Long-term prognostic characteristics of patients with clinical stage IA part-solid lung adenocarcinoma: a conditional survival analysis.

OBJECTIVES: Despite excellent 5-year survival, there are limited data on the long-term prognostic characteristics of clinical stage IA part-solid lung adenocarcinoma. The objective was to elucidate the dynamics of prognostic characteristics through conditional survival analysis. METHODS: Consecutive patients who underwent complete resection for clinical stage IA part-solid lung adenocarcinoma between 2011 and 2015 were retrospectively reviewed. Conditional survival is defined as the probability of surviving further y years, conditional on the patient has already survived x years. The conditional recurrence-free survival (CRFS) and conditional overall survival (COS) were analysed to evaluate prognosis over time, with conditional Cox regression analysis performed to identify time-dependent prognostic factors. RESULTS: A total of 1539 patients were included with a median follow-up duration of 98.4 months, and 80 (5.2%) patients experienced recurrence. Among them, 20 (1.3%) recurrence cases occurred after 5 years of follow-up with 100% intrathoracic recurrence. The 5-year CRFS increased from 95.8% to 97.4%, while the 5-year COS maintained stable. Multivariable Cox analysis revealed that histologic subtype was always an independent prognostic factor for CRFS even after 5 years of follow-up, while the independent prognostic value of consolidation-to-tumour ratio, visceral pleural invasion and lymph node metastasis was observed only within 5 years. Besides, age, pathologic size and lymph node metastasis maintained independent predictive value for COS during long-term follow-up, while consolidation-to-tumour ratio was predictive for COS only within 5 years of follow-up. CONCLUSIONS: The independent prognostic factors for clinical stage IA part-solid lung adenocarcinoma changed over time, along with gradually increasing 5-year CRFS and stable 5-year COS.

Dynamic estimates of survival of patients with poorly differentiated thyroid carcinoma: a population-based study.

Background: The real-time prognostic data of patients with poorly differentiated thyroid carcinoma (PDTC) after surviving for several years was unclear. This study aimed to employ a novel method to dynamically estimate survival for PDTC patients. Methods: A total of 913 patients diagnosed with PDTC between 2014 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, was recruited in our study. Kaplan-Meier method was used to estimate the overall survival (OS). The conditional survival (CS) outcomes of PDTC were analyzed and CS rates were calculated using the formula CS(y/x) = OS(y+x)/OS(x), whereby CS(y/x) denotes the probability of a patient enduring an additional y years subsequent to surviving x years following the diagnosis of PDTC. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify prognostic predicters and multivariate Cox regression was utilized to develop a CS-nomogram. Finally, the performance of this model was evaluated and validated. Results: Kaplan-Meier survival analysis unveiled patient outcomes demonstrating an OS rate of 83%, 75%, and 60% respectively at the end of 3, 5, and 10 years. The novel CS analysis highlighted a progressive enhancement in survival over time, with the 10-year cumulative survival rate progressively augmenting from its initiation of 60% to 66%, 69%, 73%, 77%, 81%, 83%, 88%, 93%, and finally 97% (after surviving for 1-9 years, respectively) each year. And then 11 (11/15) predictors including age at diagnosis, sex, histology type, SEER stage, T stage, N stage, M stage, tumor size, coexistence with other malignancy, radiotherapy and marital status, were selected by LASSO analysis under the condition of lambda.min. Multivariate Cox regression analysis further highlighted the significant impact of all these predictors on the OS of PDTC and we successfully established and validated a novel CS-nomogram for real-time and dynamic survival prediction. Conclusions: This was the first study to analyze the CS pattern and demonstrate a gradual improvement in CS over time in long-term PDTC survivors. We then successfully developed and validated a novel CS-nomogram for individualized, dynamic, and real-time survival forecasting, empowering clinicians to adapt and refine the patient-tailored treatment strategy promptly with consideration of evolving risks.

Conditional survival and changing risk profile in patients with gliosarcoma.

Background: Conditional survival (CS) considers the duration since the initial diagnosis and can provide supplementary informative insights. Our objective was to evaluate CS among gliosarcoma (GSM) patients and develop a CS-incorporated nomogram to predict the conditional probability of survival. Methods: This retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database included patients with GSM between 2000 and 2017. The CS was defined as the probability of surviving additional y years after already surviving for x years. The formula utilized for CS was: CS(y|x) = S(y + x)/S(x), where S(x) denotes the overall survival at x years. Univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were used for significant prognostic factors screening. Following this, backward stepwise multivariable Cox regression was utilized to refine predictor selection. Finally, a novel CS-integrated nomogram model was developed and we also employed diverse evaluation methods to assess its performance. Results: This study included a total of 1,015 GSM patients, comprising 710 patients in training cohort and 305 patients in validation cohort. CS analysis indicated a gradual increase in the probability of achieving a 5-year survival, ascending from 5% at diagnosis to 13, 31, 56, and 74% with each subsequent year survived after 1, 2, 3, and 4 years post-diagnosis, respectively. Following variable screening through univariate Cox regression, BSR, and LASSO analysis, five factors-age, tumor stage, tumor size, radiotherapy, and chemotherapy-were ultimately identified for constructing the CS-nomogram model. The performance of the nomogram model was validated through discrimination and calibration assessments in both the training and validation cohorts. Furthermore, we confirmed that the effectiveness of the CS-nomogram in stratifying GSM patient risk status. Conclusion: This nationwide study delineated the CS of patients diagnosed with GSM. Utilizing national data, a CS-nomogram could provide valuable guidance for patient counseling during follow-up and risk stratification.

Conditional survival estimates for ependymomas reveal the dynamic nature of prognostication.

BACKGROUND: Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients. METHODS: Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified. RESULTS: In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1-9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated. CONCLUSION: This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Dynamic estimates of survival in patients with follicular thyroid cancer: a retrospective cohort study.

BACKGROUND: Few studies have been conducted on the dynamic survival rates of follicular thyroid cancer (FTC). This study aimed to ascertain how the survival probability of patients with FTC changes over time. METHODS: In this retrospective analysis, 10,617 patients diagnosed with FTC between 2000 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database were included. Actuarial disease-specific survival (DSS) was estimated using the Kaplan-Meier method, and the log-rank test was used for comparisons. The annual hazard of mortality was determined using the hazard function, and the conditional survival (CS) was calculated using the life table method. RESULTS: A total of 459 (4.3%) patients died of FTC, and the 5-year and 10-year DSS rates were 96.6 ± 0.2% and 94.6 ± 0.3%, respectively. There was a statistically significant difference in the DSS rate between patients with different SEER combined summary stages (P < 0.001). The annual hazard curve for cancer mortality in the entire study cohort displayed a steep downward trend with a slight peak at 2.5 years after diagnosis, followed by a gradual decline. Patients with distant metastases exhibited a higher mortality hazard curve and more notable declining trend. CS demonstrated an upward trend across the entire study population, with the most pronounced trend in patients with distant metastases. CONCLUSION: Prognosis improved over time in a stage-dependent manner in patients with FTC after diagnosis. The most significant improvement was observed in the patients with distant metastases. Notably, dynamic survival estimations, such as death hazard and conditional survival analysis, provide more precise survival projections than traditional survival analysis for FTC survivors.

Deep Learning-Based Prediction of Post-treatment Survival in Hepatocellular Carcinoma Patients Using Pre-treatment CT Images and Clinical Data.

The objective of this study was to develop and evaluate a model for predicting post-treatment survival in hepatocellular carcinoma (HCC) patients using their CT images and clinical information, including various treatment information. We collected pre-treatment contrast-enhanced CT images and clinical information including patient-related factors, initial treatment options, and survival status from 692 patients. The patient cohort was divided into a training cohort (n = 507), a testing cohort (n = 146), and an external CT cohort (n = 39), which included patients who underwent CT scans at other institutions. After model training using fivefold cross-validation, model validation was performed on both the testing cohort and the external CT cohort. Our cascaded model employed a 3D convolutional neural network (CNN) to extract features from CT images and derive final survival probabilities. These probabilities were obtained by concatenating previously predicted probabilities for each interval with the patient-related factors and treatment options. We utilized two consecutive fully connected layers for this process, resulting in a number of final outputs corresponding to the number of time intervals, with values representing conditional survival probabilities for each interval. Performance was assessed using the concordance index (C-index), the mean cumulative/dynamic area under the receiver operating characteristics curve (mC/D AUC), and the mean Brier score (mBS), calculated every 3 months. Through an ablation study, we found that using DenseNet-121 as the backbone network and setting the prediction interval to 6 months optimized the model's performance. The integration of multimodal data resulted in superior predictive capabilities compared to models using only CT images or clinical information (C index 0.824 [95% CI 0.822-0.826], mC/D AUC 0.893 [95% CI 0.891-0.895], and mBS 0.121 [95% CI 0.120-0.123] for internal test cohort; C index 0.750 [95% CI 0.747-0.753], mC/D AUC 0.819 [95% CI 0.816-0.823], and mBS 0.159 [95% CI 0.158-0.161] for external CT cohort, respectively). Our CNN-based discrete-time survival prediction model with CT images and clinical information demonstrated promising results in predicting post-treatment survival of patients with HCC.

Conditional survival and the prognostic value of serum carcinoembryonic antigen level in oldest old with colorectal cancer.

BACKGROUND: To evaluate the clinical value of serum CEA levels and their implications on the diagnostic value of the conventional TNM staging system in the oldest-old patients with colorectal cancer (CRC). METHODS: The recruited subjects were colorectal cancer patients aged 85 and older. The cutoff value for normal CEA level is 5 ng/mL. Patients with elevated CEA levels were categorized as stage C1, and those with normal CEA levels as stage C0. A number of Cox proportional hazard regression models were established to evaluate the prognosis of different prognostic factors with hazard ratios (HRs) and 95% confidence intervals (CIs). The Kaplan-Meier method was utilized to display the disparate prognostic impact of multiple clinicopathological factors with the log-rank test. RESULTS: A total of 17,359 oldest-old patients diagnosed with CRC were recruited from the SEER database. The conditional survival of oldest-old patients with CRC was dismal with a 1-year conditional survival of only 11%, 18%, and 30% for patients surviving 1, 3, and 5 years, respectively. Patients with stage C1 exhibited a 48.5% increased risk of CRC-specific mortality compared with stage C0 (HR = 1.485, 95%CI = 1.393-1.583, using stage C0 patients as the reference, P < 0.001). All the stage C0 patients indicated lower HRs relative to the corresponding stage C1 patients. CONCLUSIONS: Dismal conditional survival of oldest-old patients with CRC should be given additional consideration. C stage influences the prognosis of oldest-old patients with CRC.

Protection from prior natural infection vs. vaccination against SARS-CoV-2-a statistical note to avoid biased interpretation.

Introduction: The fight against SARS-CoV-2 has been a major task worldwide since it was first identified in December 2019. An imperative preventive measure is the availability of efficacious vaccines while there is also a significant interest in the protective effect of a previous SARS-CoV-2 infection on a subsequent infection (natural protection rate). Methods: In order to compare protection rates after infection and vaccination, researchers consider different effect measures such as 1 minus hazard ratio, 1 minus odds ratio, or 1 minus risk ratio. These measures differ in a setting with competing risks. Nevertheless, as there is no unique definition, these metrics are frequently used in studies examining protection rate. Comparison of protection rates via vaccination and natural infection poses several challenges. For instance many publications consider the epidemiological definition, that a reinfection after a SARS-CoV-2 infection is only possible after 90 days, whereas there is no such constraint after vaccination. Furthermore, death is more prominent as a competing event during the first 90 days after infection compared to vaccination. In this work we discuss the statistical issues that arise when investigating protection rates comparing vaccination with infection. We explore different aspects of effect measures and provide insights drawn from different analyses, distinguishing between the first and the second 90 days post-infection or vaccination. Results: In this study, we have access to real-world data of almost two million people from Stockholm County, Sweden. For the main analysis, data of over 52.000 people is considered. The infected group is younger, includes more men, and is less morbid compared to the vaccinated group. After the first 90 days, these differences increased. Analysis of the second 90 days shows differences between analysis approaches and between age groups. There are age-related differences in mortality. Considering the outcome SARS-CoV-2 infection, the effect of vaccination versus infection varies by age, showing a disadvantage for the vaccinated in the younger population, while no significant difference was found in the elderly. Discussion: To compare the effects of immunization through infection or vaccination, we emphasize consideration of several investigations. It is crucial to examine two observation periods: The first and second 90-day intervals following infection or vaccination. Additionally, methods to address imbalances are essential and need to be used. This approach supports fair comparisons, allows for more comprehensive conclusions and helps prevent biased interpretations.

Individualized prediction of conditional survival for colorectal signet-ring cell carcinoma patients.

Background: Conditional survival (CS) considers the time already survived after surgery and may provide additional survival information. The authors sought to construct and validate novel conditional survival nomograms for the prediction of conditional overall survival (OS) and cancer-specific survival (CSS) of colorectal signet-ring cell carcinoma (SRCC) patients. Methods: Patients diagnosed with stage I-III SRCC between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The formula calculating CS was: CS(x|y) = S(x+y)/S(x), where S(x) represents the survival at x years. CS nomograms were then constructed to predict the 5-year conditional OS and CSS, followed by internal validation. Results: A total of 944 colorectal SRCC patients were finally identified in this study. The 5-year OS and CSS improved gradually with additional survival time. Univariate and multivariate Cox regression analysis conducted in training set revealed that age, race, T stage, LNR, and perineural invasion were independent risk factors for both OS and CSS. Two nomograms with considerable predictive ability were successfully constructed [area under the curve (AUC) for OS: 0.788; AUC for CSS: 0.847] and validated (AUC for OS: 0.773; AUC for CSS: 0.799) for the prediction of 5-year OS and CSS, based on the duration of 1-4 years post-surgery survival. Conclusions: The probability of achieving 5-year OS and 5-year CSS in colorectal SRCC patients improved gradually with additional time. Conditional nomograms considering survival time will be more reliable and informative for risk stratification and postoperative follow-up.

Conditional survival of patients with primary bone lymphoma of the spine: how survival changes after initial diagnosis.

Background: The current survival prediction methodologies for primary bone lymphoma (PBL) of the spine are deficient. This study represents the inaugural utilization of conditional survival (CS) to assess the outcome of this disease. Moreover, our objective was to devise a CS-based nomogram for predicting overall survival (OS) in real-time for spinal PBL. Methods: Patients with PBL of the spine diagnosed between January 2000 and December 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The OS was determined through the Kaplan-Meier method. The CS characteristic of patients with spinal PBL was delineated, with the CS being estimated utilizing the formula: CS(α|ß) = OS(α+ß)/OS(ß). CS(α|ß) denotes the probability of additional α-year survivorship, assuming the patient has already survived ß years after the time of observation. Three methods including univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) regression were used to identify predictors for CS-based nomogram construction. Results: Kaplan-Meier analysis was executed to determine the OS rate for these patients, revealing a survival rate of 68% and subsequently 63% at the 3-year and 5-year mark respectively. We then investigated the CS patterning exhibited by these patients and discovered the survival of PBL in the spine progressively improved with time. Meanwhile, through three different prognostic factor selection methods, we identified the best predicter subset including age, tumor histology, tumor stage, chemotherapy and marital status, for survival prediction model construction. Finally, we successfully established and validated a novel CS-based nomogram model for real-time and dynamic survival estimation. Moreover, we further designed a risk stratification system to facilitate the identification of high-risk patients. Conclusions: This is the first study to analyze the CS pattern of PBL of the spine. And we have also developed a CS-based nomogram that provide dynamic prognostic data in real-time, thereby aiding in the formulation of personalized treatment strategies in clinical practice.

Conditional survival nomogram for patients with colon mucinous adenocarcinoma to predict prognosis: a dynamic survival analysis.

The aim was to assess conditional survival for colon mucinous adenocarcinoma (MAC) patients, and to construct nomograms to predict conditional survival probability. Survival analysis was done using conditional survival, which was defined as the probability of surviving additional y years for patients who have survived for x years. The mathematical definition was express as: CS (y|x) = S (x + y)/S (x). Cox regression analyses were used to identify prognostic factors. A nomogram is constructed to predict conditional disease-free survival (DFS) and overall survival (OS) probability according to years that already survive. A total of 179 colon MAC patients were included. The 5-year DFS was 67% after surgery, and the 5-year survival probability of patients, who already survived 1, 2, 3, and 4 years were 75%, 87%, 95%, and 98%, respectively. The 5-year OS was 73% after surgery and increased to 76%, 82%, 88%, and 92% at 1, 2, 3, and 4 years, respectively. Subgroup analyses demonstrated the superiority of conditional survival was more pronounced in advanced stages than in stage I. And pT stage, pN stage, and lymphovascular invasion were significantly associated with DFS and OS. Conditional survival nomograms were constructed to predict the 5-year conditional DFS and OS probability given survival for 1, 2, 3, 4 years after surgery. Conditional survival can provide dynamic survival probability according to years that already survive, especially for patients with advanced stages. Taking into account the years already survived accounted for, novel nomograms contributed to effectively predicting conditional survival.

A novel conditional survival nomogram for monitoring real-time prognosis of non-metastatic colorectal cancer.

AIMS: The aim of this study is to enhance the accuracy of monitoring and treatment information for patients diagnosed with colorectal cancer (CRC). METHODS: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, a cohort of 335,948 eligible CRC patients was included in this investigation. Conditional survival probability and actuarial overall survival were employed as methodologies to investigate the association between clinicopathological characteristics and cancer prognosis. RESULTS: Among CRC patients, the 5-year survival rate was 59%, while the 10-year survival rate was 42%. Over time, conditional survival showed a consistent increase, with rates reaching 45% and 48% for individuals surviving 1 and 2 years, respectively. Notably, patients with unfavorable tumor stages exhibited substantial improvements in conditional survival, thereby narrowing the disparity with actuarial overall survival over time. CONCLUSION: This study underscores the significance of time-dependent conditional survival probability, particularly for patients with a poorer prognosis. The findings suggest that long-term CRC survivors may experience improved cancer prognosis over time.

Conditional survival and annual hazard of death in older patients with esophageal cancer receiving definitive chemoradiotherapy.

BACKGROUND: Definitive chemoradiotherapy is one of the primary treatment modalities for older patients with esophageal cancer (EC). However, the evolution of prognosis over time and the factors affected non-EC deaths remain inadequately studied. We examined the conditional survival and annual hazard of death in older patients with EC after chemoradiotherapy. METHODS: We collected data from patients aged 65 or older with EC registered in the Surveillance, Epidemiology, and End Results database during 2000-2019. Conditional survival was defined as the probability of survival given a specific time survived. Annual hazard of death was defined the yearly event rate. Restricted cubic spline (RCS) analysis identified the association of age at diagnosis with mortality. RESULTS: Among 3739 patients, the 3-year conditional overall survival increased annually by 7-10%. Non-EC causes accounted for 18.8% of deaths, predominantly due to cardio-cerebrovascular diseases. The hazard of death decreased from 40 to 10% in the first 6 years and then gradually increased to 20% in the tenth year. Non-EC causes surpassed EC causes in hazard starting 5 years post-treatment. RCS indicated a consistent increase in death hazard with advancing age, following a linear relationship. The overall cohort was divided into two groups: 65-74 and ≥ 75 years old, with the ≥ 75-year-old group showing poorer survival and earlier onset of non-EC deaths (HR = 1.36, 95% CI: 1.15-1.62, P < 0.001). Patients with early-stage disease (I-II) had higher risks of death from non-EC causes (HR = 0.82, 95% CI: 0.68-0.98, P = 0.035). Tumor histology had no significant impact on non-EC death risk (HR = 1.17, 95% CI: 0.98-1.39, P = 0.081). CONCLUSIONS: Survival probability increases with time for older patients with EC treated with chemoradiotherapy. Clinicians and patients should prioritize managing and preventing age-related comorbidities, especially in older cohorts and those with early-stage disease.

Contemporary conditional cancer-specific survival rates in surgically treated nonmetastatic primary urethral carcinoma.

BACKGROUND: We examined the effect of disease-free interval (DFI) duration on cancer-specific mortality (CSM)-free survival, otherwise known as the effect of conditional survival, in radical urethrectomy nonmetastatic primary urethral carcinoma (PUC) patients. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER) database 2000-2020, patient (age, sex, race/ethnicity, and marital status) and tumor (stage and histology) characteristics, as well as systemic therapy exposure status of nonmetastatic PUC patients were tabulated. Conditional survival estimates at 5-year were assessed based on DFI duration and according to stage at presentation (T1 -2N0 vs. T3-4N0-2). RESULTS: Of all 512 radical urethrectomy PUC patients, 278 (54%) harbored T1-2N0 stage versus 234 (46%) harbored T3-4N0-2 stage. In 512 PUC patients, 5-year CSM-free survival at initial diagnosis was 61.8%. Provided a DFI duration of 36 months, 5-year CSM-free survival was 85.6%. In 278 T1-2N0 PUC patients, 5-year CSM-free survival at initial diagnosis was 68.4%. Provided a DFI duration of 36 months, 5-year CSM-free survival was 86.9%. In 234 T3-4N0-2 PUC patients, 5-year CSM-free survival at initial diagnosis was 53.8%. Provided a DFI duration of 36 months, 5-year CSM-free survival was 83.6%. CONCLUSIONS: Although intuitively, clinicians and patients are well aware of the concept that increasing DFI duration improves survival probability, only a few clinicians can accurately estimate the magnitude of survival improvement, as was done within the current study. Such information is crucial to survivors, especially in those diagnosed with rare malignancies, where the survival estimation according to DFI duration is even more challenging.

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients.

Background: Merkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual's prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model. Methods: This study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient's survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors' effect on overall survival and establish a novel CS-based nomogram. Results: A total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41-50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance. Conclusion: CS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma.

BACKGROUND: As the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on outcomes for intracranial combined solitary-fibrous tumor and hemangiopericytoma (SFT/HPC). This study aimed to explore conditional survival (CS) pattern and develop a survival prediction tool for intracranial SFT/HPC patients. METHODS: Data of intracranial SFT/HPC patients was gathered from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. The patients were split into training and validation groups at a 7:3 ratio for our analysis. CS is defined as the likelihood of surviving for a specified period of time (y years), given that the patient has survived x years after initial diagnosis. Then, we used this definition of CS to analyze the intracranial SFT/HPC patients. The least absolute shrinkage and selection operator (LASSO) regression and best subset regression (BSR) were employed to identify predictive factors. The Multivariate Cox regression analysis was applied to establish a novel CS-based nomogram, and a risk stratification system was developed using this model. RESULTS: From the SEER database, 401 patients who were diagnosed with intracranial SFT/HPC between 2000 and 2019 were identified. Among them, 280 were included in the training group and 121 were included in the internal validation group for analysis. Our study revealed that in intracranial SFT/HPC, 5-year survival rates saw significant improvement ranging from 78% at initial diagnosis to rates of 83%, 87%, 90%, and 95% with each successive year after surviving for 1-4 years. The LASSO regression and BSR identified patient age, tumor behavior, surgery and radiotherapy as predictors of CS-based nomogram development. A risk stratification system was also successfully constructed to facilitate the identification of high-risk patients. CONCLUSION: The CS pattern of intracranial SFT/HPC patients was outlined, revealing a notable improvement in 5-year survival rates after an added period of survival. Our newly-established CS-based nomogram and risk stratification system can provide a real-time dynamic survival estimation and facilitate the identification of high-risk patients, allowing clinicians to better guide treatment decision for these patients.

Conditional survival to assess prognosis in patients with chronic lymphocytic leukemia.

Biomarkers in chronic lymphocytic leukemia (CLL) allow assessment of prognosis. However, the validity of current prognostic biomarkers based on a single assessment point remains unclear for patients who have survived one or more years. Conditional survival (CS) studies that address how prognosis may change over time, especially in prognostic subgroups, are still rare. We performed CS analyses to estimate 5-year survival in 1-year increments, stratified by baseline disease characteristics and known risk factors in two community-based cohorts of CLL patients (Freiburg University Hospital (n = 316) and Augsburg University Hospital (n = 564)) diagnosed between 1984 and 2021. We demonstrate that 5-year CS probability is stable (app. 75%) for the entire CLL patient cohort over 10 years. While age, sex, and stage have no significant impact on CS, patients with high-risk disease features such as non-mutated IGHV, deletion 17p, and high-risk CLL-IPI have a significantly worse prognosis at diagnosis, and 5-year CS steadily decreases with each additional year survived. Our results confirm that CLL patients have a stable survival probability with excess mortality and that the prognosis of high-risk CLL patients declines over time. We infer that CS-based prognostic information is relevant for disease management and counseling of CLL patients.

Prognosis and conditional survival among women with newly diagnosed ovarian cancer.

OBJECTIVE: An important question in determining long-term prognosis for women with ovarian cancer is whether risk of death changes the longer a woman lives. Large real-world datasets permit assessment of conditional survival (CS) given both prior overall survival (OS) and real-world progression-free survival (rwPFS). METHODS: Using a longitudinal dataset from US oncology centers, this study included 6778 women with ovarian cancer. We calculated CS rates as the Kaplan-Meier probability of surviving an additional 1 or 5 years, given no mortality (OS) or disease progression (rwPFS) event in the previous 0.5-5 years since first-line chemotherapy initiation, adjusted for factors associated with OS based on multivariable Cox regression. RESULTS: Median study follow-up was 9 years (range, 1-44) from first-line initiation to data cutoff (17-Feb-2021). Median OS was 58.0 months (95% CI, 54.9-60.8); median rwPFS was 18.4 months (17.4-19.4). The adjusted 1-year CS rate (ie, rate of 1 year additional survival) did not vary based on time alive, whereas the adjusted 5-year CS rate increased from 48.5% (47.0%-50.1%) for women who had already survived 6 months to 66.4% (63.3%-69.6%) for those already surviving 5 years (thus surviving 10 years total). The adjusted 1-year CS rate increased from 90.4% (89.5%-91.4%) with no rwPFS event at 6 months to 97.6% (96.4%-98.8%) with no rwPFS event at 5 years; adjusted 5-year CS rate increased from 53.7% (52.0%-55.5%) to 85.0% (81.2%-88.9%), respectively. CONCLUSIONS: This analysis extends the concept of CS by also conditioning on time progression-free. Patients with longer rwPFS experience longer survival than patients with shorter rwPFS.

Survival in oral and pharyngeal cancers is catching up with laryngeal cancer in the NORDIC countries through a half century.

BACKGROUND: Cancers of the head and neck (HN) are heterogeneous tumors with incidence rates varying globally. In Northern Europe oral and oropharyngeal cancers are the most common individual types. Survival for HN varies by individual tumor type but for most of them survival trends are not well known over extended periods of time. METHODS: Data for a retrospective survival study were obtained for Danish, Finnish, Norwegian, and Swedish patients from the NORDCAN database from 1971 to 2020. Relative 1- and 5-year survival rates and 5/1-year conditional survival for years 2-5 were calculated. RESULTS: Both 1- and 5-year survival improved for all HN cancers but only marginally for laryngeal cancer. For the other cancers a 50-year increase in 5-year survival was about 30% units for nasopharyngeal and oropharyngeal cancers, 20% units for oral cancer and somewhat less for hypopharyngeal cancer. CONCLUSIONS: 5-year survival reached about 65% for all HN cancers, except for hypopharyngeal cancer (30%). Human papilloma virus infection is becoming a dominant risk factor for the rapidly increasing oropharyngeal cancer, the prevention of which needs to emphasize oral sex as a route of infection.

The impact of the HER2-low status on conditional survival in patients with breast cancer.

Introduction: With recent advances in breast cancer (BC) treatment, the disease-free survival (DFS) of patients is increasing and the risk factors for recurrence and metastasis are changing. However, a dynamic approach to assessing the risk of recurrent metastasis in BC is currently lacking. This study aimed to develop a dynamically changing prediction model for recurrent metastases based on conditional survival (CS) analysis. Methods: Clinical and pathological data from patients with BC who underwent surgery at the Affiliated Hospital of Qingdao University between August 2011 and August 2022 were retrospectively analysed. The risk of recurrence and metastasis in patients with varying survival rates was calculated using CS analysis, and a risk prediction model was constructed. Results: A total of 4244 patients were included in this study, with a median follow-up of 83.16 ± 31.59 months. Our findings suggested that the real-time DFS of patients increased over time, and the likelihood of DFS after surgery correlated with the number of years of prior survival. We explored different risk factors for recurrent metastasis in baseline patients, 3-year, and 5-year disease-free survivors, and found that low HER2 was a risk factor for subsequent recurrence in patients with 5-year DFS. Based on this, conditional nomograms were developed. The nomograms showed good predictive ability for recurrence and metastasis in patients with BC. Conclusion: Our study showed that the longer patients with BC remained disease-free, the greater their chances of remaining disease-free again. Predictive models for recurrence and metastasis risk based on CS analysis can help improve the confidence of patients fighting cancer and help doctors personalise treatment and follow-up plans.

Conditional survival in breast cancer With recent advances in breast cancer (BC) treatment, the disease-free survival of patients is increasing and the risk factors for recurrence and metastasis are changing. One of the key risk factor is the human epidermal growth factor receptor 2 (HER2). However, the recent advent of anti-HER2 antibody-drug conjugates (ADC) has challenged the traditional binary classification based on HER2. Patients in the traditional HER2-negative group can now be further classified as HER2-low (ISH-negative with IHC1 or IHC2) or HER2-0 (ISH-negative and IHC-0). Does this categorisation also have some value for the prognosis of BC? To figure this out, we retrospectively analysed the clinical and pathological data of BC patients who underwent surgery at the Affiliated Hospital of Qingdao University between August 2011 and August 2022. The risk of recurrence and metastasis in patients with varying survival rates was calculated using conditional survival analysis, and a risk prediction model was constructed.Our findings suggested that the real-time disease-free survival (DFS) of patients increased over time, and the likelihood of DFS after surgery correlated with the number of years of prior survival. Conditional nomograms were developed for baseline patients, 3-year and 5-year disease-free survivors. The nomograms showed good predictive ability for recurrence and metastasis in patients with BC.