Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

Smith, Jane Kapler; Shew, Matthew; Karadaghy, Omar A.; Nallani, Rohit; Sykes, Kevin J.; Gan, Gregory N.; Brant, Jason A.; Bur, Andrés M.

doi:10.1002/hed.26246

Cited by 9 publications

(9 citation statements)

References 73 publications

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“…Several ML studies have evaluated national cancer databases rich in sociodemographic and health care quality data. Smith et al evaluated data from 16,400 subjects in the National Cancer Database who received primary laryngeal radiotherapy for laryngeal squamous cell carcinoma, of whom 2.4% underwent salvage total laryngectomy 41 . They developed a model to predict salvage total laryngectomy based on 9 variables identified by recursive feature elimination (the successive deletion of features akin to pruning less important features), including days between diagnosis and the start of treatment, as well as distance from patient residence to treatment facility.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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Objective This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). Data Sources Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. Review Methods A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. Conclusions AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. Implications for Practice AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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“…Another limitation to ML is the lack of transparency in the analysis that causes difficult interpretation of the process 44,45 . Predictions generated by the ML algorithm are based on multiple layers of analysis, but the specific process is not directly accessible.…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation to ML is the lack of transparency in the analysis that causes difficult interpretation of the process. 44,45 Predictions generated by the ML algorithm are based on multiple layers of analysis, but the specific process is not directly accessible. As already stated, the impact of individual variables and the relationship among them cannot be displayed in a comprehensible format.…”

Section: Discussionmentioning

confidence: 99%

Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS)

et al. 2022

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Purpose To develop machine learning (ML) models for predicting positive margins in patients undergoing transoral robotic surgery (TORS). Methods Data from 453 patients with laryngeal, hypopharyngeal, and oropharyngeal squamous cell carcinoma were retrospectively collected at a tertiary referral center to train (n = 316) and validate (n = 137) six two‐class supervised ML models employing 14 variables available pre‐operatively. Results The accuracy of the six ML models ranged between 0.67 and 0.75, while the measured AUC between 0.68 and 0.75. The ML algorithms showed high specificity (range: 0.75–0.89) and low sensitivity (range: 0.26–0.64) in detecting patients with positive margins after TORS. NPV was higher (range: 0.73–0.83) compared to PPV (range: 0.45–0.63). T classification and tumor site were the most important predictors of positive surgical margins. Conclusions ML algorithms can identify patients with low risk of positive margins and therefore amenable to TORS.

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“…Machine learning (ML) techniques are currently being used as powerful and reliable tools for outcome assessment. Compared with standard methods of statistical model establishment, ML methods are capable of processing a larger number of variables and tend to output more accurate and precise results (7). A nomogram is an ancient calculator similar to the slide rule, (8) that provides graphical depictions of the logistic or Cox regression model.…”

Section: Introductionmentioning

confidence: 99%

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

Liu

Zhang

et al. 2022

Front. Med.

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ObjectiveEarly prediction of long-term outcomes in patients with sepsis-induced cardiorenal syndrome (CRS) remains a great challenge in clinical practice. Herein, we aimed to construct a nomogram and machine learning model for predicting the 1-year mortality risk in patients with sepsis-induced CRS.MethodsThis retrospective study enrolled 340 patients diagnosed with sepsis-induced CRS in Shanghai Tongji Hospital between January 2015 and May 2019, as a discovery cohort. Two predictive models, the nomogram and machine learning model, were used to predict 1-year mortality. The prognostic variables used to develop the nomogram were identified based on a forward stepwise binary logistic regression, and the predictive ability of the nomogram was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve. Meanwhile, machine learning (ML) techniques, such as support vector machine, random forest (RF), and gradient boosted decision tree, were assessed mainly by accuracy and AUC. Feature ranking analysis was performed using the ML algorithm. Both nomogram and ML models were externally validated by an independent cohort of 103 patients diagnosed with sepsis-induced CRS between June 2019 and December 2020.ResultsAge, sequential sepsis-related organ failure score (SOFA), serum myoglobin (MYO), vasopressor use, and mechanical ventilation were identified as independent risk factors for 1-year mortality in the nomogram predictive model. In the discovery cohort, the nomogram yielded higher AUC for predicting mortality than did the SOFA score (0.855 [95% CI: 0.815–0.895] vs. 0.756 [95% CI: 0.705–0.808]). For ML, the model developed by RF showed the highest accuracy (0.765) and AUC (0.854). In feature ranking analysis, factors such as age, MYO, SOFA score, vasopressor use, and baseline serum creatinine were identified as important features affecting 1-year prognosis. Moreover, the nomogram and RF model both performed well in external validation, with an AUC of 0.877 and 0.863, respectively.ConclusionOur nomogram and ML models showed that age, SOFA score, serum MYO levels, and the use of vasopressors during hospitalization were the main factors influencing the risk of long-term mortality. Our models may serve as useful tools for assessing long-term prognosis in patients with sepsis-induced CRS.

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Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

Cited by 9 publications

References 73 publications

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Development of machine learning models for the prediction of positive surgical margins in transoral robotic surgery (TORS)

Nomogram and Machine Learning Models Predict 1-Year Mortality Risk in Patients With Sepsis-Induced Cardiorenal Syndrome

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