Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

Seijó, Luis; Peled, Nir; Ajona, Daniel; Boeri, Mattia; Field, John K.; Sozzi, Gabriella; Pı́o, Rubén; Zulueta, Javier; Spira, Avrum; Massion, Pierre P.; Mazzone, Peter J.; Montuenga, Luis M.

doi:10.1016/j.jtho.2018.11.023

Cited by 333 publications

(283 citation statements)

References 129 publications

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“…42 Molecular testing from airway epithelial cells, peripheral blood, and exhaled breath are under investigation as novel tools to better refine populations for screening and for determination of cancer risk in screen-detected nodules. 43…”

Section: Future Considerations and Challengesmentioning

confidence: 99%

Update on Lung Cancer Screening

Brownlee

Donington

2020

Semin Respir Crit Care Med

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Over the past 10 years, there has been substantial progress in the study and implementation of lung cancer screening using low-dose computed tomography (LDCT). The National Lung Screening Trial, the recently reported NELSON (NEderlands-Leuvens Longkanker Screenings ONderzoek) trial, and other European trials provide strong evidence for the efficacy of LDCT to reduce lung cancer mortality. This has resulted in the United State's Preventative Task Force and numerous professional medical societies adopting lung cancer screening recommendations. Despite the general acceptance of the positive effect of screening, low adoption and implementation rates remain nationally. In this article, the authors discuss the evolution and current state of the evidence for LDCT screening for lung cancer. The authors will also review the associated risks, cost, and challenges of implementation of an LDCT screening program.

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Section: Future Considerations and Challengesmentioning

confidence: 99%

Update on Lung Cancer Screening

Brownlee

Donington

2020

Semin Respir Crit Care Med

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“…Future studies could add deep learning techniques alongside biomarker discovery to develop more accurate and robust prediction models. Furthermore, an enriched classifier could provide a personalised timeframe for follow-up screenings for early-stage lung cancer survivors and low pCA subjects and predict the outcome and response to adjuvant therapy for those at high-risk of recurrence [23].…”

Section: Implications For Practicementioning

confidence: 99%

Management of incidental nodules in lung cancer screening: ready for prime-time?

Kanellakis

Lamote

2019

Breathe

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Lung cancer is the leading cause of cancer-related mortality. Two independent trials from the USA and the Netherlands and Belgium demonstrated that annual low-dose computed tomography (LDCT) screening reduced mortality by 20% and 26%, respectively [1–3]. LDCT is therefore widely accepted as the preferred method for detecting pulmonary nodules, but raises an important clinical challenge concerning nodule evaluation and patient management [4–7]. Current pulmonary nodule evaluation and clinical management starts by estimating the probability of cancer (pCA) based on clinical (age, symptoms, smoking history, performance status, associated lung diseases, family history and previous clinical history) and radiological (size, growth and morphology) parameters, and takes into account the patient's preferences. Certain lung nodule features suggest a high likelihood of malignancy (such as spiculation, lobulation and pleural retraction) whereas others favour a benign aetiology (internal fat, calcifications and round shape) [8–10]. Furthermore, the likelihood of malignancy and nodule diameter are positively correlated. However, nodule size is not a reliable standalone malignancy biomarker as slow growing adenocarcinoma nodules will appear small, and benign lesions may show growth and volume doubling time in the range of malignant nodules [11]. Hence, the assessment of pulmonary nodules remains a diagnostic challenge. Cohorts with low to moderate malignancy risk lung nodules pose the clinical dilemma between invasive procedures and serial surveillance. A score that could reliably predict pulmonary nodule aetiology would improve patient management by minimising the number of invasive procedures and reducing healthcare costs and patients' discomfort [12]. To this end, patients with a probably benign nodule could be managed by serial surveillance avoiding invasive tests, whereas those patients with a probably malignant nodule could be stratified to the most appropriate treatment more quickly [13]. These authors previously developed a biomarker-driven lung nodule classifier based upon 222 subjects [14]. In this study, Silvestri et al. [15]. designed and implemented a multicentre, double-blinded, prospective, observational study with a retrospective evaluation to validate the accuracy of this lung nodule classifier.

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“…Because of minimal invasion and easy sample collection, serological biomarkers for lung cancer diagnosis or screening are of great interest . Several studies have recently evaluated long noncoding RNA MALAT1 expression in serum or plasma of NSCLC patients and discussed its diagnostic performance .…”

Section: Introductionmentioning

confidence: 99%

Long noncoding RNA MALAT1 as a candidate serological biomarker for the diagnosis of non‐small cell lung cancer: A meta‐analysis

et al. 2019

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Background: To investigate the diagnostic efficacy of long noncoding RNA metastasis-associated in lung adenocarcinoma transcript l (MALAT1) as a candidate serological biomarker for non-small cell lung cancer (NSCLC). Methods: Diagnostic studies relevant to circulation long noncoding RNA MALAT1 as a candidate serological biomarker for NSCLC were electronically systematically searched in PubMed, EMBASE, EBSCO and CNKI databases. Suitable studies were included in the meta-analysis by pooling the diagnostic sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (−LR), diagnostic odds ratio (DOR) and area under the symmetric ROC curve (AUC) through a random or fixed effects model. Deeks' funnel plot was applied for publication bias evaluation. Results: Six studies with eight datasets were finally included in the meta-analysis after a systematic search of the databases was performed. The pooled diagnostic sensitivity, specificity, +LR, −LR and DOR were 0.81 (95% CI:0.78-0.84), 0.67 (95% CI:0.63-0.71), 2.61 (95% CI:1.81-3.71), 0.28 (95% CI:0.19-0.43) and 13.73 (95% CI:6.19-30.44), respectively. The pooled area under the ROC curve (AUC) were 0.8663 and 0.8658, respectively by symmetric and asymmetric methods. Conclusion: Based on the results of our study, serum long noncoding RNA MALAT1 is a promising biomarker for NSCLC screening. However, due to its low specificity, MALAT1 positive cases need further validation for NSCLC by other diagnostic methods such as radiology, cytology, etc.

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Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

Cited by 333 publications

References 129 publications

Update on Lung Cancer Screening

Update on Lung Cancer Screening

Management of incidental nodules in lung cancer screening: ready for prime-time?

Long noncoding RNA MALAT1 as a candidate serological biomarker for the diagnosis of non‐small cell lung cancer: A meta‐analysis

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