ArticleHead & neck2020
Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning.
Article in Head & neck, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Current role of artificial intelligence in head and neck cancer surgery: a systematic review of literature.Exploration of targeted anti-tumor therapy · 2023Pooled it
- Review
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- Missed Adjuvant Therapy Following Upfront Laryngectomy for Laryngeal Squamous Cell Carcinoma.Laryngoscope investigative otolaryngology · 2025Article
- Prediction of mortality risk in critically ill patients with systemic lupus erythematosus: a machine learning approach using the MIMIC-IV database.Lupus science & medicine · 2025Observational
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome.Frontiers in medicine · 2022Article
- Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMachine learning (ML) algorithms may predict patients who will require salvage total laryngectomy (STL) after primary radiotherapy with or without chemotherapy for laryngeal squamous cell carcinoma (SCC).
methodsPatients treated for T1-T3a laryngeal SCC were identified from the National Cancer Database. Multiple ML algorithms were trained to predict which patients would go on to require STL after primary nonsurgical treatment.
resultsA total of 16 440 cases were included. The best classification performance was achieved with a gradient boosting algorithm, which achieved accuracy of 76.0% (95% CI 74.5-77.5) and area under the curve = 0.762. The most important variables used to construct the model were distance from residence to treating facility and days from diagnosis to start of treatment.
conclusionWe can identify patients likely to fail primary radiotherapy with or without chemotherapy and who will go on to require STL by applying ML techniques and argue for high-quality, multidisciplinary regionalized care.
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Registered trials
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.