This preprint is under consideration at Journal of Ovarian Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
chao-yi xu
Second Affiliated Hospital of Wenzhou Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1684-0007
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This work is licensed under a CC BY 4.0 License. Read Full License
Objective
Emerging evidence indicates that nearly 20% of patients with incompletely staged early-stage ovarian cancer develop to advanced stage because of lymph node metastases. The aim of the present study is to establish machine learning (ML) based predictive models for lymph node metastasis (LNM) in early-stage epithelial ovarian cancer (EOC).
Methods
The Surveillance, Epidemiology, and End Results (SEER) database was used to select patients diagnosed with early T classification epithelial ovarian cancer (EOC) between 2010 and 2015. The possibility of LNM was predicted by comparing the six ML algorithms. Model performance was compared in terms of accuracy, sensitivity, specificity, F1 score, and the area under the curve (AUC). The Shapley Additive Explanation (SHAP) analysis was employed to generate explanations and was presented as patient-specific visualizations.
Results
Screening of the SEER database yielded 3400 patients with early-stage epithelial ovarian cancer, and the data were divided randomly into a training set (70%), validation set (15%), and test set (15%). A grid search with 10-fold cross-validation was performed on the training set to tune the parameters. Overall, the Random Forest (RF) (accuracy of 79.8% and AUC of 0.878) was the best performing classifier and the Extreme Gradient Boosting (XGBoost) (accuracy of 77.2% and AUC of 0.857) demonstrated a similar high performance. The RF model performance was highly dependent on five top-rank features, including histology, grade, marital status, chemotherapy, and tumor size. SHAP analysis provided model-agnostic interpretation illustrating significant clinical contributors associated with risks of LNM in early-stage epithelial ovarian cancer (EOC).
Conclusions
The established ML-based prediction model for LNM in early-stage EOC is valid and valuable in improving clinical decision-making.
Keywords
Early-stage, Epithelial ovarian cancer, Lymph node metastasis, Machine learning, Surveillance, epidemiology, and end results
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This preprint is under consideration at Journal of Ovarian Research. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
chao-yi xu
Second Affiliated Hospital of Wenzhou Medical University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-1684-0007
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 25 Oct, 2021
No community comments so far
Reviewers invited
Invitations sent on 05 Nov, 2021
Editor assigned
On 21 Oct, 2021
First submitted
On 20 Oct, 2021
Submission checks complete
On 20 Oct, 2021
Editor invited
On 20 Oct, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Objective
Emerging evidence indicates that nearly 20% of patients with incompletely staged early-stage ovarian cancer develop to advanced stage because of lymph node metastases. The aim of the present study is to establish machine learning (ML) based predictive models for lymph node metastasis (LNM) in early-stage epithelial ovarian cancer (EOC).
Methods
The Surveillance, Epidemiology, and End Results (SEER) database was used to select patients diagnosed with early T classification epithelial ovarian cancer (EOC) between 2010 and 2015. The possibility of LNM was predicted by comparing the six ML algorithms. Model performance was compared in terms of accuracy, sensitivity, specificity, F1 score, and the area under the curve (AUC). The Shapley Additive Explanation (SHAP) analysis was employed to generate explanations and was presented as patient-specific visualizations.
Results
Screening of the SEER database yielded 3400 patients with early-stage epithelial ovarian cancer, and the data were divided randomly into a training set (70%), validation set (15%), and test set (15%). A grid search with 10-fold cross-validation was performed on the training set to tune the parameters. Overall, the Random Forest (RF) (accuracy of 79.8% and AUC of 0.878) was the best performing classifier and the Extreme Gradient Boosting (XGBoost) (accuracy of 77.2% and AUC of 0.857) demonstrated a similar high performance. The RF model performance was highly dependent on five top-rank features, including histology, grade, marital status, chemotherapy, and tumor size. SHAP analysis provided model-agnostic interpretation illustrating significant clinical contributors associated with risks of LNM in early-stage epithelial ovarian cancer (EOC).
Conclusions
The established ML-based prediction model for LNM in early-stage EOC is valid and valuable in improving clinical decision-making.
Figure 1
Figure 2
Figure 3
Figure 4
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