Clinical predictors of survival in young patients with small cell lung cancer: Results from the California Cancer Registry

Lara, Joshua D; Brunson, Ann; Riess, Jonathan W.; Kelly, Karen; Lara, Primo N.; Gandara, David R.

doi:10.1016/j.lungcan.2017.08.015

Cited by 16 publications

(15 citation statements)

References 13 publications

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“…Twenty-two studies reported hazard ratios on the association between an area-based SES index measure and lung cancer survival ( 5 , 6 , 22 , 37 , 63 , 70 , 72 – 74 , 79 , 81 – 83 , 86 , 91 – 94 , 101 , 105 – 107 , 116 ) (Table S4 ). Group comparisons of 18 studies ( 5 , 22 , 37 , 63 , 70 , 72 , 74 , 79 , 81 , 82 , 86 , 91 – 94 , 101 , 105 – 107 ) showed significant associations between lower income areas and a lower survival after lung cancer diagnosis in 10 studies ( 5 , 22 , 37 , 70 , 79 , 82 , 92 , 93 , 101 , 106 ), with a range of HR 1.05–2.21 (Figure 5 ). Nine studies ( 5 , 22 , 74 , 79 , 91 – 93 , 105 , 107 ) adjusted for stage at diagnosis (Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…Data sources for cancer survival were usually national cancer registries but also cohort studies and clinical trials ( 50 , 53 ). Most studies reported on all types of lung cancer, but 20 studies restricted analyses to non-small-cell lung cancer (NSCLC) patients ( 5 , 34 , 44 , 45 , 50 , 56 , 63 , 66 , 68 , 72 , 76 , 80 , 88 – 90 , 93 , 96 , 97 , 101 , 112 , 115 ) and three studies were restricted to small-cell lung cancer (SCLC) patients ( 6 , 92 , 114 ).…”

Section: Resultsmentioning

confidence: 99%

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Socioeconomic Differences and Lung Cancer Survival—Systematic Review and Meta-Analysis

et al. 2018

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Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Socioeconomic Differences and Lung Cancer Survival—Systematic Review and Meta-Analysis

et al. 2018

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“…Similar to other lung neoplasms, SCLC commonly occurred in older individuals with a smoking history . Younger adults with SCLC have been described to account for a very small proportion and are underrepresented in clinical trials . Older patients with SCLC always faced hormonal decline, immune response deterioration and comorbidities which may delay the beginning of treatment and limit treatment options.…”

Section: Introductionmentioning

confidence: 99%

“…However, considering adolescents' malignancies were treated in children's hospital in China, we defined the young‐onset patients in the 18‐39 age range. Limited by the proportion of young patients, only a few studies have addressed whether patients with SCLC under 40 years have distinct characteristics and the prognostic factors have been rarely studied . Additionally, although young patients with SCLC are a small population, considerable attention should be given considering their remaining expected lifespan, financial productivity and roles in caring for their families …”

Section: Introductionmentioning

confidence: 99%

Small cell lung cancer in the young: Characteristics, diagnosis and outcome data

Jiang

Hao

et al. 2019

Clinical Respiratory J

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Background: Patients with small cell lung cancer (SCLC) younger than 40 years are limited in number. Our research aimed to assess the characteristics, diagnosis and outcomes of this patient population. Methods: Records of patients under the age of 40 with SCLC at the Chinese Results: One hundred and three patients (67.0% limited stage, 33.0% extensive stage) were included, along with 54 (52.4%) never-smokers. The median diagnostic interval and the median survival time (MST) were 51.0 days and 24.0 months, respectively. A total of 41 (39.8%) patients claimed to have undergone antibiotic treatment before diagnosis, with a median duration of 2 weeks. In univariate analysis, survival was better for the limited stage group than the extensive stage group (MST, 28.0 vs. 13.0 months, P < 0.0001). Also, patients who received concurrent radiochemotherapy had better survival than those who received chemotherapy alone (MST, 29.0 vs. 18.0 months, P = 0.001). Patients with antibiotic treatment before SCLC diagnosis have worse prognosis than those without (MST, 21.0 vs. 27.0 months, P = 0.008). Moreover, a timely diagnosis (≤1 month) exerted a positive impact on the overall survival in limited stage patients (48.0 vs. 26.0 months, P = 0.047) and on progression-free survival in extensive stage patients (6.0 vs. 3.0 months, P = 0.030). Multivariate analysis suggested that disease stage, history of antibiotic treatment before SCLC diagnosis and performance status independently correlated with survival. Conclusion: Our study identified distinct characteristics and prognostic factors of SCLC patients under 40 years. More timely care may improve patient prognosis. K E Y W O R D S diagnosis, small cell lung cancer, survival, time, young adult | 99 JIANG et Al.

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“…Demographics, histology and pathological staging are among clinical indicators that have been proven in the literature [34][35][36], hence previous works on predicting survival among NSCLC patients are concentrated on mixing these readily available clinical factors with AUCs range between 0.62-0.79 [19][20][27][28]. To the best of our knowledge, this is the first study establishing the fusion of both clinical with imaging covariates, which has been proven to better predict survival (AUCs between 0.77 and 0.97).…”

Section: Discussionmentioning

confidence: 99%

Leveraging hybrid biomarkers in clinical endpoint prediction

Saad

Lee

2020

Preprint

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Background Clinical endpoint prediction remains challenging for health providers. Although predictors such as age, gender, and disease staging are of considerable predictive value, the accuracy often ranges between 60% and 80%. An accurate prognosis assessment is required for making effective clinical decisions. Methods We proposed an extended prognostic model based on clinical covariates with adjustment for additional variables that were radio-graphically induced, termed imaging biomarkers. Eight imaging biomarkers were introduced and investigated in a cohort of 68 non-small cell lung cancer subjects with tumor internal characteristic. The subjects comprised of 40 males and 28 females with mean age at 68.7 years. The imaging biomarkers used to quantify the solid component and non-solid component of a tumor. The extended model comprises of additional frameworks that correlate these markers to the survival ends through uni- and multi-variable analysis to determine the most informative predictors, before combining them with existing clinical predictors. Performance was compared between traditional and extended approaches using Receiver Operating Characteristic (ROC) curves, Area under the ROC curves (AUC), Kaplan-Meier (KM) curves, Cox Proportional Hazard, and log-rank tests (p-value). Results The proposed hybrid model exhibited an impressive boosting pattern over the traditional approach of prognostic modelling in the survival prediction (AUC ranging from 77– 97%). Four developed imaging markers were found to be significant in distinguishing between subjects having more and less dense components: (P = 0.002–0.006). The correlation to survival analysis revealed that patients with denser composition of tumor (solid dominant) lived 1.6–2.2 years longer (mean survival) and 0.5–2.0 years longer (median survival), than those with less dense composition (non-solid dominant). Conclusion The present study provides crucial evidence that there is an added value for incorporating additional image-based predictors while predicting clinical endpoints. Though the hypotheses were confirmed in a customized case study, we believe the proposed model is easily adapted to various clinical cases, such as predictions of complications, treatment response, and disease evolution.

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Clinical predictors of survival in young patients with small cell lung cancer: Results from the California Cancer Registry

Cited by 16 publications

References 13 publications

Socioeconomic Differences and Lung Cancer Survival—Systematic Review and Meta-Analysis

Socioeconomic Differences and Lung Cancer Survival—Systematic Review and Meta-Analysis

Small cell lung cancer in the young: Characteristics, diagnosis and outcome data

Leveraging hybrid biomarkers in clinical endpoint prediction

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