Evaluating Long-term survival of patients with esophageal cancer using parametric non-mixture cure rate models

Zaeran, Elahe; Looha, Mehdi Azizmohammad; Amini, Payam; Azimi, Tara; Mahmoudi, Mahmoud

doi:10.30699/jambs.27.120.43

Cited by 5 publications

(6 citation statements)

References 35 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was a large multicenter study comprising of 5283 patients across several provinces in China. The latest population‐based study in China in 2009‐2011 showed that the age‐standardized 5‐year EC survival rate was 30.3%, and the 5‐year EC net survival of the population of the United States in 2010‐2014 was 20.0% [17‐19]. Findings from the present study showed that the 5‐year OS rate was 39.07% in the investigated 18 hospitals and we found that gender, marital status, occupation, family history of any cancer, tumor topographical site, differentiation status, and pathological stage were associated with the survival rate of EC.…”

Section: Discussionsupporting

confidence: 50%

Clinical characteristics and survival of 5283 esophageal cancer patients: A multicenter study from eighteen hospitals across six regions in China

Liang

et al. 2020

Cancer Communications

View full text Add to dashboard Cite

This study aimed to investigate the potential determining epidemiological and clinical risk factors affecting the survival of esophageal cancer (EC) patients across multiple hospitals in China. Methods: This was a multicenter study comprising of newly diagnosed EC cases from Beijing, Hebei, Henan, Hubei, Zhejiang, and Guangdong Province of China. Their baseline characteristics and treatment methods data were collected from their medical records. The EpiData software was used for data quality control. The Kaplan-Meier method was used to estimate their overall survival (OS), and the Cox's proportional hazard regression model was used to estimate hazard ratios (HR) and 95% confidence interval (CI). Results: The 3-and 5-year OS rates of the 5283 investigated EC patients were 49.98% and 39.07%, respectively. Their median survival was 36.00 months. The median survival time of females was longer than that of males (females vs. males: 45.00 vs. 33.00, P < 0.001). The 5-year OS rate of patients who never-smoked was higher than that of smokers (never-smokers vs smokers: 40.73% vs. 37.84%, P = 0.001). There was no significant difference in the 5-year OS rate between

show abstract

Section: Discussionsupporting

confidence: 50%

Clinical characteristics and survival of 5283 esophageal cancer patients: A multicenter study from eighteen hospitals across six regions in China

Liang

et al. 2020

Cancer Communications

View full text Add to dashboard Cite

show abstract

“…Surgery, chemotherapy and radiotherapy alone or in combination are treatments for EC. However, the estimated 5-year survival rate of EC ranges from 10 to 39.7% [3][4][5][6][7]. In recent years, immunotherapy for EC has been reported [8,9], and clinical trials are also underway, providing novel strategies for improving patient's survival with EC.…”

Section: Introductionmentioning

confidence: 99%

The intra-class heterogeneity of immunophenotyping and immune landscape in oesophageal cancer and clinical implications

et al. 2021

View full text Add to dashboard Cite

Background The response rate and survival benefit of immunotherapy vary among patients, implying specific immune status of an individual could be associated with the effect of immunotherapy. However, in-depth studies of immune subtypes (ISs), immune landscape and tumour microenvironment of oesophageal cancer (ESCA) and their clinical implications are less reported. Methods We first accessed data from publicly available databases and preprocessed it based on a standard protocol. Then, ISs were identified by unsupervised learning. Thereafter, the association of these ISs and tumour mutation burden (TMB), biomarkers of chemotherapy-induced immune response, tumour markers were also assessed. In addition, the immune characteristics, immune landscape, co-expression network of immune genes, and clinical implications were visualized and analysed. Results We identified three immunoclusters based on immune-associated genes with intra-class heterogeneity and prognostic value. Cluster-specific associations with TMB, markers of chemotherapy-induced immune response, and tumour markers were revealed. A 4-gene signature (risk score= −0.16514291× BHLHE22 −0.03964046× MXRA8 −0.15242778× SLIT2 −0.05553572× SPON1 ) based on co-expressed genes in the immunoclusters was developed and externally validated. Conclusions In summary, we identified clinically relevant immunoclusters in both adenocarcinoma and squamous cell carcinoma of oesophagus, revealing the necessity of assessing the complexity and diversity of immune microenvironment for cancer immunotherapy.

show abstract

“…(2018), and Zaeran et al. (2019). According to the PH specification for the latency part, the hazard and survival conditional functions of the random variable

T^*

can be written as follows, respectively:

\begin{eqnarray} h(t|Y=1,{\bf x})&=&h_0(t|Y=1)\exp ({\bf x}^{\top }\bm{\beta }), \end{eqnarray}

\begin{eqnarray} S(t|Y=1,{\bf x})&=&\exp {\left(-\exp ({\bf x}^{\top }\bm{\beta })H_0(t|Y=1)\right)}=S_0(t|Y=1)^{\exp ({\bf x}^{\top }\bm{\beta })}.

…”

Section: Model Formulationmentioning

confidence: 97%

“…(2018), and Zaeran et al. (2019). This model is based on the assumption that the population under study is a mixture of two groups; some related works are Sy and Taylor (2000), Peng and Dear (2000), Gu et al.…”

Section: Introductionmentioning

confidence: 97%

Modified score function for monotone likelihood in the semiparametric mixture cure model

2021

View full text Add to dashboard Cite

The cure fraction models are intended to analyze lifetime data from populations where some individuals are immune to the event under study, and allow a joint estimation of the distribution related to the cured and susceptible subjects, as opposed to the usual approach ignoring the cure rate. In situations involving small sample sizes with many censored times, the detection of nonfinite coefficients may arise via maximum likelihood. This phenomenon is commonly known as monotone likelihood (ML), occurring in the Cox and logistic regression models when many categorical and unbalanced covariates are present. An existing solution to prevent the issue is based on the Firth correction, originally developed to reduce the estimation bias. The method ensures finite estimates by penalizing the likelihood function. In the context of mixture cure models, the ML issue is rarely discussed in the literature; therefore, this topic can be seen as the first contribution of our paper. The second major contribution, not well addressed elsewhere, is the study of the ML issue in cure mixture modeling under the flexibility of a semiparametric framework to handle the baseline hazard. We derive the modified score function based on the Firth approach and explore finite sample size properties of the estimators via a Monte Carlo scheme. The simulation results indicate that the performance of coefficients related to the binary covariates are strongly affected to the imbalance degree. A real illustration, in the melanoma dataset, is discussed using a relatively novel data set collected in a Brazilian university hospital.

show abstract

Evaluating Long-term survival of patients with esophageal cancer using parametric non-mixture cure rate models

Cited by 5 publications

References 35 publications

Clinical characteristics and survival of 5283 esophageal cancer patients: A multicenter study from eighteen hospitals across six regions in China

Clinical characteristics and survival of 5283 esophageal cancer patients: A multicenter study from eighteen hospitals across six regions in China

The intra-class heterogeneity of immunophenotyping and immune landscape in oesophageal cancer and clinical implications

Modified score function for monotone likelihood in the semiparametric mixture cure model

Contact Info

Product

Resources

About