Stage-dependent conditional survival and failure hazard of non-metastatic nasopharyngeal carcinoma after intensity-modulated radiation therapy: Clinical implications for treatment strategies and surveillance

Real-time artificial intelligence-assisted detection and segmentation of nasopharyngeal carcinoma using multimodal endoscopic data: a multi-center, prospective study

Background

Nasopharyngeal carcinoma (NPC) is a common malignancy in southern China, and often underdiagnosed due to reliance on physician expertise. Artificial intelligence (AI) can enhance diagnostic accuracy and efficiency using large datasets and advanced algorithms.

Methods

Nasal endoscopy videos with white light imaging (WLI) and narrow-band imaging (NBI) modes from 707 patients treated at one center in China from June 2020 to December 2022 were prospectively collected. A total of 8816 frames were obtained through standardized data procedures. Nasopharyngeal Carcinoma Diagnosis Segmentation Network Framework (NPC-SDNet) was developed and internally tested based on these frames. Two hundred frames were randomly selected to compare the diagnostic performance between NPC-SDNet and rhinologists. Two external testing sets with 2818 images from other hospitals validated the robustness and generalizability of the model. This study was registered at clinicaltrials.gov (NCT04547673).

Findings

The diagnostic accuracy, precision, recall, and specificity of NPC-SDNet using WLI were 95.0% (95% CI: 94.1%-96.2%), 93.5% (95% CI: 90.2%-95.2%), 97.2% (95% CI: 96.2%-98.3%), and 93.5% (95% CI: 91.7%-94.0%), respectively, and using NBI were 95.8% (95% CI: 94.0%-96,8%), 93.1% (95% CI: 91.0%-95.6%), 96.0% (95% CI: 95.7%-96.8%), and 97.2% (95% CI: 97.1%-97.4%), respectively. Segmentation performance was also robust, with mean Intersection over Union scores of 83.4% (95% CI: 81.8%-85.6%; NBI) and 83.7% (95% CI: 85.1%-90.1%; WLI). In head-to-head comparisons with rhinologists, NPC-SDNet achieved a diagnostic accuracy of 94.0% (95% CI: 91.5%-95.8%) and processed 1000 frames per minute, outperforming clinicians (68.9%-88.2%) across different expertise levels. External validation further supported the reliability of NPC-SDNet, with area under the receiver operating characteristic curve (AUC) values of 0.998 and 0.977 in NBI images, 0.977 and 0.970 in WLI images.

Interpretation

NPC-SDNet demonstrates excellent real-time diagnostic and segmentation accuracy, offering a promising tool for enhancing the precision of NPC diagnosis.

Funding

This work was supported by National Key R&D Program of China (2020YFC1316903), the National Natural Science Foundation of China (NSFC) grants (81900918, 82020108009), Natural Science Foundation of Guangdong Province (2022A1515010002), Key-Area Research and Development of Guangdong Province (2023B1111040004, 2020B1111190001), and Key Clinical Technique of Guangzhou (2023P-ZD06).

Tumor differentiation-dependent conditional survival of patients with operable thyroid cancer

Objective

Little is known about the changing risk profile of death and conditional survival in patients with operable thyroid cancer. This study aimed to investigate the annual hazard rate of cancer death, actuarial disease-specific survival (DSS), and conditional DSS in patients with thyroid cancer and explore the effects of tumor differentiation.

Methods

Patients diagnosed with thyroid cancer (N = 132,354) between 2004 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The hazard function was used to estimate the annual hazard rate of death. The Kaplan-Meier method and log-rank test were used for the calculation and between-group comparison of actuarial DSS, respectively. The life table was used to estimate the conditional DSS.

Results

A total of 1896 (1.4%) patients died due to thyroid cancer during the follow-up period. Patients with ATC (68.9%, 313/454) were more likely to die than those with PDTC (19.4%, 171/883) or DTC (1.1%, 1412/131017). For the entire cohort, patients with DTC and PDTC had excellent and relatively stable one-year conditional survival, respectively; patients with ATC had the worst one-year conditional survival, but they achieved the greatest improvements. The worst one-year conditional survival and the most obvious improvement were seen in patients with ATC regardless of any SEER Summary Stage.

Conclusion

Prognosis improved over time in a tumor differentiation-dependent manner in patients with operable thyroid cancer after diagnosis. This information provides more precise dynamic evaluations of the long-term prognosis of thyroid cancer survivors and paramount clinical implications for individualized treatment and surveillance.

Recent advances in early detection of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) arises from the mucosal epithelium of the nasopharynx and is frequently located in the pharyngeal crypts. This is a highly aggressive malignant tumor that frequently leads to distant metastases in many cases and poses a significant public health challenge, particularly in certain geographic regions globally. This review discusses the epidemiology, risk factors, diagnosis, and treatment options for NPC, emphasizing the importance of early detection and comprehensive management strategies in improving patient outcomes. Moreover, the article explores the intricate mechanisms that cause NPC. Comprehending these fundamental principles can assist in creating specific prevention and therapy approaches for NPC. Recent advances in diagnostic methods, including imaging tests and molecular biomarkers, are emphasized to improve early diagnosis and individualized treatment strategies for individuals with NPC. The review also explores the most recent advancements in treating early-stage (stage I and II) NPC patients, highlighting the changing landscape of individualized therapy approaches for this particular set of patients.

Evidence of being cured for nasopharyngeal carcinoma: results of a multicenter patient-based study in China

Background

The survival rates of patients with nasopharyngeal carcinoma (NPC) have improved significantly, but there is no consensus on whether they can be considered cured. We aimed to determine whether a statistical cure could be achieved for patients with NPC in the contemporary therapeutic landscape.

Methods

This retrospective multicenter study enrolled 6315 patients with nonmetastatic NPC from nonendemic and endemic regions of China from 2007 to 2020. We applied mixture and nonmixture cure models to estimate the cure probabilities and cure times by incorporating background mortality for the general population, matching by gender, age, and diagnosed year.

Findings

With death as the uncured event, the probability of patients with NPC achieving a life expectancy at par with the general population was 78.1%. Considering progression as the uncured event, the likelihood of patients attaining a life expectancy without progression equivalent to that of the general population was 72.4%. For individuals, the probabilities of achieving cure were conditional and time-dependent, requiring approximately 7.1 and 4.7 years with 95% certainty, respectively. The corresponding cure times for uncured patients were 8.9 and 6.8 years, respectively. The cure probability was correlated with age, Eastern Cooperative Oncology Group score, TNM staging, Epstein-Barr virus DNA copies, and lactate dehydrogenase. The correlation was excellent between 5-year overall survival/progression-free survival and cure fractions.

Interpretation

Statistical cure is potentially achievable among patients with NPC undergoing contemporary treatment modalities. The results hold significant potential implications for both clinical practice and patient perspectives.

Funding

National High Level Hospital Clinical Research Funding; Beijing Xisike Clinical Oncology Research Foundation; Beijing hope run fund.

Dynamic estimates of survival in oncocytic cell carcinoma of the thyroid

BACKGROUND

Little is known about death hazard and conditional survival of oncocytic cell carcinoma of the thyroid (OCC).

METHODS

Patients diagnosed with OCC between 2004 to 2019 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method was used to estimate the actuarial disease-specific survival (DSS). The annual hazard rate of death was depicted employing the hazard function. Based on the life-table method, the conditional DSS was calculated.

RESULTS

In terms of DSS rates, there were statistically significant differences among the different stages (P < 0.01). Annual hazard curves for mortality from OCC in the entire study participants demonstrated an overall decreasing tendency with two peaks at 3 and 10 years. In patients with distant disease, the death risk curve was the steepest and decreased quickly and evidently. Conditional DSS tended to increase over time in the entire study population. Patients with distant disease showed more significant alterations than those patients with local or regional disease.

CONCLUSIONS

Prognosis improved over time in patients with OCC. The largest increase in conditional DSS was observed in patients with distant disease. Conditional survival may provide more relevant prognostic information than conventional survival estimates and allow personalized follow-up and counseling.

Risk-dependent conditional survival analysis and annual hazard rate of inflammatory breast cancer

PURPOSE

The real-time prognosis of patients with inflammatory breast cancer (IBC) after surviving for several years was unclear. We aimed to estimate survival over time in IBC using conditional survival (CS) and annual hazard functions.

PATIENTS AND METHODS

This study recruited 679 patients diagnosed with IBC between 2010 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. We used the Kaplan-Meier method to estimate overall survival (OS). CS was the probability of surviving for another y years after surviving for x years after the diagnosis, and the annual hazard rate was the cumulative mortality rate of follow-up patients. Cox regression analyses were used to identify prognostic factors, and changes in real-time survival and immediate mortality in surviving patients were assessed within these prognostic factors.

RESULTS

CS analysis showed real-time improvement in survival, with 5-year OS updated annually from the initial 43.5% to 52.2%, 65.3%, 78.5%, and 89.0% (surviving 1-4 years, respectively). However, this improvement was relatively small in the first two years after diagnosis, and the smoothed annual hazard rate curve showed increasing mortality during this period. Cox regression identified seven unfavorable factors at diagnosis, but only distant metastases remained after five years of survival. Analysis of the annual hazard rate curves showed that mortality continued to decrease for most survivors, except for metastatic IBC.

CONCLUSION

Real-time survival of IBC improved dynamically over time, and the magnitude of this improvement was non-linear, depending on survival time and clinicopathological characteristics.

Conditional survival nomogram for monitoring real-time survival of young non-metastatic nasopharyngeal cancer survivors

BACKGROUND

The aim of this study was to clarify the improvement of the overall survival (OS) over time in young non-metastatic nasopharyngeal carcinoma (NPC) survivors by conditional survival (CS) analysis and to construct a CS-nomogram for updating individualized real-time prognosis.

METHODS

The study included 3409 young non-metastatic NPC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2004-2019). OS was estimated using the Kaplan-Meier method. CS was calculated based on CS(y|x) = OS(y + x)/OS(x), defined as the probability that a patient would survive for another y years after surviving for x years since diagnosis. We identified predictors using the least absolute shrinkage and selection operator (LASSO) regression and developed the CS-nomogram using multivariate Cox regression and the CS formula.

RESULTS

CS analysis showed a continuous increase in 10-year OS for young non-metastatic NPC from the initial 60.4% to 65.0%, 70.2%, 74.2%, 78.2%, 82.6%, 86.9%, 91.1%, 96.2% and 97.0% (surviving 1-9 years after diagnosis, respectively). After screening by LASSO regression, age, race, marital status, histological type, T- and N-status were used as predictors to construct the CS-nomogram. The model accurately estimated the real-time prognosis of survivors during follow-up with a stable time-dependent area under the curve (AUC).

CONCLUSIONS

CS analysis based on SEER database calibrated the real-time prognosis of young non-metastatic NPC survivors, revealing a dynamic improvement during follow-up time. We developed a novel CS-nomogram to update survival data for real-time optimization of monitoring strategies, medical resource allocation, and patient counseling. However, it was important to note that the model still needed external data validation and continuous improvement.

Constructing an individualized surveillance framework for nasopharyngeal carcinoma based on a dynamic risk-adapted approach

BACKGROUND AND PURPOSE

This study aims to evaluate the dynamic survival and recurrence hazard of nasopharyngeal carcinoma(NPC) patients after definitive chemoradiotherapy utilizing conditional survival(CS) analysis, and to propose a personalized surveillance strategy at different clinical stages.

MATERIALS AND METHODS

Non-metastatic NPC patients who received curative chemotherapy between June 2005 and December 2011 were included. The Kaplan-Meier method was used to calculate the CS rate.

RESULTS

A total of 1616 patients were analyzed. With the prolongation of survival time, both conditional locoregional recurrence free survival and distant metastatic free survival increased gradually. Changing pattern of annual recurrence risk over time varied among different clinical stages. The annual locoregional recurrence(LRR) risk in stage I-II was always less than 2%, while in stage III-IVa, it was greater than 2% for the first three years and decreased to below 2% only after the third year. The annual distant metastases (DM) risk was always less than 2% in stage I, but higher than 2% in stage II for the first 3 years (2.5-3.8%). For those with stage III-IVa, the annual DM risk retained at a high level(>5%), and only decreased to < 5% after the third year. Based on the dynamic changes in survival probability over time, we established a surveillance plan with different follow-up intensities and frequencies for different clinical stages.

CONCLUSION

The annual risk of LRR and DM decrease over time. Our individual surveillance model will provide critical prognostic information to optimize clinical decision-making, and promote to formulate surveillance counseling and help with resources allocation.

Conditional survival analysis and real-time prognosis prediction for cervical cancer patients below the age of 65 years

Background

Survival prediction for cervical cancer is usually based on its stage at diagnosis or a multivariate nomogram. However, few studies cared whether long-term survival improved after they survived for several years. Meanwhile, traditional survival analysis could not calculate this dynamic outcome. We aimed to assess the improvement of survival over time using conditional survival (CS) analysis and developed a novel conditional survival nomogram (CS-nomogram) to provide individualized and real-time prognostic information.

Methods

Cervical cancer patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan-Meier method estimated cancer-specific survival (CSS) and calculated the conditional CSS (C-CSS) at year y+x after giving x years of survival based on the formula C-CSS(y|x) =CSS(y+x)/CSS(x). y indicated the number of years of further survival under the condition that the patient was determined to have survived for x years. The study identified predictors by the least absolute shrinkage and selection operator (LASSO) regression and used multivariate Cox regression to demonstrate these predictors' effect on CSS and to develop a nomogram. Finally, the CSS possibilities predicted by the nomogram were brought into the C-CSS formula to create the CS-nomogram.

Results

A total of 18,511 patients aged <65 years with cervical cancer from 2004 to 2019 were included in this study. CS analysis revealed that the 15-year CSS increased year by year from the initial 72.6% to 77.8%, 84.5%, 88.8%, 91.5%, 93.5%, 94.8%, 95.7%, 96.4%, 97.3%, 98.0%, 98.5%, 99.1%, and 99.4% (after surviving for 1-13 years, respectively), and found that when survival exceeded 5-6 years, the risk of death from cervical cancer would be less than 5% in 10-15 years. The CS-nomogram constructed using tumor size, lymph node status, distant metastasis status, and histological grade showed strong predictive performance with a concordance index (C-index) of 0.805 and a stable area under the curve (AUC) between 0.795 and 0.816 over 15 years.

Conclusions

CS analysis in this study revealed the gradual improvement of CSS over time in long-term survived cervical cancer patients. We applied CS to the nomogram and developed a CS-nomogram successfully predicting individualized and real-time prognosis.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic triple-negative breast cancer

BACKGROUND

Conditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival.

METHODS

We recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram.

RESULTS

CS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86.

CONCLUSIONS

Survival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.

Development and validation of a risk prediction model for overall survival in patients with nasopharyngeal carcinoma: a prospective cohort study in China

OBJECTIVE

Nasopharyngeal carcinoma (NPC) is prevailing in Southern China, characterized by distinct geographical distribution. Aimed to predict the overall survival (OS) of patients with nasopharyngeal carcinoma, this study developed and validated nomograms considering demographic variables, hematological biomarkers, and oncogenic pathogens in China.

METHODS

The clinicopathological and follow-up data of the nasopharyngeal carcinoma patients obtained from a prospective longitudinal cohort study in the Chongqing University Cancer Hospital between Jan 1, 2017 and Dec 31, 2019 ([Formula: see text]). Cox regression model was used to tested the significance of all available variables as prognostic factors of OS. And independent prognostic factors were identified based on multivariable analysis to model nomogram. Concordance index (C-index), area under the receiver operating characteristic (AUC), calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of nomogram.

RESULTS

Data was randomly divided into a training cohort (1227 observers, about 70% of data) and a validation group (408 observers, about 30% of data). At multivariable analysis, the following were independent predictors of OS in NPC patients and entered into the nomogram: age (hazard ratio [HR]: 1.03), stage (stage IV vs. stage I-II, HR: 4.54), radiotherapy (Yes vs. No, HR: 0.43), EBV ([Formula: see text] vs.[Formula: see text], HR: 1.92), LAR ([Formula: see text] vs.[Formula: see text], HR: 2.05), NLR ([Formula: see text] vs. [Formula: see text] HR: 1.54), and PLR ([Formula: see text] vs.[Formula: see text], HR: 1.79). The C-indexes for training cohort at 1-, 3- and 5-year were 0.73, 0.83, 0.80, respectively, in the validation cohort, the C-indexes were 0.74 (95% CI 0.63-0.86), 0.80 (95% CI 0.73-0.87), and 0.77 (95% CI 0.67-0.86), respectively. The calibration curve demonstrated that favorable agreement between the predictions of the nomograms and the actual observations in the training and validation cohorts. In addition, the decision curve analysis proved that the nomogram model had the highest overall net benefit.

CONCLUSION

A new prognostic model to predict OS of patients with NPC was developed. This can offer clinicians treatment making and patient counseling. Furthermore, the nomogram was deployed into a website server for use.

Spatial architecture of regulatory T-cells correlates with disease progression in patients with nasopharyngeal cancer

Purpose

This study aims to investigate the prognostic value of composition and spatial architecture of tumor-infiltrating lymphocytes (TILs) as well as PDL1 expression on TILs subpopulations in nasopharyngeal carcinoma (NPC).

Methods

A total of 121 patients with NPC were included and divided into two groups: favorable (n = 68) and unfavorable (n = 53). The archived tumor tissues of the included patients were retrieved, and a tissue microarray was constructed. The density and spatial distribution of TILs infiltration were analyzed using the multiplex fluorescent immunohistochemistry staining for CD3, CD4, CD8, Foxp3, cytokeratin (CK), PDL1, and 4′,6-diamidino-2-phenylindole (DAPI). The infiltration density of TILs subpopulations and PDL1 expression were compared between the two groups. The Gcross function was calculated to quantify the relative proximity of any two types of cells. The Cox proportional hazards regression model was used to identify factors associated with overall survival (OS) and disease-free survival (DFS).

Results

The densities of regulatory T-cells (Tregs), effector T-cells (Teffs), PDL1+ Tregs, and PDL1+ Teffs were significantly higher in patients with unfavorable outcomes. PDL1 expression on tumor cells (TCs) or overall TILs was not associated with survival. Multivariate analysis revealed that higher PDL1+ Tregs infiltration density was independently associated with inferior OS and DFS, whereas Tregs infiltration density was only a prognostic marker for DFS. Spatial analysis revealed that unfavorable group had significantly stronger Tregs and PDL1+ Tregs engagement in the proximity of TCs and cytotoxic T lymphocyte (CTLs). Gcross analysis further revealed that Tregs and PDL1+ Tregs were more likely to colocalize with CTLs. Moreover, increased G_TC : _Treg (Tregs engagement surrounding TCs) and G_CTL : _{PDL1+ Treg} were identified as independent factors correlated with poor outcomes.

Conclusion

TILs have a diverse infiltrating pattern and spatial distribution in NPC. Increased infiltration of Tregs, particularly PDL1+ Tregs, as well as their proximity to TCs and CTLs, correlates with unfavorable outcomes, implying the significance of intercellular immune regulation in mediating disease progression.

Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Background

Locally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.

Methods

LABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan-Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.

Results

CS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1-6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.

Conclusions

The real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.