Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck

Tighe, David; Lewis-Morris, Timothy; Freitas, A

doi:10.1016/j.bjoms.2019.05.026

Cited by 11 publications

(8 citation statements)

References 15 publications

(12 reference statements)

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“…ML is increasingly used in the medical community, particularly in the field of oncology. Previous studies have demonstrated that ML models can provide better accuracy and discrimination for the prediction of prognoses for lung adenocarcinoma (12) and breast cancer (13), chemoradiation therapy response in rectal cancer (14), radiotherapy response for acromegaly (15), surgical outcomes for head and neck cancer (16), and diagnosis for leukemia (17). For sellar region tumors, ML could be more effective for predicting a patient's clinical outcome and could provide better clinical decision support for neuroendocrinologists and neurosurgeons (18).…”

Section: Introductionmentioning

confidence: 99%

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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Background: Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperative treatment during long-term follow-up; this phenomenon is defined as postoperative delayed remission (DR). DR may complicate the interpretation of surgical outcomes in patients with acromegaly and interfere with decision-making regarding postoperative adjuvant therapy. Objective: We aimed to develop and validate machine learning (ML) models for predicting DR in acromegaly patients who have not achieved remission within 6 months of surgery. Methods: We enrolled 306 acromegaly patients and randomly divided them into training and test datasets. We used the recursive feature elimination (RFE) algorithm to select features and applied six ML algorithms to construct DR prediction models. The performance of these ML models was validated using receiver operating characteristics analysis. We used permutation importance, SHapley Additive exPlanations (SHAP), and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. Results: Fifty-five (17.97%) acromegaly patients met the criteria for DR, and five features (post-1w rGH, post-1w nGH, post-6m rGH, post-6m IGF-1, and post-6m nGH) were significantly associated with DR in both the training and the test datasets. After the RFE feature selection, the XGboost model, which comprised the 15 important features, had the greatest discriminatory ability (area under the curve = 0.8349, sensitivity = 0.8889, Youden's index = 0.6842). The XGboost model showed good discrimination ability and provided significantly better estimates of DR of patients with acromegaly compared with using only the Knosp grade. The results obtained from permutation importance, SHAP, and LIME algorithms showed that post-6m IGF-1 is the most important feature in XGboost algorithm prediction and showed the reliability and the clinical practicability of the XGboost model in DR prediction. Dai et al. ML Models Predict Acromegaly DR Conclusions: ML-based models can serve as an effective non-invasive approach to predicting DR and could aid in determining individual treatment and follow-up strategies for acromegaly patients who have not achieved remission within 6 months of surgery.

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Section: Introductionmentioning

confidence: 99%

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Dai

Fan

et al. 2020

Front. Endocrinol.

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“…Two other groups have applied ML models to predict LOS after major HNC surgery. Tighe et al used several ML models across a multicenter cohort to predict LOS less than 15 or 20 days 24 . Limited predictors were included, and sparse details were provided regarding which predictors were significant.…”

Section: Discussionmentioning

confidence: 99%

“…They include age, baseline creatinine, monocyte count, and duration of surgery. Older age is a known characteristic for predicting prolonged LOS for patients undergoing head and neck surgery 2,24 . Creatinine may be a surrogate factor for patient comorbidities and has been shown to be a predictor of medical complications in other surgical populations 46 .…”

Section: Discussionmentioning

confidence: 99%

“…Although it is reliable in certain populations, various studies have highlighted its limited predictive utility for outcomes in HNC patients, including LOS 17–22 . Although risk factors for prolonged LOS in HNC patients have been extensively described, few validated risk prediction models exist 23–25 . Work in this area has relied primarily on traditional statistical methods.…”

Section: Introductionmentioning

confidence: 99%

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Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS‐NSQIP

Namavarian,

Gabinet‐Equihua,

Deng

et al. 2024

The Laryngoscope

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ObjectiveAccurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS‐NSQIP) calculator in predicting LOS following surgery for OCC.Materials and MethodsA retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy.ResultsTotally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4‐day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS‐NSQIP calculator's performance (0.23, 59%).ConclusionWe developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS‐NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice.Level of EvidenceLevel 3 Laryngoscope, 2024

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“…We summarise results by presenting the 'champion models' of four metrics: complications within 30 days; severe complications (Clavien-Dindo >3) within 30 days; length of hospital stay (days); and positivity of surgical margins (Table 2). Further details, including calibration test results, are included in their respective publications (10)(11)(12) and model outputs (Supplementary Material 1-4).…”

Section: Methodsmentioning

confidence: 99%

Risk adjustment in audit of outcome after head and neck surgery applied to cumulative sum chart methodology to monitor of free flap failure

Tighe¹,

McMahon²,

Ho³

et al. 2022

Front Oral Maxillofac Med

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Soon, there will be a time where our scholars & colleagues will not be satisfied with general comments on surgical quality outcomes-instead, they will call any physician charlatan who is incapable to quantify his results."-Theodore Billroth 1860 IntroductionSurgeons' efforts to audit post-operative patient outcomes, in order to measure quality of care systematically, have increased over recent years. National Audits, within the National Clinical Audit Patient Outcomes Program (NCAPOP) provide information on quality of surgical care. The annual reports produced by the National Audits produce are accessible to the public. Cardiothoracic surgeons led the modern era of national audit in a major response to the Bristol Royal Infirmary Inquiry into Paediatric Heart Surgery in the 1980's and 1990's (1).

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Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 11 publications

References 15 publications

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up

Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS‐NSQIP

Risk adjustment in audit of outcome after head and neck surgery applied to cumulative sum chart methodology to monitor of free flap failure

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