Development and validation of a nomogram for prediction of survival and local control in laryngeal carcinoma patients treated with radiotherapy alone: A cohort study based on 994 patients

A novel ubiquitin-related genes-based signature demonstrated values in prognostic prediction, immune landscape sculpture and therapeutic options in laryngeal cancer

Background

Laryngeal cancer (LC) is characterized by high mortality and remains challenging in prognostic evaluation and treatment benefits. Ubiquitin-related genes (UbRGs) are widely involved in cancer initiation and progression, but their potential value in LC is unknown.

Methods

RNA-seq and clinical data of LC were obtained from TCGA and GEO. UbRGs that independently influenced the overall survival (OS) of LC patients were screened with differential expression, COX and LASSO regression analyses. A prognostic signature was then established and assessed for its predictive value, stability and applicability using Kaplan-Meier analysis and receiver operating characteristic curves. The nomogram was further generated in combination with the signature and clinical characteristics. Characterization of immune properties and prediction of drug sensitivity were investigated on the signature-based subgroups using a panel of in silico platforms. Verification of gene expression was conducted with Western blot, qRT-PCR and ELISA, ultimately.

Results

PPARG, LCK and LHX1 were identified and employed to construct the UbRGs-based prognostic signature, showing a strong ability to discriminate LC patients with distinct OS in TCGA-LC and GSE65858, and excellent applicability in most clinical conditions. The nomogram showed higher predictive value and net clinical benefit than traditional indicators. As evaluated, the low-risk group had a more activated immune function, higher infiltration of anti-cancer immune cells and stronger expression of immune-promoting cytokines than the high-risk group. Immune properties were also correlated with individual signature genes. PPARG and LHX1 were negatively correlated, whereas LCK positively correlated, with the immuno-promoting microenvironment. Additionally, chemotherapy would be more effective in high-risk patients, while immune checkpoint inhibitors would be more effective in low-risk patients. Finally, dysregulation of the signature genes was confirmed in LC cell lines by Western blot, and PPARG knockdown significantly reduced the expression of the immunosuppressive cytokines IL6, TGFB1, TGFB2 and VEGFC by qRT-PCR and ELISA.

Conclusion

We have developed a UbRGs-based signature for LC prognostic evaluation that is valuable in clinical application, indicative of the immune microenvironment and beneficial for individualized treatment guidance.

Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis

Background

The limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC. We assessed models' characteristics and methodologies, as well as performance, risk of bias (RoB), and applicability.

Methods

Literature searches were conducted in MEDLINE (OVID), Embase (OVID) and IEEE databases from January 2005 to November 2023. The search algorithm used comprehensive text word and index term combinations without language or publication type restrictions. Independent reviewers screened titles and abstracts using a predefined Population, Index, Comparator, Outcomes, Timing and Setting (PICOTS) framework. We included externally validated (EV) multivariable models, with at least one clinical predictor, that provided recurrence or survival predictions. The SR-MA followed PRISMA reporting guidelines, and PROBAST framework for RoB assessment. Model discrimination was assessed using C-index/AUC, and was presented for all models using forest plots. MA was only performed for models that were externally validated in two or more cohorts, using random-effects model. The main outcomes were model discrimination and calibration measures for survival (OS) and/or local recurrence (LR) prediction. All measures and assessments were preplanned prior to data collection.

Results

The SR-MA identified 11 models, reported in 16 studies. Seven models for OS showed good discrimination on development, with only one excelling (C-index >0.9), and three had weak or poor discrimination. Inclusion of a radiomics score as a model parameter achieved relatively better performance. Most models had poor generalisability, demonstrated by worse discrimination performance on EV, but they still outperformed the TNM system. Only two models met the criteria for MA, with pooled EV AUCs 0.73 (95% CI 0.71-0.76) and 0.67 (95% CI 0.6-0.74). RoB was high for all models, particularly in the analysis domain.

Conclusions

This review highlighted the shortcomings of currently available models, while emphasizing the need for rigorous independent evaluations. Despite the proliferation of models, most exhibited methodological limitations and bias. Currently, no models can confidently be recommended for routine clinical use.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021248762, identifier CRD42021248762.

Inter-hospital variation in data collection, radiotherapy treatment, and survival in patients with head and neck cancer: A multisite study

BACKGROUND AND PURPOSE

Inter-hospital inequalities in head and neck cancer (HNC) survival may exist due to variation in radiotherapy treatment-related factors. This study investigated inter-hospital variation in data collection, primary radiotherapy treatment, and survival in HNC patients from an Australian setting.

MATERIALS AND METHODS

Data collected in oncology information systems (OIS) from seven Australian hospitals was extracted for 3,182 adults treated with curative radiotherapy, with or without surgery or chemotherapy, for primary, non-metastatic squamous cell carcinoma of the head and neck (2000-2017). Death data was sourced from the National Death Index using record linkage. Multivariable Cox regression was used to assess the association between survival and hospital.

RESULTS

Inter-hospital variation in data collection, primary radiotherapy dose, and five-year HNC-related death was detected. Completion of eleven fields ranged from 66%-98%. Primary radiotherapy treated Tis-T1N0 glottic and any stage oral cavity and oropharynx cancers received significantly different time-corrected biologically equivalent dose in two gray fractions (EQD2T) by hospital, with observed deviation from Australian radiotherapy guidelines. Increased EQD2T dose was associated with a reduced risk of five-year HNC-related death in all patients and those treated with primary radiotherapy. Hospital, tumour site, and T and N classification were also identified as independent prognostic factors for five-year HNC-related death in all patients treated with radiotherapy.

CONCLUSION

Unexplained variation exists in HNC-related death in patients treated at Australian hospitals. Available routinely collected data in OIS are insufficient to explain variation in survival. Innovative data collection, extraction, and classification practices are needed to inform clinical practice.

Prognostic Significance of a Model Based on Acetaldehyde Dehydrogenase 2 Genetic Polymorphisms in Laryngeal Carcinoma

OBJECTIVE

Because of the high costs associated with early-stage laryngeal carcinoma diagnosis and prognosis prediction, this study attempts to find valuable targets to establish a novel predictive model by focusing on the aldehyde dehydrogenase 2 (ALDH2) genotype and other peripheral blood markers.

STUDY DESIGN

Retrospective study.

SETTING

Tertiary comprehensive hospital.

METHODS

From January 2011 to January 2021, 362 cases of laryngeal carcinoma were included and divided into 2 groups in this retrospective analysis. Information on medical history, alcohol, and tobacco consumption habits, ALDH2 genotypes, and other peripheral blood markers was collected. Endpoints of the current study included disease-free survival and overall survival. A nomogram model for overall survival was established and evaluated using receiver operating characteristic (ROC) curves.

RESULTS

A total of 236 patients were included in the training cohort, and the other 126 were included in the validation cohort. The median follow-up of the patients was 9.6 years (interquartile range: 7.5-12.5 years). Peripheral fibrinogen, hemoglobin, and ALDH2 genotypes were significantly associated with an increase in laryngeal carcinoma mortality rate on Kaplan-Meier curves. The ROC curve showed that the effectiveness of overall survival prediction by the nomogram model was better than that of traditional clinical staging.

CONCLUSION

A prognostic nomogram of laryngeal carcinoma patients involving ALDH2 and peripheral blood markers and T and N stages was constructed and validated.

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.

Outcome prediction models incorporating clinical variables for Head and Neck Squamous cell Carcinoma: A systematic review of methodological conduct and risk of bias

Multiple outcome prediction models have been developed for Head and Neck Squamous Cell Carcinoma (HNSCC). This systematic review aimed to identify HNSCC outcome prediction model studies, assess their methodological quality and identify those with potential utility for clinical practice. Inclusion criteria were mucosal HNSCC prognostic prediction model studies (development or validation) incorporating clinically available variables accessible at time of treatment decision making and predicting tumour-related outcomes. Eligible publications were identified from PubMed and Embase. Methodological quality and risk of bias were assessed using the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies (CHARMS) and prediction model risk of bias assessment tool (PROBAST). Eligible publications were categorised by study type for reporting. 64 eligible publications were identified; 55 reported model development, 37 external validations, with 28 reporting both. CHARMS checklist items relating to participants, predictors, outcomes, handling of missing data, and some model development and evaluation procedures were generally well-reported. Less well-reported were measures accounting for model overfitting and model performance measures, especially model calibration. Full model information was poorly reported (3/55 model developments), specifically model intercept, baseline survival or full model code. Most publications (54/55 model developments, 28/37 external validations) were found to have high risk of bias, predominantly due to methodological issues in the PROBAST analysis domain. The identified methodological issues may affect prediction model accuracy in heterogeneous populations. Independent external validation studies in the local population and demonstration of clinical impact are essential for the clinical implementation of outcome prediction models.

Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models

BACKGROUND

Knowledge of the prognostic factors and performance of machine learning predictive models for 2-year cancer-specific survival (CSS) is limited in the head and neck cancer (HNC) population.

METHODS

Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck.

RESULTS

Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identified as predictors of 2-year CSS in both datasets. Hypothyroidism and fitness for operation were further identified in the research dataset.

CONCLUSIONS

Datasets extracted from an OIS for routine clinical practice and research purposes demonstrated high utility for informing 2-year head and neck CSS.

The Utility of Oncology Information Systems for Prognostic Modelling in Head and Neck Cancer

Cancer centres rely on electronic information in oncology information systems (OIS) to guide patient care. We investigated the completeness and accuracy of routinely collected head and neck cancer (HNC) data sourced from an OIS for suitability in prognostic modelling and other research. Three hundred and fifty-three adults diagnosed from 2000 to 2017 with head and neck squamous cell carcinoma, treated with radiotherapy, were eligible. Thirteen clinically relevant variables in HNC prognosis were extracted from a single-centre OIS and compared to that compiled separately in a research dataset. These two datasets were compared for agreement using Cohen's kappa coefficient for categorical variables, and intraclass correlation coefficients for continuous variables. Research data was 96% complete compared to 84% for OIS data. Agreement was perfect for gender (κ = 1.000), high for age (κ = 0.993), site (κ = 0.992), T (κ = 0.851) and N (κ = 0.812) stage, radiotherapy dose (κ = 0.889), fractions (κ = 0.856), and duration (κ = 0.818), and chemotherapy treatment (κ = 0.871), substantial for overall stage (κ = 0.791) and vital status (κ = 0.689), moderate for grade (κ = 0.547), and poor for performance status (κ = 0.110). Thirty-one other variables were poorly captured and could not be statistically compared. Documentation of clinical information within the OIS for HNC patients is routine practice; however, OIS data was less correct and complete than data collected for research purposes. Substandard collection of routine data may hinder advancements in patient care. Improved data entry, integration with clinical activities and workflows, system usability, data dictionaries, and training are necessary for OIS data to generate robust research. Data mining from clinical documents may supplement structured data collection.

Machine Learning and Nomogram Prognostic Modeling for 2-Year Head and Neck Cancer-Specific Survival Using Electronic Health Record Data: A Multisite Study

PURPOSE

There is limited knowledge of the prediction of 2-year cancer-specific survival (CSS) in the head and neck cancer (HNC) population. The aim of this study is to develop and validate machine learning models and a nomogram for the prediction of 2-year CSS in patients with HNC using real-world data collected by major teaching and tertiary referral hospitals in New South Wales (NSW), Australia.

MATERIALS AND METHODS

Data collected in oncology information systems at multiple NSW Cancer Centres were extracted for 2,953 eligible adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck. Death data were sourced from the National Death Index using record linkage. Machine learning and Cox regression/nomogram models were developed and internally validated in Python and R, respectively.

RESULTS

Machine learning models demonstrated highest performance (C-index) in the larynx and nasopharynx cohorts (0.82), followed by the oropharynx (0.79) and the hypopharynx and oral cavity cohorts (0.73). In the whole HNC population, C-indexes of 0.79 and 0.70 and Brier scores of 0.10 and 0.27 were reported for the machine learning and nomogram model, respectively. Cox regression analysis identified age, T and N classification, and time-corrected biologic equivalent dose in two gray fractions as independent prognostic factors for 2-year CSS. N classification was the most important feature used for prediction in the machine learning model followed by age.

CONCLUSION

Machine learning and nomogram analysis predicted 2-year CSS with high performance using routinely collected and complete clinical information extracted from oncology information systems. These models function as visual decision-making tools to guide radiotherapy treatment decisions and provide insight into the prediction of survival outcomes in patients with HNC.

Larynx cancer survival model developed through open-source federated learning

INTRODUCTION

Federated learning has the potential to perfrom analysis on decentralised data; however, there are some obstacles to survival analyses as there is a risk of data leakage. This study demonstrates how to perform a stratified Cox regression survival analysis specifically designed to avoid data leakage using federated learning on larynx cancer patients from centres in three different countries.

METHODS

Data were obtained from 1821 larynx cancer patients treated with radiotherapy in three centres. Tumour volume was available for all 786 of the included patients. Parameter selection among eleven clinical and radiotherapy parameters were performed using best subset selection and cross-validation through the federated learning system, AusCAT. After parameter selection, β regression coefficients were estimated using bootstrap. Calibration plots were generated at 2 and 5-years survival, and inner and outer risk groups' Kaplan-Meier curves were compared to the Cox model prediction.

RESULTS

The best performing Cox model included log(GTV), performance status, age, smoking, haemoglobin and N-classification; however, the simplest model with similar statistical prediction power included log(GTV) and performance status only. The Harrell C-indices for the simplest model were for Odense, Christie and Liverpool 0.75[0.71-0.78], 0.65[0.59-0.71], and 0.69[0.59-0.77], respectively. The values are slightly higher for the full model with C-index 0.77[0.74-0.80], 0.67[0.62-0.73] and 0.71[0.61-0.80], respectively. Smoking during treatment has the same hazard as a ten-years older nonsmoking patient.

CONCLUSION

Without any patient-specific data leaving the hospitals, a stratified Cox regression model based on data from centres in three countries was developed without data leakage risks. The overall survival model is primarily driven by tumour volume and performance status.

Disease outcome and associated factors after definitive platinum based chemoradiotherapy for advanced stage HPV-negative head and neck cancer

BACKGROUND

Definitive concomitant cisplatin-based chemoradiotherapy (CRT) is the current gold standard for most patients with advanced stage head and neck squamous cell carcinoma (HNSCC) of the pharynx and larynx. Since previous meta-analysis on CRT outcomes in HNSCC have been reported, advances have been made in radiotherapy techniques and clinical management, while HPV-status has been identified as a strong confounding prognostic factor in oropharyngeal cancer. Here, we present real-world outcome data from a large multicenter cohort of HPV-negative advanced stage HNSCC treated with CRT using contemporary IMRT-based techniques.

METHOD

Retrospective data were collected from a multicenter cohort of 513 patients treated with definitive concurrent platinum-based CRT with curative intent between January 2009 and August 2017. Only patients with HPV-negative advanced stage (III-IV) HNSCC were included. A prognostic model for outcome was developed based on clinical parameters and compared to TNM.

RESULTS

Nearly half of the 513 patients (49%) had an oropharyngeal tumor, often locally advanced (73.3% T3-T4b) and with involvement of the regional lymph nodes (84%). Most patients (84%) received cisplatin as single agent. 66% received the planned number of cycles and 75% reached a cumulative cisplatin dose of ≥200 mg/m². Locoregional control was achieved in 324 (63%) patients during follow-up, and no association with tumor sites was observed (p = 0.48). Overall survival at 5 year follow-up was 47%, with a better survival for laryngeal cancer (p = 0.02) compared to other sites. A model with clinical variables (gender, high pre-treatment weight loss, N2c/N3-stage and <200 mg/m² dose of cisplatin) provided a noticeably stronger association with overall survival than TNM-staging (C- index 0.68 vs 0.55). Simultaneous Integrated Boosting (SIB) significantly outperformed Sequential Boosting (SEQ) to reduce the development of distant metastasis (SEQ vs SIB: OR 1.91 (1.11 - 3.26; p = 0.02).

CONCLUSION

Despite advances in clinical management, more than a third of patients with HPV-negative HNSCC do not complete CRT treatment protocols due to cisplatin toxicity. A model that consists of clinical variables and treatment parameters including cisplatin dose provided the strongest association with overall survival. Since cisplatin toxicity is a major obstacle in completing definitive CRT, the development of alternative and less toxic radiosensitizers is therefore warranted to improve treatment results. The association of RT-boost technique with distant metastasis is an important finding and requires further study.

Open-source distributed learning validation for a larynx cancer survival model following radiotherapy

INTRODUCTION

Prediction models are useful to design personalised treatment. However, safe and effective implementation relies on external validation. Retrospective data are available in many institutions, but sharing between institutions can be challenging due to patient data sensitivity and governance or legal barriers. This study validates a larynx cancer survival model performed using distributed learning without any sensitive data leaving the institution.

METHODS

Open-source distributed learning software based on a stratified Cox proportional hazard model was developed and used to validate the Egelmeer et al. MAASTRO survival model across two hospitals in two countries. The validation optimised a single scaling parameter multiplied by the original predicted prognostic index. All analyses and figures were based on the distributed system, ensuring no information leakage from the individual centres. All applied software is provided as freeware to facilitate distributed learning in other institutions.

RESULTS

1745 patients received radiotherapy for larynx cancer in the two centres from Jan 2005 to Dec 2018. Limiting to a maximum of one missing value in the parameters of the survival model reduced the cohort to 1095 patients. The Harrell C-index was 0.74 (CI95%, 0.71-0.76) and 0.70 (0.66-0.75) for the two centres. However, the model needed a scaling update. In addition, it was found that survival predictions of patients undergoing hypofractionation were less precise.

CONCLUSION

Open-source distributed learning software was able to validate, and suggest a minor update to the original survival model without central access to patient sensitive information. Even without the update, the original MAASTRO survival model of Egelmeer et al. performed reasonably well, providing similar results in this validation as in its original validation.

Dual-energy CT may predict post-operative recurrence in early-stage glottic laryngeal cancer: a novel nomogram and risk stratification system

OBJECTIVES

To establish and validate a predictive model integrating with clinical and dual-energy CT (DECT) variables for individual recurrence-free survival (RFS) prediction in early-stage glottic laryngeal cancer (EGLC) after larynx-preserving surgery.

METHODS

This retrospective study included 212 consecutive patients with EGLC who underwent DECT before larynx-preserving surgery between January 2015 and December 2018. Using Cox proportional hazard regression model to determine independent predictors for RFS and presented on a nomogram. The model's performance was assessed using Harrell's concordance index (C-index), time-dependent area under curve (TD-AUC) plot, and calibration curve. A risk stratification system was established using the nomogram with median scores of all cases to divide all patients into two prognostic groups.

RESULTS

Recurrence occurred in 39/212 (18.4%) cases. Normalized iodine concentration in arterial (NICAP) and venous phases (NICVP) were verified as significant predictors of RFS in multivariate Cox regression (hazard ratio [HR], 4.2; 95% confidence interval [CI]: 2.3, 7.7, p < .001 and HR, 3.0; 95% CI: 1.5, 5.9, p = .002, respectively). Nomogram based on clinical and DECT variables was better than did only clinical variables. The prediction model proved well-calibrated and had good discriminative ability in the training and validation samples. A risk stratification system was built that could effectively classify EGLC patients into two risk groups.

CONCLUSIONS

DECT could provide independent RFS indicators in patients with EGLC, and the nomogram based on DECT and clinical variables was useful in predicting RFS at several time points.

KEY POINTS

• Dual-energy CT(DECT) variables can predict recurrence-free survival (RFS) after larynx-preserving surgery in patients with early-stage glottic laryngeal cancer (EGLC). • The model that integrates clinical and DECT variables predicted RFS better than did only clinical variables. • A risk stratification system based on the nomogram could effectively classify EGLC patients into two risk groups.

Key Aspects of Prognostic Model Development and Interpretation From a Clinical Perspective

Importance

Prognostication is an important aspect of clinical decision-making, but it is often challenging. Previous studies show that both patients and physicians tend to overestimate survival chances. Prediction models may assist in estimating and quantifying prognosis. However, insufficient understanding of the development, possibilities, and limitations of such models can lead to misinterpretations. Although many excellent books and comprehensive methodological articles on prognostic model research are published, they may not be accessible enough for the clinical audience. Our aim is to provide an overview on the main issues regarding prediction research for health care professionals to achieve better interpretation and increase the use of prognostic models in daily clinical practice.

Observations

The first steps of model development include coding of predictors, model specification, and estimation. Next, we discuss the assessment of the performance of a prediction model, including discrimination and calibration aspects, followed by approaches to internal and external validation and updating. Finally, model reporting, presentation, and steps toward clinical implementation are presented.

Conclusions and Relevance

After thorough consideration of the research question, data inspection, and coding of predictors, one can start with the specification of a prediction model. The number of candidate predictors should be kept limited, in view of the number of events in the data, to prevent overfitting. Calibration and discrimination are 2 aspects of model performance that complement each other and should be assessed preferably at external validation. Model development should be accompanied by qualitative research among patients and physicians to facilitate the development of a valuable tool and maximize possibilities for successful implementation. After model presentation is optimized, impact studies are required to assess the clinical value of a prediction model.

Risk groups of laryngeal cancer treated with chemoradiation according to nomogram scores - A pooled analysis of RTOG 0129 and 0522

OBJECTIVES

To develop nomograms predicting overall survival (OS), freedom from locoregional recurrence (FFLR), and freedom from distant metastasis (FFDM) for patients receiving chemoradiation for laryngeal squamous cell carcinoma (LSCC).

MATERIAL AND METHODS

Clinical and treatment data for patients with LSCC enrolled on NRG Oncology/RTOG 0129 and 0522 were extracted from the RTOG database. The dataset was partitioned into 70% training and 30% independent validation datasets. Significant predictors of OS, FFLR, and FFDM were obtained using univariate analysis on the training dataset. Nomograms were built using multivariate analysis with four a priori variables (age, gender, T-stage, and N-stage) and significant predictors from the univariate analyses. These nomograms were internally and externally validated using c-statistics (c) on the training and validation datasets, respectively.

RESULTS

The OS nomogram included age, gender, T stage, N stage, and number of cisplatin cycles. The FFLR nomogram included age, gender, T-stage, N-stage, and time-equivalent biologically effective dose. The FFDM nomogram included age, gender, N-stage, and number of cisplatin cycles. Internal validation of the OS nomogram, FFLR nomogram, and FFDM nomogram yielded c = 0.66, c = 0.66 and c = 0.73, respectively. External validation of these nomograms yielded c = 0.59, c = 0.70, and c = 0.73, respectively. Using nomogram score cutoffs, three risk groups were separated for each outcome.

CONCLUSIONS

We have developed and validated easy-to-use nomograms for LSCC outcomes using prospective cooperative group trial data.

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.

A Comprehensive Analysis of Treatment Management and Survival Outcomes in Nasopharyngeal Carcinoma

OBJECTIVES

To comprehensively investigate nasopharyngeal carcinoma (NPC) treatment, overall survival (OS), and the influence of clinical/sociodemographic factors on outcome.

STUDY DESIGN

Retrospective database study.

SETTING

National Cancer Database.

METHODS

The 2004-2015 National Cancer Database was queried for all patients with NPC receiving definitive treatment. Log-rank tests and Cox proportional hazards models were used for statistical analyses.

RESULTS

A total of 8260 patients with NPC were included (71.4% male; 42.5% with keratinizing histology; mean ± SD age, 52.1 ± 15.1 years), with a 5-year OS of 63.4%. Multivariate predictors of mortality included age ≥65 years (hazard ratio [HR], 1.81; P < .001), Charlson/Deyo score ≥1 (HR, 1.27; P = .001), American Joint Committee on Cancer clinical stage III to IV (HR, 1.85; P < .001), and government insurance or no insurance (HR, 1.53; P < .001). Predictors of survival included female sex (HR, 0.82; P = .002), Asian/Pacific Islander race (HR, 0.74; P < .001), nonkeratinizing/undifferentiated histology (HR, 0.79; P = .004), and receiving treatment at academic centers (HR, 0.87; P = .02). Chemoradiotherapy (CRT) demonstrated improved OS as compared with radiotherapy (RT) only for stage II (P = .006) and stage III (P = .005) and with RT or chemotherapy only in stage IVA NPC (P < .001). When compared with CRT alone, surgery plus CRT provided OS benefits in keratinizing (P = .013) or stage IVA (P = .030) NPC. When compared with RT, CRT provided OS benefits in keratinizing (P = .005) but not nonkeratinizing (P = .240) or undifferentiated (P = .390) NPC. Substandard radiation dosing of <60 Gy and <30 fractions were associated with inferior OS (both P < .001).

CONCLUSIONS

NPC survival is dependent on a variety of clinical/sociodemographic factors. Stage-specific treatments with optimal OS include CRT or RT for stages I to II and CRT for stage III to IV. The large representation of nonendemic histology is valuable, as these cases are not well characterized.

Proton vs photon: A model-based approach to patient selection for reduction of cardiac toxicity in locally advanced lung cancer

PURPOSE/OBJECTIVE

To use a model-based approach to identify a sub-group of patients with locally advanced lung cancer who would benefit from proton therapy compared to photon therapy for reduction of cardiac toxicity.

MATERIAL/METHODS

Volumetric modulated arc photon therapy (VMAT) and robust-optimised intensity modulated proton therapy (IMPT) plans were generated for twenty patients with locally advanced lung cancer to give a dose of 70 Gy (relative biological effectiveness (RBE)) in 35 fractions. Cases were selected to represent a range of anatomical locations of disease. Contouring, treatment planning and organs-at-risk constraints followed RTOG-1308 protocol. Whole heart and ub-structure doses were compared. Risk estimates of grade⩾3 cardiac toxicity were calculated based on normal tissue complication probability (NTCP) models which incorporated dose metrics and patients baseline risk-factors (pre-existing heart disease (HD)).

RESULTS

There was no statistically significant difference in target coverage between VMAT and IMPT. IMPT delivered lower doses to the heart and cardiac substructures (mean, heart V5 and V30, P < .05). In VMAT plans, there were statistically significant positive correlations between heart dose and the thoracic vertebral level that corresponded to the most inferior limit of the disease. The median level at which the superior aspect of the heart contour began was the T7 vertebrae. There was a statistically significant difference in dose (mean, V5 and V30) to the heart and all substructures (except mean dose to left coronary artery and V30 to sino-atrial node) when disease overlapped with or was inferior to the T7 vertebrae. In the presence of pre-existing HD and disease overlapping with or inferior to the T7 vertebrae, the mean estimated relative risk reduction of grade⩾3 toxicities was 24-59%.

CONCLUSION

IMPT is expected to reduce cardiac toxicity compared to VMAT by reducing dose to the heart and substructures. Patients with both pre-existing heart disease and tumour and nodal spread overlapping with or inferior to the T7 vertebrae are likely to benefit most from proton over photon therapy.

Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project

Background

Despite advances in treatments, 30% to 50% of stage III‐IV head and neck squamous cell carcinoma (HNSCC) patients relapse within 2 years after treatment. The Big Data to Decide (BD2Decide) project aimed to build a database for prognostic prediction modeling.

Methods

Stage III‐IV HNSCC patients with locoregionally advanced HNSCC treated with curative intent (1537) were included. Whole transcriptomics and radiomics analyses were performed using pretreatment tumor samples and computed tomography/magnetic resonance imaging scans, respectively.

Results

The entire cohort was composed of 71% male (1097)and 29% female (440): oral cavity (429, 28%), oropharynx (624, 41%), larynx (314, 20%), and hypopharynx (170, 11%); median follow‐up 50.5 months. Transcriptomics and imaging data were available for 1284 (83%) and 1239 (80%) cases, respectively; 1047 (68%) patients shared both.

Conclusions

This annotated database represents the HNSCC largest available repository and will enable to develop/validate a decision support system integrating multiscale data to explore through classical and machine learning models their prognostic role.

Nomogram for Predicting Central Lymph Node Metastasis in Papillary Thyroid Cancer: A Retrospective Cohort Study of Two Clinical Centers

Purpose

Central lymph node metastasis (CNM) are highly prevalent but hard to detect preoperatively in papillary thyroid carcinoma (PTC) patients, while the significance of prophylactic compartment central lymph node dissection (CLND) remains controversial as a treatment option. We aim to establish a nomogram assessing risks of CNM in PTC patients, and explore whether prophylactic CLND should be recommended.

Materials and Methods

One thousand four hundred thirty-eight patients from two clinical centers that underwent thyroidectomy with CLND for PTC within the period 2016–2019 were retrospectively analyzed. Univariate and multivariate analysis were performed to examine risk factors associated with CNM. A nomogram for predicting CNM was established, thereafter internally and externally validated.

Results

Seven variables were found to be significantly associated with CNM and were used to construct the model. These were as follows: thyroid capsular invasion, multifocality, creatinine > 70 μmol/L, age < 40, tumor size > 1 cm, body mass index < 22, and carcinoembryonic antigen > 1 ng/mL. The nomogram had good discrimination with a concordance index of 0.854 (95% confidence interval [CI], 0.843 to 0.867), supported by an external validation point estimate of 0.825 (95% CI, 0.793 to 0.857). A decision curve analysis was made to evaluate nomogram and ultrasonography for predicting CNM.

Conclusion

A validated nomogram utilizing readily available preoperative variables was developed to predict the probability of central lymph node metastases in patients presenting with PTC. This nomogram may help surgeons make appropriate surgical decisions in the management of PTC, especially in terms of whether prophylactic CLND is warranted.

Randomized controlled trial to identify the optimal radiotherapy scheme for palliative treatment of incurable head and neck squamous cell carcinoma

BACKGROUND

No randomized controlled trials (RCT) have yet identified the optimal palliative radiotherapy scheme in patients with incurable head and neck squamous cell carcinoma (HNSCC). We conducted RCT to compare two radiation schemes in terms of efficacy, toxicity and quality-of-life (QoL).

MATERIALS AND METHODS

Patients with locally-advanced HNSCC who were ineligible for radical treatment and those with limited metastatic disease were randomly assigned in 1:1 ratio to arm 1 (36 Gy in 6 fractions, twice a week) or arm 2 (50 Gy in 16 fractions, four times a week).

RESULTS

The trial was discontinued early because of slow accrual (34 patients enrolled). Objective response rates were 38.9% and 57.1% for arm 1 and 2 respectively (p = 0.476). The median time to loco-regional progression was not reached. The loco-regional control rates at 1 year was 57.4% and 69.3% in arm 1 and 2 (p = 0.450, HR = 0.56, 95%CI 0.12-2.58). One-year overall survival was 33.3% and 57.1%, with medians of 35.4 and 59.5 weeks, respectively (p = 0.215, HR = 0.55, 95%CI 0.21-1.43). Acute grade ≥3 toxicity was lower in arm 1 (16.7% versus 57.1%, p = 0.027), with the largest difference in grade 3 mucositis (5.6% versus 42.9%, p = 0.027). However, no significant deterioration in any of the patient-reported QoL-scales was found.

CONCLUSION

No solid conclusion could be made on this incomplete study which is closed early. Long-course radiotherapy did not show significantly better oncologic outcomes, but was associated with more acute grade 3 mucositis. No meaningful differences in QoL-scores were found. Therefore, the shorter schedule might be carefully advocated. However, this recommendation should be interpreted with great caution because of the inadequate statistical power.

Development and validation of nomograms to accurately predict risk of recurrence for patients with laryngeal squamous cell carcinoma: Cohort study

BACKGROUND

Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC.

METHODS

A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram.

RESULTS

Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort.

CONCLUSION

A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.

Outcomes of curative (chemo)radiotherapy for patients with non-p16 positive head and neck squamous cell carcinoma who are borderline for curative treatment

INTRODUCTION

Appropriate selection of head and neck squamous cell cancer (HNSCC) patients for curative treatment is difficult, and it is a very understudied issue. The aim of this study was to review the outcomes of curative intent treatment in non-p16 positive HNSCC patients assessed as having borderline curability.

METHODS

A single institution retrospective review of the clinical outcomes of non-p16 positive HNSCC patients with borderline curability. Predefined criteria for borderline curability were as follows: (i) T4 and/or N3 disease; or (ii) ECOG status ≥2; or (iii) age ≥75 years.

RESULTS

A total of 114 patients were identified. A total of 56 had N3/T4, 32 were >ECOG 2 and 57 were >75 years. A total of 29 had two or more borderline curability criteria. Progression-free survival rate (PFS) at 1 and 2 years was 72% (95% confidence interval (CI), 63-79) and 53% (95% CI, 43-62), respectively. Overall survival (OS) at 1 and 2 years was 76% (95% CI, 67-83) and 61% (95% CI, 51-69), respectively. On multivariable analysis, the only independent prognostic factor for OS was the adult comorbidity evaluation-27 (ACE-27) grade (HR 1.4; 95% CI, 1.1-1.8; P = 0.018).

CONCLUSIONS

Patients with borderline curability criteria treated with curative intent achieved good PFS and OS. ACE-27 was an important prognostic factor in this population.

Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population-based study

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.

A Genomic-Clinicopathologic Nomogram Predicts Survival for Patients with Laryngeal Squamous Cell Carcinoma

Background

Long noncoding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNA signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC).

Methods

A LSCC RNA-sequencing (RNA-seq) dataset and the matched clinicopathologic information were downloaded from The Cancer Genome Atlas (TCGA). Using univariable Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen-lncRNA signature related to prognosis. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with the TNM staging system by C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the clinical value of our nomogram.

Results

Thirteen overall survival- (OS-) related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroups, with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status, and lncRNA signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram coincided well with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of the nomogram in predicting overall survival (OS) was 0.938, both of which were significantly higher than the traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage.

Conclusion

An inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared with TNM stage alone, but more data are needed before the nomogram is used in clinical practice.

The Impact of Clinical Trial Quality Assurance on Outcome in Head and Neck Radiotherapy Treatment

Purpose: To investigate the impact of radiation treatment quality assurance (RTQA) on treatment outcomes in a phase III trial for advanced head and neck cancer.

Materials and Methods: A total of 767 patients from NRG/RTOG 0522 were included in this study. The contours of target volume (TV) and organ at risk (OAR), and dose-volume coverage of targets were reviewed and scored (per-protocol, variation-acceptable and deviation-unacceptable) according to the protocol. We performed log-rank tests for RTQA scores with patients' outcomes, including local control (LC), distant control (DC) and overall survival (OS). Cox models with and without RTQA score data were established. To obtain a more reasonable model, per-protocol and variation acceptable were combined into a single acceptable score.

Results: The log-rank test showed that all RTQA scores correlated with LC, which was significantly different between the per-protocol and variation-acceptable patients in target and OAR contouring (p-value = 0.004 and 0.043). For dose-volume score, the per-protocol and variation-acceptable patients were significantly different from unacceptable patients in the LC, with a p-value = 0.020 and 0.006, respectively. The DC of patients with variation-acceptable was significantly different than that of the unacceptable patients (p-value = 0.043). There were no correlations between RTQA scores with other outcomes. By incorporating RTQA scores into outcome modeling, the performance of LC model can be improved from 0.62 to 0.63 (c-index). The RTQA scores had no impact on DC and OS.

Conclusion: RTQA scores are related to patients' local control rates in head and neck cancer radiotherapy.

A prognostic nomogram for predicting risk of recurrence in laryngeal squamous cell carcinoma patients after tumor resection to assist decision making for postoperative adjuvant treatment

BACKGROUND

This study aimed to create a nomogram for postoperative prediction of the risk of recurrence in laryngeal squamous cell carcinoma patients who received laryngectomy alone and to assess indications for postoperative adjuvant treatments (POAT).

METHODS

A retrospective analysis of 1571 newly diagnosed laryngeal carcinoma patients was conducted. Those patients were divided into two groups-the development cohort (n = 1102) and the validation cohort (n = 469). Patients were classified into three subgroups according to their individual points calculated from the nomogram. The efficiency of POAT was examined among various subgroups.

RESULTS

Five variables, including pT classification, pN classification, surgical margin, tumor differentiation, and primary location, were included in the nomogram. The C-index was 0.753 in development cohort and 0.744 in validation cohort. Patients were classified into three subgroups with incremental risks of recurrence. In the high-risk group, patients receiving POAT showed significantly better recurrence-free survival (RFS) than did those receiving surgery alone, while POAT was not significantly associated with RFS in either the low- or moderate-risk groups.

CONCLUSIONS

The risk of tumor recurrence in patients with laryngeal carcinoma was quantified by our newly constructed nomogram. Patients categorized as high-risk were found to benefit from POAT in RFS.

Personalized prognostication in head and neck cancer: A systematic review of nomograms according to the AJCC precision medicine core (PMC) criteria

BACKGROUND

The American Joint Committee on Cancer (AJCC) Precision Medicine Core (PMC) has recognized the need for more personalized probabilistic predictions above the "TNM" staging system and has recently released a checklist of inclusion and exclusion criteria for evaluating prognostic models.

METHODS

A systematic review of articles in which nomograms were created for head and neck cancer (HNC) was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The AJCC PMC criteria were used to score the individual studies.

RESULTS

Forty-four studies were included in the final qualitative analysis. The mean number of inclusion criteria met was 9.3 out of 13, and the mean number of exclusion criteria met was 2.1 out of 3. Studies were generally of high quality, but no single study fulfilled all of the AJCC PMC criteria.

CONCLUSION

This is the first study to utilize the AJCC checklist to comprehensively evaluate the published prognostic nomograms in HNC. Future studies should attempt to adhere to the AJCC PMC criteria. Recommendations for future research are given.

SUMMARY

The AJCC recently released a set of criteria to grade the quality of prognostic cancer models. In this study, we grade all published nomograms for head and neck cancer according to the new guidelines.

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Purpose:

Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction.

Methods:

We collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, Logit-Boost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All classifiers were run on each dataset in a 100-repeated nested fivefold cross-validation with hyperparameter tuning. Performance metrics (AUC, calibration slope and intercept, accuracy, Cohen’s kappa, and Brier score) were computed. We ranked classifiers by AUC to determine which classifier is likely to also perform well in future studies. We simulated the benefit for potential investigators to select a certain classifier for a new dataset based on our study (pre-selection based on other datasets) or estimating the best classifier for a dataset (set-specific selection based on information from the new dataset) compared with uninformed classifier selection (random selection).

Results:

Random forest (best in 6/12 datasets) and elastic net logistic regression (best in 4/12 datasets) showed the overall best discrimination, but there was no single best classifier across datasets. Both classifiers had a median AUC rank of 2. Preselection and set-specific selection yielded a significant average AUC improvement of 0.02 and 0.02 over random selection with an average AUC rank improvement of 0.42 and 0.66, respectively.

Conclusion:

Random forest and elastic net logistic regression yield higher discriminative performance in (chemo)radiotherapy outcome and toxicity prediction than other studied classifiers. Thus, one of these two classifiers should be the first choice for investigators when building classification models or to benchmark one’s own modeling results against. Our results also show that an informed preselection of classifiers based on existing datasets can improve discrimination over random selection.

Development of comprehensive nomograms for evaluating overall and cancer-specific survival of laryngeal squamous cell carcinoma patients treated with neck dissection

Background

Neck dissection for laryngeal squamous cell carcinoma (LSCC) patients could provide complementary prognostic information for AJCC N staging, like lymph node ratio (LNR). The aim of this study was to develop effective nomograms to better predict survival for LSCC patients treated with neck dissection.

Results

2752 patients were identified and randomly divided into training (n = 2477) and validation (n = 275) cohorts. The 3- and 5-year probabilities of cancer-specific mortality (CSM) were 30.1% and 37.2% while 3- and 5-year death resulting from other causes (DROC) rate were 6.2% and 11.3%, respectively. 13 significant prognostic factors including LNR for overall (OS) and 12 (except race) for CSS were enrolled in the nomograms. Concordance index as a commonly used indicator of predictive performance, showed the nomograms had superiority over the no-LNR models and TNM classification (Training-cohort: OS: 0.713 vs 0.703 vs 0.667, CSS: 0.725 vs 0.713 vs 0.688; Validation-cohort: OS: 0.704 vs 0.690 vs 0.658, cancer-specific survival (CSS): 0.709 vs 0.693 vs 0.672). All calibration plots revealed good agreement between nomogram prediction and actual survival.

Materials and Methods

We identified LSCC patients undergoing neck dissection diagnosed between 1988 and 2008 from Surveillance, Epidemiology, and End Results (SEER) database. Optimal cutoff points were determined by X-tile program. Cumulative incidence function was used to analyze cancer-specific mortality (CSM) and death resulting from other causes (DROC). Significant predictive factors were used to establish nomograms estimating overall (OS) and cancer-specific survival (CSS). The nomograms were bootstrapped validated both internally and externally.

Conclusions

Comprehensive nomograms were constructed to predict OS and CSS for LSCC patients treated with neck dissection more accurately.

Implementation of a rapid learning platform: Predicting 2-year survival in laryngeal carcinoma patients in a clinical setting

Background and Purpose

To improve quality and personalization of oncology health care, decision aid tools are needed to advise physicians and patients. The aim of this work is to demonstrate the clinical relevance of a survival prediction model as a first step to multi institutional rapid learning and compare this to a clinical trial dataset.

Materials and Methods

Data extraction and mining tools were used to collect uncurated input parameters from Illawarra Cancer Care Centre's (clinical cohort) oncology information system. Prognosis categories previously established from the Maastricht Radiation Oncology (training cohort) dataset, were applied to the clinical cohort and the radiotherapy only arm of the RTOG-9111 (trial cohort).

Results

Data mining identified 125 laryngeal carcinoma patients, ending up with 52 patients in the clinical cohort who were eligible to be evaluated by the model to predict 2-year survival and 177 for the trial cohort. The model was able to classify patients and predict survival in the clinical cohort, but for the trial cohort it failed to do so.

Conclusions

The technical infrastructure and model is able to support the prognosis prediction of laryngeal carcinoma patients in a clinical cohort. The model does not perform well for the highly selective patient population in the trial cohort.

A novel prognostic score model incorporating CDGSH iron sulfur domain2 (CISD2) predicts risk of disease progression in laryngeal squamous cell carcinoma

Background

The role of CDGSH iron sulfur domain 2 (CISD2) in laryngeal squamous cell carcinoma (LSCC) remains unclear.

Results

CISD2 were up-regulated in LSCC tissues compared with adjacent noncancerous tissues both at mRNA and protein levels. CISD2 was significantly correlated with T stage, lymph node metastasis, clinical stage and disease progression. A prognostic model (C-N model) for PFS was subsequently constructed based on independent prognostic factors including CISD2 and N classification. This model significantly divided LSCC patients into three risk subgroups and was more accurate than the prediction efficacy of TNM classification in the training cohort (C-index, 0.710 vs 0.602, P = 0.027) and validation cohort (C-index, 0.719 vs 0.578, P = 0.014).

Methods

Real-time PCR and Western blotting were employed to examine the expression of CISD2 in eight fresh paired LSCC samples. Immunohistochemistry was performed to assess CISD2 expression in 490 paraffin-embedded archived LSCC samples. A prognostic model for progression-free survival (PFS) was built using independent factors. The concordance index (C-Index) was used to evaluate the prognostic ability of the model.

Conclusions

CISD2 was up-regulated in LSCC. The novel C-N model, which includes CISD2 levels and N classification, is more accurate than conventional TNM classification for predicting PFS in LSCC.

Individualized outcome prognostication for patients with laryngeal cancer

BACKGROUND

Accurate prognostication is essential to the optimal management of laryngeal cancer. Predictive models have been developed to calculate the risk of oncologic outcomes, but extensive external validation of accuracy and reliability is necessary before implementing them into clinical practice.

METHOD

Four published prognostic calculators that predict 5-year overall survival for patients with laryngeal cancer were evaluated using patient information from a prospective epidemiology study cohort (n = 246; median follow-up, 60 months) with previously untreated, stage I through IVb laryngeal squamous cell carcinoma.

RESULTS

Different calculators yielded substantially different predictions for individual patients. The observed 5-year overall survival was significantly higher than the averaged predicted 5-year overall survival of the 4 calculators (71.9%; 95% confidence interval [CI], 65%-78%] vs 47.7%). Statistical analyses demonstrated the calculators' limited capacity to discriminate outcomes for risk-stratified patients. The area under the receiver operating characteristic curve ranged from 0.68 to 0.72. C-index values were similar for each of the 4 models (range, 0.66-0.68). There was a lower than expected hazard of death for patients who received induction (bioselective) chemotherapy (hazard ratio, 0.46; 95% CI, 0.24-0.88; P = .024) or primary surgical intervention (hazard ratio, 0.43; 95 % CI, 0.21-0.90; P = .024) compared with those who received concurrent chemoradiation.

CONCLUSIONS

Suboptimal reliability and accuracy limit the integration of existing individualized prediction tools into routine clinical decision making. The calculators predicted significantly worse than observed survival among patients who received induction chemotherapy and primary surgery, suggesting a need for updated consideration of modern treatment modalities. Further development of individualized prognostic calculators may improve risk prediction, treatment planning, and counseling for patients with laryngeal cancer. Cancer 2018;124:706-16. © 2017 American Cancer Society.

Decision support systems for personalized and participative radiation oncology

A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being supported by the development of clinical decision support systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, decision support systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, decision support systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.

A Novel Inflammation- and Nutrition-Based Prognostic System for Patients with Laryngeal Squamous Cell Carcinoma: Combination of Red Blood Cell Distribution Width and Body Mass Index (COR-BMI)

Background

Laryngeal squamous cell carcinoma (LSCC) is a head and neck cancer type. In this study, we introduced a novel inflammation- and nutrition-based prognostic system, referred to as COR-BMI (Combination of red blood cell distribution width and body mass index), for LSCC patients.

Methods

A total of 807 LSCC patients (784 male and 23 female, 22–87 y of age) who underwent surgery were enrolled in this retrospective cohort study. The patients were stratified by COR-BMI into three groups: COR-BMI (0) (RDW ≤ 13.1 and BMI ≥ 25); COR-BMI (1) (RDW ≤ 13.1 and BMI < 18.5 or 18.5 ≤ BMI < 25; RDW > 13.1 and 18.5 ≤ BMI < 25 or BMI ≥ 25); or COR-BMI (2) (RDW > 13.1 and BMI < 18.5). Cox regression models were used to investigate the association between COR-BMI and cancer-specific survival (CSS) rate among LSCC patients.

Results

The 5-y, 10-y, and 15-y CSS rates were 71.6%, 60.1%, and 55.4%, respectively. There were significant differences among the COR-BMI groups in age (< 60 versus ≥ 60 y; P = 0.005) and T stage (T1, T2, T3, or T4; P = 0.013). Based on the results, COR-BMI (1 versus 0: HR = 1.76; 95% CI = 0.98–3.15; 2 versus 0: HR = 2.91; 95% CI = 1.53–5.54, P = 0.001) was a significant independent predictor of CSS.

Conclusion

COR-BMI is a novel inflammation- and nutrition-based prognostic system, which could predict long-term survival in LSCC patients who underwent surgery.

Protein function in precision medicine: deep understanding with machine learning

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

Treatment failure prediction for head-and-neck cancer radiation therapy

PURPOSE

Treatment outcome prediction is an important emerging topic in oncologic care. To support radiation oncologists on their decisions, with individualized, tailored treatment regimens increasingly becoming the standard of care, accurate tools to predict tumour response to treatment are needed. The goal of this work is to identify the most determinant factor(s) for treatment response aiming to develop prediction models that robustly estimate tumour response to radiation therapy in patients with head-and-neck cancer.

PATIENTS AND METHODS

A population-based cohort study was performed on 92 patients with head-and-neck cancer treated with radiation from 2007 until 2014 at the Portuguese Institute of Oncology of Coimbra (IPOCFG). Correlation analysis and multivariate binary logistic regression analysis were conducted in order to explore the predictive power of the considered predictors. Performance of the models is expressed as the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. A nomogram to predict treatment failure was developed.

RESULTS

Significant prognostic factors for treatment failure, after multivariate regression, were older age, non-concomitant radiation therapy and larger primary tumour volume. A regression model with these predictors revealed an AUC of .78 for an independent data set.

CONCLUSION

For patients with head-and-neck cancer treated with definitive radiation, we have developed a prediction nomogram based on models that presented good discriminative ability in making predictions of tumour response to treatment. The probability of treatment failure is higher for older patients with larger tumours treated with non-concomitant radiation.

Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine

BACKGROUND

Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.

AIM

The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementary alternatives to traditional evidence-based medicine, specifically rapid learning health care and cohort multiple randomised controlled trial design. Rapid learning health care is an approach that proposes to extract and apply knowledge from routine clinical care data rather than exclusively depending on clinical trial evidence, (please watch the animation: http://youtu.be/ZDJFOxpwqEA). The cohort multiple randomised controlled trial design is a pragmatic method which has been proposed to help overcome the weaknesses of conventional randomised trials, taking advantage of the standardised follow-up approaches more and more used in routine patient care. This approach is particularly useful when the new intervention is a priori attractive for the patient (i.e. proton therapy, patient decision aids or expensive medications), when the outcomes are easily collected, and when there is no need of a placebo arm.

DISCUSSION

Truly personalised cancer treatment is the goal in modern radiotherapy. However, personalised cancer treatment is also an immense challenge. The vast variety of both cancer patients and treatment options makes it extremely difficult to determine which decisions are optimal for the individual patient. Nevertheless, rapid learning health care and cohort multiple randomised controlled trial design are two approaches (among others) that can help meet this challenge.

Cost-Effectiveness of Reduced Waiting Time for Head and Neck Cancer Patients due to a Lean Process Redesign

BACKGROUND

Compared with new technologies, the redesign of care processes is generally considered less attractive to improve patient outcomes. Nevertheless, it might result in better patient outcomes, without further increasing costs. Because early initiation of treatment is of vital importance for patients with head and neck cancer (HNC), these care processes were redesigned.

OBJECTIVES

This study aimed to assess patient outcomes and cost-effectiveness of this redesign.

METHODS

An economic (Markov) model was constructed to evaluate the biopsy process of suspicious lesion under local instead of general anesthesia, and combining computed tomography and positron emission tomography for diagnostics and radiotherapy planning. Patients treated for HNC were included in the model stratified by disease location (larynx, oropharynx, hypopharynx, and oral cavity) and stage (I-II and III-IV). Probabilistic sensitivity analyses were performed.

RESULTS

Waiting time before treatment start reduced from 5 to 22 days for the included patient groups, resulting in 0.13 to 0.66 additional quality-adjusted life-years. The new workflow was cost-effective for all the included patient groups, using a ceiling ratio of €80,000 or €20,000. For patients treated for tumors located at the larynx and oral cavity, the new workflow resulted in additional quality-adjusted life-years, and costs decreased compared with the regular workflow. The health care payer benefited €14.1 million and €91.5 million, respectively, when individual net monetary benefits were extrapolated to an organizational level and a national level.

CONCLUSIONS

The redesigned care process reduced the waiting time for the treatment of patients with HNC and proved cost-effective. Because care improved, implementation on a wider scale should be considered.

Prognostic value of hypoxia-associated markers in advanced larynx and hypopharynx squamous cell carcinoma

OBJECTIVES/HYPOTHESIS

To determine the prognostic value of hypoxia-associated markers carbonic anhydrase-9 (CA-9) and hypoxia-inducible factor-1α (HIF-1α) in advanced larynx and hypopharynx squamous cell carcinoma (SCCa) treated by organ preservation strategies.

STUDY DESIGN

Retrospective cohort study.

METHODS

Pretreatment CA-9 and HIF-1α expression, clinicopathologic data, and tumor volume were analyzed in a series of 114 patients with T3-4 SCCa larynx or hypopharynx treated by (chemo)radiation.

RESULTS

Adverse prognostic factors for locoregional control were T4 classification (P = 0.008), and for disease-specific survival were CA-9 positivity (P = 0.039), T4 classification (P = 0.001), larger tumor volume (P = 0.004), N1-3 classification (P = 0.002), and pretreatment hemoglobin < 13.0 g/dl (P = 0.014). With increasing CA-9 expression, there was a trend to increasing tumor recurrence (P trend = 0.009) and decreasing survival (P trend = 0.002). On multivariate analysis, independent variables were T4 classification (hazard ratio [HR] 13.54, P = 0.01) for locoregional failure, and CA-9 positivity (HR = 8.02, P = 0.042) and higher tumor volume (HR = 3.33, P = 0.007) for disease-specific mortality.

CONCLUSION

This is the first study to look specifically at T3 and T4 SCCa larynx and hypopharynx for a relationship between hypoxia-associated marker expression and clinical outcome. Pretreatment immunohistochemical CA-9 expression is an adverse prognostic factor for disease-specific survival, indicating that CA-9 expression may confer a more aggressive tumor phenotype.

Squamous cell carcinoma of the larynx with subglottic extension: is larynx preservation possible?

PURPOSE

Squamous cell carcinoma of larynx with subglottic extension (sSCC) is a rare location described to carry a poor prognosis. The aim of this study was to analyze outcomes and feasibility of larynx preservation in sSCC patients.

PATIENTS AND METHODS

Between 1996 and 2012, 197 patients with sSCC were treated at our institution and included in the analysis. Stage III-IV tumors accounted for 76%. Patients received surgery (62%), radiotherapy (RT) (18%), or induction chemotherapy (CT) (20%) as front-line therapy.

RESULTS

The 5-year actuarial overall survival (OS), locoregional control (LRC), and distant control rate were 59% (95% CI 51-68), 83% (95% CI 77-89), and 88% (95% CI 83-93), respectively, with a median follow-up of 54.4 months. There was no difference in OS and LRC according to front-line treatments or between primary subglottic cancer and glottosupraglottic cancers with subglottic extension. In the multivariate analysis, age > 60 years and positive N stage were the only predictors for OS (HR 2, 95% CI 1.2-3.6; HR1.9, 95% CI 1-3.5, respectively). A lower LRC was observed for T3 patients receiving a larynx preservation protocol as compared with those receiving a front-line surgery (HR 14.1, 95% CI 2.5-136.7; p = 0.02); however, no difference of ultimate LRC was observed according to the first therapy when including T3 patients who underwent salvage laryngectomy (p = 0.6). In patients receiving a larynx preservation protocol, the 5-year larynx-preservation rate was 55% (95% CI 43-68), with 36% in T3 patients. The 5-year larynx preservation rate was 81% (95% CI 65-96) and 35% (95% CI 20-51) for patients who received RT or induction CT as a front-line treatment, respectively.

CONCLUSION

Outcomes of sSCC are comparable with other laryngeal cancers when managed with modern therapeutic options. Larynx-preservation protocols could be a suitable option in T1-T2 (RT or chemo-RT) and selected T3 sSCC patients (induction CT).

[Results of definitive radiotherapy for squamous cell carcinomas of the larynx patients with subglottic extension]

BACKGROUND

Squamous cell carcinoma of larynx with subglottic extension is a rare location described to carry a poor prognosis. The aim of this study was to analyze outcomes and feasibility of definitive radiotherapy in patients with squamous cell carcinoma.

PATIENTS AND METHODS

Between 1998 and 2012, 56 patients with squamous cell carcinoma were treated at our institution and included in the analysis. Patients received definitive radiotherapy/chemoradiotherapy alone (63%) or after induction chemotherapy (37%) at our institute.

RESULTS

The 5-year actuarial overall survival, progression-free survival and specific survival were 64% (CI 95%: 48-90), 45% (CI 95%: 28-61), 88% (CI 95%: 78-98), respectively, with median follow-up of 74 months. The 5-year locoregional control was 69% (CI 95%: 56-83) and the 5-year distant control was 95% (CI 95%: 89-100). There was no difference in overall survival and locoregional control according to front-line treatments or between primary subglottic cancer and glotto-supraglottic cancers with subglottic extension. In the multivariate analysis, performance status of at least 1 and positive N stage were the only predictors for overall survival (hazard ratio [HR] [CI 95%]: 6.5 [1.3-34; P=0.03] and 11 [1.6-75; P=0.02], respectively). No difference of locoregional control was observed according to the first received therapy. The univariate analysis retrieved that T3-T4 patients had a lower locoregional control (HR: 3.1; CI 95%: 1.1-9.2, P=0.04), but no prognostic factor was retrieved in the multivariate analysis. In patients receiving a larynx preservation protocol, 5-year larynx preservation rate was 88% (CI 95%: 78-98), and 58% in T3 patients. The 5-year larynx preservation rate was 91% (CI 95%: 79-100) and 83% (CI 95%: 66-100) for patients who received radiotherapy/chemoradiotherapy or induction chemotherapy as a front-line treatment, respectively.

CONCLUSION

This analysis suggests that the results for squamous cell carcinoma patients treated with radiotherapy/chemoradiotherapy are comparable to those obtained for other laryngeal tumors. This thus suggests the feasibility of laryngeal preservation protocols for infringement subglottic for selected cases. Further studies are needed to clarify these preliminary data.

International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining

Extensive, multifactorial data sharing is a crucial prerequisite for current and future (radiotherapy) research. However, the cost, time and effort to achieve this are often a roadblock. We present an open-source based data-sharing infrastructure between two radiotherapy departments, allowing seamless exchange of de-identified, automatically translated clinical and biomedical treatment data.