2018
DOI: 10.3802/jgo.2018.29.e66
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Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer

Abstract: ObjectiveAccumulating 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.MethodsThis is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance… Show more

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Cited by 35 publications
(29 citation statements)
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“…Another AI model predicted the outcome of surgery and again showed that ANN could predict outcome (optimal cytoreduction vs. suboptimal cytoreduction) with 77% accuracy and an AUC of 0.73. Application of AI weighted the importance of factors predicting CCR at secondary cytoreductive surgery for recurrent ovarian cancer [20].…”
Section: Discussionmentioning
confidence: 99%
“…Another AI model predicted the outcome of surgery and again showed that ANN could predict outcome (optimal cytoreduction vs. suboptimal cytoreduction) with 77% accuracy and an AUC of 0.73. Application of AI weighted the importance of factors predicting CCR at secondary cytoreductive surgery for recurrent ovarian cancer [20].…”
Section: Discussionmentioning
confidence: 99%
“…[9][10][11] In fact, the absence of peritoneal dissemination makes these patients the ideal candidates for a local treatment such as surgery. Several studies reported outcomes of patients having SCS owing to isolated nodal recurrence, [9][10][11][12][22][23][24][25] but to date no study investigated how the extension of surgery influences oncologic outcomes.…”
Section: Discussionmentioning
confidence: 99%
“…62 Such AI-assisted imaging-related clinical tasks can increase the efficiency of health-care delivery by reducing the cognitive burden of human experts. 63 In the latest research mentioned in this review, it is found that the application performance of AI in gynecologic oncology at present mostly exceeds the existing methods and models in prognosis and diagnosis 21,27,29,31,53,54,[56][57][58][59][60] and it is also superior to the less experienced clinicians, 20 or equivalent to the most experienced clinicians. 22,23,30 In the comparison of AI itself, it is also found that the performance of the ensemble classifier combining DL and SL is often the best, 28,34 DL (ANN, CNN, FFBPNN, and PNN, etc) is often better than SL (CARTs, SVM, and RF, etc), 20,21,26,29,57,59 but a small part of them are the same 28 or even the opposite.…”
Section: Ai In Gynecologic Malignant Tumor Diagnosis and Treatment Prmentioning
confidence: 96%
“… 52 AI is thus used to measure the importance of individual patients and disease variables to determine who among the many recurrent ovarian cancer patients is worth SCS. Bogani et al 53 conducted a retrospective study to evaluate 194 patients with recurrent ovarian cancer who have been treated by SCS. ANN analysis was used to estimate the importance of different variables and predict complete cytoreduction (CC) and survival.…”
Section: Application Of Ai In Predicting Prognosis Of Gynecological Mmentioning
confidence: 99%