Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Lustberg, Tim; Bailey, Michael; Thwaites, David; Miller, Andrew; Carolan, Martin G; Holloway, Lois; Velazquez, Emmanuel Rios; Hoebers, Frank; Dekker, André

doi:10.18632/oncotarget.8755

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…There are no available models predicting 2‐year head and neck CSS in patients at the site and subsite level, however, a similar study has used a model developed by Egelmeer et al 29 to externally validate 2‐year OS in a laryngeal carcinoma cohort using a related class of routinely collected clinical features. The authors reported a model with acceptable performance, supporting the models use in prognosis prediction 6 . Though a direct comparison cannot be made, our best model predicted 2‐year laryngeal CSS using similar clinical features and population size with excellent performance (GBT and RF, AUC = 0.98).…”

Section: Discussionsupporting

confidence: 62%

“…To the best of our knowledge, there are no publicly available models which have predicted 2‐year CSS in HNC populations using clinical information, nor are there any available models whereby 2‐year CSS can be predicted for individual patients, since overall survival (OS) is the more commonly reported survival outcome in the HNC population. One study investigated 2‐year OS in a laryngeal carcinoma cohort using clinical information from trial and clinical cohorts, reporting poor and acceptable model performance, respectively 6 . Other studies predicted OS at 5‐years with excellent performance in tongue 7 and oral 8 cancer cohorts, and at any time in an oral cancer cohort, reporting excellent performance 9 …”

Section: Introductionmentioning

confidence: 99%

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Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models

et al. 2022

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Background Knowledge of the prognostic factors and performance of machine learning predictive models for 2‐year cancer‐specific survival (CSS) is limited in the head and neck cancer (HNC) population. Methods Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Results Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2‐year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset. Conclusions Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2‐year head and neck CSS.

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Section: Discussionsupporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models

et al. 2022

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“…ML techniques can discover and identify patterns and relationships between treatment methods and outcomes. Using complex datasets, ML algorithms are increasingly able to predict outcomes for a specific cancer type [16,29,[31][32][33][34].…”

Section: Prediction Modelsmentioning

confidence: 99%

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Lee¹,

Ju²,

Choi³

et al. 2019

Artificial Intelligence - Scope and Limitations

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Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described.

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“…validated a non–small cell lung cancer (NSCLC) two‐year overall survival model 9 on a single‐centre Australian cohort. Lustberg et al 10 . repeated this for laryngeal carcinoma at a different Australian centre and compared results to an RTOG trial dataset.…”

Section: Introductionmentioning

confidence: 99%

Implementation of the Australian Computer‐Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

et al. 2021

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Summary Introduction There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non‐existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres. Methods A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort. Results The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model‐based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG‐9410, RTOG‐0617). External validation of a 2‐year overall survival model for non–small cell lung cancer (NSCLC) gave an AUC of 0.65 and C‐index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice‐changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024). Conclusion Population‐based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.

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Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Cited by 9 publications

References 27 publications

Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models

Empirical comparison of routinely collected electronic health record data for head and neck cancer‐specific survival in machine‐learnt prognostic models

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Implementation of the Australian Computer‐Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

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