Individualized risk prediction of outcomes for oral cavity cancer patients

A New Personalized Oral Cancer Survival Calculator to Estimate Risk of Death From Both Oral Cancer and Other Causes

Importance

Standard cancer prognosis models typically do not include much specificity in characterizing competing illnesses or general health status when providing prognosis estimates, limiting their utility for individuals, who must consider their cancer in the context of their overall health. This is especially true for patients with oral cancer, who frequently have competing illnesses.

Objective

To describe a statistical framework and accompanying new publicly available calculator that provides personalized estimates of the probability of a patient surviving or dying from cancer or other causes, using oral cancer as the first data set.

Design, Setting, and Participants

The models used data from the Surveillance, Epidemiology, and End Results (SEER) 18 registry (2000 to 2011), SEER-Medicare linked files, and the National Health Interview Survey (NHIS) (1986 to 2009). Statistical methods developed to calculate natural life expectancy in the absence of the cancer, cancer-specific survival, and other-cause survival were applied to oral cancer data and internally validated with 10-fold cross-validation. Eligible participants were aged between 20 and 94 years with oral squamous cell carcinoma.

Exposures

Histologically confirmed oral cancer, general health status, smoking, and selected serious comorbid conditions.

Main Outcomes and Measures

Probabilities of surviving or dying from the cancer or from other causes, and life expectancy in the absence of the cancer.

Results

A total of 22 392 patients with oral squamous cell carcinoma (13 544 male [60.5%]; 1476 Asian and Pacific Islander [6.7%]; 1792 Black [8.0%], 1589 Hispanic [7.2%], 17 300 White [78.1%]) and 402 626 NHIS interviewees were included in this calculator designed for public use for patients ages 20 to 86 years with newly diagnosed oral cancer to obtain estimates of health status-adjusted age, life expectancy in the absence of the cancer, and the probability of surviving, dying from the cancer, or dying from other causes within 1 to 10 years after diagnosis. The models in the calculator estimated that patients with oral cancer have a higher risk of death from other causes than their matched US population, and that this risk increases by stage.

Conclusions and relevance

The models developed for the calculator demonstrate that survival estimates that exclude the effects of coexisting conditions can lead to underestimates or overestimates of survival. This new calculator approach will be broadly applicable for developing future prognostic models of cancer and noncancer aspects of a person's health in other cancers; as registries develop more linkages, available covariates will become broader, strengthening future tools.

Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer

BACKGROUND

Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a 'one-dose-fits-all' approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international 'big-data' to facilitate risk-based stratification of patients with HNC.

METHODS

The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts: a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups.

RESULTS

Performance score, AJCC8thstage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66-0.77]) and in all three validation cohorts (c-indices: 0.76 [0.69-0.83], 0.73 [0.68-0.77], and 0.75 [0.68-0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17-46% for the high-risk group compared to 92-98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64-0.81]). These models are integrated in a clinic-ready interactive web interface: https://uic-evl.github.io/hnc-predictor/ CONCLUSIONS: Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.

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.

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.

Examining Bias and Reporting in Oral Health Prediction Modeling Studies

Recent efforts to improve the reliability and efficiency of scientific research have caught the attention of researchers conducting prediction modeling studies (PMSs). Use of prediction models in oral health has become more common over the past decades for predicting the risk of diseases and treatment outcomes. Risk of bias and insufficient reporting present challenges to the reproducibility and implementation of these models. A recent tool for bias assessment and a reporting guideline—PROBAST (Prediction Model Risk of Bias Assessment Tool) and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis)—have been proposed to guide researchers in the development and reporting of PMSs, but their application has been limited. Following the standards proposed in these tools and a systematic review approach, a literature search was carried out in PubMed to identify oral health PMSs published in dental, epidemiologic, and biostatistical journals. Risk of bias and transparency of reporting were assessed with PROBAST and TRIPOD. Among 2,881 papers identified, 34 studies containing 58 models were included. The most investigated outcomes were periodontal diseases (42%) and oral cancers (30%). Seventy-five percent of the studies were susceptible to at least 4 of 20 sources of bias, including measurement error in predictors ( n = 12) and/or outcome ( n = 7), omitting samples with missing data ( n = 10), selecting variables based on univariate analyses ( n = 9), overfitting ( n = 13), and lack of model performance assessment ( n = 24). Based on TRIPOD, at least 5 of 31 items were inadequately reported in 95% of the studies. These items included sampling approaches ( n = 15), participant eligibility criteria ( n = 6), and model-building procedures ( n = 16). There was a general lack of transparent reporting and identification of bias across the studies. Application of the recommendations proposed in PROBAST and TRIPOD can benefit future research and improve the reproducibility and applicability of prediction models in oral health.

Early squamous cell carcinoma of the oral tongue with histologically benign lymph nodes: A model predicting local control and vetting of the eighth edition of the American Joint Committee on Cancer pathologic T stage

BACKGROUND

The eighth edition of the American Joint Committee on Cancer staging manual (AJCC8) added depth of invasion to the definition of pathologic T stage (pT). In the current study, the authors assess pT stage migration and the prognostic performance of the updated pT stage and compare it with other clinicopathologic variables in patients with early squamous cell carcinoma of the oral tongue (OTSCC; tumors measuring ≤4 cm) with histologically benign lymph nodes (pN0).

METHODS

A multi-institutional cohort of patients with early OTSCC was restaged as per AJCC8. Primary endpoints were local recurrence (LR) and locoregional recurrence (LRR). Influential variables were identified and an LR/LRR prediction model was developed.

RESULTS

There were a total of 494 patients, with 49 LR and 73 LRR. AJCC8 pT criteria resulted in upstaging of 37.9% of patients (187 of 494 patients), including 34.5% (64 of 185 patients) from pT2 to pT3, without improving the prognostication for LR or LRR. Both LR and LRR were found to be similar for patients with AJCC8 pT2 and pT3 disease. On multivariate analysis, LR was only found to be associated with distance to the closest margin (hazard ratio, 0.36; 95% CI, 0.20-0.64 [P = .0007]) and perineural invasion (hazard ratio, 1.92; 95% CI, 1.10-0.64 [P = .046]). Based on these 2 predictors, a final proportional hazards regression model (which may be used similar to a nomogram) was developed. The proposed model appeared to be superior to AJCC pT stage for estimating the probability of LR and LRR for individual patients with early OTSCC.

CONCLUSIONS

AJCC8 pT criteria resulted in pT upstaging of patients with pN0 disease without improved LR or LRR prognostication. The proposed model based on distance to the closest margin and perineural invasion, status outperformed pT as a predictor of LR and LRR in patients with early OTSCC.

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.

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.