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Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence

SIMPLE SUMMARY: The current scoring systems fail to reflect the patient’s real anatomy, as seen by the surgeon upon cytoreduction for advanced-stage epithelial ovarian cancer (EOC). Using artificial intelligence, we developed a novel intra-operative score based on specific weights assigned to the pa...

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Autores principales: Laios, Alexandros, Kalampokis, Evangelos, Johnson, Racheal, Munot, Sarika, Thangavelu, Amudha, Hutson, Richard, Broadhead, Tim, Theophilou, Georgios, Nugent, David, De Jong, Diederick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913185/
https://www.ncbi.nlm.nih.gov/pubmed/36765924
http://dx.doi.org/10.3390/cancers15030966
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author Laios, Alexandros
Kalampokis, Evangelos
Johnson, Racheal
Munot, Sarika
Thangavelu, Amudha
Hutson, Richard
Broadhead, Tim
Theophilou, Georgios
Nugent, David
De Jong, Diederick
author_facet Laios, Alexandros
Kalampokis, Evangelos
Johnson, Racheal
Munot, Sarika
Thangavelu, Amudha
Hutson, Richard
Broadhead, Tim
Theophilou, Georgios
Nugent, David
De Jong, Diederick
author_sort Laios, Alexandros
collection PubMed
description SIMPLE SUMMARY: The current scoring systems fail to reflect the patient’s real anatomy, as seen by the surgeon upon cytoreduction for advanced-stage epithelial ovarian cancer (EOC). Using artificial intelligence, we developed a novel intra-operative score based on specific weights assigned to the patterns of cancer dissemination. We employed an explainable artificial intelligence (XAI) framework to explain feature effects associated with complete cytoreduction (CC0). The presence of cancer dissemination in specific anatomical sites, including the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, could be more predictive of CC0 than the existing scoring tools. Early intra-operative assessment of these areas only accurately predicts CC0 in 9 out of 10 patients and can guide patient selection. The novel score remains predictive of adverse survival outcomes. ABSTRACT: Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient’s real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan–Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854–0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849–0.963). We identified a “turning point”, ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03–1.09, p < 0.005) and overall survival (HR: 1.04; 95% CI: 1.01–1.07), by 4% and 6%, respectively. (4) Conclusions: The presence of cancer disseminated in specific anatomical sites, including small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, can be more predictive of CC0 and survival than the entire PCI and IMO scores. Early intra-operative assessment of these areas only may reveal whether CC0 is achievable. In contrast to the PCI and IMO scores, the novel score remains predictive of adverse survival outcomes.
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spelling pubmed-99131852023-02-11 Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence Laios, Alexandros Kalampokis, Evangelos Johnson, Racheal Munot, Sarika Thangavelu, Amudha Hutson, Richard Broadhead, Tim Theophilou, Georgios Nugent, David De Jong, Diederick Cancers (Basel) Article SIMPLE SUMMARY: The current scoring systems fail to reflect the patient’s real anatomy, as seen by the surgeon upon cytoreduction for advanced-stage epithelial ovarian cancer (EOC). Using artificial intelligence, we developed a novel intra-operative score based on specific weights assigned to the patterns of cancer dissemination. We employed an explainable artificial intelligence (XAI) framework to explain feature effects associated with complete cytoreduction (CC0). The presence of cancer dissemination in specific anatomical sites, including the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, could be more predictive of CC0 than the existing scoring tools. Early intra-operative assessment of these areas only accurately predicts CC0 in 9 out of 10 patients and can guide patient selection. The novel score remains predictive of adverse survival outcomes. ABSTRACT: Background: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient’s real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan–Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854–0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849–0.963). We identified a “turning point”, ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03–1.09, p < 0.005) and overall survival (HR: 1.04; 95% CI: 1.01–1.07), by 4% and 6%, respectively. (4) Conclusions: The presence of cancer disseminated in specific anatomical sites, including small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, can be more predictive of CC0 and survival than the entire PCI and IMO scores. Early intra-operative assessment of these areas only may reveal whether CC0 is achievable. In contrast to the PCI and IMO scores, the novel score remains predictive of adverse survival outcomes. MDPI 2023-02-03 /pmc/articles/PMC9913185/ /pubmed/36765924 http://dx.doi.org/10.3390/cancers15030966 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Laios, Alexandros
Kalampokis, Evangelos
Johnson, Racheal
Munot, Sarika
Thangavelu, Amudha
Hutson, Richard
Broadhead, Tim
Theophilou, Georgios
Nugent, David
De Jong, Diederick
Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title_full Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title_fullStr Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title_full_unstemmed Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title_short Development of a Novel Intra-Operative Score to Record Diseases’ Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence
title_sort development of a novel intra-operative score to record diseases’ anatomic fingerprints (anafi score) for the prediction of complete cytoreduction in advanced-stage ovarian cancer by using machine learning and explainable artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9913185/
https://www.ncbi.nlm.nih.gov/pubmed/36765924
http://dx.doi.org/10.3390/cancers15030966
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