Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers

Multiple primary lung cancer: Updates and perspectives

Management of multiple primary lung cancer (MPLC) remains challenging, partly due to its increasing incidence, especially with the significant rise in cases of multiple lung nodules caused by low-dose computed tomography screening. Moreover, the indefinite pathogenesis, diagnostic criteria, and treatment selection add to the complexity. In recent years, there have been continuous efforts to dissect the molecular characteristics of MPLC and explore new diagnostic approaches as well as treatment modalities, which will be reviewed here, with a focus on newly emerging evidence and future perspectives, hope to provide new insights into the management of MPLC and serve as inspiration for future research related to MPLC.

Diagnostic Accuracy of a Computed Tomography-Guided Transthoracic Needle Biopsy for Ground-Glass Opacities and Subsolid Pulmonary Nodules

Purpose The increasing use of computed tomography (CT) imaging has led to the detection of more ground-glass nodules (GGNs) and subsolid nodules (SSNs), which may be malignant and require a biopsy for proper diagnosis. Approximately 75% of persistent GGNs can be attributed to adenocarcinoma in situ or minimally invasive adenocarcinoma. A CT-guided biopsy has been proven to be a reliable procedure with high diagnostic performance. However, the diagnostic accuracy and safety of a CT-guided biopsy for GGNs and SSNs with solid components ≤6 mm are still uncertain. The aim of this study is to assess the diagnostic accuracy of a CT-guided core needle biopsy (CNB) for GGN and SSNs with solid components ≤6 mm. Methods This is a retrospective study of patients who underwent CT-guided CNB for the evaluation of GGNs and SSNs with solid components ≤6 mm between February 2020 and January 2023. Biopsy findings were compared to the final diagnosis determined by definite histopathologic examination and clinical course. Results A total of 22 patients were enrolled, with a median age of 74 years (IQR: 68-81). A total of 22 nodules were assessed, comprising 15 (68.2%) SSNs with a solid component measuring ≤6 mm and seven (31.8%) pure GGNs. The histopathological examination revealed that 12 (54.5%) were diagnosed as malignant, nine (40.9%) as benign, and one (4.5%) as non-diagnostic. The overall diagnostic accuracy and sensitivity for malignancy were 86.36% and 85.7%, respectively. Conclusion A CT-guided CNB for GGNs and SSNs with solid components measuring ≤6 mm appears to have a high diagnostic accuracy.

Lack of clinical utility of serum macrophage migration inhibitory factor (MIF) for monitoring therapy response and estimating prognosis in advanced lung cancer

BACKGROUND

Lung cancer is a major burden to global health and is still among the most frequent and most lethal malignant diseases. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine involved in a variety of processes including tumorigenesis, formation of a tumor microenvironment and metastasis. It is therefore a potential prognostic biomarker in malignant diseases.

OBJECTIVE

In this study, we investigated the applicability of MIF in serum samples as a biomarker in lung cancer.

METHODS

In a retrospective approach, we analyzed the sera of 79 patients with non-small-cell lung cancer (NSCLC) and 14 patients with small-cell lung cancer (SCLC) before the start of chemotherapy, as well as before the second and third chemotherapy cycle, respectively. Serum MIF levels were measured using a sandwich immunoassay with a sulfo-tag-labelled detection antibody, while pro-gastrin releasing peptide (proGRP) levels were determined with an enzyme-linked immunosorbent assay.

RESULTS

No difference in serum MIF levels between responders and non-responders to chemotherapy was observed at all time points, while proGRP levels were significantly lower in responders before the second chemotherapy cycle (p = 0.012). No differences in biomarker levels depending on the histopathological classification of NSCLC patients was found. Moreover, in ROC curve analyses MIF was not able to distinguish between responders and non-responders to therapy. proGRP could differentiate between responders and non-responders before the second chemotherapy cycle (p = 0.015) with sensitivities of 43% at 90% and 95% specificity, respectively. Likewise, proGRP yielded significantly longer survival times of patients with low proGRP concentrations before the second chemotherapy cycle (p = 0.015) in Kaplan-Meier analyses, yet MIF showed no significant differences in survival times at all time points. Comparison with the biomarkers CEA and CYFRA 21-1 in the same cohort showed that these established biomarkers clearly performed superior to MIF and proGRP.

CONCLUSIONS

From the present results, there is no indication that serum MIF may serve as a biomarker in prognosis and monitoring of response to therapy in lung cancer. Limitations of this study include its retrospective design, the inclusion of a larger NSCLC and a smaller SCLC subgroup, the classical chemotherapeutic treatment, the use of a non-diagnostic immunoassay (RUO-test) for MIF measurement and the lack of a validation cohort. Strengths of the study are its highly standardized procedures concerning sample collection, preanalytic treatment, measurements and quality control of the laboratory assays.

An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy of pulmonary nodules and its practical value for patients with different clinical characteristics

Objectives

This study aimed to explore the value of an artificial intelligence (AI)-assisted diagnostic system in the prediction of pulmonary nodules.

Methods

The AI system was able to make predictions of benign or malignant nodules. 260 cases of solitary pulmonary nodules (SPNs) were divided into 173 malignant cases and 87 benign cases based on the surgical pathological diagnosis. A stratified data analysis was applied to compare the diagnostic effectiveness of the AI system to distinguish between the subgroups with different clinical characteristics.

Results

The accuracy of AI system in judging benignity and malignancy of the nodules was 75.77% (p < 0.05). We created an ROC curve by calculating the true positive rate (TPR) and the false positive rate (FPR) at different threshold values, and the AUC was 0.755. Results of the stratified analysis were as follows. (1) By nodule position: the AUC was 0.677, 0.758, 0.744, 0.982, and 0.725, respectively, for the nodules in the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe. (2) By nodule size: the AUC was 0.778, 0.771, and 0.686, respectively, for the nodules measuring 5-10, 10-20, and 20-30 mm in diameter. (3) The predictive accuracy was higher for the subsolid pulmonary nodules than for the solid ones (80.54 vs. 66.67%).

Conclusion

The AI system can be applied to assist in the prediction of benign and malignant pulmonary nodules. It can provide a valuable reference, especially for the diagnosis of subsolid nodules and small nodules measuring 5-10 mm in diameter.

Tips and Tricks in Thoracic Radiology for Beginners: A Findings-Based Approach

This review has the purpose of illustrating schematically and comprehensively the key concepts for the beginner who approaches chest radiology for the first time. The approach to thoracic imaging may be challenging for the beginner due to the wide spectrum of diseases, their overlap, and the complexity of radiological findings. The first step consists of the proper assessment of the basic imaging findings. This review is divided into three main districts (mediastinum, pleura, focal and diffuse diseases of the lung parenchyma): the main findings will be discussed in a clinical scenario. Radiological tips and tricks, and relative clinical background, will be provided to orient the beginner toward the differential diagnoses of the main thoracic diseases.

Deep learning predicts malignancy and metastasis of solid pulmonary nodules from CT scans

In the clinic, it is difficult to distinguish the malignancy and aggressiveness of solid pulmonary nodules (PNs). Incorrect assessments may lead to delayed diagnosis and an increased risk of complications. We developed and validated a deep learning-based model for the prediction of malignancy as well as local or distant metastasis in solid PNs based on CT images of primary lesions during initial diagnosis. In this study, we reviewed the data from multiple patients with solid PNs at our institution from 1 January 2019 to 30 April 2022. The patients were divided into three groups: benign, Ia-stage lung cancer, and T1-stage lung cancer with metastasis. Each cohort was further split into training and testing groups. The deep learning system predicted the malignancy and metastasis status of solid PNs based on CT images, and then we compared the malignancy prediction results among four different levels of clinicians. Experiments confirmed that human-computer collaboration can further enhance diagnostic accuracy. We made a held-out testing set of 134 cases, with 689 cases in total. Our convolutional neural network model reached an area under the ROC (AUC) of 80.37% for malignancy prediction and an AUC of 86.44% for metastasis prediction. In observer studies involving four clinicians, the proposed deep learning method outperformed a junior respiratory clinician and a 5-year respiratory clinician by considerable margins; it was on par with a senior respiratory clinician and was only slightly inferior to a senior radiologist. Our human-computer collaboration experiment showed that by simply adding binary human diagnosis into model prediction probabilities, model AUC scores improved to 81.80-88.70% when combined with three out of four clinicians. In summary, the deep learning method can accurately diagnose the malignancy of solid PNs, improve its performance when collaborating with human experts, predict local or distant metastasis in patients with T1-stage lung cancer, and facilitate the application of precision medicine.

Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis

BACKGROUND

Screening using low-dose computed tomography (LDCT) is a more effective approach and has the potential to detect lung cancer more accurately. We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.

METHODS

MEDLINE, Excerpta Medica Database, and Web of Science were searched for articles published up to April 10, 2022. According to the inclusion and exclusion criteria, the data of true positives, false-positives, false negatives, and true negatives in the screening test were extracted. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature. A bivariate random effects model was used to estimate pooled sensitivity and specificity. The area under the curve (AUC) was calculated by using hierarchical summary receiver-operating characteristics analysis. Heterogeneity between studies was measured using the Higgins I2 statistic, and publication bias was evaluated using a Deeks' funnel plot and linear regression test.

RESULTS

A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis; most of them were from Europe and America (38 studies), ten were from Asia, and one was from Oceania. The recruitment period was 1992 to 2018, and most of the subjects were 40 to 75 years old. The analysis showed that the AUC of lung cancer screening by LDCT was 0.98 (95% CI: 0.96-0.99), and the overall sensitivity and specificity were 0.97 (95% CI: 0.94-0.98) and 0.87 (95% CI: 0.82-0.91), respectively. The funnel plot and test results showed that there was no significant publication bias among the included studies.

CONCLUSIONS

Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer. However, long-term follow-up of the whole study population (including those with a negative baseline screening result) should be performed to enhance the accuracy of LDCT screening.

Fluoroscopia por tomografia computadorizada - biópsia percutânea guiada de nódulos pulmonares ≤ 10 mm: análise retrospectiva de procedimentos realizados no período de pandemia de COVID-19

Resumo Objetivo: Avaliar o desempenho diagnóstico da biópsia pulmonar percutânea transtorácica (BPPT) guiada por fluoroscopia associada a tomografia computadorizada (FTC) em nódulos pulmonares ≤ 10 mm no período de pandemia de COVID-19. Materiais e Métodos: No período de 1º de janeiro de 2020 a 30 de abril de 2022, 359 BPPTs guiadas por FTC foram realizadas em um centro terciário de radiologia intervencionista. As lesões pulmonares mediam entre 2 mm e 108 mm. Dessas 359 BPPTs, 27 (7,5%) foram realizadas com agulha 18G em nódulos de 2 mm a 10 mm. Resultados: Das 27 BPPTs realizadas nos nódulos ≤ 10 mm, quatro lesões tinham dimensões menores que 5 mm e 23 lesões mediam entre 5 e 10 mm. Sensibilidade e acurácia diagnóstica das BPPTs guiadas por FTC foram de 100% e 92,3%, respectivamente. A dose média de radiação ionizante para os pacientes durante o procedimento de BPPT guiada por FTC foi de 581,33 mGy*cm, variando de 303 a 1129 mGy*cm. A média de tempo dos procedimentos de biópsia foi de 6,6 minutos, variando de 2 a 12 minutos. Nas 27 BPPTs, nenhuma complicação maior foi descrita. Conclusão: A BBPT guiada por FTC resultou em alto rendimento diagnóstico e baixas taxas de complicações.

Computed tomography fluoroscopy-guided percutaneous biopsy of pulmonary nodules ≤ 10 mm: retrospective analysis of procedures performed during the COVID-19 pandemic

Objective

To evaluate the diagnostic performance of computed tomography (CT) fluoroscopy-guided percutaneous transthoracic needle biopsy (PTNB) in pulmonary nodules ≤ 10 mm during the coronavirus disease 2019 pandemic.

Materials and Methods

Between January 1, 2020 and April 30, 2022, a total of 359 CT fluoroscopy-guided PTNBs were performed at an interventional radiology center. Lung lesions measured between 2 mm and 108 mm. Of the 359 PTNBs, 27 (7.5%) were performed with an 18G core needle on nodules ≤ 10 mm in diameter.

Results

Among the 27 biopsies performed on nodules ≤ 10 mm, the lesions measured < 5 mm in four and 5-10 mm in 23. The sensitivity and overall diagnostic accuracy of PTNB were 100% and 92.3%, respectively. The mean dose of ionizing radiation during PTNB was 581.33 mGy*cm (range, 303-1,129 mGy*cm), and the mean biopsy procedure time was 6.6 min (range, 2-12 min). There were no major postprocedural complications.

Conclusion

CT fluoroscopy-guided PTNB appears to provide a high diagnostic yield with low complication rates.

Silent region barcode particle arrays for ultrasensitive multiplexed SERS detection

Suspension arrays are a critical components of next generation multiplexed detection technologies. Current fluorescence suspension arrays are limited by a multiplexed coding ceiling and difficulties with ultrasensitive detection. Raman mode is a promising substitute, but the complex spectral peak distributions and extremely weak intrinsic signal intensity severely diminish Raman signal performance in suspension arrays. To address these limitations, we constructed a Raman suspension array system using plasmonic microbeads as barcode substrates and Au nanoflowers as reporter carriers. The well-designed shell morphology and plasmonic microbead composition enabled significant surface enhancement Raman scattering (SERS) such that we were able to adjust silent region Raman-coding intensity levels. Due to synergistic SERS effects from the plasmonic shell and the multi-branched Au nanoflower nanostructure, the reporting signal was greatly improved, enabling ultrasensitive detection of 5-plexed lung cancer markers. Detection in patient serum samples demonstrated good consistency with the standard electrochemiluminescence method. Thus, this silent region SERS barcode-based suspension array is a developmental advance for modern multiplexed biodetection, potentially providing a powerful early disease screening and diagnosis tool.

Understanding patient barriers and facilitators to uptake of lung screening using low dose computed tomography: a mixed methods scoping review of the current literature

BACKGROUND

Targeted lung cancer screening is effective in reducing mortality by upwards of twenty percent. However, screening is not universally available and uptake is variable and socially patterned. Understanding screening behaviour is integral to designing a service that serves its population and promotes equitable uptake. We sought to review the literature to identify barriers and facilitators to screening to inform the development of a pilot lung screening study in Scotland.

METHODS

We used Arksey and O'Malley's scoping review methodology and PRISMA-ScR framework to identify relevant literature to meet the study aims. Qualitative, quantitative and mixed methods primary studies published between January 2000 and May 2021 were identified and reviewed by two reviewers for inclusion, using a list of search terms developed by the study team and adapted for chosen databases.

RESULTS

Twenty-one articles met the final inclusion criteria. Articles were published between 2003 and 2021 and came from high income countries. Following data extraction and synthesis, findings were organised into four categories: Awareness of lung screening, Enthusiasm for lung screening, Barriers to lung screening, and Facilitators or ways of promoting uptake of lung screening. Awareness of lung screening was low while enthusiasm was high. Barriers to screening included fear of a cancer diagnosis, low perceived risk of lung cancer as well as practical barriers of cost, travel and time off work. Being health conscious, provider endorsement and seeking reassurance were all identified as facilitators of screening participation.

CONCLUSIONS

Understanding patient reported barriers and facilitators to lung screening can help inform the implementation of future lung screening pilots and national lung screening programmes.

Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals

Introduction

To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals.

Methods

A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols.

Results

The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%).

Conclusions

LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.

Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma

BACKGROUND

Squamous cell carcinoma (SqCC) is a subtype of non-small cell lung cancer for which patient prognosis remains poor. The extracellular matrix (ECM) is critical in regulating cell behavior; however, its importance in tumor aggressiveness remains to be comprehensively characterized.

METHODS

Multi-omics data of SqCC human tumor specimens was combined to characterize ECM features associated with initiation and recurrence. Penalized logistic regression was used to define a matrix risk signature for SqCC tumors and its performance across a panel of tumor types and in SqCC premalignant lesions was evaluated. Consensus clustering was used to define prognostic matreotypes for SqCC tumors. Matreotype-specific tumor biology was defined by integration of bulk RNAseq with scRNAseq data, cell type deconvolution, analysis of ligand-receptor interactions and enriched biological pathways, and through cross comparison of matreotype expression profiles with aging and idiopathic pulmonary fibrosis lung profiles.

RESULTS

This analysis revealed subtype-specific ECM signatures associated with tumor initiation that were predictive of premalignant progression. We identified an ECM-enriched tumor subtype associated with the poorest prognosis. In silico analysis indicates that matrix remodeling programs differentially activate intracellular signaling in tumor and stromal cells to reinforce matrix remodeling associated with resistance and progression. The matrix subtype with the poorest prognosis resembles ECM remodeling in idiopathic pulmonary fibrosis and may represent a field of cancerization associated with elevated cancer risk.

CONCLUSIONS

Collectively, this analysis defines matrix-driven features of poor prognosis to inform precision medicine prevention and treatment strategies towards improving SqCC patient outcome.

Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features

To develop and validate a predictive model based on clinical radiology and radiomics to enhance the ability to distinguish between benign and malignant solitary solid pulmonary nodules. In this study, we retrospectively collected computed tomography (CT) images and clinical data of 286 patients with isolated solid pulmonary nodules diagnosed by surgical pathology, including 155 peripheral adenocarcinomas and 131 benign nodules. They were randomly divided into a training set and verification set at a 7:3 ratio, and 851 radiomic features were extracted from thin-layer enhanced venous phase CT images by outlining intranodal and perinodal regions of interest. We conducted preprocessing measures of image resampling and eigenvalue normalization. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (lasso) methods were used to downscale and select features. At the same time, univariate and multifactorial analyses were performed to screen clinical radiology features. Finally, we constructed a nomogram based on clinical radiology, intranodular, and perinodular radiomics features. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC), and the clinical decision curve (DCA) was used to evaluate the clinical practicability of the models. Univariate and multivariate analyses showed that the two clinical factors of sex and age were statistically significant. Lasso screened four intranodal and four perinodal radiomic features. The nomogram based on clinical radiology, intranodular, and perinodular radiomics features showed the best predictive performance (AUC=0.95, accuracy=0.89, sensitivity=0.83, specificity=0.96), which was superior to other independent models. A nomogram based on clinical radiology, intranodular, and perinodular radiomics features is helpful to improve the ability to predict benign and malignant solitary pulmonary nodules.

Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality

BACKGROUND

Lung cancer is the most common cause of cancer-related death in the world, however lung cancer screening has not been implemented in most countries at a population level. A previous Cochrane Review found limited evidence for the effectiveness of lung cancer screening with chest radiography (CXR) or sputum cytology in reducing lung cancer-related mortality, however there has been increasing evidence supporting screening with low-dose computed tomography (LDCT).  OBJECTIVES: To determine whether screening for lung cancer using LDCT of the chest reduces lung cancer-related mortality and to evaluate the possible harms of LDCT screening.

SEARCH METHODS

We performed the search in collaboration with the Information Specialist of the Cochrane Lung Cancer Group and included the Cochrane Lung Cancer Group Trial Register, Cochrane Central Register of Controlled Trials (CENTRAL, the Cochrane Library, current issue), MEDLINE (accessed via PubMed) and Embase in our search. We also searched the clinical trial registries to identify unpublished and ongoing trials. We did not impose any restriction on language of publication. The search was performed up to 31 July 2021.  SELECTION CRITERIA: Randomised controlled trials (RCTs) of lung cancer screening using LDCT and reporting mortality or harm outcomes.  DATA COLLECTION AND ANALYSIS: Two review authors were involved in independently assessing trials for eligibility, extraction of trial data and characteristics, and assessing risk of bias of the included trials using the Cochrane RoB 1 tool. We assessed the certainty of evidence using GRADE. Primary outcomes were lung cancer-related mortality and harms of screening. We performed a meta-analysis, where appropriate, for all outcomes using a random-effects model. We only included trials in the analysis of mortality outcomes if they had at least 5 years of follow-up. We reported risk ratios (RRs) and hazard ratios (HRs), with 95% confidence intervals (CIs) and used the I2 statistic to investigate heterogeneity.  MAIN RESULTS: We included 11 trials in this review with a total of 94,445 participants. Trials were conducted in Europe and the USA in people aged 40 years or older, with most trials having an entry requirement of ≥ 20 pack-year smoking history (e.g. 1 pack of cigarettes/day for 20 years or 2 packs/day for 10 years etc.). One trial included male participants only. Eight trials were phase three RCTs, with two feasibility RCTs and one pilot RCT. Seven of the included trials had no screening as a comparison, and four trials had CXR screening as a comparator. Screening frequency included annual, biennial and incrementing intervals. The duration of screening ranged from 1 year to 10 years. Mortality follow-up was from 5 years to approximately 12 years.  None of the included trials were at low risk of bias across all domains. The certainty of evidence was moderate to low across different outcomes, as assessed by GRADE. In the meta-analysis of trials assessing lung cancer-related mortality, we included eight trials (91,122 participants), and there was a reduction in mortality of 21% with LDCT screening compared to control groups of no screening or CXR screening (RR 0.79, 95% CI 0.72 to 0.87; 8 trials, 91,122 participants; moderate-certainty evidence). There were probably no differences in subgroups for analyses by control type, sex, geographical region, and nodule management algorithm. Females appeared to have a larger lung cancer-related mortality benefit compared to males with LDCT screening. There was also a reduction in all-cause mortality (including lung cancer-related) of 5% (RR 0.95, 95% CI 0.91 to 0.99; 8 trials, 91,107 participants; moderate-certainty evidence).  Invasive tests occurred more frequently in the LDCT group (RR 2.60, 95% CI 2.41 to 2.80; 3 trials, 60,003 participants; moderate-certainty evidence). However, analysis of 60-day postoperative mortality was not significant between groups (RR 0.68, 95% CI 0.24 to 1.94; 2 trials, 409 participants; moderate-certainty evidence).  False-positive results and recall rates were higher with LDCT screening compared to screening with CXR, however there was low-certainty evidence in the meta-analyses due to heterogeneity and risk of bias concerns. Estimated overdiagnosis with LDCT screening was 18%, however the 95% CI was 0 to 36% (risk difference (RD) 0.18, 95% CI -0.00 to 0.36; 5 trials, 28,656 participants; low-certainty evidence). Four trials compared different aspects of health-related quality of life (HRQoL) using various measures. Anxiety was pooled from three trials, with participants in LDCT screening reporting lower anxiety scores than in the control group (standardised mean difference (SMD) -0.43, 95% CI -0.59 to -0.27; 3 trials, 8153 participants; low-certainty evidence). There were insufficient data to comment on the impact of LDCT screening on smoking behaviour.  AUTHORS' CONCLUSIONS: The current evidence supports a reduction in lung cancer-related mortality with the use of LDCT for lung cancer screening in high-risk populations (those over the age of 40 with a significant smoking exposure). However, there are limited data on harms and further trials are required to determine participant selection and optimal frequency and duration of screening, with potential for significant overdiagnosis of lung cancer. Trials are ongoing for lung cancer screening in non-smokers.

Segmentation of Lung Nodules on CT Images Using a Nested Three-Dimensional Fully Connected Convolutional Network

In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is important for analyzing image features of lung nodules on computed tomography (CT) images and distinguishing malignant nodules from benign ones. However, it is difficult to accurately and robustly segment lung nodules attached to the chest wall or with ground-glass opacities using conventional image processing methods. Therefore, this study aimed to develop a method for robust and accurate three-dimensional (3D) segmentation of lung nodule regions using deep learning. In this study, a nested 3D fully connected convolutional network with residual unit structures was proposed, and designed a new loss function. Compared with annotated images obtained under the guidance of a radiologist, the Dice similarity coefficient (DS) and intersection over union (IoU) were 0.845 ± 0.008 and 0.738 ± 0.011, respectively, for 332 lung nodules (lung adenocarcinoma) obtained from 332 patients. On the other hand, for 3D U-Net and 3D SegNet, the DS was 0.822 ± 0.009 and 0.786 ± 0.011, respectively, and the IoU was 0.711 ± 0.011 and 0.660 ± 0.012, respectively. These results indicate that the proposed method is significantly superior to well-known deep learning models. Moreover, we compared the results obtained from the proposed method with those obtained from conventional image processing methods, watersheds, and graph cuts. The DS and IoU results for the watershed method were 0.628 ± 0.027 and 0.494 ± 0.025, respectively, and those for the graph cut method were 0.566 ± 0.025 and 0.414 ± 0.021, respectively. These results indicate that the proposed method is significantly superior to conventional image processing methods. The proposed method may be useful for accurate and robust segmentation of lung nodules to assist radiologists in the diagnosis of lung nodules such as lung adenocarcinoma on CT images.

Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule

Aim

To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN).

Materials and Methods

A total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model.

Results

A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings.

Conclusion

The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.

Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation

Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%.

Integrity of clinical information in radiology reports documenting pulmonary nodules

OBJECTIVE

Quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations.

MATERIALS AND METHODS

This Institutional Review Board-approved, retrospective cohort study was performed at an academic medical center. Natural language processing was performed on radiology reports of CT scans of chest, abdomen, or spine completed in 2016 to assess presence of pulmonary nodules, excluding patients with lung cancer, of which 300 reports were randomly sampled to form the study cohort. Documentation of nodule characteristics were manually extracted from reports by 2 authors with 20% overlap. CT images corresponding to 60 randomly selected reports were further reviewed by a thoracic radiologist to record nodule characteristics. Documentation completeness for all characteristics were reported in percentage and compared using χ2 analysis. Concordance with a thoracic radiologist was reported as percentage agreement; impact on making follow-up recommendations was assessed using kappa.

RESULTS

Documentation completeness for pulmonary nodule characteristics differed across variables (range = 2%-90%, P < .001). Concordance with a thoracic radiologist was 75% for documenting nodule laterality and 29% for size. Follow-up recommendations were in agreement in 67% and 49% of reports when there was lack of completeness and concordance in documenting nodule size, respectively.

DISCUSSION

Essential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up.

CONCLUSION

Lack of documentation of pulmonary nodule characteristics in radiology reports is common, with potential for compromising patient care and clinical decision support tools.

Lung cancers and pulmonary nodules detected by computed tomography scan: a population-level analysis of screening cohorts

Background

An increasing number and proportion of younger lung cancer patients have been observed worldwide, raising concerns on the optimal age to begin screening. This study aimed to investigate the association between age and findings in initial CT scans.

Methods

We searched for low-dose CT screening cohorts from electronic databases. Single-arm syntheses weighted by sample size were performed to calculate the detection rates of pulmonary nodules, lung cancers (all stages and stage I), and the proportion of stage I diseases in lung cancers. In addition, we included patients who underwent chest CT in our center as a supplementary cohort. The correlation between the detection rates and age was evaluated by the Pearson Correlation Coefficient.

Results

A total of 37 studies involving 163,442 participants were included. We found the detection rates of pulmonary nodules and lung cancers increased with age. However, the proportion of stage I diseases in lung cancers declined with increased starting age and was significantly higher in the 40-year group than in other groups (40 vs. 45, 50, 55, P<0.001). In addition, the ratio of early-stage lung cancer to the number of nodules declined with age. Similarly, in our center, the detection rates of nodules (R²=0.86, P≤0.001), all lung cancer (R²=0.99, P≤0.001) and stage I diseases (R²=0.87, P=0.001) increased with age, while the proportion of stage I diseases consistently declined with age (R²=0.97, P≤0.001).

Conclusions

Starting lung cancer screening at an earlier age is associated with a higher probability of identifying a curable disease, urging future research to determine the optimal starting age.

A computed tomography-based nomogram to predict pneumothorax caused by preoperative localization of ground glass nodules using hook wire

OBJECTIVE

To develop and validate a CT-based nomogram to predict the occurrence of loculated pneumothorax due to hook wire placement.

METHODS

Patients (n = 177) were divided into pneumothorax (n = 72) and non-pneumothorax (n = 105) groups. Multivariable logistic regression analysis was applied to build a clinical prediction model using significant predictors identified by univariate analysis of imaging features and clinical factors. Receiver operating characteristic (ROC) was applied to evaluate the discrimination of the nomogram, which was calibrated using calibration curve.

RESULTS

Based on the results of multivariable regression analysis, transfissure approach [odds ratio (OR): 757.94; 95% confidence interval CI (21.20-27099.30) p < 0.0001], transemphysema [OR: 116.73; 95% CI (12.34-1104.04) p < 0.0001], localization of multiple nodules [OR: 8.04; 95% CI (2.09-30.89) p = 0.002], and depth of nodule [OR: 0.77; 95% CI (0.71-0.85) p < 0.0001] were independent risk factors for pneumothorax and were included in the predictive model (p < 0.05). The area under the ROC curve value for the nomogram was 0.95 [95% CI (0.92-0.98)] and the calibration curve indicated good consistency between risk predicted using the model and actual risk.

CONCLUSION

A CT-based nomogram combining imaging features and clinical factors can predict the probability of pneumothorax before localization of ground-glass nodules. The nomogram is a decision-making tool to prevent pneumothorax and determine whether to proceed with further treatment.

ADVANCES IN KNOWLEDGE

A nomogram composed of transfissure, transemphysema, multiple nodule localization, and depth of nodule has been developed to predict the probability of pneumothorax before localization of GGNs.

Feasibility of Using the O-Arm Imaging System During ENB-rEBUS-guided Peripheral Lung Biopsy: A Dual-center Experience

BACKGROUND

There is a paucity of real-time imaging modalities available for the bronchoscopic biopsy of peripheral lung nodules. We aim to demonstrate the feasibility of the O-arm imaging system to guide real-time biopsies of peripheral lung nodules during electromagnetic navigation bronchoscopy.

METHODS

A retrospective review was performed at 2 academic medical centers utilizing O-arm guidance.

RESULTS

The average nodule size was 2.1×2.0 cm and were mostly solid (66%) with a positive bronchus sign (83%). O-arm imaging confirmed tool-in-lesion in all cases. The diagnostic yield was 33%. Four cases were nondiagnostic of the 6 cases performed. In these cases, necrotic tissue was the most common (75%) and showed resolution following subsequent imaging. The average 3-dimensional (3D) spin time was 23.5 seconds. The average number of 3D spins performed per case was 4.33. The average effective dose per 3D spin was 3.73 mSv.

CONCLUSION

We have demonstrated the O-arm's feasibility with electromagnetic navigation bronchoscopy for peripheral lung nodules. The O-arm was able to confirm tool-in-lesion in all cases which added confidence to the biopsy. Four high-resolution 3D spins per case may limit the total computed tomography effective dose. We also noted that both metal and radiation scatter were minimal when appropriate radiation safety standards were met. Although additional experience and data will be required to verify the O-arm approach for routine use, our initial experience is promising.

Incidental Findings of Malignancy of the Chest by Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT-CT MPI): One Year Follow-Up Report

Introduction

We recently reported six cases of pulmonary/hilar malignancies as the result of incidental findings (IF) on CT attenuation correction (CTAC) during Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT-CT MPI). In this study, clinical features, diagnostic procedures, and clinical outcomes were examined on all patients who had malignancies or significant IF that required further follow-up.

Methods

Of 1,098 consecutive patients who underwent cardiac SPECT-CT MPI from September 1, 2017 to August 31, 2018, their MPI and CTAC were reviewed contemporaneously. Patients with known history of prior pulmonary or chest malignancy were excluded.

Results

A total of 79 (7.2%) patients were identified to have significant IF on CTAC. After diagnostic CT, 47 patients had significant findings that warranted further follow-up and included in this study. Eight of 1,098 patients (0.73%) and 8/79 patients (10.1%) were found to have malignancy of the chest because of IF on the CTAC. There were no statistically significant differences in baseline characteristics and cancer risk factors among patients who had cancer versus those without. At the time of diagnosis, four patients had cancer at an advanced stage, resulting in death within 12 months. Three others had early stage lung cancer and one had mantle cell lymphoma; they were alive at a mean follow-up of 17.5+/−2.1 months. Biopsy for tissue diagnosis was performed safely with needle biopsy. Major complication occurred in one patient (1/9 or 11.1%) with needle biopsy; none with surgical biopsy.

Conclusion

This study underscored the importance of reviewing CTAC images obtained during cardiac SPECT-CT MPI to detect clinically important IF.

Screening for Lung Cancer

Lung cancer screening with low-dose computed tomography provides an opportunity to save lives by early detection of the deadliest cancer in the United States. Uptake of lung cancer screening has been quite low but may be improving. Clinician and patient education, integration of lung cancer screening protocols into electronic medical records, support for shared decision making and tobacco cessation, and improved communication between referral centers and clinicians are all important areas for improvement for lung cancer screening to reach its potential in improving morbidity and mortality from lung cancer.

Safety and diagnostic accuracy of percutaneous CT-guided transthoracic biopsy of small lung nodules (≤20 mm) adjacent to the pericardium or great vessels

PURPOSE

We aimed to evaluate the safety and diagnostic accuracy of computed tomography (CT)-guided transthoracic biopsy of small lung nodules (≤20 mm) adjacent to the pericardium or great vessels.

METHODS

This retrospective study examined the safety and diagnostic accuracy of percutaneous CT-guided biopsy for small lung nodules (≤20 mm) located within 10 mm of the pericardium or great vessels. Technical aspects and factors influencing complications were assessed, and diagnostic accuracy was calculated.

RESULTS

A total of 168 biopsies were performed in 168 patients. The complications were mainly pneumothorax (34.5%; 58 of 168 patients), chest tube insertion (5.3%; 9 of 168 patients), and pulmonary hemorrhage (61.3%; 103 of 168 procedures), with no patient mortality. One patient (0.6%) was admitted because of hemorrhage complications. Significant independent risk factors for pneumothorax were nodules resided in upper or middle lobes and lateral patient position, and for hemorrhage, longer distance from structures and longer needle trajectory through the lung parenchyma. Overall, the sensitivity, accuracy, and specificity were 91.0%, 92.2%, and 100%, respectively.

CONCLUSION

Percutaneous CT-guided transthoracic biopsy was highly accurate in small lung nodules (≤20 mm) adjacent to the pericardium or great vessels. Complications are common, but most were minor and self-limited.

Performance of low-dose computed tomography on lung cancer screening in high-risk populations: The experience over five screening rounds in Sichuan, China

OBJECTIVE

To evaluate the performance of low-dose computed tomography (LDCT) on lung cancer screening in high-risk populations in Sichuan.

METHODS

From April 2014 to July 2018, LDCT was performed annually on 3185 subjects aged 50-74 years who had smoked ≥ 20 pack-years (or subjects having quit smoking within 5 years). Information about all deaths and lung cancer diagnoses were obtained by active investigation, or passive matching to disease surveillance system.

RESULTS

The screening population had a median age of 60 years. 62.4 % of which were current smokers and had smoked 30 pack-years. After participating in the baseline screening, the compliance rates of subjects consecutively completing one round, two rounds, three rounds, and four rounds of annual screening were 67.22 %, 52.84 %, 43.24 %, and 40.04 %, respectively. The positive rates in baseline and annual screening were 6.53 % and 5.79 %, respectively. During the 5 rounds, a total of 9522 person-times were screened by LDCT with a screening sensitivity of 89.13 % (95 % CI: 76.96-95.27), specificity of 94.36 % (95 % CI: 93.88-94.81), positive predictive value of 7.13 % (95 % CI: 5.30-9.53), and negative predictive value of 99.94 % (95 % CI: 99.87-99.98). There were no statistically significant performance differences between baseline and annual screening. The difference in the proportion of screen-detected stage I lung cancer between baseline screening and annual screening was not statistically significant, neither.

CONCLUSION

The application of LDCT on lung cancer screening in high-risk populations shows favorable compliance and a high screening performance in the project area of Sichuan,China.

Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval

Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured.

Contralateral Dependent Position During Percutaneous CT-Guided Core Needle Biopsy for Small (≤ 20 mm) Lung Lesions Adjacent to the Pericardium: Effect on Procedures and Complications

PURPOSE

To assess the effect of contralateral dependent position on procedures and complications of percutaneous computed tomography (CT)-guided core needle biopsy (PCT-CNB) for small (≤ 20 mm) lung lesions adjacent to the pericardium.

MATERIALS AND METHODS

Retrospective view was performed to identify patients with small (≤ 20 mm) lung lesions located within 10 mm of the pericardium and who underwent PCT-CNB in the standard supine or prone position (n = 66) or in contralateral dependent position ( n = 35) between March 2010 and January 2020. In 35 patients, CT images in the contralateral dependent position were compared with images in the supine position to assess the mean distance of the lesion from the pericardium and the mean length of interface between these two positions. Complications including rates of pneumothorax, chest tube insertion, and pulmonary hemorrhage were assessed.

RESULTS

In comparison with axial images in supine position, the pericardium were located farther from the lesion in the contralateral dependent position; the mean distance of lesions from the pericardium became farther (P < 0.001), and the mean length of interface with the pericardium became shorter (P = 0.008). There was no difference in the complication rates between supine or prone position and contralateral dependent position (pneumothorax, P = 0.098; pulmonary hemorrhage, P = 0.791).

CONCLUSION

Placing patients in contralateral dependent position may confer some advantages, including maximizing distance and minimizing length of interface of the lesion to the pericardium during PCT-CNB for small (≤ 20 mm) lung lesions adjacent to the pericardium.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

OBJECTIVE

Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).

MATERIALS AND METHODS

This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.

RESULTS

Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).

CONCLUSION

DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

CT-guided biopsy of pulmonary nodules ≤10 mm: Diagnostic yield based on nodules' lobar and segmental distribution

PURPOSE

The aim of our study is to evaluate the diagnostic performance of CT-guided biopsy of lung nodules ≤10 mm based on their lobar and segmental location.

MATERIALS AND METHODS

This was a retrospective study performed on 193 CT-guided percutaneous transthoracic needle biopsies of lung nodules ≤10 mm in greatest dimension, between January 1, 2013 and April 30, 2019. Biopsies were classified as either diagnostic or non-diagnostic based on final cytology and surgical pathology reports. Diagnostic results were those that met parameters for malignancy or a specific benign diagnosis, whereas atypical cells, non-specific benignity, or insufficient specimen were considered non-diagnostic.

RESULTS

A total of 1577 CT-guided percutaneous transthoracic needle biopsies were reviewed. Of these, 193 nodules (12.24%) measured ≤10 mm and were selected for further analysis. Of the 193 biopsies, 138 yielded diagnostic results while 56 yielded nondiagnostic results (71% vs 29%, respectively). When analyzed by nodule location, the superior segments of the lower lobes boasted the highest diagnostic yield compared to nodules located in the basal segments of the lower lobes which had the lowest diagnostic yield (84.2% vs 64.7%, respectively). Nodules in the upper lobes and in the middle lobes had a diagnostic yield of 70% and 66.7%, respectively.

CONCLUSION

The diagnostic performance of CT-guided biopsy of lung nodules ≤10 mm in diameter may be affected by lobar and segmental location. While the overall performance was good (diagnostic yield of 71%), the yield varied nearly 20% depending on location.

Cardiac motion non-influential in percutaneous computed tomography-guided biopsies of small (≤ 20 mm) lung nodules near pericardium

PURPOSE

To assess the impact of cardiac motion during percutaneous computed tomography (CT)-guided core needle biopsy (PCT-CNB) of small lung lesions near pericardium, focusing on safety and diagnostic accuracy.

MATERIALS AND METHODS

Seventy-eight PCT-CNBs were performed between March 2010 and June 2018 in 78 patients with small (≤ 20 mm) lung nodules, each within 10 mm of pericardium. Shifts in distance and length of interface separating lesions from pericardium were calculated and compared by cardiac chambers (left atrium, left ventricle, right atrium, or right ventricle). Risk factors for complications were subjected to univariate analysis, and diagnostic accuracy was assessed.

RESULTS

The respective mean values were 0.8 ± 1.1 mm (range 0-5.1 mm) for shifts in distance and 1.5 ± 2.1 mm (range 0-10.8 mm) for length of interface. Neither parameter shifted significantly with respect to cardiac chambers (p > 0.05, both). Pneumothorax ensued in 28 patients (35.9%), and pulmonary hemorrhage occurred in 41 (52.6%). The overall sensitivity, specificity, and accuracy of PCT-CNB were 91.2%, 100%, and 93.2%, respectively.

CONCLUSION

Our data indicate that cardiac motion has no impact on either the incidence of complications or the diagnostic accuracy of PCT-CNB in patients with small (≤ 20 mm) lung lesions near pericardium.

Low-Dose CT Screening for Lung Cancer: Evidence from 2 Decades of Study

Lung cancer remains the overwhelmingly greatest cause of cancer death in the United States, accounting for more annual deaths than breast, prostate, and colon cancer combined. Accumulated evidence since the mid to late 1990s, however, indicates that low-dose CT screening of high-risk patients enables detection of lung cancer at an early stage and can reduce the risk of dying from lung cancer. CT screening is now a recommended clinical service in the United States, subject to guidelines and reimbursement requirements intended to standardize practice and optimize the balance of benefits and risks. In this review, the evidence on the effectiveness of CT screening will be summarized and the current guidelines and standards will be described in the context of knowledge gained from lung cancer screening studies. In addition, an overview of the potential advances that may improve CT screening will be presented, and the need to better understand the performance in clinical practice outside of the research trial setting will be discussed. © RSNA, 2020.

Performance of lung cancer screening with low-dose CT in Gejiu, Yunnan: A population-based, screening cohort study

Background

The performance of lung cancer screening with low‐dose computed tomography (CT) (LDCT) in China is uncertain. This study aimed to evaluate the performance of LDCT lung cancer screening in the Chinese setting.

Methods

In 2014, a screening cohort of lung cancer with LDCT was established in Gejiu, Yunnan Province, a screening center of the Lung Cancer Screening Program in Rural China (LungSPRC). Participants received a baseline screening and four rounds of annual screening with LDCT in two local hospitals until June 2019. We analyzed the rates of participation, detection, early detection, and the clinical characteristics of lung cancer.

Results

A total of 2006 participants had complete baseline screening results with a compliance rate of 98.4%. Of these, 1411 were high‐risk and 558 were nonhigh‐risk participants. During this period, 40 lung cancer cases were confirmed, of these, 35 were screen‐detected, four were post‐screening and one was an interval case. The positive rate of baseline and annual screening was 9.7% and 9.0%, while the lung cancer detection rate was 0.4% and 0.6%, respectively. The proportion of early lung cancer increased from 37.5% in T0 to 75.0% in T4. Adenocarcinoma was the most common histological subtype. Lung cancer incidence according to the criteria of LungSPRC and National Lung Cancer Screening Trial (NLST) was 513.31 and 877.41 per 100 000 person‐years, respectively.

Conclusions

The program of lung cancer screening with LDCT showed a successful performance in Gejiu, Yunnan. However, further studies are warranted to refine a high‐risk population who will benefit most from LDCT screening and reduce the high false positive results.

Key points

This study reports the results of lung cancer screening with LDCT in Gejiu, Yunnan, a high‐risk area of lung cancer, and it demonstrates that lung cancer screening with LDCT is effective in detecting early‐stage lung cancer. Our program provides an opportunity to explore the performance of LDCT lung cancer screening in the Chinese context.

Adenocarcinoma of the Lung Mimicking Miliary Tuberculosis

Miliary shadows on chest imaging have wide differential diagnoses. The most common etiology is infectious, such as miliary tuberculosis (TB) and histoplasmosis, but miliary shadows can be the presentation of sarcoidosis, pneumoconiosis, and secondary metastasis to the lungs from primary cancers of the thyroid, kidney, and trophoblasts as well as sarcomas. Here we present the case of a 35-year-old Indian male who presented with a 2-month history of dry cough and shortness of breath. Chest imaging showed diffuse bilateral miliary nodules. The initial impression was that of miliary pulmonary TB. Subsequent bronchoscopy with a transbronchial biopsy confirmed the diagnosis of pulmonary mucinous adenocarcinoma with brain metastasis, which is a rare and unusual presentation of primary lung cancer. The tumor was positive for ALK5A4 and PD-L1, and the patient was started on tyrosine kinase inhibitor immunotherapy, with a favorable response.

A Task-dependent Investigation on Dose and Texture in CT Image Reconstruction

Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.

Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology

Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.

Surgical resection is sufficient for incidentally discovered solitary pulmonary nodule caused by nontuberculous mycobacteria in asymptomatic patients

Incidentally discovered solitary pulmonary nodules (SPN) caused by nontuberculous mycobacteria (NTM) is uncommon, and its optimal treatment strategy remains uncertain. This cohort study determined the clinical characteristics and outcome of asymptomatic patients with NTM-SPN after surgical resection. Resected SPNs with culture-positive for NTM in six hospitals in Taiwan during January, 2010 to January, 2017 were identified. Asymptomatic patients without a history of NTM-pulmonary disease (PD) or same NTM species isolated from the respiratory samples were selected. All were followed until May 1, 2019. A total of 43 patients with NTM-SPN were enrolled. Mycobacterium avium complex (60%) and M. kansasii (19%) were the most common species. The mean age was 61.7 ± 13.4. Of them, 60% were female and 4% had history of pulmonary tuberculosis. The NTM-SPN was removed by wedge resection in 38 (88%), lobectomy in 3 (7%) and segmentectomy in 2 (5%). Caseating granuloma was the most common histologic feature (58%), while chronic inflammation accounts for 23%. Mean duration of the follow-up was 5.2 ± 2.8 years (median: 4.2 years [2.5–7.0]), there were no mycobacteriology recurrence or NTM-PD development. In conclusion, surgical resection is likely to curative for incidentally discovered NTM-SPN in asymptomatic patients without culture evidence of the same NTM species from respiratory specimens, and routine mycobacterium culture for resected SPN might be necessary for differentiating pulmonary tuberculosis and NTM because further treatment differs.

Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance

Purpose

To compare sensitivity in the detection of lung nodules between the deep learning (DL) model and radiologists using various patient population and scanning parameters and to assess whether the radiologists' detection performance could be enhanced when using the DL model for assistance.

Materials and Methods

A total of 12 754 thin-section chest CT scans from January 2012 to June 2017 were retrospectively collected for DL model training, validation, and testing. Pulmonary nodules from these scans were categorized into four types: solid, subsolid, calcified, and pleural. The testing dataset was divided into three cohorts based on radiation dose, patient age, and CT manufacturer. Detection performance of the DL model was analyzed by using a free-response receiver operating characteristic curve. Sensitivities of the DL model and radiologists were compared by using exploratory data analysis. False-positive detection rates of the DL model were compared within each cohort. Detection performance of the same radiologist with and without the DL model were compared by using nodule-level sensitivity and patient-level localization receiver operating characteristic curves.

Results

The DL model showed elevated overall sensitivity compared with manual review of pulmonary nodules. No significant dependence regarding radiation dose, patient age range, or CT manufacturer was observed. The sensitivity of the junior radiologist was significantly dependent on patient age. When radiologists used the DL model for assistance, their performance improved and reading time was reduced.

Conclusion

DL shows promise to enhance the identification of pulmonary nodules and benefit nodule management.© RSNA, 2019Supplemental material is available for this article.

A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

Purpose

Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD.

Methods

From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation.

Results

Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively.

Conclusion

The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.

CT-guided percutaneous core needle biopsy of pulmonary nodules smaller than 2 cm: technical aspects and factors influencing accuracy

OBJECTIVE

To evaluate the diagnostic accuracy of CT-guided percutaneous core needle biopsy (CT-CNB) of pulmonary nodules ≤ 2 cm, as well as to identify factors influencing the accuracy of the procedure and its morbidity.

METHODS

This was a retrospective, single-center study of 170 consecutive patients undergoing CT-CNB of small pulmonary nodules (of ≤ 2 cm) between January of 2010 and August of 2015.

RESULTS

A total of 156 CT-CNBs yielded a definitive diagnosis, the overall diagnostic accuracy being 92.3%. Larger lesions were associated with a higher overall accuracy (OR = 1.30; p = 0.007). Parenchymal hemorrhage occurring during the procedure led to lower accuracy rates (OR = 0.13; p = 0.022). Pneumothorax was the most common complication. A pleura-to-lesion distance &gt; 3 cm was identified as a risk factor for pneumothorax (OR = 16.94), whereas performing a blood patch after biopsy was a protective factor for pneumothorax (OR = 0.18).

CONCLUSIONS

Small nodules (of < 2 cm) represent a technical challenge for diagnosis. CT-CNB is an excellent diagnostic tool, its accuracy being high.

Variable radiological lung nodule evaluation leads to divergent management recommendations

Radiological evaluation of incidentally detected lung nodules on computed tomography (CT) influences management. We assessed international radiological variation in 1) pulmonary nodule characterisation; 2) hypothetical guideline-derived management; and 3) radiologists' management recommendations.107 radiologists from 25 countries evaluated 69 CT-detected nodules, recording: 1) first-choice composition (solid, part-solid or ground-glass, with percentage confidence); 2) morphological features; 3) dimensions; 4) recommended management; and 5) decision-influencing factors. We modelled hypothetical management decisions on the 2005 and updated 2017 Fleischner Society, and both liberal and parsimonious interpretations of the British Thoracic Society 2015 guidelines.Overall agreement for first-choice nodule composition was good (Fleiss' κ=0.65), but poorest for part-solid nodules (weighted κ 0.62, interquartile range 0.50-0.71). Morphological variables, including spiculation (κ=0.35), showed poor-to-moderate agreement (κ=0.23-0.53). Variation in diameter was greatest at key thresholds (5 mm and 6 mm). Agreement for radiologists' recommendations was poor (κ=0.30); 21% disagreed with the majority. Although agreement within the four guideline-modelled management strategies was good (κ=0.63-0.73), 5-10% of radiologists would disagree with majority decisions if they applied guidelines strictly.Agreement was lowest for part-solid nodules, while significant measurement variation exists at important size thresholds. These variations resulted in generally good agreement for guideline-modelled management, but poor agreement for radiologists' actual recommendations.

Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists

Background

The study was conducted to evaluate the performance of a state‐of‐the‐art commercial deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing pulmonary nodules.

Methods

Pulmonary nodules in 346 healthy subjects (male: female = 221:125, mean age 51 years) from a lung cancer screening program conducted from March to November 2017 were screened using a DL‐CAD system and double reading independently, and their performance in nodule detection and characterization were evaluated. An expert panel combined the results of the DL‐CAD system and double reading as the reference standard.

Results

The DL‐CAD system showed a higher detection rate than double reading, regardless of nodule size (86.2% vs. 79.2%; P < 0.001): nodules ≥ 5 mm (96.5% vs. 88.0%; P = 0.008); nodules < 5 mm (84.3% vs. 77.5%; P < 0.001). However, the false positive rate (per computed tomography scan) of the DL‐CAD system (1.53, 529/346) was considerably higher than that of double reading (0.13, 44/346; P < 0.001). Regarding nodule characterization, the sensitivity and specificity of the DL‐CAD system for distinguishing solid nodules > 5 mm (90.3% and 100.0%, respectively) and ground‐glass nodules (100.0% and 96.1%, respectively) were close to that of double reading, but dropped to 55.5% and 93%, respectively, when discriminating part solid nodules.

Conclusion

Our DL‐CAD system detected significantly more nodules than double reading. In the future, false positive findings should be further reduced and characterization accuracy improved.

Low-dose computed tomography (LDCT) versus other cancer screenings in early diagnosis of lung cancer: A meta-analysis

BACKGROUND

Lung cancer is the leading cause of cancer mortality worldwide. It is often diagnosed at an advanced stage when treatment is no longer possible. Early population-based screening may provide an opportunity for early diagnosis and reduce mortality rates.

METHODS

Study characteristics were collected and outcome data (lung cancer diagnosis and mortality) were extracted and used for meta-analysis. Statistical analyses were performed using OpenMetaAnalyst-0.1503 software. The odds ratio (OR) and 95% confidence interval (CI) were used to assess LDCT compared to other screening methods under the random-effects model. The I2 statistic was used to assess heterogeneity.

RESULTS

Pooling data from 4 studies (64,129 patients) showed a higher incidence of diagnosed lung cancer with LDCT screening (OR = 1.86, 95% CI: 1.02-3.37), compared to other screening tools. However, no significant difference (OR = 1.13, 95% CI: 0.78-1.64) was found in lung cancer mortality between both groups.

CONCLUSIONS

Although no significant difference was found between LDCT and other control groups in terms of lung cancer mortality, this meta-analysis suggests an increased diagnosis of lung cancer with LDCT as compared with other screening modalities. This meta-analysis displays the potential but also the limitations of LDCT for early lung cancer detection.

Radiological Report of Pilot Study for the Korean Lung Cancer Screening (K-LUCAS) Project: Feasibility of Implementing Lung Imaging Reporting and Data System

Objective

To report the radiological results of a pilot study for the Korean Lung Cancer Screening project conducted to evaluate the feasibility of lung cancer screening using low-dose chest computed tomography (LDCT) in Korea.

Materials and Methods

The National Cancer Center and three regional cancer centers participated in this study. Asymptomatic current or ex-smokers aged 55–74 years with a smoking history of at least 30 pack-years who had used tobacco within the last 15 years were considered eligible. In total, 256 participants underwent LDCT November 2016 through March 2017. The American College of Radiology Lung Imaging Reporting and Data System (Lung-RADS) was used to categorize the LDCT findings.

Results

In total, 57%, 35.5%, 3.9%, and 3.5% participants belonged to Lung-RADS categories 1, 2, 3, and 4, respectively. Accordingly, 7.4% participants exhibited positive findings (category 3 or 4). Lung cancer was diagnosed in one participant (stage IA, small cell lung cancer). Other LDCT findings included pulmonary emphysema (32.8%), coronary artery calcification (30.9%), old pulmonary tuberculosis (11.7%), bronchiectasis (12.9%), interstitial lung disease with a usual interstitial pneumonia pattern (1.2%), and pleural effusion (0.8%).

Conclusion

Even though the size of our study population was small, the positive rate of 7.4% was like or lower than those in other lung cancer screening studies. Early lung cancer was detected using LDCT screening in one participant. Lung-RADS may be applicable to participants in Korea, where pulmonary tuberculosis is endemic.

Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer

Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy "1","2" as benign and "4","5" as malignant), configuration-2 (composite rank of malignancy "1","2", "3" as benign and "4","5" as malignant), and configuration-3 (composite rank of malignancy "1","2" as benign and "3","4","5" as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy "1" and "2" as benign and "4" and "5" as malignant), configuration 2 (composite rank of malignancy "1","2", and "3" as benign and "4" and "5" as malignant), and configuration 3 (composite rank of malignancy "1" and "2" as benign and "3","4" and "5" as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.