scholarly journals Lung Cancer Screening, towards a Multidimensional Approach: Why and How?

Cancers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 212 ◽  
Author(s):  
Jonathan Benzaquen ◽  
Jacques Boutros ◽  
Charles Marquette ◽  
Hervé Delingette ◽  
Paul Hofman
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
False Positive ◽  
Early Stage ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Target Population ◽  
High Rate ◽  
Multidisciplinary Teams ◽  
Surgical Interventions

Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.

scholarly journals Artificial Intelligence Tools for Refining Lung Cancer Screening

2020 ◽  
Vol 9 (12) ◽  
pp. 3860
Author(s):  
J. Luis Espinoza ◽  
Le Thanh Dong
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Computer Tomography ◽  
False Positive ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Disease Stage ◽  
Screening Programs ◽  
Positive Rate

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

scholarly journals Clinical Utility of a Plasma-Based miRNA Signature Classifier Within Computed Tomography Lung Cancer Screening: A Correlative MILD Trial Study

2014 ◽  
Vol 32 (8) ◽  
pp. 768-773 ◽  
Author(s):  
Gabriella Sozzi ◽  
Mattia Boeri ◽  
Marta Rossi ◽  
Carla Verri ◽  
Paola Suatoni ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Validation Study ◽  
False Positive ◽  
Diagnostic Performance ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Risk Groups ◽  
Positive Rate

Purpose Recent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients. However, high false-positive rates, costs, and potential harms highlight the need for complementary biomarkers. The diagnostic performance of a noninvasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicenter Italian Lung Detection (MILD) trial. Patients and Methods Plasma samples from 939 participants, including 69 patients with lung cancer and 870 disease-free individuals (n = 652, LDCT arm; n = 287, observation arm) were analyzed by using a quantitative reverse transcriptase polymerase chain reaction–based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study that used prespecified risk groups. Results The diagnostic performance of MSC for lung cancer detection was 87% for sensitivity and 81% for specificity across both arms, and 88% and 80%, respectively, in the LDCT arm. For all patients, MSC had a negative predictive value of 99% and 99.86% for detection and death as a result of disease, respectively. LDCT had sensitivity of 79% and specificity of 81% with a false-positive rate of 19.4%. Diagnostic performance of MSC was confirmed by time dependency analysis. Combination of both MSC and LDCT resulted in a five-fold reduction of LDCT false-positive rate to 3.7%. MSC risk groups were significantly associated with survival (χ12 = 49.53; P < .001). Conclusion This large validation study indicates that MSC has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT, thus improving the efficacy of lung cancer screening.

scholarly journals High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yeshwant Reddy Chillakuru ◽  
Kyle Kranen ◽  
Vishnu Doppalapudi ◽  
Zhangyuan Xiong ◽  
Letian Fu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Object Detection ◽  
High Precision ◽  
False Positive ◽  
False Positive Rate ◽  
Lung Nodule ◽  
Lung Cancer Screening ◽  
Radiology Reports ◽  
Axial Slice

Abstract Background Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions. Methods 888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation. Results Our nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions. Conclusions Utilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

scholarly journals Second round results from the Manchester ‘Lung Health Check’ community-based targeted lung cancer screening pilot

Thorax ◽  
2018 ◽  
Vol 74 (7) ◽  
pp. 700-704 ◽  
Author(s):  
Phil A Crosbie ◽  
Haval Balata ◽  
Matthew Evison ◽  
Melanie Atack ◽  
Val Bayliss-Brideaux ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Early Stage ◽  
False Positive Rate ◽  
Lung Cancer Screening ◽  
Health Check ◽  
Stereotactic Ablative Radiotherapy ◽  
Community Based ◽  
Screening Adherence ◽  
Lung Health

We report results from the second annual screening round (T1) of Manchester’s ‘Lung Health Check’ pilot of community-based lung cancer screening in deprived areas (undertaken June to August 2017). Screening adherence was 90% (n=1194/1323): 92% of CT scans were classified negative, 6% indeterminate and 2.5% positive; there were no interval cancers. Lung cancer incidence was 1.6% (n=19), 79% stage I, treatments included surgery (42%, n=9), stereotactic ablative radiotherapy (26%, n=5) and radical radiotherapy (5%, n=1). False-positive rate was 34.5% (n=10/29), representing 0.8% of T1 participants (n=10/1194). Targeted community-based lung cancer screening promotes high screening adherence and detects high rates of early stage lung cancer.

LOW-DOSE COMPUTED TOMOGRAPHY IN MOSCOW FOR LUNG CANCER SCREENING (LDCT-MLCS): BASELINE RESULTS

2019 ◽  
Vol 65 (2) ◽  
pp. 224-233
Author(s):  
Sergey Morozov ◽  
Viktor Gombolevskiy ◽  
Anton Vladzimirskiy ◽  
Albina Laypan ◽  
Pavel Kononets ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Early Stage ◽  
Lung Cancer Screening ◽  
Stage I ◽  
Malignant Neoplasms ◽  
Number Needed To Screen ◽  
Low Dose Computed Tomography

Study aim. To justify selective lung cancer screening via low-dose computed tomography and evaluate its effectiveness. Materials and methods. In 2017 we have concluded the baseline stage of “Lowdose computed tomography in Moscow for lung cancer screening (LDCT-MLCS)” trial. The trial included 10 outpatient clinics with 64-detector CT units (Toshiba Aquilion 64 and Toshiba CLX). Special low-dose protocols have been developed for each unit with maximum effective dose of 1 mSv (in accordance with the requirements of paragraph 2.2.1, Sanitary Regulations 2.6.1.1192-03). The study involved 5,310 patients (53% men, 47% women) aged 18-92 years (mean age 62 years). Diagnosis verification was carried out in the specialized medical organizations via consultations, additional instrumental, laboratory as well as pathohistological studies. The results were then entered into the “National Cancer Registry”. Results. 5310 patients (53% men, 47% women) aged 18 to 92 years (an average of 62 years) participated in the LDCT-MLCS. The final cohort was comprised of 4762 (89.6%) patients. We have detected 291 (6.1%) Lung-RADS 3 lesions, 228 (4.8%) Lung- RADS 4A lesions and 196 (4.1%) Lung-RADS 4B/4X lesions. All 4B and 4X lesions were routed in accordance with the project's methodology and legislative documents. Malignant neoplasms were verified in 84 cases (1.76% of the cohort). Stage I-II lung cancer was actively detected in 40.3% of these individuals. For the first time in the Russian Federation we have calculated the number needed to screen (NNS) to identify one lung cancer (NNS=57) and to detect one Stage I lung cancer (NNS=207). Conclusions. Based on the global experience and our own practices, we argue that selective LDCT is the most systematic solution to the problem of early-stage lung cancer screening.

Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals

2013 ◽  
Vol 23 (7) ◽  
pp. 1836-1845 ◽  
Author(s):  
Marjolein A. Heuvelmans ◽  
Matthijs Oudkerk ◽  
Geertruida H. de Bock ◽  
Harry J. de Koning ◽  
Xueqian Xie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
False Positive ◽  
Doubling Time ◽  
Lung Cancer Screening ◽  
Lung Nodules ◽  
Fast Growing ◽  
Volume Doubling Time ◽  
Volume Doubling

scholarly journals Systematic review of behavioural smoking cessation interventions for older smokers from deprived backgrounds

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e032727
Author(s):  
Pamela Smith ◽  
Ria Poole ◽  
Mala Mann ◽  
Annmarie Nelson ◽  
Graham Moore ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Older Adults ◽  
Smoking Cessation ◽  
Cancer Screening ◽  
Positive Impact ◽  
Lung Cancer Screening ◽  
Target Population ◽  
Smoking Abstinence ◽  
High Quality Research

IntroductionThe associations between smoking prevalence, socioeconomic group and lung cancer outcomes are well established. There is currently limited evidence for how inequalities could be addressed through specific smoking cessation interventions (SCIs) for a lung cancer screening eligible population. This systematic review aims to identify the behavioural elements of SCIs used in older adults from low socioeconomic groups, and to examine their impact on smoking abstinence and psychosocial variables.MethodSystematic searches of Medline, EMBASE, PsychInfo and CINAHL up to November 2018 were conducted. Included studies examined the characteristics of SCIs and their impact on relevant outcomes including smoking abstinence, quit motivation, nicotine dependence, perceived social influence and quit determination. Included studies were restricted to socioeconomically deprived older adults who are at (or approaching) eligibility for lung cancer screening. Narrative data synthesis was conducted.ResultsEleven studies met the inclusion criteria. Methodological quality was variable, with most studies using self-reported smoking cessation and varying length of follow-up. There were limited data to identify the optimal form of behavioural SCI for the target population. Intense multimodal behavioural counselling that uses incentives and peer facilitators, delivered in a community setting and tailored to individual needs indicated a positive impact on smoking outcomes.ConclusionTailored, multimodal behavioural interventions embedded in local communities could potentially support cessation among older, deprived smokers. Further high-quality research is needed to understand the effectiveness of SCIs in the context of lung screening for the target population.PROSPERO registration numberCRD42018088956.

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