking down of FABP4, which was accompanied by elevated expression of E-cadherin and downregulated expression of N-cadherin, Vimentin and p-AKT.

Conclusions

FABP4 might be a promising biomarker of LNM and survival in patients with early-stage CCa and therefore could significantly contribute to the development of personalized prognosis prediction and therapy optimization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02273-4.