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Do we need to see to believe?—radiomics for lung nodule classification and lung cancer risk stratification.

ABSTRACT: Despite multiple recent advances, the diagnosis and management of lung cancer remain challenging and it continues to be the deadliest malignancy. In 2011, the National Lung Screening Trial (NLST) reported 20% reduction in lung cancer related mortality using annual low dose chest computed tomography (CT). These results led to the approval and nationwide establishment of lung cancer CT-based lung cancer screening programs. These findings have been further validated by the recently published Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) and Multicentric Italian Lung Detection (MILD) trials, the latter showing benefit of screening even beyond the 5 years. However, the implementation of lung cancer screening has been impeded by several challenges, including the differentiation between benign and malignant nodules, the large number of false positive studies and the detection of indolent, potentially clinically insignificant lung cancers (overdiagnosis). Hence, the development of non-invasive strategies to accurately classify and risk stratify screen-detected pulmonary nodules in order to individualize clinical management remains a high priority area of research. Radiomics is a recently coined term which refers to the process of imaging feature extraction and quantitative analysis of clinical diagnostic images to characterize the nodule phenotype beyond what is possible with conventional radiologist assessment. Even though it is still in early phase, several studies have already demonstrated that radiomics approaches are potentially useful for lung nodule classification, risk stratification, individualized management and prediction of overall prognosis. The goal of this review is to summarize the current literature regarding the radiomics of screen-detected lung nodules, highlight potential challenges and discuss its clinical application along with future goals and challenges.

SUBMITTER: Khawaja A

PROVIDER: S-EPMC7330769 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Project description:We conducted two independent nested case-control studies to identify circulating inflammation markers reproducibly associated with lung cancer risk and to investigate the utility of replicated markers for lung cancer risk stratification.Nested within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, the previously published discovery study included 526 lung cancer patients and 592 control subjects and the replication study included 526 lung cancer case patients and 625 control subjects. Control subjects were matched by sex, age, smoking, study visit, and years of blood draw and exit. Serum levels of 51 inflammation markers were measured. Odds ratios (ORs) were estimated with conditional logistic regression. All statistical tests were two-sided.Of 11 markers identified in the discovery study, C-reactive protein (CRP) (odds ratio [OR] [highest vs. lowest category] = 1.77, 95% confidence interval [CI] = 1.23 to 2.54), serum amyloid A (SAA) (OR = 1.88, 95% CI = 1.28 to 2.76), soluble tumor necrosis factor receptor-2 (sTNFRII) (OR = 1.70, 95% CI = 1.18 to 2.45), and monokine induced by gamma interferon (CXCL9/MIG) (OR = 2.09, 95% CI = 1.41 to 3.00) were associated with lung cancer risk in the replication study (P trend < .01). In pooled analyses, CRP, SAA, and CXCL9/MIG remained associated with lung cancer more than six years before diagnosis (P trend < .05). The incorporation of an inflammation score combining these four markers did not improve the sensitivity (77.6% vs 75.8%, P = .33) or specificity (56.1% vs 56.1%, P = .98) of risk-based lung cancer models.Circulating levels of CRP, SAA, sTNFRII, and CXCL9/MIG were reproducibly associated with lung cancer risk in two independent studies within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, underscoring an etiologic role for inflammation in lung carcinogenesis, though replication is needed in other populations. Markers did not improve lung cancer risk stratification beyond standard demographic and behavioral characteristics.

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Do we need to see to believe?—radiomics for lung nodule classification and lung cancer risk stratification.

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