Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer

Giorgio Bogani, Diego Rossetti, Antonino Ditto, Fabio Martinelli, Valentina Chiappa, Lavinia Mosca, Umberto Leone Roberti Maggiore, Stefano Ferla, Domenica Lorusso, Francesco Raspagliesi

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)e66
JournalJournal of Gynecologic Oncology
Volume29
Issue number5
DOIs
Publication statusPublished - Sep 2018

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Artificial Intelligence
Ovarian Neoplasms
Weights and Measures
Survival
Gynecology
Obstetrics
Aptitude
Disease-Free Survival
Decision Making
Retrospective Studies
Learning
Neurons
Recurrence

Cite this

@article{4afc8bbdd25c4425b86a0cebb3977ac7,
title = "Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer",
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.",
author = "Giorgio Bogani and Diego Rossetti and Antonino Ditto and Fabio Martinelli and Valentina Chiappa and Lavinia Mosca and {Leone Roberti Maggiore}, Umberto and Stefano Ferla and Domenica Lorusso and Francesco Raspagliesi",
note = "Copyright {\circledC} 2018. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology.",
year = "2018",
month = "9",
doi = "10.3802/jgo.2018.29.e66",
language = "English",
volume = "29",
pages = "e66",
journal = "Journal of Gynecologic Oncology",
issn = "2005-0380",
publisher = "Korean Society of Gynecologic Oncology and Colposcopy",
number = "5",

}

TY - JOUR

T1 - Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer

AU - Bogani, Giorgio

AU - Rossetti, Diego

AU - Ditto, Antonino

AU - Martinelli, Fabio

AU - Chiappa, Valentina

AU - Mosca, Lavinia

AU - Leone Roberti Maggiore, Umberto

AU - Ferla, Stefano

AU - Lorusso, Domenica

AU - Raspagliesi, Francesco

N1 - Copyright © 2018. Asian Society of Gynecologic Oncology, Korean Society of Gynecologic Oncology.

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

U2 - 10.3802/jgo.2018.29.e66

DO - 10.3802/jgo.2018.29.e66

M3 - Article

C2 - 30022630

VL - 29

SP - e66

JO - Journal of Gynecologic Oncology

JF - Journal of Gynecologic Oncology

SN - 2005-0380

IS - 5

ER -