Validation of the Integrated Prediction Model algorithm for outcome of cytoreduction in advanced ovarian cancer

Article Oncology

Using a machine learning algorithm to predict outcome of primary cytoreductive surgery in advanced ovarian cancer

Sabrina Piedimonte et al.

Summary: This study developed a machine learning algorithm to predict the outcome of primary cytoreductive surgery in patients with advanced ovarian cancer. The algorithm demonstrated high accuracy in predicting optimal cytoreduction and may assist in decision-making.

JOURNAL OF SURGICAL ONCOLOGY (2023)

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Article Oncology

Integrated prediction model of patient factors, resectability scores and surgical complexity to predict cytoreductive outcome and guide treatment plan in advanced ovarian cancer

Sabrina Piedimonte et al.

Summary: This study reports the performance of an integrated predictive model algorithm based on patient factors, surgical resectability, and surgical complexity to predict the outcome of primary cytoreductive surgery in advanced epithelial ovarian cancer patients. The algorithm showed high sensitivity and specificity for optimal cytoreduction with acceptable morbidity without delaying adjuvant therapy.

GYNECOLOGIC ONCOLOGY (2022)

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Article Oncology

Appropriate triage allows aggressive primary debulking surgery with rates of morbidity and mortality comparable to interval surgery after chemotherapy

Deepa M. Narasimhulu et al.

Summary: Using an evidence-based algorithm to identify patients fit for surgery in advanced epithelial ovarian cancer demonstrated that despite more complex surgery, patients undergoing primary debulking surgery had comparable morbidity and mortality outcomes to those undergoing interval debulking surgery, with similar rates of complete resection and overall survival.

GYNECOLOGIC ONCOLOGY (2021)

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Article Oncology