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Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study

OBJECTIVE: To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. DESIGN: Multicentre cohort study. SETTING: 36 oncology referral centres (tertiary centres with a specific gynaecological oncology u...

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Autores principales: Van Calster, Ben, Valentin, Lil, Froyman, Wouter, Landolfo, Chiara, Ceusters, Jolien, Testa, Antonia C, Wynants, Laure, Sladkevicius, Povilas, Van Holsbeke, Caroline, Domali, Ekaterini, Fruscio, Robert, Epstein, Elisabeth, Franchi, Dorella, Kudla, Marek J, Chiappa, Valentina, Alcazar, Juan L, Leone, Francesco P G, Buonomo, Francesca, Coccia, Maria Elisabetta, Guerriero, Stefano, Deo, Nandita, Jokubkiene, Ligita, Savelli, Luca, Fischerová, Daniela, Czekierdowski, Artur, Kaijser, Jeroen, Coosemans, An, Scambia, Giovanni, Vergote, Ignace, Bourne, Tom, Timmerman, Dirk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391073/
https://www.ncbi.nlm.nih.gov/pubmed/32732303
http://dx.doi.org/10.1136/bmj.m2614
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author Van Calster, Ben
Valentin, Lil
Froyman, Wouter
Landolfo, Chiara
Ceusters, Jolien
Testa, Antonia C
Wynants, Laure
Sladkevicius, Povilas
Van Holsbeke, Caroline
Domali, Ekaterini
Fruscio, Robert
Epstein, Elisabeth
Franchi, Dorella
Kudla, Marek J
Chiappa, Valentina
Alcazar, Juan L
Leone, Francesco P G
Buonomo, Francesca
Coccia, Maria Elisabetta
Guerriero, Stefano
Deo, Nandita
Jokubkiene, Ligita
Savelli, Luca
Fischerová, Daniela
Czekierdowski, Artur
Kaijser, Jeroen
Coosemans, An
Scambia, Giovanni
Vergote, Ignace
Bourne, Tom
Timmerman, Dirk
author_facet Van Calster, Ben
Valentin, Lil
Froyman, Wouter
Landolfo, Chiara
Ceusters, Jolien
Testa, Antonia C
Wynants, Laure
Sladkevicius, Povilas
Van Holsbeke, Caroline
Domali, Ekaterini
Fruscio, Robert
Epstein, Elisabeth
Franchi, Dorella
Kudla, Marek J
Chiappa, Valentina
Alcazar, Juan L
Leone, Francesco P G
Buonomo, Francesca
Coccia, Maria Elisabetta
Guerriero, Stefano
Deo, Nandita
Jokubkiene, Ligita
Savelli, Luca
Fischerová, Daniela
Czekierdowski, Artur
Kaijser, Jeroen
Coosemans, An
Scambia, Giovanni
Vergote, Ignace
Bourne, Tom
Timmerman, Dirk
author_sort Van Calster, Ben
collection PubMed
description OBJECTIVE: To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. DESIGN: Multicentre cohort study. SETTING: 36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre. PARTICIPANTS: Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up. MAIN OUTCOME MEASURES: Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain. RESULTS: The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125. CONCLUSIONS: Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively. TRIAL REGISTRATION: ClinicalTrials.gov NCT01698632.
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spelling pubmed-73910732020-08-11 Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study Van Calster, Ben Valentin, Lil Froyman, Wouter Landolfo, Chiara Ceusters, Jolien Testa, Antonia C Wynants, Laure Sladkevicius, Povilas Van Holsbeke, Caroline Domali, Ekaterini Fruscio, Robert Epstein, Elisabeth Franchi, Dorella Kudla, Marek J Chiappa, Valentina Alcazar, Juan L Leone, Francesco P G Buonomo, Francesca Coccia, Maria Elisabetta Guerriero, Stefano Deo, Nandita Jokubkiene, Ligita Savelli, Luca Fischerová, Daniela Czekierdowski, Artur Kaijser, Jeroen Coosemans, An Scambia, Giovanni Vergote, Ignace Bourne, Tom Timmerman, Dirk BMJ Research OBJECTIVE: To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively. DESIGN: Multicentre cohort study. SETTING: 36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre. PARTICIPANTS: Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up. MAIN OUTCOME MEASURES: Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain. RESULTS: The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125. CONCLUSIONS: Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively. TRIAL REGISTRATION: ClinicalTrials.gov NCT01698632. BMJ Publishing Group Ltd. 2020-07-30 /pmc/articles/PMC7391073/ /pubmed/32732303 http://dx.doi.org/10.1136/bmj.m2614 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.
spellingShingle Research
Van Calster, Ben
Valentin, Lil
Froyman, Wouter
Landolfo, Chiara
Ceusters, Jolien
Testa, Antonia C
Wynants, Laure
Sladkevicius, Povilas
Van Holsbeke, Caroline
Domali, Ekaterini
Fruscio, Robert
Epstein, Elisabeth
Franchi, Dorella
Kudla, Marek J
Chiappa, Valentina
Alcazar, Juan L
Leone, Francesco P G
Buonomo, Francesca
Coccia, Maria Elisabetta
Guerriero, Stefano
Deo, Nandita
Jokubkiene, Ligita
Savelli, Luca
Fischerová, Daniela
Czekierdowski, Artur
Kaijser, Jeroen
Coosemans, An
Scambia, Giovanni
Vergote, Ignace
Bourne, Tom
Timmerman, Dirk
Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title_full Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title_fullStr Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title_full_unstemmed Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title_short Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
title_sort validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391073/
https://www.ncbi.nlm.nih.gov/pubmed/32732303
http://dx.doi.org/10.1136/bmj.m2614
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