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Background: International guidelines recommend tailoring the radicality of hysterectomy according to the known preoperative tumor characteristics in patients with early-stage cervical cancer. Objective: This study aimed to assess whether increased radicality had an effect on 5-year disease-free survival in patients with early-stage cervical cancer undergoing radical hysterectomy. The secondary aims were 5-year overall survival and pattern of recurrence. Study Design: This was an international, multicenter, retrospective study from the Surveillance in Cervical CANcer (SCCAN) collaborative cohort. Patients with the International Federation of Gynecology and Obstetrics 2009 stage IB1 and IIA1 who underwent open type B/C1/C2 radical hysterectomy according to Querleu-Morrow classification between January 2007 and December 2016, who did not undergo neoadjuvant chemotherapy and who had negative lymph nodes and free surgical margins at final histology, were included. Descriptive statistics and survival analyses were performed. Patients were stratified according to pathologic tumor diameter. Propensity score match analysis was performed to balance baseline characteristics in patients undergoing nerve-sparing and non–nerve-sparing radical hysterectomy. Results: A total of 1257 patients were included. Of note, 883 patients (70.2%) underwent nerve-sparing radical hysterectomy, and 374 patients (29.8%) underwent non–nerve-sparing radical hysterectomy. Baseline differences between the study groups were found for tumor stage and diameter (higher use of non–nerve-sparing radical hysterectomy for tumors >2 cm or with vaginal involvement; P<.0001). The use of adjuvant therapy in patients undergoing nerve-sparing and non–nerve-sparing radical hysterectomy was 27.3% vs 28.6%, respectively (P=.63). Five-year disease-free survival in patients undergoing nerve-sparing vs non–nerve-sparing radical hysterectomy was 90.1% (95% confidence interval, 87.9–92.2) vs 93.8% (95% confidence in

OBJECTIVE: To evaluate the association of number of radical hysterectomies performed per year in each center with disease-free survival and overall survival. METHODS: We conducted an international, multicenter, retrospective study of patients previously included in the Surveillance in Cervical Cancer collaborative studies. Individuals with International Federation of Gynecology and Obstetrics (FIGO) 2009 stage IB1-IIA1 cervical cancer who underwent radical hysterectomy and had negative lymph nodes at final histology were included. Patients were treated at referral centers for gynecologic oncology according to updated national and international guidelines. Optimal cutoffs for surgical volume were identified using an unadjusted Cox proportional hazard model, with disease-free survival as the outcome and defined as the value that minimizes the P-value of the split in groups in terms of disease-free survival. Propensity score matching was used to create statistically similar cohorts at baseline. RESULTS: A total of 2,157 patients were initially included. The two most significant cutoffs for surgical volume were identified at seven and 17 surgical procedures, dividing the entire cohort into low-volume, middle-volume, and high-volume centers. After propensity score matching, 1,238 patients were analyzed - 619 (50.0%) in the high-volume group, 523 (42.2%) in the middle-volume group, and 96 (7.8%) in the low-volume group. Patients who underwent surgery in higher-volume institutions had progressively better 5-year disease-free survival than those who underwent surgery in lower-volume centers (92.3% vs 88.9% vs 83.8%, P=.029). No difference was noted in 5-year overall survival (95.9% vs 97.2% vs 95.2%, P=.70). Cox multivariable regression analysis showed that FIGO stage greater than IB1, presence of lymphovascular space invasion, grade greater than 1, tumor diameter greater than 20 mm, minimally invasive surgical approach, nonsquamous cell carcinoma histology, and lower-volum

Purpose: Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the same strategy for all survivors. The aim of this study was to develop a robust model allowing for individualised surveillance based on a patient's risk profile. Methods: Data of 4343 early-stage patients with cervical cancer treated between 2007 and 2016 were obtained from the international SCCAN (Surveillance in Cervical Cancer) consortium. The Cox proportional hazards model predicting disease-free survival (DFS) was developed and internally validated. The risk score, derived from regression coefficients of the model, stratified the cohort into significantly distinctive risk groups. On its basis, the annual recurrence risk model (ARRM) was calculated. Results: Five variables were included in the prognostic model: maximal pathologic tumour diameter; tumour histotype; grade; number of positive pelvic lymph nodes; and lymphovascular space invasion. Five risk groups significantly differing in prognosis were identified with a five-year DFS of 97.5%, 94.7%, 85.2% and 63.3% in increasing risk groups, whereas a two-year DFS in the highest risk group equalled 15.4%. Based on the ARRM, the annual recurrence risk in the lowest risk group was below 1% since the beginning of follow-up and declined below 1% at years three, four and >5 in the medium-risk groups. In the whole cohort, 26% of recurrences appeared at the first year of the follow-up, 48% by year two and 78% by year five. Conclusion: The ARRM represents a potent tool for tailoring the surveillance strategy in early-stage patients with cervical cancer based on the patient's risk status and respective annual recurrence risk. It can easily be used in routine clinical settings internationally.

Item does not contain fulltext, IMPORTANCE: Patients with low-grade (ie, grade 1-2) endometrial cancer (EC) are characterized by their favorable prognosis compared with patients with high-grade (ie, grade 3) EC. With the implementation of molecular profiling, the prognostic relevance of tumor grading might lose attention. As most patients present with low-grade EC and have an excellent outcome, the value of molecular profiling for these patients is unclear. OBJECTIVE: To determine the association of molecular profiling with outcomes among patients with low-grade EC. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included a multicenter international European cohort of patients diagnosed with EC between 1994 and 2018, with a median follow-up of 5.9 years. Molecular subgroups were determined by next-generation sequencing using single-molecule molecular inversion probes and by immunohistochemistry. Subsequently, tumors were classified as polymerase epsilon (POLE)-altered, microsatellite instable (MSI), tumor protein p53 (TP53)-altered, or no specific molecular profile (NSMP). Patients diagnosed with any histological subtypes and FIGO (International Federation of Gynecology and Obstetrics) stages of EC were included, but patients with early-stage EC (FIGO I-II) were only included if they had known lymph node status. Data were analyzed February 20 to June 16, 2022. EXPOSURES: Molecular testing of the 4 molecular subgroups. MAIN OUTCOMES AND MEASURES: The main outcome was disease-specific survival (DSS) within the molecular subgroups. RESULTS: A total of 393 patients with EC were included, with a median (range) age of 64.0 (31.0-86.0) years and median (range) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 29.1 (18.0-58.3). Most patients presented with early-stage (290 patients [73.8%]) and low-grade (209 patients [53.2%]) disease. Of all patients, 33 (8.4%) had POLE-altered EC, 78 (19.8%) had MSI EC, 72 (18.3%) had TP53-altered EC, and

Item does not contain fulltext, There is no consensus on the cutoff for positivity of estrogen receptor (ER) and progesterone receptor (PR) in endometrial cancer (EC). Therefore, we determined the cutoff value for ER and PR expression with the strongest prognostic impact on the outcome. Immunohistochemical expression of ER and PR was scored as a percentage of positive EC cell nuclei. Cutoff values were related to disease-specific survival (DSS) and disease-free survival (DFS) using sensitivity, specificity, and multivariable regression analysis. The results were validated in an independent cohort. The study cohort (n = 527) included 82% of grade 1-2 and 18% of grade 3 EC. Specificity for DSS and DFS was highest for the cutoff values of 1-30%. Sensitivity was highest for the cutoff values of 80-90%. ER and PR expression were independent markers for DSS at cutoff values of 10% and 80%. Consequently, three subgroups with distinct clinical outcomes were identified: 0-10% of ER/PR expression with, unfavorable outcome (5-year DSS = 75.9-83.3%); 20-80% of ER/PR expression with, intermediate outcome (5-year DSS = 93.0-93.9%); and 90-100% of ER/PR expression with, favorable outcome (5-year DSS = 97.8-100%). The association between ER/PR subgroups and outcomes was confirmed in the validation cohort (n = 265). We propose classification of ER and PR expression based on a high-risk (0-10%), intermediate-risk (20-80%), and low-risk (90-100%) group.

Contains fulltext : 232535.pdf (Publisher’s version ) (Closed access), There is no consensus on the cutoff for positivity of estrogen receptor (ER) and progesterone receptor (PR) in endometrial cancer (EC). Therefore, we determined the cutoff value for ER and PR expression with the strongest prognostic impact on the outcome. Immunohistochemical expression of ER and PR was scored as a percentage of positive EC cell nuclei. Cutoff values were related to disease-specific survival (DSS) and disease-free survival (DFS) using sensitivity, specificity, and multivariable regression analysis. The results were validated in an independent cohort. The study cohort (n = 527) included 82% of grade 1-2 and 18% of grade 3 EC. Specificity for DSS and DFS was highest for the cutoff values of 1-30%. Sensitivity was highest for the cutoff values of 80-90%. ER and PR expression were independent markers for DSS at cutoff values of 10% and 80%. Consequently, three subgroups with distinct clinical outcomes were identified: 0-10% of ER/PR expression with, unfavorable outcome (5-year DSS = 75.9-83.3%); 20-80% of ER/PR expression with, intermediate outcome (5-year DSS = 93.0-93.9%); and 90-100% of ER/PR expression with, favorable outcome (5-year DSS = 97.8-100%). The association between ER/PR subgroups and outcomes was confirmed in the validation cohort (n = 265). We propose classification of ER and PR expression based on a high-risk (0-10%), intermediate-risk (20-80%), and low-risk (90-100%) group.

Contains fulltext : 232535.pdf (Publisher’s version ) (Closed access), There is no consensus on the cutoff for positivity of estrogen receptor (ER) and progesterone receptor (PR) in endometrial cancer (EC). Therefore, we determined the cutoff value for ER and PR expression with the strongest prognostic impact on the outcome. Immunohistochemical expression of ER and PR was scored as a percentage of positive EC cell nuclei. Cutoff values were related to disease-specific survival (DSS) and disease-free survival (DFS) using sensitivity, specificity, and multivariable regression analysis. The results were validated in an independent cohort. The study cohort (n = 527) included 82% of grade 1-2 and 18% of grade 3 EC. Specificity for DSS and DFS was highest for the cutoff values of 1-30%. Sensitivity was highest for the cutoff values of 80-90%. ER and PR expression were independent markers for DSS at cutoff values of 10% and 80%. Consequently, three subgroups with distinct clinical outcomes were identified: 0-10% of ER/PR expression with, unfavorable outcome (5-year DSS = 75.9-83.3%); 20-80% of ER/PR expression with, intermediate outcome (5-year DSS = 93.0-93.9%); and 90-100% of ER/PR expression with, favorable outcome (5-year DSS = 97.8-100%). The association between ER/PR subgroups and outcomes was confirmed in the validation cohort (n = 265). We propose classification of ER and PR expression based on a high-risk (0-10%), intermediate-risk (20-80%), and low-risk (90-100%) group.

Purpose: Current guidelines for surveillance strategy in cervical cancer are rigid, recommending the same strategy for all survivors. The aim of this study was to develop a robust model allowing for individualised surveillance based on a patient's risk profile. Methods: Data of 4343 early-stage patients with cervical cancer treated between 2007 and 2016 were obtained from the international SCCAN (Surveillance in Cervical Cancer) consortium. The Cox proportional hazards model predicting disease-free survival (DFS) was developed and internally validated. The risk score, derived from regression coefficients of the model, stratified the cohort into significantly distinctive risk groups. On its basis, the annual recurrence risk model (ARRM) was calculated. Results: Five variables were included in the prognostic model: maximal pathologic tumour diameter; tumour histotype; grade; number of positive pelvic lymph nodes; and lymphovascular space invasion. Five risk groups significantly differing in prognosis were identified with a five-year DFS of 97.5%, 94.7%, 85.2% and 63.3% in increasing risk groups, whereas a two-year DFS in the highest risk group equalled 15.4%. Based on the ARRM, the annual recurrence risk in the lowest risk group was below 1% since the beginning of follow-up and declined below 1% at years three, four and >5 in the medium-risk groups. In the whole cohort, 26% of recurrences appeared at the first year of the follow-up, 48% by year two and 78% by year five. Conclusion: The ARRM represents a potent tool for tailoring the surveillance strategy in early-stage patients with cervical cancer based on the patient's risk status and respective annual recurrence risk. It can easily be used in routine clinical settings internationally.

Contains fulltext : 220465.pdf (publisher's version ) (Open Access), BACKGROUND: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte

Contains fulltext : 220465.pdf (publisher's version ) (Open Access), BACKGROUND: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte

Contains fulltext : 220465.pdf (publisher's version ) (Open Access), BACKGROUND: Bayesian networks (BNs) are machine-learning-based computational models that visualize causal relationships and provide insight into the processes underlying disease progression, closely resembling clinical decision-making. Preoperative identification of patients at risk for lymph node metastasis (LNM) is challenging in endometrial cancer, and although several biomarkers are related to LNM, none of them are incorporated in clinical practice. The aim of this study was to develop and externally validate a preoperative BN to predict LNM and outcome in endometrial cancer patients. METHODS AND FINDINGS: Within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC), we performed a retrospective multicenter cohort study including 763 patients, median age 65 years (interquartile range [IQR] 58-71), surgically treated for endometrial cancer between February 1995 and August 2013 at one of the 10 participating European hospitals. A BN was developed using score-based machine learning in addition to expert knowledge. Our main outcome measures were LNM and 5-year disease-specific survival (DSS). Preoperative clinical, histopathological, and molecular biomarkers were included in the network. External validation was performed using 2 prospective study cohorts: the Molecular Markers in Treatment in Endometrial Cancer (MoMaTEC) study cohort, including 446 Norwegian patients, median age 64 years (IQR 59-74), treated between May 2001 and 2010; and the PIpelle Prospective ENDOmetrial carcinoma (PIPENDO) study cohort, including 384 Dutch patients, median age 66 years (IQR 60-73), treated between September 2011 and December 2013. A BN called ENDORISK (preoperative risk stratification in endometrial cancer) was developed including the following predictors: preoperative tumor grade; immunohistochemical expression of estrogen receptor (ER), progesterone receptor (PR), p53, and L1 cell adhesion molecule (L1CAM); cancer antigen 125 serum level; thrombocyte

Contains fulltext : 165692.pdf (publisher's version ) (Open Access), BACKGROUND: Identification of aggressive endometrioid endometrial carcinomas (EECs) and non-endometrioid carcinomas (NEECs) is essential to improve outcome. L1 cell adhesion molecule (L1CAM) expression is a strong prognostic marker in stage I EECs, but less is known about L1CAM expression in advanced-stage EECs and NEECs. This study analyses L1CAM expression in a clinically representative cohort of endometrial carcinomas. METHODS: The expression of L1CAM was immunohistochemically determined in 1199 endometrial carcinomas, treated at one of the European Network for Individualized Treatment of Endometrial Cancer (ENITEC) centres. Staining was considered positive when >10% of the tumour cells expressed L1CAM. The association between L1CAM expression and several clincopathological characteristics and disease outcome was calculated. RESULTS: In all, L1CAM was expressed in 10% of the 935 stage I EECs, 18% of the 160 advanced stage EECs, and 75% of the 104 NEECs. The expression of L1CAM was associated with advanced stage, nodal involvement, high tumour grade, non-endometrioid histology, lymphovascular space invasion, and distant recurrences in all cases, and with reduced survival in the EECs, but not in the NEECs. CONCLUSIONS: The expression of L1CAM is a strong predictor of poor outcome in EECs, but not NEECs. It is strongly associated with non-endometrioid histology and distant spread, and could improve the postoperative selection of high-risk endometrial carcinomas. The value of L1CAM expression in the preoperative selection of high-risk endometrial carcinomas should be studied.

Contains fulltext : 165692.pdf (publisher's version ) (Open Access), BACKGROUND: Identification of aggressive endometrioid endometrial carcinomas (EECs) and non-endometrioid carcinomas (NEECs) is essential to improve outcome. L1 cell adhesion molecule (L1CAM) expression is a strong prognostic marker in stage I EECs, but less is known about L1CAM expression in advanced-stage EECs and NEECs. This study analyses L1CAM expression in a clinically representative cohort of endometrial carcinomas. METHODS: The expression of L1CAM was immunohistochemically determined in 1199 endometrial carcinomas, treated at one of the European Network for Individualized Treatment of Endometrial Cancer (ENITEC) centres. Staining was considered positive when >10% of the tumour cells expressed L1CAM. The association between L1CAM expression and several clincopathological characteristics and disease outcome was calculated. RESULTS: In all, L1CAM was expressed in 10% of the 935 stage I EECs, 18% of the 160 advanced stage EECs, and 75% of the 104 NEECs. The expression of L1CAM was associated with advanced stage, nodal involvement, high tumour grade, non-endometrioid histology, lymphovascular space invasion, and distant recurrences in all cases, and with reduced survival in the EECs, but not in the NEECs. CONCLUSIONS: The expression of L1CAM is a strong predictor of poor outcome in EECs, but not NEECs. It is strongly associated with non-endometrioid histology and distant spread, and could improve the postoperative selection of high-risk endometrial carcinomas. The value of L1CAM expression in the preoperative selection of high-risk endometrial carcinomas should be studied.

Contains fulltext : 165692.pdf (publisher's version ) (Open Access), BACKGROUND: Identification of aggressive endometrioid endometrial carcinomas (EECs) and non-endometrioid carcinomas (NEECs) is essential to improve outcome. L1 cell adhesion molecule (L1CAM) expression is a strong prognostic marker in stage I EECs, but less is known about L1CAM expression in advanced-stage EECs and NEECs. This study analyses L1CAM expression in a clinically representative cohort of endometrial carcinomas. METHODS: The expression of L1CAM was immunohistochemically determined in 1199 endometrial carcinomas, treated at one of the European Network for Individualized Treatment of Endometrial Cancer (ENITEC) centres. Staining was considered positive when >10% of the tumour cells expressed L1CAM. The association between L1CAM expression and several clincopathological characteristics and disease outcome was calculated. RESULTS: In all, L1CAM was expressed in 10% of the 935 stage I EECs, 18% of the 160 advanced stage EECs, and 75% of the 104 NEECs. The expression of L1CAM was associated with advanced stage, nodal involvement, high tumour grade, non-endometrioid histology, lymphovascular space invasion, and distant recurrences in all cases, and with reduced survival in the EECs, but not in the NEECs. CONCLUSIONS: The expression of L1CAM is a strong predictor of poor outcome in EECs, but not NEECs. It is strongly associated with non-endometrioid histology and distant spread, and could improve the postoperative selection of high-risk endometrial carcinomas. The value of L1CAM expression in the preoperative selection of high-risk endometrial carcinomas should be studied.