Association of pre-treatment lymphocyte-monocyte ratio with survival outcome in patients with head and neck cancer treated with chemoradiation

Yu, Brian; Jun, Sung; Khan, Michael; Gill, J.S.; Iovoli, Austin J.; Fekrmandi, Fatemeh; Farrugia, Mark; Wooten, Kimberly; Gupta, Vishal; McSpadden, Ryan; Kuriakose, Moni Abraham; Markiewicz, Michael R.; Al-Afif, Ayham; Hicks, Wesley L.; Seshadri, Mukund; Ray, Andrew D.; Repasky, Elizabeth A.; Singh, Anurag K.

doi:10.1186/s12885-023-11062-3

Cited by 5 publications

(2 citation statements)

References 28 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23 A study of 476 patients by Yu et al demonstrated that a lymphocyte-monocyte ratio < 3.8 (corresponding to MLR > 0.26) was associated with lower overall survival. 24 Our findings supported prognostic value of PLR,NLR, and MLR and quantified thresholds for clinical guidance (FCR=1.25)…”

Section: Survivalsupporting

confidence: 82%

Data Farming to Table: Combined Use of a Learning Health System Infrastructure, Statistical Profiling, and Artificial Intelligence for Automating Toxicity and 3-year Survival for Quantified Predictive Feature Discovery from Real-World Data for Patients Having Head and Neck Cancers

Mayo,

Su,

Rosen

et al. 2023

Preprint

View full text Add to dashboard Cite

Introduction: Clinicians iteratively adjust treatment approaches to improve outcomes but to date, automatable approaches for continuous learning of risk factors as these adjustments are made are lacking. We combined a large-scale comprehensive real-world Learning Health System infrastructure (LHSI), with automated statistical profiling, visualization, and artificial intelligence (AI) approach to test evidence-based discovery of clinical factors for three use cases: dysphagia, xerostomia, and 3-year survival for head and neck cancer patients. Our hypothesis was that the combination would enable automated discovery of prognostic features generating testable insights. Methods: Records for 964 patients treated at a single instiution for head and neck cancers with conventional fractionation between 2017 and 2022 were used. Combined information on demographics, diagnosis and staging, social determinants of health measures, chemotherapy, radiation therapy dose volume histogram curves, and treatment details, laboratory values, and outcomes from the LHSI to winnow evidence for 485 candidate prognostic features. Univariate statistical profiling using benchmark resampling to detail confidence intervals for thresholds and metrics: area under the curve (AUC), sensitivity (SN), specificity (SP), F1, diagnostic odds ratio (DOR), p values for Wilcoxon Rank Sum (WRS), Kolmogorov-Smirnov (KS), and logistic fits of distributions detailed predictive evidence of individual features. Statistical profiling was used to benchmark, parsimonious XGBoost models were constructed with 10-fold cross validation using training (70%), validation (10%), and test (20%) sets. Probabilistic models utilizing statistical profiling logistic fits of distributions were used to benchmark XGBoost models. Results: Automated standardized analysis identified novel features and clinical thresholds. Validity of automated findings were affirmed with supporting literature benchmarks. Average incidence of dysphagia ≥grade 3 within 1 year of treatment was low (11%). Xerostomia ≥ grade 2 (39% to 16%) and survival ≤ 3 years decreased (25% to 15%) over the time range. Standard planning constraints used limited contribution of those features: : Musc_Constrict_S: Mean[Gy] < 50, Glnd_Submand_High: Mean[Gy] ≤ 30, Glnd_Submand_Low: Mean[Gy] ≤ 10, Parotid_High: Mean[Gy] ≤ 24, Parotid_Low: Mean[Gy] ≤ 10 Additional prognostic features identified for dysphagia included Glnd_Submand_High:D1%[Gy] ≥ 71.1, Glnd_Submand_Low:D4%[Gy] ≥ 55.1 , Musc_Constric_S:D10%[Gy] ≥ 56.5 , GTV_Low:Mean[Gy] ≥ 71.3. Strongest grade 2 xerostomia feature was Glnd_Submand_Low: D15%[Gy] ≥ 45.2 with a logistic model quantifying a gradual rather than an abrupt increase in probability 13.5 + 0.18 (x-41.0 Gy). Strongest prognostic factors for lower likelihood of death by 3 years were GTV_High: Volume[cc] ≤ 21.1, GTV_Low: Volume[cc] ≤ 57.5, Baseline Neutrophil-Lymphocyte Ratio (NLR) ≤ 5.6, Monocyte-Lymphocyte Ratio (MLR) ≤0.56, Platelet-Lymphocyte ratio (PLR) ≤ 202.5. All predictors had WRS and KS p values < 0.02. Statistical profiling enabled detailing gains of XGBoost models with respect to individual features. Time period reductions in distribution of GTV volumes correlated with reductions in death by 3 years. Discussion: Confirming our hypothesis, automated, standardized statistical profiling of a set of statistical metrics and visualizations supported detailing predictive strength and confidence intervals of individual features, benchmarking of subsequent AI models, and clinical assessment. Association of high dose values to submandibular gland volumes, highlighted relevance as surrogate measures for proximal un-contoured muscles including digastric muscles. Higher values of PLR, NLR, and MLR were associated with lower survival rates. Combined use of Learning Health System Infrastructure, Statistical Profiling and Artificial Intelligence provided a basis for faster, more efficient evidence-based continuous learning of risk factors and development of clinical trial testable hypothesis. Benchmarking AI models with simple probabilistic models provided a means of understanding when results are driven by general areas of overall risk vs. more complex interactions.

show abstract

Section: Survivalsupporting

confidence: 82%

Data Farming to Table: Combined Use of a Learning Health System Infrastructure, Statistical Profiling, and Artificial Intelligence for Automating Toxicity and 3-year Survival for Quantified Predictive Feature Discovery from Real-World Data for Patients Having Head and Neck Cancers

Mayo,

Su,

Rosen

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…There are several patient-and tumor-related factors that influence clinical outcomes in HNC. Therefore, the identification of prognostic biomarkers that can improve risk stratification and guide treatment decisions has attracted attention [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Efficacy of the Geriatric Nutritional Risk Index for Predicting Overall Survival in Patients with Head and Neck Cancer: A Meta-Analysis

Yiu,

Liu,

et al. 2023

Nutrients

View full text Add to dashboard Cite

Head and neck cancer (HNC) is a prevalent malignancy with a poor prognosis, necessitating the identification of prognostic biomarkers to guide management. The geriatric nutritional risk index (GNRI), calculated from serum albumin and body weight, may predict survival in patients with HNC. We performed a systematic review and meta-analysis to clarify this relationship. Databases were searched for studies examining the association between pretreatment GNRI and overall survival in patients with HNC. Ten studies with 2793 patients were included. Meta-analysis demonstrated that low GNRI was associated with significantly worse overall survival compared to high GNRI (hazard ratio [HR]:2.84, 95% CI 2.07–3.91, p < 0.00001). Older age (HR:1.73; 95% CI, 1.35–2.22; p < 0.0001), male sex (HR:1.7; 95% CI, 1.12–2.6; p = 0.01), advanced tumor stage (HR: 2.5; 95% CI, 1.72–3.63; p < 0.00001), and higher T-/N-stage (HR = 1.69 and 1.98, respectively) were also predictive of unfavorable outcomes. The GNRI had the highest HR, suggesting potent predictive ability. Despite limitations, including retrospective design and potential publication bias, our study indicates that low pretreatment GNRI predicts poor overall survival in patients with HNC. The GNRI is an inexpensive, routinely available biomarker that could improve prognostication and guide management decisions. Additional research is warranted to validate these findings.

show abstract

Comprehensive approach to cancer immunotherapy – Simultaneous targeting of myeloid-derived suppressor cells and cancer cells with AFP conjugates

Pak,

Sherman

2024

Critical Reviews in Oncology/Hematology

View full text Add to dashboard Cite

Association of pre-treatment lymphocyte-monocyte ratio with survival outcome in patients with head and neck cancer treated with chemoradiation

Cited by 5 publications

References 28 publications

Data Farming to Table: Combined Use of a Learning Health System Infrastructure, Statistical Profiling, and Artificial Intelligence for Automating Toxicity and 3-year Survival for Quantified Predictive Feature Discovery from Real-World Data for Patients Having Head and Neck Cancers

Data Farming to Table: Combined Use of a Learning Health System Infrastructure, Statistical Profiling, and Artificial Intelligence for Automating Toxicity and 3-year Survival for Quantified Predictive Feature Discovery from Real-World Data for Patients Having Head and Neck Cancers

Efficacy of the Geriatric Nutritional Risk Index for Predicting Overall Survival in Patients with Head and Neck Cancer: A Meta-Analysis

Comprehensive approach to cancer immunotherapy – Simultaneous targeting of myeloid-derived suppressor cells and cancer cells with AFP conjugates

Contact Info

Product

Resources

About