Individualized outcome prognostication for patients with laryngeal cancer

Development and validation of a prognostic nomogram for patients with laryngeal cancer with synchronous or metachronous lung cancer

BACKGROUND

The objective of this study was to examine the independent prognostic factors of laryngeal cancer with synchronous or metachronous lung cancer (LCSMLC), and to generate and verify a clinical prediction model.

METHODS

In this study, laryngeal cancer alone and LCSMLC were defined using the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors of patients with LCSMLC were analyzed through univariate and multivariate logistic regression analysis. Independent prognostic factors were selected by Cox regression analyses, on the basis of which a nomogram was constructed using R code. Kaplan-Meier survival analyses were applied to test the application of a risk stratification system. Finally, we conducted a comparison of the American Joint Committee on Cancer (AJCC) staging system of laryngeal cancer with the new model of nomogram and risk stratification. For further validation of the nomogram, data from patients at two Chinese independent institutions were also analyzed.

RESULTS

According to the eligibility criteria, 32 429 patients with laryngeal cancer alone and 641 patients with LCSMLC from the SEER database (the training cohort) and additional 61 patients from two Chinese independent institutions (the external validation cohort) were included for final analyses. Compared with patients with laryngeal cancer who did not have synchronous or metachronous lung cancer, age, sex, race, primary site of laryngeal cancer, grade, and stage were risk factors for LCSMLC, while marriage, surgery, radiation therapy, and chemotherapy are not their risk factors. Age, two cancers' interval, pathological type, stage, surgery, radiation, primary lung site, and primary throat site were independent prognostic predictors of LCSMLC. The risk stratification system of high-, medium-, and low-risk groups significantly distinguished the prognosis in different patients with LCSMLC, regardless of the training cohort or the validation cohort. Compared with the 6th AJCC TNM stage of laryngeal cancer, the new model of nomogram and risk stratification showed an improved net benefit.

CONCLUSIONS

Age, sex, race, primary site of laryngeal cancer, grade, and stage were risk factors for LCSMLC. An individualized clinical prognostic predictive model by nomogram was generated and validated, which showed superior prediction ability for LCSMLC.

Eugene Nicholas Myers' Lecture on Head and Neck Cancer, 2020: The Surgeon as a Prognostic Factor in Head and Neck Cancer Patients Undergoing Surgery

This paper is a transcript of the 29 th Eugene N. Myers, MD International Lecture on Head and Neck Cancer presented at the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) in 2020. By the end of the 19 th century, the survival rate in treated patients was 10%. With the improvements in surgical techniques, currently, about two thirds of patients survive for > 5 years. Teamwork and progress in surgical reconstruction have led to advancements in ablative surgery; the associated adjuvant treatments have further improved the prognosis in the last 30 years. However, prospective trials are lacking; most of the accumulated knowledge is based on retrospective series and some real-world data analyses. Current knowledge on prognostic factors plays a central role in an efficient treatment decision-making process. Although the influence of most tumor- and patient-related prognostic factors in head and neck cancer cannot be changed by medical interventions, some environmental factors-including treatment, decision-making, and quality-can be modified. Ideally, treatment strategy decisions should be taken in dedicated multidisciplinary team meetings. However, evidence suggests that surgeons and hospital volume and specialization play major roles in patient survival after initial or salvage head and neck cancer treatment. The metrics of surgical quality assurance (surgical margins and nodal yield) in neck dissection have a significant impact on survival in head and neck cancer patients and can be influenced by the surgeon's expertise. Strategies proposed to improve surgical quality include continuous performance measurement, feedback, and dissemination of best practice measures.

The use of artificial intelligence models to predict survival in patients with laryngeal squamous cell carcinoma

Most recent survival prediction has been based on TNM staging, which does not provide individualized information. However, clinical factors including performance status, age, sex, and smoking might influence survival. Therefore, we used artificial intelligence (AI) to analyze various clinical factors to precisely predict the survival of patients with larynx squamous cell carcinoma (LSCC). We included patients with LSCC (N = 1026) who received definitive treatment from 2002 to 2020. Age, sex, smoking, alcohol consumption, Eastern Cooperative Oncology Group (ECOG) performance status, location of tumor, TNM stage, and treatment methods were analyzed using deep neural network (DNN) with multi-classification and regression, random survival forest (RSF), and Cox proportional hazards (COX-PH) model for prediction of overall survival. Each model was confirmed with five-fold cross validation, and performance was evaluated using linear slope, y-intercept, and C-index. The DNN with multi-classification model demonstrated the highest prediction power (1.000 ± 0.047, 0.126 ± 0.762, and 0.859 ± 0.018 for slope, y-intercept, and C-index, respectively), and the prediction survival curve showed the strongest agreement with the validation survival curve, followed by DNN with regression (0.731 ± 0.048, 9.659 ± 0.964, and 0.893 ± 0.017, respectively). The DNN model produced with only T/N staging showed the poorest survival prediction. When predicting the survival of LSCC patients, various clinical factors should be considered. In the present study, DNN with multi-class was shown to be an appropriate method for survival prediction. AI analysis may predict survival more accurately and improve oncologic outcomes.

Prognostic Nomogram for Early Gastric Cancer After Surgery to Assist Decision-Making for Treatment With Adjuvant Chemotherapy

Background: Most patients with early gastric cancer (EGC) can achieve a better 5-year survival rate after endoscopic resection or surgery. However, indications for adjuvant chemotherapy (ACT) after surgery have not yet been determined.

Methods: A total of 4,108 patients with EGC diagnosed in 2004–2016 were retrospectively analyzed using the Surveillance, Epidemiology, and End Results (SEER) database. Of these, 3,521 patients received postoperative ACT and 587 patients did not. Propensity score matching was used to balance the two groups’ confounding factors. Kaplan-Meier method was utilized to perform survival analysis. Log-rank test was used to compare the differences between survival curves. Cox proportional-hazards regression model was used to screen independent risk factors and build a nomogram for the non-ACT group. The X-tile software was employed to artificially divide all patients into low-, moderate-, and high-risk groups according to the overall survival score prediction based on the nomogram. A total of 493 patients with EGC diagnosed between 2010 and 2014 in our hospital were included for external validation.

Results: Multivariate analysis found that age, sex, race, marital status, primary site, surgical extent, and metastatic lymph node ratio in the non-ACT group were independent prognostic factors for EGC and were included in the construction of the nomogram. The model C-index was 0.730 (95% confidence interval: 0.677–0.783). The patients were divided into three different risk groups based on the nomogram prediction score. Patients in the low-risk group did not benefit from ACT, while patients in the moderate- and high-risk groups did. External validation also demonstrated that moderate- and high-risk patients benefited from ACT.

Conclusion: The study nomogram can effectively evaluate postoperative prognosis of patients with EGC. Postoperative ACT is therefore recommended for moderate- and high-risk patients, but not for low-risk patients.

Development and Assessment of a Model for Predicting Individualized Outcomes in Patients With Oropharyngeal Cancer

IMPORTANCE

Recent insights into the biologic characteristics and treatment of oropharyngeal cancer may help inform improvements in prognostic modeling. A bayesian multistate model incorporates sophisticated statistical techniques to provide individualized predictions of survival and recurrence outcomes for patients with newly diagnosed oropharyngeal cancer.

OBJECTIVE

To develop a model for individualized survival, locoregional recurrence, and distant metastasis prognostication for patients with newly diagnosed oropharyngeal cancer, incorporating clinical, oncologic, and imaging data.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic study, a data set was used comprising 840 patients with newly diagnosed oropharyngeal cancer treated at a National Cancer Institute-designated center between January 2003 and August 2016; analysis was performed between January 2019 and June 2020. Using these data, a bayesian multistate model was developed that can be used to obtain individualized predictions. The prognostic performance of the model was validated using data from 447 patients treated for oropharyngeal cancer at Erasmus Medical Center in the Netherlands.

EXPOSURES

Clinical/oncologic factors and imaging biomarkers collected at or before initiation of first-line therapy.

MAIN OUTCOMES AND MEASURES

Overall survival, locoregional recurrence, and distant metastasis after first-line cancer treatment.

RESULTS

Of the 840 patients included in the National Cancer Institute-designated center, 715 (85.1%) were men and 268 (31.9%) were current smokers. The Erasmus Medical Center cohort comprised 300 (67.1%) men, with 350 (78.3%) current smokers. Model predictions for 5-year overall survival demonstrated good discrimination, with area under the curve values of 0.81 for the model with and 0.78 for the model without imaging variables. Application of the model without imaging data in the independent Dutch validation cohort resulted in an area under the curve of 0.75. This model possesses good calibration and stratifies patients well in terms of likely outcomes among many competing events.

CONCLUSIONS AND RELEVANCE

In this prognostic study, a multistate model of oropharyngeal cancer incorporating imaging biomarkers appeared to estimate and discriminate locoregional recurrence from distant metastases. Providing personalized predictions of multiple outcomes increases the information available for patients and clinicians. The web-based application designed in this study may serve as a useful tool for generating predictions and visualizing likely outcomes for a specific patient.

A dynamic prognostic nomogram to predict the benefit from surgical treatment modality for patients with laryngeal squamous cell carcinoma

BACKGROUND

Although nonsurgical treatment strategy is increasingly adopted in patients with locoregionally advanced laryngeal squamous cell carcinoma (LSCC), survival disparities were reported between surgical treatment modality and organ preservation protocols, highlighting the great importance for accurate patients' selection.

METHOD

This secondary analysis used data from the Surveillance, Epidemiology, and End Results database between 2010 and 2015 with follow-up data up to 2018. We developed and validated a dynamic prognostic nomogram for overall survival (OS) in 4237 patients with LSCC and subgroup of 2087 patients with locoregionally advanced laryngeal squamous cell carcinoma (LALSCC). Based on the total risk score derived from the dynamic nomogram, two well-matched risk groups (i.e., low- and high-risk) were created via X-tile software and 1-to-1 propensity score matching (PSM); surgical treatment modality was compared with nonsurgical one in each risk group.

RESULTS

A more accurate and convenient dynamic prognostic nomogram based on age, marital status, T category, N category, M category, tumor size, and tumor differentiation was developed and validated, of which the predictive performance was superior to that of TNM staging system. For high-risk LALSCC selected by the dynamic nomogram, after 1-to-1 PSM, significantly improved OS was observed in patients with receiving surgical treatment compared to those receipt of nonsurgical management (restricted mean survival time at 36-month: 26.6 vs 22.7, p < 0.001; restricted mean survival time at 60-month: 36.7 vs 31.0, p = 0.003).

CONCLUSION

We establish and validate a more accurate and convenient dynamic prognostic nomogram for patients with LSCC, which may predict the benefit from surgical treatment modality for patients with high-risk LALSCC.

Development and validation of nomogram to predict risk of survival in patients with laryngeal squamous cell carcinoma

To the best of our knowledge, this is the first study established a nomogram to predict survival probability in Asian patients with LSCC. A risk prediction nomogram for patients with LSCC, incorporating easily assessable clinicopathologic factors, generates more precise estimations of the survival probability when compared TNM stage alone, but still need additional data before being used in clinical application.

Background: Due to a wide variation of tumor behavior, prediction of survival in laryngeal squamous cell carcinoma (LSCC) patients received curative-intent surgery is an important but formidable challenge. We attempted to establish a nomogram to precisely predict survival probability in LSCC patients.

Methods: A total of 369 consecutive LSCC patients underwent curative resection between 2008 and 2012 at Hunan Province Cancer Hospital were included in the present study. Subsequently, 369 LSCC patients were assigned to a training set (N=261) and a validation set (N=108) at random. On the basis of multivariable Cox regression analysis results, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis.

Results: Six independent parameters to predict prognosis were age, pack years, N-stage, lymph node ratio (LNR), anemia and albumin, which were all assembled into the nomogram. The calibration curve verified excellent models’ concordance. The C-index of the nomogram was 0.73 (0.68–0.78), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.766, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had a larger net benefit than the TNM stage.

Conclusion: A risk prediction nomogram for patients with LSCC, incorporating easily assessable clinicopathologic factors, generates more precise estimations of the survival probability when compared TNM stage alone, but still need additional data before being used in clinical application.

Identification and validation of methylation-driven genes prognostic signature for recurrence of laryngeal squamous cell carcinoma by integrated bioinformatics analysis

Background

Recurrence remains a major obstacle to long-term survival of laryngeal squamous cell carcinoma (LSCC). We conducted a genome-wide integrated analysis of methylation and the transcriptome to establish methylation-driven genes prognostic signature (MDGPS) to precisely predict recurrence probability and optimize therapeutic strategies for LSCC.

Methods

LSCC DNA methylation datasets and RNA sequencing (RNA-seq) dataset were acquired from the Cancer Genome Atlas (TCGA). MethylMix was applied to detect DNA methylation-driven genes (MDGs). By univariate and multivariate Cox regression analyses, five genes of DNA MDGs was developed a recurrence-free survival (RFS)-related MDGPS. The predictive accuracy and clinical value of the MDGPS were evaluated by receiver operating characteristic (ROC) and decision curve analysis (DCA), and compared with TNM stage system. Additionally, prognostic value of MDGPS was validated by external Gene Expression Omnibus (GEO) database. According to 5 MDGs, the candidate small molecules for LSCC were screen out by the CMap database. To strengthen the bioinformatics analysis results, 30 pairs of clinical samples were evaluated by digoxigenin-labeled chromogenic in situ hybridization (CISH).

Results

A total of 88 DNA MDGs were identified, and five RFS-related MDGs (LINC01354, CCDC8, PHYHD1, MAGEB2 and ZNF732) were chosen to construct a MDGPS. The MDGPS can effectively divide patients into high-risk and low-risk group, with the area under curve (AUC) of 0.738 (5-year RFS) and AUC of 0.74 (3-year RFS). Stratification analysis affirmed that the MDGPS was still a significant statistical prognostic model in subsets of patients with different clinical variables. Multivariate Cox regression analysis indicated the efficacy of MDGPS appears independent of other clinicopathological characteristics. In terms of predictive capacity and clinical usefulness, the MDGPS was superior to traditional TNM stage. Additionally, the MDGPS was confirmed in external LSCC cohorts from GEO. CMap matched the 9 most significant small molecules as promising therapeutic drugs to reverse the LSCC gene expression. Finally, CISH analysis in 30 LSCC tissues and paired adjacent normal tissues revealed that MAGEB2 has significantly higher expression of LSCC compared to adjacent non-neoplastic tissues; LINC01354, CCDC8, PHYHD1, and ZNF732 have significantly lower expression of LSCC compared to adjacent non-neoplastic tissues, which were in line with bioinformatics analysis results.

Conclusion

A MDGPS, with five DNA MDGs, was identified and validated in LSCC patients by combining transcriptome and methylation datasets analysis. Compared TNM stage alone, it generates more accurate estimations of the recurrence prediction and maybe offer novel research directions and prospects for individualized treatment of patients with LSCC.

Development of a Machine Learning Model for Survival Risk Stratification of Patients With Advanced Oral Cancer

IMPORTANCE

A tool for precisely stratifying postoperative patients with advanced oral cancer is crucial for the treatment plan, such as intensifying or deintensifying the regimen to improve their quality of life and prognosis.

OBJECTIVE

To develop and validate a machine learning-based algorithm that can provide survival risk stratification for patients with advanced oral cancer who have comprehensive clinicopathologic and genetic data.

DESIGN, SETTING, AND PARTICIPANTS

In this prognostic cohort study, the elastic net penalized Cox proportional hazards regression-based risk stratification model was developed and validated using single-center data collected between January 1, 1996, and December 31, 2011. In total, comprehensive clinicopathologic and genetic data (including clinical, pathologic, and 44 cancer-related gene variant profiles) of 334 patients with stage III or IV oral squamous cell carcinoma were used to develop and validate the algorithm in this 15-year cohort study. Data analysis was conducted between February 1, 2018, and May 6, 2020.

MAIN OUTCOMES AND MEASURES

The main outcomes were cancer-specific survival, distant metastasis-free survival, and locoregional recurrence-free survival. Model performance was compared in terms of the Akaike information criterion and the Harrell concordance index (C index).

RESULTS

Complete data were available for 334 patients (315 men; median age at onset, 48 years [interquartile range, 42-56 years]). The predictive models using comprehensive clinicopathologic and genetic data outperformed those using clinicopathologic data alone. In the groups of postoperative patients receiving adjuvant concurrent chemoradiotherapy, the models demonstrated higher classification performance than those using clinicopathologic data alone in cancer-specific survival (mean [SD] C index, 0.689 [0.050] vs 0.673 [0.051]; P = .02) and locoregional recurrence-free survival (mean [SD] C index, 0.693 [0.039] vs 0.678 [0.035]; P = .004). The classification performance in distant metastasis-free survival was not different (mean [SD] C index, 0.702 [0.056] vs 0.688 [0.048]; P = .09).

CONCLUSIONS AND RELEVANCE

A risk stratification model using comprehensive clinicopathologic and genetic data accurately differentiated the high-risk group from the low-risk group in cancer-specific survival and locoregional recurrence-free survival for postoperative patients with advanced oral cancer. This algorithm could be used through an online calculator to provide additional personalized information for postoperative management of patients with advanced oral squamous cell carcinoma.

Individualized survival prediction for patients with oropharyngeal cancer in the human papillomavirus era

BACKGROUND

Accurate, individualized prognostication in patients with oropharyngeal squamous cell carcinoma (OPSCC) is vital for patient counseling and treatment decision making. With the emergence of human papillomavirus (HPV) as an important biomarker in OPSCC, calculators incorporating this variable have been developed. However, it is critical to characterize their accuracy prior to implementation.

METHODS

Four OPSCC calculators were identified that integrate HPV into their estimation of 5-year overall survival. Treatment outcomes for 856 patients with OPSCC who were evaluated at a single institution from 2003 through 2016 were analyzed. Predicted survival probabilities were generated for each patient using each calculator. Calculator performance was assessed and compared using Kaplan-Meier plots, receiver operating characteristic curves, concordance statistics, and calibration plots.

RESULTS

Correlation between pairs of calculators varied, with coefficients ranging from 0.63 to 0.90. Only 3 of 6 pairs of calculators yielded predictions within 10% of each other for at least 50% of patients. Kaplan-Meier curves of calculator-defined risk groups demonstrated reasonable stratification. Areas under the receiver operating characteristic curve ranged from 0.74 to 0.80, and concordance statistics ranged from 0.71 to 0.78. Each calculator demonstrated superior discriminatory ability compared with clinical staging according to the seventh and eighth editions of the American Joint Committee on Cancer staging manual. Among models, the Denmark calculator was found to be best calibrated to observed outcomes.

CONCLUSIONS

Existing calculators exhibited reasonable estimation of survival in patients with OPSCC, but there was considerable variability in predictions for individual patients, which limits the clinical usefulness of these calculators. Given the increasing role of personalized treatment in patients with OPSCC, further work is needed to improve accuracy and precision, possibly through the identification and incorporation of additional biomarkers.