Time-varying survival effects for squamous cell carcinomas at oropharyngeal and nonoropharyngeal head and neck sites in the United States, 1973-2015

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017

PURPOSE

Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period.

METHODS

Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks.

RESULTS

Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival.

CONCLUSION

These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Cross-site prognosis prediction for nasopharyngeal carcinoma from incomplete multi-modal data

Accurate prognosis prediction for nasopharyngeal carcinoma based on magnetic resonance (MR) images assists in the guidance of treatment intensity, thus reducing the risk of recurrence and death. To reduce repeated labor and sufficiently explore domain knowledge, aggregating labeled/annotated data from external sites enables us to train an intelligent model for a clinical site with unlabeled data. However, this task suffers from the challenges of incomplete multi-modal examination data fusion and image data heterogeneity among sites. This paper proposes a cross-site survival analysis method for prognosis prediction of nasopharyngeal carcinoma from domain adaptation viewpoint. Utilizing a Cox model as the basic framework, our method equips it with a cross-attention based multi-modal fusion regularization. This regularization model effectively fuses the multi-modal information from multi-parametric MR images and clinical features onto a domain-adaptive space, despite the absence of some modalities. To enhance the feature discrimination, we also extend the contrastive learning technique to censored data cases. Compared with the conventional approaches which directly deploy a trained survival model in a new site, our method achieves superior prognosis prediction performance in cross-site validation experiments. These results highlight the key role of cross-site adaptability of our method and support its value in clinical practice.

Using information criteria to select smoothing parameters when analyzing survival data with time-varying coefficient hazard models

Analyzing the large-scale survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program may help guide the management of cancer. Detecting and characterizing the time-varying effects of factors collected at the time of diagnosis could reveal important and useful patterns. However, fitting a time-varying effect model by maximizing the partial likelihood with such large-scale survival data is not feasible with most existing software. Moreover, estimating time-varying coefficients using spline based approaches requires a moderate number of knots, which may lead to unstable estimation and over-fitting issues. To resolve these issues, adding a penalty term greatly aids estimation. The selection of penalty smoothing parameters is difficult in this time-varying setting, as traditional ways like using Akaike information criterion do not work, while cross-validation methods have a heavy computational burden, leading to unstable selections. We propose modified information criteria to determine the smoothing parameter and a parallelized Newton-based algorithm for estimation. We conduct simulations to evaluate the performance of the proposed method. We find that penalization with the smoothing parameter chosen by a modified information criteria is effective at reducing the mean squared error of the estimated time-varying coefficients. Compared to a number of alternatives, we find that the estimates of the variance derived from Bayesian considerations have the best coverage rates of confidence intervals. We apply the method to SEER head-and-neck, colon, prostate, and pancreatic cancer data and detect the time-varying nature of various risk factors.

Patterns of care and prognostic evaluation for stage I-III upper esophageal squamous cell carcinoma: a population-based study

Background

There is no strong evidence regarding the optimal treatment and specific prognosis prediction model for upper esophageal squamous cell carcinoma (UESCC). This study aimed to investigate the real-world treatment patterns and develop models to predict overall survival (OS) and esophageal cancer-specific survival (ECSS) in patients with stage I-III UESCC.

Methods

Patients with T1-4N0-3M0 UESCC in the Surveillance, Epidemiology, and End Results (SEER) database were identified from 2010 to 2017, and randomized to a training cohort and a validation cohort. The effect of treatment patterns on survival were comprehensively analyzed. Nomograms were developed by incorporating independent prognostic factors analyzed by Cox regression in the training cohort and evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) in two cohorts.

Results

A total of 677 patients were identified, including 452 in the training cohort and 225 in the validation cohort. Among all populations, 71.9% (487) received chemoradiotherapy without surgery, and chemoradiotherapy or/and surgery showed better survival than other treatments. However, surgery was rarely carried out for patients with stage II-III. T stage, N stage, surgery, chemotherapy, and radiotherapy were independent risks for both OS and ECSS, while age was also an independent risk for OS. The C-indexes for nomograms to predict OS (0.71 and 0.72) and ECSS (0.70 and 0.73) were greater than 7th AJCC staging system to predict OS (0.61 and 0.64) and ECSS (0.64 and 0.64) in both the training cohort and the validation cohort. Time-dependent ROC curves and DCA also suggested that nomograms performed consistently better than 7th AJCC staging system. The calibration curves demonstrated good consistency in predicting survival.

Conclusions

Chemoradiotherapy was a major treatment with preferable survival for patients with stage I-III UESCC. We have firstly developed and validated prognostic nomograms in patients with stage I-III UESCC, which would play a supplementary role in the current staging system.

Sociodemographic Survival Disparities for Lung Cancer in the United States, 2000-2016

BACKGROUND

Understanding the impact of patient and tumor characteristics on lung cancer survival can help build personalized prognostic models and identify health disparities.

METHODS

We identified 557 555 patients aged 25 years and older diagnosed with lung or bronchus carcinoma from the Surveillance, Epidemiology, and End Results database, 2000-2016. We estimated hazard ratios (HR) for demographic (sex, age, race and ethnicity), tumor (stage, histology, year of diagnosis), and geographic characteristics (census tract-level urbanicity, socioeconomic status [SES]), as well as selected interactions, on the rate of lung cancer-specific death using multivariable proportional hazards models.

RESULTS

Women had a higher survival (lower hazard) of lung cancer-specific death than men (HR = 0.83, 95% confidence interval [CI] = 0.82 to 0.83). Hazards differed by race and ethnicity. Regional (HR = 2.41, 95% CI = 2.37 to 2.44) and distant (HR = 6.61, 95% CI = 6.53 to 6.69) tumors were associated with a lower survival (higher hazard) than localized tumors. Small cell tumors were associated with a lower survival (HR = 1.19, 95% CI = 1.18 to 1.20) than non-small cell tumors. Patients diagnosed after 2009 had lower hazards (HR = 0.86, 95% CI = 085 to 0.86) than those diagnosed 2000-2009. Lung cancer-specific survival did not depend on urbanicity after adjusting for census tract-level SES, but survival decreased with decreasing census tract-level SES. Differences in survival between non-Hispanic Black and White patients were greater for younger patients and localized tumors and increased with census tract-level SES. Differences by sex were greatest for young patients and localized tumors.

CONCLUSIONS

Disparities in survival after lung cancer diagnosis remain, with intersectional patterns suggesting differential access to and quality of care. Efforts are needed to ensure that high-risk groups receive guideline-concordant treatment.

Genomic Hippo Pathway Alterations and Persistent YAP/TAZ Activation: New Hallmarks in Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSCC) represents a highly prevalent and deadly malignancy worldwide. The prognosis for locoregionally advanced HNSCC has not appreciably improved over the past 30 years despite advances in surgical, radiation, and targeted therapies and less than 20% of HNSCC patients respond to recently approved immune checkpoint inhibitors. The Hippo signaling pathway, originally discovered as a mechanism regulating tissue growth and organ size, transduces intracellular and extracellular signals to regulate the transcriptional co-activators YAP and TAZ. Alterations in the Hippo pathway resulting in persistent YAP and TAZ activation have emerged as major oncogenic drivers. Our analysis of the human HNSCC oncogenome revealed multiple genomic alterations impairing Hippo signaling and activating YAP and TAZ, which in turn contribute to HNSCC development. This includes mutations and deletions of the FAT1 gene (29%) and amplification of the WWTR1 (encoding TAZ, 14%) and YAP1 genes (8%), together representing one of the most genetically altered signaling mechanisms in this malignancy. Here, we discuss key elements of the mammalian Hippo pathway, detail mechanisms by which perturbations in Hippo signaling promote HNSCC initiation and progression and outline emerging strategies to target Hippo signaling vulnerabilities as part of novel multimodal precision therapies for HNSCC.

Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients

Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.

Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma

Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.