Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence
The Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/15/22/5386 |
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| author | Alexandros Laios Evangelos Kalampokis Marios Evangelos Mamalis Amudha Thangavelu Richard Hutson Tim Broadhead David Nugent Diederick De Jong |
| author_facet | Alexandros Laios Evangelos Kalampokis Marios Evangelos Mamalis Amudha Thangavelu Richard Hutson Tim Broadhead David Nugent Diederick De Jong |
| author_sort | Alexandros Laios |
| collection | DOAJ |
| description | The Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not all potential surgical procedures are described by this score. Lately, the European Society for Gynaecological Oncology (ESGO) has established standard outcome quality indicators pertinent to achieving complete cytoreduction (CC0). There is a need to define what weight all these surgical sub-procedures comprising CC0 would be given. Prospectively collected data from 560 surgically cytoreduced advanced stage EOC patients were analysed at a UK tertiary referral centre.We adapted the structured ESGO ovarian cancer report template. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to model a long list of surgical sub-procedures. We applied the Shapley Additive explanations (SHAP) framework to provide global (cohort) explainability. We used Cox regression for survival analysis and constructed Kaplan-Meier curves. The XGBoost model predicted CC0 with an acceptable accuracy (area under curve [AUC] = 0.70; 95% confidence interval [CI] = 0.63–0.76). Visual quantification of the feature importance for the prediction of CC0 identified upper abdominal peritonectomy (UAP) as the most important feature, followed by regional lymphadenectomies. The UAP best correlated with bladder peritonectomy and diaphragmatic stripping (Pearson’s correlations > 0.5). Clear inflection points were shown by pelvic and para-aortic lymph node dissection and ileocecal resection/right hemicolectomy, which increased the probability for CC0. When UAP was solely added to a composite model comprising of engineered features, it substantially enhanced its predictive value (AUC = 0.80, CI = 0.75–0.84). The UAP was predictive of poorer progression-free survival (HR = 1.76, CI 1.14–2.70, P: 0.01) but not overall survival (HR = 1.06, CI 0.56–1.99, P: 0.86). The SCS did not have significant survival impact. Machine Learning allows for operational feature selection by weighting the relative importance of those surgical sub-procedures that appear to be more predictive of CC0. Our study identifies UAP as the most important procedural predictor of CC0 in surgically cytoreduced advanced-stage EOC women. The classification model presented here can potentially be trained with a larger number of samples to generate a robust digital surgical reference in high output tertiary centres. The upper abdominal quadrants should be thoroughly inspected to ensure that CC0 is achievable. |
| first_indexed | 2024-03-09T16:56:45Z |
| format | Article |
| id | doaj.art-767d7744064544d08a2535fb01ce299b |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T16:56:45Z |
| publishDate | 2023-11-01 |
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| series | Cancers |
| spelling | doaj.art-767d7744064544d08a2535fb01ce299b2023-11-24T14:34:12ZengMDPI AGCancers2072-66942023-11-011522538610.3390/cancers15225386Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial IntelligenceAlexandros Laios0Evangelos Kalampokis1Marios Evangelos Mamalis2Amudha Thangavelu3Richard Hutson4Tim Broadhead5David Nugent6Diederick De Jong7Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKInformation Systems Lab, Department of Business Administration, University of Macedonia, 54636 Thessaloniki, GreeceInformation Systems Lab, Department of Business Administration, University of Macedonia, 54636 Thessaloniki, GreeceDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKDepartment of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UKThe Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not all potential surgical procedures are described by this score. Lately, the European Society for Gynaecological Oncology (ESGO) has established standard outcome quality indicators pertinent to achieving complete cytoreduction (CC0). There is a need to define what weight all these surgical sub-procedures comprising CC0 would be given. Prospectively collected data from 560 surgically cytoreduced advanced stage EOC patients were analysed at a UK tertiary referral centre.We adapted the structured ESGO ovarian cancer report template. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to model a long list of surgical sub-procedures. We applied the Shapley Additive explanations (SHAP) framework to provide global (cohort) explainability. We used Cox regression for survival analysis and constructed Kaplan-Meier curves. The XGBoost model predicted CC0 with an acceptable accuracy (area under curve [AUC] = 0.70; 95% confidence interval [CI] = 0.63–0.76). Visual quantification of the feature importance for the prediction of CC0 identified upper abdominal peritonectomy (UAP) as the most important feature, followed by regional lymphadenectomies. The UAP best correlated with bladder peritonectomy and diaphragmatic stripping (Pearson’s correlations > 0.5). Clear inflection points were shown by pelvic and para-aortic lymph node dissection and ileocecal resection/right hemicolectomy, which increased the probability for CC0. When UAP was solely added to a composite model comprising of engineered features, it substantially enhanced its predictive value (AUC = 0.80, CI = 0.75–0.84). The UAP was predictive of poorer progression-free survival (HR = 1.76, CI 1.14–2.70, P: 0.01) but not overall survival (HR = 1.06, CI 0.56–1.99, P: 0.86). The SCS did not have significant survival impact. Machine Learning allows for operational feature selection by weighting the relative importance of those surgical sub-procedures that appear to be more predictive of CC0. Our study identifies UAP as the most important procedural predictor of CC0 in surgically cytoreduced advanced-stage EOC women. The classification model presented here can potentially be trained with a larger number of samples to generate a robust digital surgical reference in high output tertiary centres. The upper abdominal quadrants should be thoroughly inspected to ensure that CC0 is achievable.https://www.mdpi.com/2072-6694/15/22/5386epithelial ovarian cancercomplete cytoreductionupper abdominal peritonectomymachine learningexplainable artificial intelligencesurvival |
| spellingShingle | Alexandros Laios Evangelos Kalampokis Marios Evangelos Mamalis Amudha Thangavelu Richard Hutson Tim Broadhead David Nugent Diederick De Jong Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence Cancers epithelial ovarian cancer complete cytoreduction upper abdominal peritonectomy machine learning explainable artificial intelligence survival |
| title | Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence |
| title_full | Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence |
| title_fullStr | Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence |
| title_full_unstemmed | Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence |
| title_short | Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence |
| title_sort | exploring the potential role of upper abdominal peritonectomy in advanced ovarian cancer cytoreductive surgery using explainable artificial intelligence |
| topic | epithelial ovarian cancer complete cytoreduction upper abdominal peritonectomy machine learning explainable artificial intelligence survival |
| url | https://www.mdpi.com/2072-6694/15/22/5386 |
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