Abstract:
OBJECTIVE:Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. METHODS:This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. RESULTS:Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included: disease-free interval (DFI) (importance: 0.231), retroperitoneal recurrence (importance: 0.178), residual disease at primary surgical treatment (importance: 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance: 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance: 0.306). Other important variables included: CC (importance: 0.217), and FIGO stage at presentation (importance: 0.100). CONCLUSION:According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process.
journal_name
J Gynecol Oncoljournal_title
Journal of gynecologic oncologyauthors
Bogani G,Rossetti D,Ditto A,Martinelli F,Chiappa V,Mosca L,Leone Roberti Maggiore U,Ferla S,Lorusso D,Raspagliesi Fdoi
10.3802/jgo.2018.29.e66subject
Has Abstractpub_date
2018-09-01 00:00:00pages
e66issue
5eissn
2005-0380issn
2005-0399pii
29.e66journal_volume
29pub_type
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journal_title:Journal of gynecologic oncology
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journal_title:Journal of gynecologic oncology
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abstract::This corrects the article on p. e43 in vol. 29, PMID: 29770616. ...
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更新日期:2018-09-01 00:00:00
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journal_title:Journal of gynecologic oncology
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journal_title:Journal of gynecologic oncology
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更新日期:2019-07-01 00:00:00
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更新日期:2011-09-01 00:00:00
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更新日期:2017-11-01 00:00:00
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journal_title:Journal of gynecologic oncology
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doi:10.3802/jgo.2016.27.e29
更新日期:2016-05-01 00:00:00
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journal_title:Journal of gynecologic oncology
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doi:10.3802/jgo.2015.26.4.293
更新日期:2015-10-01 00:00:00
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journal_title:Journal of gynecologic oncology
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doi:10.3802/jgo.2017.28.e8
更新日期:2017-03-01 00:00:00
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更新日期:2018-05-01 00:00:00
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更新日期:2018-11-01 00:00:00
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journal_title:Journal of gynecologic oncology
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更新日期:2018-07-01 00:00:00
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doi:10.3802/jgo.2011.22.1.9
更新日期:2011-03-31 00:00:00
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doi:10.3802/jgo.2016.27.e4
更新日期:2016-01-01 00:00:00
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doi:10.3802/jgo.2008.19.4.241
更新日期:2008-12-01 00:00:00
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更新日期:2019-01-01 00:00:00
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更新日期:2008-12-01 00:00:00
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journal_title:Journal of gynecologic oncology
pub_type: 杂志文章
doi:10.3802/jgo.2020.31.e10
更新日期:2020-01-01 00:00:00
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doi:10.3802/jgo.2010.21.3.174
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