Histological grading in lung cancer: one system for all or separate systems for each histological type?

Travis, William D.; Brambilla, Élisabeth; Geisinger, Kim R.

doi:10.1183/13993003.00035-2016

Cited by 27 publications

(11 citation statements)

References 34 publications

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“…Odd ratios (OR) and 95% confidence intervals (95% CI) were calculated for each predictor. Tumor grade was not included in this study’s model due to the large number of missing data (N = 272) and due to the lack of a widely-accepted grading system for lung cancer that is used consistently by all health facilities [ 17 , 18 ]. Tumor behavior variable also was not included in the model due to collinearity, where all observations are recorded as malignant behavior.…”

Section: Methodsmentioning

confidence: 99%

An overview of mortality & predictors of small-cell and non-small cell lung cancer among Saudi patients

Alghamdi

Alshehri

Farhat³

2017

JEGH

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Lung cancer ranks as the top cancer worldwide in terms of incidence and constitutes a major health problem. About 90% of lung cancer cases are diagnosed at advance stage where treatment is not available. Despite evidence that lung cancer screening improves survival, guidelines for lung cancer screening are still a subject for debate. In Saudi Arabia, only 14% of lung cancers are diagnosed at early stage and researches on survival and its predictors are lacking. This overview analysis was conducted on predictors of lung cancer mortality according to the two major cancer types, small-cell lung cancers (SCLCs) and non-small cell lung cancers (NSCLCs) in Saudi Arabia. A secondary data analysis was performed on small-cell lung cancers (SCLCs) and Non-small cell lung cancers (NSCLCs) registered in the Saudi Cancer Registry (SCR) for the period 2009-2013 to estimate predictors of mortality for both lung cancer types. A total of 404 cases (197 SCLC and 207 NSCLC) were included in the analysis, all Saudi nationals. A total of 213 (52.75%) deaths occurred among lung cancer patients, 108 (54.82%) among SCLCs and 105 (50.72%) among NCSLCs. Three quarter of patients are diagnosis with advance stage for both SCLC & NSCLC. Univariate analysis revealed higher mean age at diagnosis in dead patients compared to alive patients for SCLCs (p=0.04); but not NSCLCs, a lower mortality for NSCLCs diagnosed in 2013 (p=0.025) and a significant difference in stage of tumor (p=0.006) and (p=0.035) for both SCLC and NSCLC respectively. In multiple logistic regression, stage of tumor was a strong predictor of mortality, where distant metastasis increased morality by 6-fold (OR=5.87, 95% CI: 2.01 - 17.19) in SCLC and by 3-fold (OR=3.29, 95% CI: 1.22 - 8.85) in NSCLC, compared to localized tumors. Those with NSCLC who were diagnosed in 2013 were less likely to die by 64% compared to NSCLC diagnosed in 2009 (OR=0.36, 95% CI: 0.14 - 0.93). Age, sex, topography and laterality were not associated with mortality for both types of lung cancer. We observed that the stage of the tumor is the strongest predictor of mortality for both SCLCs and NSCLs. This confirms the impact of diagnostic stage on survival. However, establishing Saudi-specific lung cancer screening guidelines will require further research on the benefits and harms of screening modalities in the Saudi population.

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Section: Methodsmentioning

confidence: 99%

An overview of mortality & predictors of small-cell and non-small cell lung cancer among Saudi patients

Alghamdi

Alshehri

Farhat³

2017

JEGH

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“…According to the latest WHO Classification (2), histopathological LUAD classification is based on pattern prevalence, which is a major prognostic indicator (25,26). Indeed, tumors with a prevalent lepidic pattern are typically considered less aggressive and associated with the early phases of the disease, whereas solid-prevalent tumors are indicative of poor prognosis.…”

Section: Introductionmentioning

confidence: 99%

Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression

et al. 2021

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Cancer evolution determines molecular and morphological intra-tumor heterogeneity and challenges the design of effective treatments. In lung adenocarcinoma, disease progression and prognosis are associated with the appearance of morphologically diverse tumor regions, termed histologic patterns. However, the link between molecular and histological features remains elusive. Here, we generated multi-omics and spatially resolved molecular profiles of histologic patterns from primary lung adenocarcinoma, which we integrated with molecular data from >2,000 patients. The transition from indolent to aggressive patterns was not driven by genetic alterations but by epigenetic and transcriptional reprogramming reshaping cancer cell identity. A signature quantifying this transition was an independent predictor of patient prognosis in multiple human cohorts. Within individual tumors, highly multiplexed protein spatial profiling revealed coexistence of immune desert, inflamed, and excluded regions, which matched histologic pattern composition. Our results provide a detailed molecular map of lung adenocarcinoma intra-tumor spatial heterogeneity, tracing non-genetic routes of cancer evolution. Statement of significance Lung adenocarcinomas are classified based on histologic pattern prevalence. However, individual tumors exhibit multiple patterns with unknown molecular features. We characterized non-genetic mechanisms underlying intra-tumor patterns and molecular markers predicting patient prognosis. Intra-tumor patterns determined diverse immune microenvironments warranting their study in the context of current immunotherapies.

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“…More than 85% of lung cancers are non-small cell lung cancer (NSCLC), which is a smoking-related disease and the leading cancer killer in the USA and worldwide. NSCLC mainly consists of two major histological subtypes: squamous cell carcinoma (SCC) and adenocarcinoma (AC) [ 1 ]. Since chemotherapy and radiation therapy of NSCLC differ according to the subtypes, accurately differentiating SCC from AC is clinically important for the personalized treatment of the malignancy [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

Jiang

Leng

et al. 2017

Oncotarget

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Histological grading in lung cancer: one system for all or separate systems for each histological type?

Cited by 27 publications

References 34 publications

An overview of mortality & predictors of small-cell and non-small cell lung cancer among Saudi patients

An overview of mortality & predictors of small-cell and non-small cell lung cancer among Saudi patients

Nongenetic Evolution Drives Lung Adenocarcinoma Spatial Heterogeneity and Progression

A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma

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