Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics?
Background: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study...
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MDPI AG
2023-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/16/1/172 |
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| author | Luca Giannella Francesco Piva Giovanni Delli Carpini Jacopo Di Giuseppe Camilla Grelloni Matteo Giulietti Francesco Sopracordevole Giorgio Giorda Anna Del Fabro Nicolò Clemente Barbara Gardella Giorgio Bogani Orsola Brasile Ruby Martinello Marta Caretto Alessandro Ghelardi Gianluca Albanesi Guido Stevenazzi Paolo Venturini Maria Papiccio Marco Cannì Maggiorino Barbero Massimiliano Fambrini Veronica Maggi Stefano Uccella Arsenio Spinillo Francesco Raspagliesi Pantaleo Greco Tommaso Simoncini Felice Petraglia Andrea Ciavattini |
| author_facet | Luca Giannella Francesco Piva Giovanni Delli Carpini Jacopo Di Giuseppe Camilla Grelloni Matteo Giulietti Francesco Sopracordevole Giorgio Giorda Anna Del Fabro Nicolò Clemente Barbara Gardella Giorgio Bogani Orsola Brasile Ruby Martinello Marta Caretto Alessandro Ghelardi Gianluca Albanesi Guido Stevenazzi Paolo Venturini Maria Papiccio Marco Cannì Maggiorino Barbero Massimiliano Fambrini Veronica Maggi Stefano Uccella Arsenio Spinillo Francesco Raspagliesi Pantaleo Greco Tommaso Simoncini Felice Petraglia Andrea Ciavattini |
| author_sort | Luca Giannella |
| collection | DOAJ |
| description | Background: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. Methods: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). Results: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. Conclusions: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable. |
| first_indexed | 2024-03-08T15:10:23Z |
| format | Article |
| id | doaj.art-5f97186928c245848b1684f3468e1ed2 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-08T15:10:23Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-5f97186928c245848b1684f3468e1ed22024-01-10T14:52:55ZengMDPI AGCancers2072-66942023-12-0116117210.3390/cancers16010172Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics?Luca Giannella0Francesco Piva1Giovanni Delli Carpini2Jacopo Di Giuseppe3Camilla Grelloni4Matteo Giulietti5Francesco Sopracordevole6Giorgio Giorda7Anna Del Fabro8Nicolò Clemente9Barbara Gardella10Giorgio Bogani11Orsola Brasile12Ruby Martinello13Marta Caretto14Alessandro Ghelardi15Gianluca Albanesi16Guido Stevenazzi17Paolo Venturini18Maria Papiccio19Marco Cannì20Maggiorino Barbero21Massimiliano Fambrini22Veronica Maggi23Stefano Uccella24Arsenio Spinillo25Francesco Raspagliesi26Pantaleo Greco27Tommaso Simoncini28Felice Petraglia29Andrea Ciavattini30Woman’s Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, ItalyDepartment of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, 60131 Ancona, ItalyWoman’s Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, ItalyWoman’s Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, ItalyWoman’s Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, ItalyDepartment of Specialistic Clinical and Odontostomatological Sciences, Polytechnic University of Marche, 60131 Ancona, ItalyGynecologic Oncology Unit, IRCCS—Centro di Riferimento Oncologico di Aviano, 33081 Aviano, ItalyGynecologic Oncology Unit, IRCCS—Centro di Riferimento Oncologico di Aviano, 33081 Aviano, ItalyGynecologic Oncology Unit, IRCCS—Centro di Riferimento Oncologico di Aviano, 33081 Aviano, ItalyGynecologic Oncology Unit, IRCCS—Centro di Riferimento Oncologico di Aviano, 33081 Aviano, ItalyDepartment of Obstetrics and Gynecology, Fondazione IRCCS Policlinico San Matteo, Università degli Studi di Pavia, 27100 Pavia, ItalyGynecological Oncology Unit, Fondazione IRCCS—Istituto Nazionale Tumori, 20133 Milano, ItalySection of Obstetrics and Gynecology, Department of Medical Sciences, University of Ferrara, 44124 Ferrara, ItalySection of Obstetrics and Gynecology, Department of Medical Sciences, University of Ferrara, 44124 Ferrara, ItalyDivision of Obstetrics and Gynecology, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, ItalyUOC Ostetricia e Ginecologia, Ospedale Apuane, Azienda Usl Toscana Nord-Ovest, 54100 Massa, ItalyUOC Ostetricia e Ginecologia, Ospedale Apuane, Azienda Usl Toscana Nord-Ovest, 54100 Massa, ItalyDepartment of Obstetrics and Gynaecology, ASST Ovest MI, Legnano (Milan) Hospital, 20025 Legnano, ItalyDivision of Obstetrics and Gynecology, AUSL di Modena, 41012 Carpi, ItalyDivision of Obstetrics and Gynecology, AUSL di Modena, 41012 Carpi, ItalyDepartment of Obstetrics and Gynecology, Asti Community Hospital, 14100 Asti, ItalyDepartment of Obstetrics and Gynecology, Asti Community Hospital, 14100 Asti, ItalyObstetrics and Gynecology, Department of Experimental, Clinical, and Biomedical Sciences, Careggi University Hospital, University of Florence, 50121 Florence, ItalyDepartment of Obstetrics and Gynecology, University of Verona, 37129 Verona, ItalyDepartment of Obstetrics and Gynecology, University of Verona, 37129 Verona, ItalyDepartment of Obstetrics and Gynecology, Fondazione IRCCS Policlinico San Matteo, Università degli Studi di Pavia, 27100 Pavia, ItalyGynecological Oncology Unit, Fondazione IRCCS—Istituto Nazionale Tumori, 20133 Milano, ItalySection of Obstetrics and Gynecology, Department of Medical Sciences, University of Ferrara, 44124 Ferrara, ItalyDivision of Obstetrics and Gynecology, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, ItalyObstetrics and Gynecology, Department of Experimental, Clinical, and Biomedical Sciences, Careggi University Hospital, University of Florence, 50121 Florence, ItalyWoman’s Health Sciences Department, Gynecologic Section, Polytechnic University of Marche, 60123 Ancona, ItalyBackground: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. Methods: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). Results: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. Conclusions: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable.https://www.mdpi.com/2072-6694/16/1/172atypical endometrial hyperplasiaendometrial cancerartificial intelligenceregression modelsprediction modelpatient characteristics |
| spellingShingle | Luca Giannella Francesco Piva Giovanni Delli Carpini Jacopo Di Giuseppe Camilla Grelloni Matteo Giulietti Francesco Sopracordevole Giorgio Giorda Anna Del Fabro Nicolò Clemente Barbara Gardella Giorgio Bogani Orsola Brasile Ruby Martinello Marta Caretto Alessandro Ghelardi Gianluca Albanesi Guido Stevenazzi Paolo Venturini Maria Papiccio Marco Cannì Maggiorino Barbero Massimiliano Fambrini Veronica Maggi Stefano Uccella Arsenio Spinillo Francesco Raspagliesi Pantaleo Greco Tommaso Simoncini Felice Petraglia Andrea Ciavattini Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? Cancers atypical endometrial hyperplasia endometrial cancer artificial intelligence regression models prediction model patient characteristics |
| title | Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? |
| title_full | Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? |
| title_fullStr | Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? |
| title_full_unstemmed | Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? |
| title_short | Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics? |
| title_sort | concurrent endometrial cancer in women with atypical endometrial hyperplasia what is the predictive value of patient characteristics |
| topic | atypical endometrial hyperplasia endometrial cancer artificial intelligence regression models prediction model patient characteristics |
| url | https://www.mdpi.com/2072-6694/16/1/172 |
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