A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years

Li, Zufei; Li, Tiancheng; Zhang, Pei; Wang, Xiao

doi:10.1016/j.amjoto.2023.104209

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…For the oral cavity, seven studies reported AUROC and F1-score for ML models, ranging from 0.61 to 0.91 and 0.58 to 0.86, respectively, with four of these studies also reporting on logistic regression models, which ranged from 0.52 to 0.69 for AUROC and 0.57 to 0.62 for F1-score [ 56 , 58 , 63 , 75 , 76 , 87 , 88 ]. For the larynx, four studies reported AUROC for ML models ranging from 0.76 to 0.97, and three of these studies also reported F1-scores ranging from 0.63 to 0.92; from these four, two studies also reported on logistic regression with AUROC ranging from 0.76 to 0.92 [ 75 , 76 , 81 , 85 ]. For the oropharynx, three studies reported AUROC for ML models ranging from 0.93 to 0.97 and F1-scores ranging from 0.90 to 0.92, but there was not enough information to consolidate results for logistic regression [ 74 – 76 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, in one study, a voting classifier of random forest, logistic regression, and Gaussian Naïve Bayes was superior [ 88 ]. In another study, a Support Vector Machine (SVM) excelled [ 81 ], and in one study, neural networks showed superior performance [ 67 ]. The detailed performance metrics for the best-performing models can be found in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

“…However, there were instances where other algorithms were superior to tree-based models. For example, in one study, an SVM—distinguished by its capacity to identify the optimal margin separating different classes, particularly in high-dimensional spaces—exhibited superior performance [ 81 ]. Additionally, in another study, an ensemble voting model comprising random forest, logistic regression, and Gaussian Naïve Bayes excelled.…”

Section: Discussionmentioning

confidence: 99%

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Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami,

Azimian Zavareh,

Watson

et al. 2024

PLoS ONE

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Background This systematic review aimed to evaluate the performance of machine learning (ML) models in predicting post-treatment survival and disease progression outcomes, including recurrence and metastasis, in head and neck cancer (HNC) using clinicopathological structured data. Methods A systematic search was conducted across the Medline, Scopus, Embase, Web of Science, and Google Scholar databases. The methodological characteristics and performance metrics of studies that developed and validated ML models were assessed. The risk of bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Out of 5,560 unique records, 34 articles were included. For survival outcome, the ML model outperformed the Cox proportional hazards model in time-to-event analyses for HNC, with a concordance index of 0.70–0.79 vs. 0.66–0.76, and for all sub-sites including oral cavity (0.73–0.89 vs. 0.69–0.77) and larynx (0.71–0.85 vs. 0.57–0.74). In binary classification analysis, the area under the receiver operating characteristics (AUROC) of ML models ranged from 0.75–0.97, with an F1-score of 0.65–0.89 for HNC; AUROC of 0.61–0.91 and F1-score of 0.58–0.86 for the oral cavity; and AUROC of 0.76–0.97 and F1-score of 0.63–0.92 for the larynx. Disease-specific survival outcomes showed higher performance than overall survival outcomes, but the performance of ML models did not differ between three- and five-year follow-up durations. For disease progression outcomes, no time-to-event metrics were reported for ML models. For binary classification of the oral cavity, the only evaluated subsite, the AUROC ranged from 0.67 to 0.97, with F1-scores between 0.53 and 0.89. Conclusions ML models have demonstrated considerable potential in predicting post-treatment survival and disease progression, consistently outperforming traditional linear models and their derived nomograms. Future research should incorporate more comprehensive treatment features, emphasize disease progression outcomes, and establish model generalizability through external validations and the use of multicenter datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Moharrami,

Azimian Zavareh,

Watson

et al. 2024

PLoS ONE

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Anticancer effects of punicalagin and 5-fluorouracil on laryngeal squamous cell carcinoma: an <i>in vitro</i> study

Taha,

Abd-Elshafy,

Khalifa

et al. 2024

Ital J Med

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The purpose of this study was to assess the apoptotic effects of punicalagin alone and in combination with 5-fluorouracil (5-FU) on laryngeal squamous cell carcinoma (Hep-2) cell line. Hep-2 cells were cultured and divided into four groups: Group 1 received no therapy and served as control, Group 2 received 5-FU only, Group 3 received punicalagin only, and Group 4 received a combination of 5-FU and punicalagin. After 48 hours of incubation, cellular changes were examined under an inverted microscope. The methyl thiazolyl tetrazolium assay, caspase-3 gene level, and vascular endothelial growth factor (VEGF) level were assessed. The control group showed the highest mean value of cancer cell proliferation rate (1.595±0.58), followed by the punicalagin group (1.263±0.447), then the 5-FU group (0.827±0.256), while the combination group showed the lowest proliferation rate (0.253±0.111). The combination group showed the highest mean value of caspase-3 concentration (3.177±0.736), followed by the 5-FU group (1.830±0.646), and punicalagin group (0.741±0.302), while the control group showed the lowest mean value (0.359±0.117). Regarding VEGF levels, the control group had a statistically significant higher mean value, followed by the punicalagin and 5-FU groups, and finally, the combination group which showed the lowest value. Punicalagin exerts an anticancer effect through anti-proliferative action and induction of apoptosis on Hep-2 cell line. Combining punicalagin with 5-FU potentiates its anti-proliferative, apoptotic, and anti-angiogenic actions. It, further, helps in mitigating the putative side effects of 5-FU by reducing the dose required for its therapeutic effects.

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A practical online prediction platform to predict the survival status of laryngeal squamous cell carcinoma after 5 years

Cited by 2 publications

References 13 publications

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review

Anticancer effects of punicalagin and 5-fluorouracil on laryngeal squamous cell carcinoma: an <i>in vitro</i> study

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