ArticleJournal of ovarian research2020
Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.
Article in Journal of ovarian research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Pooled it
- A folate receptor-targeted NIR-II probe improves intraoperative imaging and surgery of metastatic ovarian cancer.Science advances · 2026Article
- Ileocecal and Small Bowel Involvement Are Independently Associated with Inferior Survival Despite Complete Cytoreduction in FIGO IIIC-IV Tubo-Ovarian and Primary Peritoneal Carcinoma.Annals of surgical oncology · 2026Article
- The optimal SVM model from multi-model comparison outperforms conventional criteria in preoperative prediction of ovarian cancer residual disease.Journal of ovarian research · 2026Article
- A Review on Biomarker-Enhanced Machine Learning for Early Diagnosis and Outcome Prediction in Ovarian Cancer Management.Cancer medicine · 2025Review
- A pioneering artificial intelligence tool to predict treatment outcomes in ovarian cancer via diagnostic laparoscopy.Scientific reports · 2025Article
- Management of Deep Neck Infection Associated with Descending Necrotizing Mediastinitis: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Review
- RETRACTED: A novel spectral transformation technique based on special functions for improved chest X-ray image classification.PloS one · 2025Article
- Artificial intelligence for precision management of epithelial ovarian cancer: a comprehensive review.Frontiers in medicine · 2025Review
- Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques.Heliyon · 2024Article
- Application of artificial intelligence in the diagnosis, treatment, and recurrence prediction of peritoneal carcinomatosis.Heliyon · 2024Review
- Article
- Prerequisites to improve surgical cytoreduction in FIGO stage III/IV epithelial ovarian cancer and subsequent clinical ramifications.Journal of ovarian research · 2023Article
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- Beauty is in the explainable artificial intelligence (XAI) of the "agnostic" beholder.Translational cancer research · 2023Article
- Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score.Current oncology (Toronto, Ont.) · 2022Article
- Survival and Chemosensitivity in Advanced High Grade Serous Epithelial Ovarian Cancer Patients with and without a BRCA Germline Mutation: More Evidence for Shifting the Paradigm towards Complete Surgical Cytoreduction.Medicina (Kaunas, Lithuania) · 2022Article
- Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.BMC medical imaging · 2022Article
- Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings-Preliminary Data and a Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective systems for classification and prediction. For a disease as heterogenous as ovarian cancer, they could potentially outperform conventional predictive algorithms for routine clinical use. We investigated the performance of an AI system, the k-nearest neighbor (k-NN) classifier, to predict R0, comparing it with logistic regression. Patients diagnosed with advanced stage, high grade serous ovarian, tubal and primary peritoneal cancer, undergoing surgical cytoreduction from 2015 to 2019, was selected from the ovarian database. Performance variables included age, BMI, Charlson Comorbidity Index, timing of surgery, surgical complexity and disease score. The k-NN algorithm classified R0 vs non-R0 patients using 3-20 nearest neighbors. Prediction accuracy was estimated as percentage of observations in the training set correctly classified.
results154 patients were identified, with mean age of 64.4 + 10.5 yrs., BMI of 27.2 + 5.8 and mean SCS of 3 + 1 (1-8). Complete and optimal cytoreduction was achieved in 62 and 88% patients. The mean predictive accuracy was 66%. R0 resection prediction of true negatives was as high as 90% using k = 20 neighbors.
conclusionsThe k-NN algorithm is a promising and versatile tool for R0 resection prediction. It slightly outperforms logistic regression and is expected to improve accuracy with data expansion.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.