Can We Define the Risk of Lymph Node Metastasis in Early-Stage Cervical Cancer Patients? A Large-Scale, Retrospective Study

Feasibility and Diagnostic Accuracy of Ultrastaging in the Detection of Micrometastases in Sentinel and Non-sentinel Lymph Nodes in Cervical Cancer: A Single-Center Retrospective Study With a Five-Year Follow-Up Period

BACKGROUND

Cervical cancer is the fourth most common cause of malignant tumor-related deaths among women in developing nations. Cervical cancer has been estimated to cause 527.600 new cases and 265.700 deaths globally per year.

OBJECTIVES

This study aimed to evaluate patients with cervical cancer by ultrastaging all the lymph nodes (LN), sentinel LN (SLN) and non-SLN, to increase the sensitivity of the detection of LN metastases and the diagnostic accuracy in cervical cancer with a five-year follow-up.

MATERIALS AND METHODS

This is a retrospective study of 14 cervical cancer cases from 2017 to 2019 at the Municipal Emergency Clinical Hospital of Timisoara, Romania. The cases were selected based on their high risk of LN involvement but negative intraoperative pathologic LN. After re-evaluating all paraffin block biopsy samples from 29 cases, 14 cases were included in the study, which met all criteria for ultrastaging on surgical biopsy samples.

RESULTS

Patients' ages included in the study ranged from 43 to 70 years (median: 57.14 years). According to the International Federation of Gynecology and Obstetrics (FIGO) staging, the majority of the patients were in stage IB: seven cases (50%). The study revealed a positive correlation between patient age and FIGO staging, with Pearson's correlation coefficient of 0.707 and a p-value of less than 0.05, indicating that older patients were more likely to be diagnosed with a higher FIGO stage. The mean follow-up was 34.5 months, and the median follow-up was 36 months (range: 6-60 months). We obtained 167 nodes, with a mean of 11.92 nodes/case. Twenty-one LN were found to be positive with the ultrastaging method. We detected 11 LN with macrometastases (MAC) (52.38%), seven with micrometastasis (MIC) (33.3%), and three with tumor cell islets (14.4%). That would be 13% of newly diagnosed ultrastaging cases as positive nodes. This ultrastaging method detected nodal MIC in eight (57.1%) out of the 14 patients, who initially tested negative for LN involvement using the routine Hematoxylin and Eosin (HE) method. The detection of micrometastases in these patients underscored the superior sensitivity of ultrastaging, which was further highlighted by the subsequent relapse of four (28.57%) out of these eight patients. The study also found no correlation between the FIGO standardization and the number of MIC found in these patients.

CONCLUSIONS

Predicting cervical LN metastasis (LNM) is crucial for improving survival rates and reducing recurrence. Very few small cohort studies used an ultrastaging method to assess non-SLNs; most of them only assessed SLNs. We showed in our study that the ultrastaging method, both in the case of SLN and non-SLN, is superior compared with H&E analysis, with a 13% rate of new positive nodule diagnosis. Metastatic involvement of non-SLN was found in over 50% of all cases (8/14) according to the ultrastaging method. Additionally, our study confirms that the sensitivity of SLN ultrastaging is high for the presence of both MIC and MAC in SLN pelvic LN. As a result, we feel that ultrastaging is the most effective method for SLN analysis in patients with early-stage cervical cancer, and bilateral detection is preferable, significantly reducing false-negative results. The routine use of SLN along with ultrastaging would lead to more accurate surgical staging and better oncological follow-up of cases.

Hypertension and hyperglycaemia are positively correlated with local invasion of early cervical cancer

Background

Metabolic disorders are involved in the development of numerous cancers, but their association with the progression of cervical cancer is unclear. This study aims to investigate the association between metabolic disorders and the pathological risk factors and survival in patients with early cervical cancer.

Methods

Patients with FIGO IB1 (2009) primary cervical cancer who underwent radical hysterectomy and systematic pelvic lymph node dissection at our institution from October 2014 to December 2017 were included retrospectively. Clinical data regarding the metabolic syndrome and surgical pathology of the patient were collected. The correlations between metabolic disorders (hypertension, hyperglycemia, and obesity) and clinicopathological characteristics as well as survival after surgery were analyzed.

Results

The study included 246 patients with clinical IB1 cervical cancer, 111 (45.1%) of whom had at least one of the comorbidities of hypertension, obesity, or hyperglycemia. Hypertension was positively correlated with parametrial invasion and poorly differentiated histology; hyperglycemia was positively correlated with stromal invasion; obesity was negatively associated with lymph node metastasis; but arbitrary disorder did not show any correlation with pathologic features. Hypertension was an independent risk factor for parametrial invasion (OR=6.54, 95% CI: 1.60-26.69); hyperglycemia was an independent risk factor for stromal invasion (OR=2.05, 95% CI: 1.07-3.95); and obesity was an independent protective factor for lymph node metastasis (OR=0.07, 95% CI: 0.01-0.60). Moreover, the patients with hypertension had a significantly lower 5-year OS rate (70.0% vs. 95.3%, P<0.0001) and a significantly lower 5-year PFS rate than those without hypertension (70.0% vs. 91.2%, P=0.010).

Conclusion

Hypertension and hyperglycemia are positively associated with local invasion of early cervical cancer, which need to be verified in multi-center, large scale studies.

Prediction of lymph node status in patients with early-stage cervical cancer based on radiomic features of magnetic resonance imaging (MRI) images

BACKGROUND

Lymph node metastasis is an important factor affecting the treatment and prognosis of patients with cervical cancer. However, the comparison of different algorithms and features to predict lymph node metastasis is not well understood. This study aimed to construct a non-invasive model for predicting lymph node metastasis in patients with cervical cancer based on clinical features combined with the radiomic features of magnetic resonance imaging (MRI) images.

METHODS

A total of 180 cervical cancer patients were divided into the training set (n = 126) and testing set (n = 54). In this cross-sectional study, radiomic features of MRI images and clinical features of patients were collected. The least absolute shrinkage and selection operator (LASSO) regression was used to filter the features. Seven machine learning methods, including eXtreme Gradient Boosting (XGBoost), Logistic Regression, Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting Decision Tree (GBDT) are used to build the models. Receiver operating characteristics (ROC) curve and area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the performance of the models.

RESULTS

Of these 180 patients, 49 (27.22%) patients had lymph node metastases. Five of the 122 radiomic features and 3 clinical features were used to build predictive models. Compared with other models, the MNB model was the most robust, with its AUC, specificity, and accuracy on the testing set of 0.745 (95%CI: 0.740-0.750), 0.900 (95%CI: 0.807-0.993), and 0.778 (95%CI: 0.667-0.889), respectively. Furthermore, the AUCs of the MNB models with clinical features only, radiomic features only, and combined features were 0.698 (95%CI: 0.692-0.704), 0.632 (95%CI: 0.627-0.637), and 0.745 (95%CI: 0.740-0.750), respectively.

CONCLUSION

The MNB model, which combines the radiomic features of MRI images with the clinical features of the patient, can be used as a non-invasive tool for the preoperative assessment of lymph node metastasis.

Ultrastaging of the Parametrium in Cervical Cancer: A Clinicopathological Study

Occult parametrial involvement in apparent early-stage cervical cancer might be overlooked with standard pathologic assessment. The primary endpoint of the present study was to assess the rate of positive parametrial lymph nodes and of microscopic continuous or discontinuous parametrial involvement. This is a retrospective, single-center, observational study including patients with FIGO 2018 stage IA1-IIA1 and IIIC1p in whom bilateral sentinel lymph node (SLN) detection and ultrastaging of SLN were performed according to institutional protocol, with surgery as primary treatment performed between May 2017 and February 2021, as well as type B2/C1/C2 (Querleu-Morrow) radical hysterectomy and usual histology (squamous cell, adenocarcinoma and adenosquamous carcinoma). Thirty-one patients were included in the study period. Six (18.7%) patients had metastatic lymph nodes, of whom four had only SLN metastasis (two cases of ITC, one case of micrometastasis and one case of macrometastasis). We found a macroscopic deposit of cancer cells in the parametrial lymph node of one patient (3.1%). There was a positive statistical correlation between the incidence of parametrial lymph node involvement and the metastatic pelvic lymph nodes (p = 0.038). When performed per patient, the sensitivity, negative predictive value and accuracy of parametrial lymph node involvement in predicting pelvic lymph node metastasis were 16.7%, 83.3% and 83.9%, respectively. Ultrastaging of parametrial tissue did not identify any occult continuous or discontinuous parametrial metastasis. In conclusion, the incidence of lymph node parametrial involvement in a retrospective series of early-stage cervical cancer was 3.1% of all included patients. Lymph node involvement of the parametrium was associated with lymph node metastasis. The sensitivity of parametrial lymph node involvement to predict pelvic lymph node metastasis was low. The lack of parametrial involvement revealed by parametrial ultrastaging could be related to the number of patients with tumors with a pathologic diameter < 2 cm (54.8%). Further prospective studies are needed to analyze the role of parametrial ultrastaging in early-stage cervical cancer and to assess whether it can be considered the "sentinel" of the sentinel lymph node.

Oncologic Outcomes of Surgically Treated Cervical Cancer with No Residual Disease on Hysterectomy Specimen: A 4C (Canadian Cervical Cancer Collaborative) Working Group Study

Minimally invasive surgery for the treatment of macroscopic cervical cancer leads to worse oncologic outcomes than with open surgery. Preoperative conization may mitigate the risk of surgical approach. Our objective was to describe the oncologic outcomes in cases of cervical cancer initially treated with conization, and subsequently found to have no residual cervical cancer after hysterectomy performed via open and minimally invasive approaches. This was a retrospective cohort study of surgically treated cervical cancer at 11 Canadian institutions from 2007 to 2017. Cases initially treated with cervical conization and subsequent hysterectomy, with no residual disease on hysterectomy specimen were included. They were subdivided according to minimally invasive (laparoscopic/robotic (MIS) or laparoscopically assisted vaginal/vaginal hysterectomy (LVH)), or abdominal (AH). Recurrence free survival (RFS) and overall survival (OS) were estimated using Kaplan-Meier analysis. Chi-square and log-rank tests were used to compare between cohorts. Within the total cohort, 238/1696 (14%) had no residual disease on hysterectomy specimen (122 MIS, 103 AH, and 13 VLH). The majority of cases in the cohort were FIGO 2018 stage IB1 (43.7%) and underwent a radical hysterectomy (81.9%). There was no statistical difference between stage, histology, and radical vs simple hysterectomy between the abdominal and minimally invasive groups. There were no significant differences in RFS (5-year: MIS/LVH 97.7%, AH 95.8%, p = 0.23) or OS (5-year: MIS/VLH 98.9%, AH 97.4%, p = 0.10), although event-rates were low. There were only two recurrences. In this large study including only patients with no residual cervical cancer on hysterectomy specimen, no significant differences in survival were seen by surgical approach. This may be due to the small number of events or due to no actual difference between the groups. Further studies are warranted.

Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer

The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = 0.839, accuracy = 74.2%, sensitivity = 65.7%, and specificity = 82.8%), clinical models that combine stromal invasion depth, FIGO stage, and MTD perform poorly. The combined model had the highest AUC in the training cohort (AUC = 0.988, accuracy = 95.9%, sensitivity = 92.0%, and specificity = 100.0%) and the testing cohort (AUC = 0.922, accuracy = 87.1%, sensitivity = 85.7%, and specificity = 88.6%) when compared to the radiomics and clinical models. The study may provide valuable guidance for clinical physicians regarding the treatment strategies for early-stage cervical cancer patients.

Tumor budding of cervical squamous cell carcinoma: epithelial-mesenchymal transition-like cancer stem cells?

Recent evidence indicates that cancer stem cells (CSCs) are the origin of cancers. Scientists have identified CSCs in various tumors and have suggested the existence of a variety of states of CSCs. The existence of epithelial-mesenchymal transition (EMT)-like CSCs has been confirmed in vitro, but they have not been identified in vivo. Tumor budding was defined as single cell or clusters of ≤ 5 cells at the invasive front of cancers. Such tumor budding is hypothesized to be closely related to EMT and linked to CSCs, especially to those migrating at the invasive front. Therefore, tumor budding has been proposed to represent EMT-like stem cells. However, this hypothesis has not yet been proven. Thus, we studied the expression of EMT markers, certain CSC markers of tumor budding, and the tumor center of cervical squamous cell carcinoma (CxSCC). We performed tissue chip analyses of 95 primary CxSCCs from patients. Expression of EMT and CSC markers (E-cadherin, β-catenin, vimentin, Ki67, CD44, SOX2 , and ALDH1A1) in a set of tumor samples on tissue chips (87 cases of tumor budding/the main tumor body) were evaluated by immunohistochemistry. We found that the cell-membranous expression of β-catenin was stronger in the main tumor body than in tumor buds. Compared with the main tumor body, tumor buds had reduced proliferative activity as measured by Ki67. Moreover, vimentin expression was high and E-cadherin expression was low in tumor buds. Expression of EMT-related markers suggested that tumor buds were correlated with EMT. We noted that CxSCC tumor buds had a CD44negative/low/SOX2high/ALDH1A1high staining pattern, indicating that tumor buds of CxSCC present CSC-like immunophenotypic features. Taken together, our data indicate that tumor buds in CxSCC may represent EMT-like CSCs in vivo.

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.

Comparison of recurrence patterns in cervical cancer patients with positive lymph nodes versus negative

PURPOSE

The aim of this study was to compare patterns of recurrence in 2009 FIGO Stage IB-IIA (T_1b N₀ M₀ -T_2a N₀ M₀ ) cervical cancer patients with cN0 and cN1.

METHODS

The epidemiological and clinical data of 1352 patients who had undergone radical hysterectomy and systematic lymphadenectomy with cervical cancer treated from January 2008 to April 2019 at a tertiary teaching hospital were retrospectively collected. The primary aim was to discover the lymph node status-dependent patterns and time of recurrence.

RESULTS

Tumor recurrence and death were significantly less common in patients with cN0 than cN1. In addition, the length of time to recurrence (median 60 months cN0 vs. 43 months cN1, p < 0.001) and death (median 84 months cN0 vs. 68 months cN1 p < 0.001) were significantly longer in cN0 versus cN1 patients. The cumulative rate of relapse also showed a significant difference between cN0 and cN1 groups, especially the 1-year relapse rate (2.14% vs. 10.78%). Of the patients who recurred, there was no difference in number of recurrent sites between cN0 and cN1 groups (solitary metastases:35.8% of cN0 and 35.6% of cN1; multiple metastases: 64.2% of cN0 and 64.4% of cN1). Similarly, there was no statistical difference in recurrence sites of cervical cancer between cN0 and cN1 groups based on three categories, (p = 0.646). However, in the six categories, patients' vaginal vaults with negative lymph nodes were more prone to recurrence, while the distribution of other recurrence sites showed no significant difference between the two groups.

CONCLUSIONS

There is a significant improvement of relapse-free survival in the cN0 group, and the recurrence time of cN0 patients is significantly delayed than cN1 group. However, except for the risk of metastasis of the vaginal vault, the site of relapse remains similar.

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.

Management of stage I and II cervical cancer: a review

In the modern era, cervical cancer treatment has become more multidisciplinary in nature. Accurate and precise staging based on clinical and radiographic findings, as well as identification of pathologic and molecular risk factors, may alter treatment recommendations. Additionally, the body of evidence guiding optimal treatment recommendations continues to grow. Multiple specialists including gynecologic oncologists, radiation oncologists, medical oncologists, radiologists, pathologists, and other ancillary staff, often with subspecialty experience in gynecology or cancer care, now staff multidisciplinary gynecologic oncology teams. This review highlights the basis of multidisciplinary treatment of early-stage cervical cancer, with a focus on surgical interventions, the role of adjuvant therapy, and indications for definitive chemoradiation. We specifically focus on the treatment of cervical cancer from stage IA1 (microinvasive disease) to stage IIB (parametrial involvement without involvement of pelvic sidewall). The staging manuals referenced in this review include the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging as well as the updated American Joint Committee on Cancer (AJCC) 9th edition (2021).

MRI-based peritumoral radiomics analysis for preoperative prediction of lymph node metastasis in early-stage cervical cancer: A multi-center study

PURPOSE

To evaluate intra- and preitumoral radiomics on the contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2W) MRI for predicting the LNM, and develop a nomogram for potential clinical uses.

METHODS

We enrolled 169 cervical cancer cases who underwent CE-T1 and T2W MR scans from two hospitals between Dec. 2015 and Sep. 2021. Intra- and peritumoral features were extracted separately and selected by the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures were built using the selected features from different regions. Clinical parameters were evaluated by statistical analysis. The nomogram was developed combining the multi-regional radiomics signature and the most predictive clinical parameters.

RESULTS

Five radiomics features were finally selected from the peritumoral regions with 1 and 3 mm distances in the CE-T1 and T2W MRI, respectively. The nomogram incorporating multi-regional combined radiomics signature, MR-reported LN status and tumor diameter achieved the highest AUCs in the training (nomogram vs. combined radiomics signature vs. clinical model, 0.891 vs. 0.830 vs. 0.812), internal validation (nomogram vs. combined radiomics signature vs. clinical model, 0.863 vs. 0.853 vs. 0.816) and external validation (nomogram vs. combined radiomics signature vs. clinical model, 0.804 vs. 0.701 vs. 0.787) cohort. DCA suggested good clinical usefulness of our developed models.

CONCLUSION

The current work suggested clinical potential for intra- and peritumoral radiomics with multi-modal MRI for preoperative predicting LNM.

Clinical value of MRI, serum SCCA, and CA125 levels in the diagnosis of lymph node metastasis and para-uterine infiltration in cervical cancer

Background

Cervical cancer shows great differences in depth of invasion, metastasis, and other biological behaviors. The location of the lesion is special, so it is usually difficult to determine the clinical stage. This study aimed to explore the clinical value of magnetic resonance imaging (MRI) and tumor serum markers for the preoperative diagnosis of cervical cancer lymph node metastasis and para-uterine invasion.

Methods

A total of 200 patients with cervical cancer admitted to our hospital from January 2019 to January 2020 were collected as the research subjects. Comparing the diagnosis results of preoperative MRI scan, serum tumor markers, and postoperative pathological examination using single factor comparison, we determined the MRI scan results, the comprehensive matching rate between serum tumor markers (squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125)) and postoperative pathological results, and the differences of sensitivity, specificity, and accuracy in the prediction of lymph node metastasis and para-uterine infiltration of cervical cancer.

Results

The levels of SCCA and CA125 in patients with para-uterine invasion and lymph node metastasis were higher than those of patients without invasion and metastasis. Among them, the level of SCCA was significantly different (P<0.05). The level of CA125 was not statistically significant (P>0.05), so MRI combined with serum SCCA was selected for combined diagnosis in the later period. The sensitivity, specificity, and accuracy of MRI diagnosis of cervical cancer and para-uterine infiltrating lymph node metastasis and metastasis were 55.2, 91.6, and 89.5% and 55.2, 91.6, and 89.5%, respectively. These data in MRI combined with serum SCCA were 76.3, 95.3, and 94.3% and 63.2, 96.0, and 95.1%, respectively. The accuracy of tumor markers combined with MRI in the diagnosis of cervical cancer lymph node metastasis and para-uterine invasion was higher than that of MRI.

Conclusions

MRI combined with serum SCCA can more accurately identify cervical cancer lymph node metastasis and para-uterine invasion compared with MRI alone. Tumor marker combined with MRI diagnosis is an important auxiliary method for cervical cancer treatment and can provide comprehensive and reliable clinical evidence for evaluation before cervical cancer surgery.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

BACKGROUND

Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors.

PURPOSE

To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery.

MATERIAL AND METHODS

In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts.

RESULTS

Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer.

CONCLUSION

The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

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.

Radiomic models for lymph node metastasis prediction in cervical cancer: can we think beyond sentinel lymph node?

Liu and colleagues performed a retrospective study to validate a computed tomography (CT) scan-based radiomic model to detect lymph node metastasis in cervical cancer. The proposed model incorporating the arterial and venous phase CT-scan features represented a non-invasive method exhibiting high sensitivity in the prediction of lymph node metastasis. It is well established that lymph node metastasis is one of the most significant prognostic factors in cervical cancer. For this reason, management of cervical cancer is strictly related to lymph node status, with international guidelines recommending definitive chemo-radiation in case of metastatic lymph node. More and more evidence supports the use of sentinel lymph node in early-stage cervical cancer but its frozen section analysis may result in false negative results; in locally-advanced stages staging para-aortic lymphadenectomy is proposed by many Authors to tailor chemoradiotherapy treatment, with potential intra-and post-operative related complications. The use of a validated radiomic model able to predict lymph node metastases in radiologically normal lymph nodes may represent an essential tool to possibly spare lympadenectomy related morbidity.

A novel 2-deoxy-2-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT)-based nomogram to predict lymph node metastasis in early stage uterine cervical squamous cell cancer

Background

We aimed to establish an effective 2-deoxy-2-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) based nomogram for pelvic lymph node (PLN) metastasis prediction in early-stage uterine cervical squamous cell cancer.

Methods

A predictive model was developed in a cohort that consisted of 351 patients with stage IB-IIA [International Federation of Gynecology and Obstetrics (FIGO) 2009] uterine cervical squamous cell cancer. All patients underwent a preoperative PET/CT scan and subsequent radical surgery between 2010 and 2017, with 241 and 110 patients allotted into training and external validation cohorts. The chi-square (χ²) test and the logistic regression analysis were used to compare the clinical and PET/CT parameters with PLN metastasis. A nomogram was developed and validated by internal and external validation.

Results

In the training cohort, 82 (34.0%) patients had positive PLNs identified in the preoperative PET/CT scan. Among them, 46 (56.1%) were pathologically confirmed. There were 30 (18.9%) PET/CT scan-negative patients found to have PLN metastasis. The χ² test and logistic regression showed that only the squamous cell carcinoma antigen (SCCA) level (P=0.039) and maximum standardized uptake value (SUVmax) of PLN (nSUVmax, P=0.001) were independent predictors for PLN metastasis. A predictive nomogram based on these 2 parameters was developed with a C-index [95% confidence interval (CI)] of 0.854 (0.772-0.937) on internal validation and 0.836 (0.723-0.948) on the external validation. Compared to nSUVmax alone, our nomogram showed elevated sensitivity (70.5%, 73.1% vs. 60.5%), specificity (94.4%, 86.4% vs. 78.2%), and positive predictive value (PPV) (93.9%, 86.4% vs. 56.1%) in both the training and validation cohorts.

Conclusions

We successfully developed a noninvasive and convenient nomogram for preoperative identification of PLN metastasis in early-stage squamous cell cervical cancer.

Sentinel lymph node biopsy in early stage cervical cancer: A meta-analysis

Background

The aim of this study was to determine the specific side detection rate of the sentinel lymph node biopsy and the accuracy in predicting lymph node metastasis in early stage cervical cancer.

Methods

A systematic search of databases was performed from the inception of the databases to 27 June 2020. Studies of cervical cancer patients with FIGO stage FIGO ⅠA~ⅡB, evaluating the sentinel lymph node biopsy with blue dye, technetium 99, combined technique (blue dye with technetium 99) or indocyanine green with a reference standard of systematic pelvis lymph node dissection or clinical follow‐up were included. Stata12.0 and Meta‐Disc 1.4 were used for the meta‐analysis.

Results

Of 2825 articles found, 21 studies (2234 women) were eventually included. Out of 21 studies, 20 met the detection rate evaluation criteria and six were included for sensitivity meta‐analysis. Due to heterogeneity, it was inappropriate to pool all studies. The pooled specific side detection rates were 85% in tumors up to 2 cm, 67% in tumors over 2 cm, 75.2% for blue dye, 74.7% for technetium 99, 84% for combined technique, and 85.5% for indocyanine green. The sentinel lymph node biopsy had a pooled specific side sensitivity of 88%. Adverse effects of sentinel lymph node biopsy appear minimal for most patients and are mainly related to the injection of blue dye.

Conclusions

Sentinel lymph node biopsy using a tracer with a high detection rate and ultrastaging is highly accurate and reliable when limited to seriously selected patients, with satisfactory bilateral lymph node mapping and where enough cases for learning curve optimization exist. Indocyanine green sentinel lymph node mapping seems to be a superior sentinel lymph node mapping technique compared to other methods at present.

Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer

PURPOSE

To investigate the predictive value of MRI-based radiomics features for lymph node metastasis (LNM) and vascular endothelial growth factor (VEGF) expression in patients with cervical cancer.

METHOD

A total of 163 patients with cervical cancer were enrolled in this study. A total of 134 patients were included for LNM differentiation, and 118 were included for VEGF expression discrimination. The patients were randomly assigned to the training group or test group at a ratio of 2:1. Radiomics features were extracted from T1WI enhanced and T2WI MRI scans of each patient, and tumor stage was also documented according to the International Federation of Gynecology and Obstetrics (FIGO) guidelines. The least absolute shrinkage and selection operator algorithm was used for feature selection. The results of 5-fold cross validation were applied to select the best classification models. The performances of the constructed models were further evaluated with the test group.

RESULTS

Sixteen radiomics features and the FIGO stage were selected to construct the LNM discrimination model. The LNM prediction model achieved the best diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.95 and 0.88 in the training group and test group, respectively. Nine radiomics characteristics were screened to build the VEGF prediction model, with AUCs of 0.82 and 0.70 in the training group and test group, respectively. Decision curve analysis confirmed their clinical usefulness.

CONCLUSIONS

The presented radiomics prediction models demonstrated potential to noninvasively differentiate LNM and VEGF expression in cervical cancer.

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer.

Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness.

Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732–0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794–0.936) and 0.861 (95% CI: 0.733–0.990), respectively. Decision curve analysis confirmed its clinical usefulness.

Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

Prognostic Assessment of Cervical Cancer Patients by Clinical Staging and Surgical-Pathological Factor: A Support Vector Machine-Based Approach

Introduction: The International Federation of Gynecology and Obstetrics (FIGO) staging system is considered the most powerful prognostic factor in patients with cervical cancer. In addition, other surgical-pathological risk factors have been demonstrated to have significance in predicting the prognosis of patients. Therefore, the purpose of this study was to investigate the effects of the FIGO staging system and surgical-pathological risk factors on the prognosis of cervical cancer patients.

Methods: A retrospective study was performed on patients diagnosed with cervical cancer at FIGO stage IB1–IIA2. Kaplan–Meier, Cox proportional hazards regression analysis and the support vector machine (SVM) algorithm were used to assess and validate the high-risk factors related to recurrence and death.

Results: A total of 647 patients were included. Kaplan-Meier analysis showed that five high-risk factors, including FIGO stage, status of pelvic lymph node, parametrial involvement, tumor size, and depth of cervical cancer, had a significant effect on the prognosis of patients. In multivariate analysis, pelvic lymph node metastasis (hazard ratio [HR] 2.415, 95% confidence interval [CI] 1.471–3.965), parametrial involvement (HR 2.740, 95% CI 1.092–6.872) and >2/3 depth of cervical invasion (HR 2.263, 95% CI 1.045–4.902) were three independent risk factors of disease-free survival. Pelvic lymph node metastasis (HR 3.855, 95% CI 2.125–6.991) and parametrial involvement (HR 3.871, 95% CI 1.375–10.900) were two independent risk factors for overall survival. When all five high-risk factors were assembled and used for classification prediction through SVM, it achieved the highest prediction accuracy of recurrence (accuracy = 69.1%). The highest prediction accuracy for survival was 94.3% when only using the two independent predictors (the pathological status of lymph nodes and parametrium involvement) by SVM classifiers. Among the 13 groups of intermediate-risk factor, the combination of tumor size, histology and grade of differentiation was more accurate in predicting prognosis than the intermediate-risk factors in the Sedlis criteria (recurrence: 86.8% vs. 60.0%; death: 92.0% vs. 71.6%).

Conclusions: The combination of FIGO stage and surgical-pathological risk factors can further enhance the prediction accuracy of the prognosis in patients with early-stage cervical cancer. Histology and grade of differentiation can further improve the prediction accuracy of intermediate-risk factors in the Sedlis criteria.

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

IMPORTANCE

Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning.

OBJECTIVE

To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017.

MAIN OUTCOMES AND MEASURES

The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis.

RESULTS

A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001).

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.

Diagnostic value of frozen section examination of sentinel lymph nodes in early-stage cervical cancer at the time of ultrastaging

OBJECTIVES

We aimed to assess the diagnostic value of frozen-section pathologic examination (FSE) of sentinel lymph nodes (SLN) in patients with early-stage cervical cancer.

METHODS

Two French prospective multicentric database on SLN biopsy for cervical cancer (SENTICOL I and II) were analysed. Patients with IA to IIA1 2018 FIGO stage, who underwent SLN biopsy with both FSE and ultrastaging examination were included.

RESULTS AND DISCUSSION

Between 2005 and 2012, 313 patients from 25 centers fulfilled the inclusion criteria. Metastatic involvement of SLN was diagnosed in 52 patients (16.6%). Macrometastases, micrometastases and isolated tumor cells (ITCs) were found in 27, 12 and 13 patients respectively. Among the 928 SLNs analysed, FSE identified 23 SLNs with macrometastases in 20 patients and 5 SLNs with micrometastases in 2 patients whereas no ITCs were identified. Ultrastaging of negative SLNs by FSE found macrometastases, micrometastases and ITCs in additional 7, 11 and 17 SLNs. Ultrastaging increased significantly the rate of patients with positive SLN from 7% to 16.6% (p < 0.0001). The sensitivity and the negative predictive value of FSE were 42.3% and 89.7% respectively or 56.4% and 94.1% if ITCs were excluded. False-negative cases were more frequent with tumor size ≥ 20 mm (OR = 4.46, 95%IC = [1.45-13.66], p = 0.01) and preoperative brachytherapy (OR = 4.47, 95%IC = [1.37-14.63], p = 0.01) and less frequent with patients included in higher volume center (>5 patients/year) (OR = 0.09, 95%IC = [0.02-0.51], p = 0.01).

CONCLUSIONS

FSE of SLN had a low sensitivity for detecting micrometastases and ITCs and a high negative predictive value for SLN status. Clinical impact of false-negative cases has to be assessed by further studies.

Should the Number of Metastatic Pelvic Lymph Nodes be Integrated into the 2018 Figo Staging Classification of Early Stage Cervical Cancer?

Introduction: Lymph node status has become part of the new staging system for cervical cancer (CC). It has been shown that patients staged as IIIC1 had heterogeneous prognoses and, in some cases, experienced better outcomes than patients with lower stages. We evaluated the impact of the number of metastatic pelvic lymph nodes (MPLNs) among patients with stage IIIC1 cervical cancer. Methods: Survival analyses were conducted in order to identify the best cut-off prognostic value relative to the number of MPLNs. Disease free survival (DFS) was considered the main outcome. Results: 541 patients were included in the study. Eighty-nine patients were of stage IIIC1. The best prognostic cut-off value of the number of MPLNs was 2. Patients with >2 MPLNs (n > 2 group) had worse DFS compared with those having <2 (N1-2 group) (5 yr DFS: 54.7% vs. 78.1%, p value = 0.006). Multivariate analyses demonstrated that the extent of MPLNs had little impact on DFS and that replacement of IIIC1 staging with N1-2 and n > 2 grouping provided a better, statistically significant model (p value = 0.006). Discussion: Using a cut-off value of 2, the number of MPLNs could better predict prognostic outcomes within stage IIIC1 cervical cancer and have potential implications for therapeutic decision-making in the treatment of patients with stage IIIC1 CC.

Potential roles of lymphovascular space invasion based on tumor characteristics provide important prognostic information in T1 tumors with ER and HER2 positive breast cancer

PURPOSE

Patients presenting with lymphovascular space invasion (LVSI) had an absolute decrease in survival. In our present study, the potential roles of LVSI on tumor characteristics was explored to predict the difference in the prognosis of ER and HER2 positive T1 tumors.

METHODS

A total of 142 breast cancer patients diagnosed with ER+ and HER2+ tumors whose tumor size was ≤ 2 cm were included in this analysis. One hundred forty-two patients were divided into four groups, group 1 (lymph nodes+ and LVSI+), group 2 (lymph nodes+ and LVSI-), group 3 (lymph nodes- and LVSI+), group 4 (lymph nodes- and LVSI-). Univariate and multivariate Cox proportional hazard models were used to identify independent prognostic factors and calculate the HR and 95% CI. Kaplan-Meier and Cox regression models were used to test the prognostic significance.

RESULTS

LVSI positivity was significantly associated with patient age, menopausal status, tumor size, lymph node status, Ki67, PR, and tumor grade. In the univariate and multivariate model, LVSI, PR, and Ki67 were significantly associated with DFS, and LVSI, lymph node status, PR, and Ki67 were significantly associated with OS. LVSI was significantly related to increased risk of DFS and OS only in the PR-negative and low-positive subgroups. It was a prognostic factor for DFS but not for OS in women with low Ki67 and was associated with DFS and OS in high-Ki67 tumors. Furthermore, patients who presented with only LVSI had a significantly worse survival rate than those with lymph node metastasis without LVSI in small tumors.

CONCLUSION

The presence of LVSI was highlighted as a variable significant to survival. In further clinical practice, patients with LVSI may need more intensive treatment in certain populations.

Central Pathology Review in SENTIX, A Prospective Observational International Study on Sentinel Lymph Node Biopsy in Patients with Early-Stage Cervical Cancer (ENGOT-CX2)

The quality of pathological assessment is crucial for the safety of patients with cervical cancer if pelvic lymph node dissection is to be replaced by sentinel lymph node (SLN) biopsy. Central pathology review of SLN pathological ultrastaging was conducted in the prospective SENTIX/European Network of Gynaecological Oncological Trial (ENGOT)-CX2 study. All specimens from at least two patients per site were submitted for the central review. For cases with major or critical deviations, the sites were requested to submit all samples from all additional patients for second-round assessment. From the group of 300 patients, samples from 83 cases from 37 sites were reviewed in the first round. Minor, major, critical, and no deviations were identified in 28%, 19%, 14%, and 39% of cases, respectively. Samples from 26 patients were submitted for the second-round review, with only two major deviations found. In conclusion, a high rate of major or critical deviations was identified in the first round of the central pathology review (28% of samples). This reflects a substantial heterogeneity in current practice, despite trial protocol requirements. The importance of the central review conducted prospectively at the early phase of the trial is demonstrated by a substantial improvement of SLN ultrastaging quality in the second-round review.

Predictive Role of MRI and 18F FDG PET Response to Concurrent Chemoradiation in T2b Cervical Cancer on Clinical Outcome: A Retrospective Single Center Study

Tumor response in locally advanced cervical cancer (LACC) is generally evaluated with MRI and PET, but this strategy is not supported by the literature. Therefore, we compared the diagnostic performance of these two techniques in the response evaluation to concurrent chemoradiotherapy (CCRT) in LACC. Patients with cervical cancer (CC) stage T2b treated with CCRT and submitted to MRI and PET/CT before and after treatment were enrolled in the study. All clinical, pathological, therapeutic, radiologic and follow-up data were collected and examined. The radiological response was analyzed and compared to the follow-up data. Data of 40 patients with LACC were analyzed. Agreement between MRI and PET/CT in the evaluation response to therapy was observed in 31/40 (77.5%) of cases. The agreement between MRI, PET/CT and follow-up data showed a Cohen kappa coefficient of 0.59 (95% CI = 0.267-0.913) and of 0.84 (95% CI = 0.636-1.00), respectively. Considering the evaluation of primary tumor response, PET/CT was correct in 97.5% of cases, and MRI in 92.5% of cases; no false negative cases were observed. These results suggest the use of PET/CT as a unique diagnostic imaging tool after CCRT, to correctly assess residual and progression disease.

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

OBJECTIVE

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images.

METHODS

One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models.

RESULTS

A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort.

CONCLUSIONS

The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC.

KEY POINTS

• Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

Pathologic Protocols for Sentinel Lymph Nodes Ultrastaging in Cervical Cancer

CONTEXT.—

Ultrastaging of sentinel lymph nodes (SLNs) is a crucial aspect in the approach to SLN processing. No consensual protocol for pathologic ultrastaging has been approved by international societies to date.

OBJECTIVE.—

To provide a review of the ultrastaging protocol and all its aspects related to the processing of SLNs in patients with cervical cancer.

DATA SOURCES.—

In total, 127 publications reporting data from 9085 cases were identified in the literature. In 24% of studies, the information about SLN processing is entirely missing. No ultrastaging protocol was used in 7% of publications. When described, the differences in all aspects of SLN processing among the studies and institutions are substantial. This includes grossing of the SLN, which is not completely sliced and processed in almost 20% of studies. The reported protocols varied in all aspects of SLN processing, including the thickness of slices (range, 1-5 mm), the number of levels (range, 0-cut out until no tissue left), distance between the levels (range, 40-1000 μm), and number of sections per level (range, 1-5).

CONCLUSIONS.—

We found substantial differences in protocols used for SLN pathologic ultrastaging, which can impact sensitivity for detection of micrometastases and even small macrometastases. Since the involvement of pelvic lymph nodes is the most important negative prognostic factor, such profound discrepancies influence the referral of patients to adjuvant radiotherapy and could potentially cause treatment failure. It is urgent that international societies agree on a consensual protocol before SLN biopsy without pelvic lymphadenectomy is introduced into routine clinical practice.

Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

OBJECTIVE

To explore an MRI-based radiomics nomogram for preoperatively predicting of pelvic lymph node (PLN) metastasis in patients with early-stage cervical cancer (ECC).

METHODS

Ninety-six patients with ECC were enrolled in this study. All patients underwent T2WI and DWI scans before radical hysterectomy with PLN dissection surgery. Radiomics features extracted from T2WI and DWI were selected by least absolute shrinkage and selection operation regression for further radimoics signature calculation. The discrimination of this radiomics signature for PLN metastasis was then assessed using a support vector machine (SVM) model. Subsequently, a radiomics nomogram was constructed based on the radiomics signature and clinicopathologic risk factors using a multivariable logistic regression method. The performance of the radiomics nomogram for the preoperative prediction of PLN metastasis was evaluated for discrimination and calibration.

RESULTS

The radiomics signatures demonstrated a good discrimination for PLN metastasis. A radiomics signature derived from joint T2WI and DWI yielded higher AUC than the signatures derived from T2WI or DWI alone. The radiomics nomogram integrating the radiomics signature with clinicopathologic risk factors showed a significant improvement over the nomogram based only on clinicopathologic risk factors in the primary cohort(C-index, 0.893 vs. 0.616; P = 4.311×10^-5) and validation cohort(C-index, 0.922 vs. 0.799; P = 3.412 ×10^-2).The calibration curves also showed good agreement.

CONCLUSIONS

The radiomics nomogram based on joint T2WI and DWI demonstrated an improved prediction ability for PLN metastasis in ECC. This noninvasive and convenient tool may be used to facilitate preoperative identification of PLN metastasis in patients with ECC.

A prospective multicenter trial on sentinel lymph node biopsy in patients with early-stage cervical cancer (SENTIX)

Objective

Sentinel lymph node (SLN) biopsy has been increasingly used in the management of early-stage cervical cancer. It appears in guidelines as an alternative option to systematic pelvic lymphadenectomy. The evidence about safety is, however, based mostly on retrospective studies, in which SLN was combined with systematic lymphadenectomy.

Materials and methods

SENTIX is a prospective multicenter trial aiming to prove that less-radical surgery with SLN is non-inferior to treatment with systematic pelvic lymphadenectomy. The primary end point is recurrence rate; the secondary end point is the prevalence of lower-leg lymphedema and symptomatic pelvic lymphocele. The reference recurrence rate was set up conservatively at 7% at 24 months after treatment. With a sample size of 300 patients treated per protocol, the trial is powered to detect a non-inferiority margin of 5% (90% power, p = 0.05) for recurrence rate, 30% reduction in the prevalence of symptomatic lymphocele or lower-leg lymphedema, with reference rates of 30% and 6% at 12 months (p = 0.025, Bonferroni correction). The patients eligible for SENTIX have stage IA1/LVSI+, IA2, IB1 (<2 cm for fertility sparing), with negative LN on pre-operative imaging. Intra-operatively, patients are excluded when there is a failure to detect SLN on both sides of the pelvis in cases of more advanced cancer (stage >IB1), or a positive intra-operative SLN assessment. The quality of SLN pathology evaluation will be assessed by central review. Three interim safety analyses are pre-planned when 30, 60, 150 patients complete 12 months' follow-up.

Conclusions

The first patient was enrolled into the study in June 2016 and, by June 2018, 340 patients had been enrolled. The first analysis of secondary outcomes should be available in 2019 and the oncological outcome of 300 patients at the end of 2021. The trial is registered as a CEEGOG trial (CEEGOG CX-01), ENGOT trial (ENGOT-Cx 2), and at the ClinicalTrials.gov database (NCT02494063).

Progress in the Study of Lymph Node Metastasis in Early-stage Cervical Cancer

Spread into regional lymph node is the major route of metastasis in cervical cancer. Although lymph node status is not involved in the International Federation of Gynecology and Obstetrics staging system of uterine cervical cancer, the presence or absence of lymph node metastasis provides important information for prognosis and treatment. In this review, we have attempted to focus on the incidence and patterns of lymph node metastasis, and the issues surrounding surgical assessment of lymph nodes. In addition, the preoperative prediction of lymph node status, as well as the intraoperative assessment by sentinel nodes will be reviewed. Finally, lymph node micrometastasis also will be discussed.

Incidence of Lymph Node Metastases in Women With Low-Risk Early Cervical Cancer (<2 cm) Without Lymph-Vascular Invasion

Objective

To determine the incidence of lymph node metastasis in women with low-risk cervical cancer stage IA2 or IB1 (<2 cm) without lymph-vascular space invasion.

Methods

A multicenter retrospective study was performed in patients who underwent radical or simple hysterectomy, conization, or trachelectomy plus pelvic lymphadenectomy for cervical cancer between January 2000 and June 2016.

Results

A total of 271 patients were included in the study. Median age and body mass index were 46 years (range, 23–77 years) and 24 kg/m2 (range, 18–48 kg/m2), respectively. Twenty-two patients had stage IA2 (8.1%), and 249 (91.9%) had stage IB1. The median tumor size was 14 mm (range, 5–20 mm). Tumor grades were 1 (n = 63 [23.2%]), 2 (n = 120 [44.3%]), 3 (n = 63 [23.2%]), and unknown (25 [9.2%]). Median depth stromal invasion was 6 mm (range, 3–20 mm). Histologic subtypes included squamous (n = 171 [63.1%]), adenocarcinoma (n = 92 [33.9%]), and adenosquamous (n = 8 [3.0%]). Overall incidence of lymph node metastasis was 2.9% (n = 8). The incidence of lymph node involvement in G1, G2, and G3 was 0% (0/63), 5% (6/120), and 3.1% (2/63), respectively. No patient with stage IA2 (regardless of grade or histology) or G1 cervical cancer less than 2 cm (stage IB1) had lymph node metastasis.

Conclusions

Patients with stage IA2 or IB1 (G1) with tumor size of less than 2 cm and no lymph-vascular space invasion may not need lymph node evaluation. On the other hand, 95% and 98% of patients with grade 2 or 3 tumors, respectively, could potentially undergo an unnecessary lymphadenectomy. Further studies with bigger sample size are required to confirm these results.