Management strategy of pulmonary nodule in 2013

Advances in the Treatment of Pulmonary Nodules

BACKGROUND

Early detection and accurate diagnosis of pulmonary nodules are crucial for improving patient outcomes. While surgical resection of malignant nodules is still the preferred treatment option, it may not be feasible for all patients. We aimed to discuss the advances in the treatment of pulmonary nodules, especially stereotactic body radiotherapy (SBRT) and interventional pulmonology technologies, and provide a range of recommendations based on our expertise and experience.

SUMMARY

Interventional pulmonology is an increasingly important approach for the management of pulmonary nodules. While more studies are needed to fully evaluate its long-term outcomes and benefits, the available evidence suggests that this technique can provide a minimally invasive and effective alternative for treating small malignancies in selected patients. We conducted a systematic literature review in PubMed, designed a framework to include the advances in surgery, SBRT, and interventional pulmonology for the treatment of pulmonary nodules, and provided a range of recommendations based on our expertise and experience.

KEY MESSAGES

As such, alternative therapeutic options such as SBRT and ablation are becoming increasingly important and viable. With recent advancements in bronchoscopy techniques, ablation via bronchoscopy has emerged as a promising option for treating pulmonary nodules. This study reviewed the advances of interventional pulmonology in the treatment of peripheral lung cancer patients that are not surgical candidates. We also discussed the challenges and limitations associated with ablation, such as the risk of complications and the potential for incomplete nodule eradication. These advancements hold great promise for improving the efficacy and safety of interventional pulmonology in treating pulmonary nodules.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

PURPOSE

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance.

METHODS

187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve.

RESULTS

The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05).

CONCLUSION

For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

Comparability of Pulmonary Nodule Size Measurements among Different Scanners and Protocols: Should Diameter Be Favorized over Volume?

BACKGROUND

To assess the impact of the lung cancer screening protocol recommended by the European Society of Thoracic Imaging (ESTI) on nodule diameter, volume, and density throughout different computed tomography (CT) scanners.

METHODS

An anthropomorphic chest phantom containing fourteen different-sized (range 3-12 mm) and CT-attenuated (100 HU, -630 HU and -800 HU, termed as solid, GG1 and GG2) pulmonary nodules was imaged on five CT scanners with institute-specific standard protocols (P_S) and the lung cancer screening protocol recommended by ESTI (ESTI protocol, P_E). Images were reconstructed with filtered back projection (FBP) and iterative reconstruction (REC). Image noise, nodule density and size (diameter/volume) were measured. Absolute percentage errors (APEs) of measurements were calculated.

RESULTS

Using P_E, dosage variance between different scanners tended to decrease compared to P_S, and the mean differences were statistically insignificant (p = 0.48). P_S and P_E(REC) showed significantly less image noise than P_E(FBP) (p < 0.001). The smallest size measurement errors were noted with volumetric measurements in P_E(REC) and highest with diametric measurements in P_E(FBP). Volume performed better than diameter measurements in solid and GG1 nodules (p < 0.001). However, in GG2 nodules, this could not be observed (p = 0.20). Regarding nodule density, REC values were more consistent throughout different scanners and protocols.

CONCLUSION

Considering radiation dose, image noise, nodule size, and density measurements, we fully endorse the ESTI screening protocol including the use of REC. For size measurements, volume should be preferred over diameter.

CT-Assisted Improvements in the Accuracy of the Intraoperative Frozen Section Examination of Ground-Glass Density Nodules

Objectives. The intraoperative frozen section examination (IFSE) of pulmonary ground-glass density nodules (GGNs) is a great challenge. In the present study, through comparing the correlation between the computed tomography (CT) findings and pathological diagnosis of GGNs, the CT features as independent risk factors affecting the examination were defined, and their value in the rapid intraoperative examination of GGNs was explored. Methods. The relevant clinical data of 90 patients with GGNs on CT were collected, and all CT findings of GGNs, including the maximum transverse diameter, average CT value, spiculation, solid component, vascular sign, air sign, bronchus sign, lobulation, and pleural indentation, were recorded. All the cases received thoracoscopic surgery, and final pathological results were obtained. The cases were divided into three groups on the basis of pathological diagnosis: benign/atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). The CT findings were analyzed statistically, the independent risk factors were identified through the intergroup bivariate logistic regression analysis on variables with statistically significant differences, and a receiver operating curve (ROC) was plotted to establish a logistic regression model for diagnosing GGNs. A retrospective analysis was conducted on the coincidence rate of the rapid intraoperative and routine postoperative pathological examinations of the 90 cases with GGNs. The relevant clinical data of 49 cases with GGNs were collected. Conventional rapid intraoperative examination and CT-assisted rapid intraoperative examination were performed, and their coincidence rates with routine postoperative pathological examinations were compared. Results. No statistical differences in the onset age, gender, smoking history, and family history of malignant tumors were found among cases with GGNs in the identification of benign/AAH, AIS/MIA, and IAC ( $P=0.158$ , $P=0.947$ , $P=0.746$ , $P=0.566$ ). No statistically significant difference was found among the three groups in terms of CT findings, such as lobulation, bronchus sign, pleural indentation, spiculation, vascular sign, and solid component ( $P>0.05$ ). The air sign, the maximum transverse diameter of GGNs, and average CT value showed statistically significant differences among the groups ( $P<0.001$ , $P<0.05$ , $P<0.001$ ). Bivariate logistic regression analysis was performed on three risk factors, and the predicted probability value was obtained. A ROC curve was plotted by using the maximum transverse diameter as a predictor for analysis between the groups with benign/AAH and AIS/MIA, and the results demonstrated that the area under the curve (AUC) was 0.692. A ROC curve was plotted by using the predicted probability value, maximum transverse diameter, and average CT value as predictors for distinguishing between the groups with AIS/MIA and IAC, and the results showed that the AUC values of the predicted probability value, maximum transverse diameter, and CT value were 0.920, 0.816, and 0.772, respectively. A regression model $\left[\text{Logit}\left(P\right)=2.304-2.689\text{X}1+0.302\text{X}2+0.011\text{X}3\right]$ was established to identify GGNs as IAC, obtaining AUC values of up to 0.920 for the groups with AIS/MIA and IAC, the sensitivity of 0.821, and the specificity of 0.894. The coincidence rate of rapid intraoperative and routine postoperative pathological examinations taken for modeling was 79.3%, that of conventional IFSE and postoperative pathological examination in prospective studies was 83.7%, and that of CT-assisted rapid intraoperative and postoperative pathological examinations was 98.0%. The former two were statistically different from the last one ( $P=0.003$ and $P=0.031$ , respectively). Conclusion. The air sign, maximum transverse diameter, and average CT value of the CT findings of GGNs had superior capabilities to enhance the pathologic classification of GGNs. The auxiliary function of the comprehensive multifactor analysis of GGNs was better than that of single-factor analysis. CT-assisted diagnosis can improve the accuracy of rapid intraoperative examination, thereby increasing the accuracy of the selection of operative approaches in clinical practice.

Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review

This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.

Predicting lung nodules malignancy

BACKGROUND

It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting.

METHODS

The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy.

RESULTS

From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573.

CONCLUSIONS

The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.

Survey on deep learning for pulmonary medical imaging

As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

Automatic detection of pulmonary nodules in CT images by incorporating 3D tensor filtering with local image feature analysis

Computer-aided detection (CAD) technology has been developed and demonstrated its potential to assist radiologists in detecting pulmonary nodules especially at an early stage. In this paper, we present a novel scheme for automatic detection of pulmonary nodules in CT images based on a 3D tensor filtering algorithm and local image feature analysis. We first apply a series of preprocessing steps to segment the lung volume and generate the isotropic volumetric CT data. Next, a unique 3D tensor filtering approach and local image feature analysis are used to detect nodule candidates. A 3D level set segmentation method is used to correct and refine the boundaries of nodule candidates subsequently. Then, we extract the features of the detected candidates and select the optimal features by using a CFS (Correlation Feature Selection) subset evaluator attribute selection method. Finally, a random forest classifier is trained to classify the detected candidates. The performance of this CAD scheme is validated using two datasets namely, the LUNA16 (Lung Nodule Analysis 2016) database and the ANODE09 (Automatic Nodule Detection 2009) database. By applying a 10-fold cross-validation method, the CAD scheme yielded a sensitivity of 79.3% at an average of 4 false positive detections per scan (FP/Scan) for the former dataset, and a sensitivity of 84.62% and 2.8 FP/Scan for the latter dataset, respectively. Our detection results show that the use of 3D tensor filtering algorithm combined with local image feature analysis constitutes an effective approach to detect pulmonary nodules.

Circulating tumour cells as a potential screening tool for lung cancer (the AIR study): protocol of a prospective multicentre cohort study in France

INTRODUCTION

Lung cancer (LC) is the leading cause of death from cancer. Early diagnosis of LC is of paramount importance in terms of prognosis. The health authorities of most countries do not accept screening programmes based on low-dose chest CT (LDCT), especially in Europe, because they are flawed by a high rate of false-positive results, leading to a large number of invasive diagnostic procedures. These authorities advocated further research, including companion biological tests that could enhance the effectiveness of LC screening. The present project aims to validate early diagnosis of LC by detection and characterisation of circulating tumour cells (CTCs) in a peripheral blood sample taken from a prospective cohort of persons at high-risk of LC.

METHODS AND ANALYSIS

The AIR Project is a prospective, multicentre, double-blinded, cohort study conducted by a consortium of 21 French university centres. The primary objective is to determine the operational values of CTCs for the early detection of LC in a cohort of asymptomatic participants at high risk for LC, that is, smokers and ex-smokers (≥30 pack-years, quitted ≤15 years), aged ≥55 years, with chronic obstructive pulmonary disease (COPD). The study participants will undergo yearly screening rounds for 3 years plus a 1-year follow-up. Each round will include LDCT plus peripheral blood sampling for CTC detection. Assuming 5% prevalence of LC in the studied population and a 10% dropout rate, a total of at least 600 volunteers will be enrolled.

ETHICS AND DISSEMINATION

The study sponsor is the University Hospital of Nice. The study was approved for France by the ethical committee CPP Sud-Méditerranée V and the ANSM (Ministry of Health) in July 2015. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.

TRIAL REGISTRATION NUMBER

NCT02500693.

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. In order to differentiate between the patterns of malignant and benign nodules, we used phylogenetic diversity by means of particular indexes, that are: intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, total taxonomic distinctness, and pure diversity indexes. After that, we applied the genetic algorithm for selection of the best model. In the tests' stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules. The proposed work presents promising results at the classification into malignant and benign, achieving accuracy of 92.52%, sensitivity of 93.1% and specificity of 92.26%. The results demonstrated a good rate of correct detections using texture features. Since a precocious detection allows a faster therapeutic intervention, thus a more favorable prognostic to the patient, we propose herein a methodology that contributes to the area in this aspect.

Managing Incidental Lung Nodules in Patients With a History of Oncologic Disease: A Survey of Thoracic Radiologists

PURPOSE

The aim of this study was to analyze the impact that a clinical history of an oncologic disease may have on the management decisions for incidentally detected lung nodules on chest computed tomographic (CT) examinations.

MATERIALS AND METHODS

An electronic survey was sent to all 796 members of the Society of Thoracic Radiology regarding criteria for the management of incidentally detected lung nodules in oncologic patients, as well as recommendations for nodule follow-up. Nodule characteristics and clinical parameters used by respondents were analyzed. Differences between variables were examined using the χ test.

RESULTS

Of the 796 Society of Thoracic Radiology members, 178 (22.36%) replied. Most respondents were subspecialized in cardiothoracic imaging (92.70%) and practiced in an "academic or teaching hospital setting" (75.28%) with a "dedicated oncology center" (94.03%). "History of oncologic disease" was the most important factor (98.87%) for management decisions. In patients with such a history, respondents most commonly used "experience and common sense" (56.74%) and reported "all incidentally found lung nodules" (65.73%, P<0.0001). "Size" and "shape" were the 2 most important nodule characteristics (33.61% and 27.05%, respectively) used to consider a nodule "clinically relevant," and "size" (44.07%) was also the most important nodule characteristic prompting recommendation for short-term CT follow-up. Follow-up CT examinations in oncologic patients were recommended by 75.84% of respondents.

CONCLUSIONS

In patients with a history of oncologic disease, radiologists tend to report every detected nodule and to routinely recommend follow-up CT examinations. Although most radiologists rely on "experience and common sense" in managing these nodules, greater standardization of lung nodule management in oncologic patients is needed, ideally through guidelines tailored to this patient population.

A Systematic Review and Meta-Analysis of C-Arm Cone-Beam CT-Guided Percutaneous Transthoracic Needle Biopsy of Lung Nodules

Background

A systematic review and meta-analysis of all available publications was performed to evaluate the diagnostic accuracy of percutaneous transthoracic needle biopsy (PTNB) using a C-Arm Cone-Beam CT (CBCT) system in patients with lung nodules.

Material/Methods

Thedatabases of PUBMED, OVID, EBSCO, EMBASE, and China National Knowledge Infrastructure (CNKI) were systematically searched for relevant original articles on the diagnostic accuracy of CBCT-guided PTNB for the diagnosis of nodules in the lungs. Diagnostic indices including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and diagnostic score (DS) were calculated. Moreover,summary receiver operating characteristic curves (SROC) were constructed with Stata (version 13.0), Rev Man (version 5.3), and Meta-disc (version 1.4) software. Other clinical indices such as incidence of complications were also recorded.

Results

Eight studies met the inclusion and exclusion criteria for the meta-analysis. The pooled sensitivity, specificity, PLR, NLR, DOR, DS, and SROC with 95% confidence intervals were 0.96 (0.93–0.98), 1.00 (0.91–1.00), 711.15 (9.48–53325.89), 0.04 (0.02–0.07), 16585.29 (284.88–9.7e+05), 9.72 (5.65–13.78), and 0.99 (0.97–0.99), respectively. The incidence of pneumothorax and hemorrhage was 10–29.27% and 1.22–47.25%, respectively.

Conclusions

CBCT-guided PTNB has an acceptable rate of complications and is associated with a reasonable radiation exposure. Moreover, it is a highly accurate and safe technique for the diagnosis of lung nodules and can be recommended to be used in routine clinical practice.

Influence of PET reconstruction technique and matrix size on qualitative and quantitative assessment of lung lesions on [18F]-FDG-PET: A prospective study in 37 cancer patients

PURPOSE

To evaluate the influence of point spread function (PSF)-based reconstruction and matrix size for PET on (1) lung lesion detection and (2) standardized uptake values (SUV).

METHODS

This prospective study included oncological patients who underwent [18F]-FDG-PET/CT for staging. PET data were reconstructed with a 2D ordered subset expectation maximization (OSEM) algorithm, and a 2D PSF-based algorithm (TrueX), separately with two matrix sizes (168×168 and 336×336). The four PET reconstructions (TrueX-168; OSEM-168; TrueX-336; and OSEM-336) were read independently by two raters, and PET-positive lung lesions were recorded. Blinded to the PET findings, a third independent rater assessed lung lesions with diameters of >4mm on CT. Subsequently, PET and CT were reviewed side-by side in consensus. Multi-factorial logistic regression analyses and two-way repeated measures analyses of variance (ANOVA) were performed.

RESULTS

Thirty-seven patients with 206 lung lesions were included. Lesion-based PET sensitivities differed significantly between reconstruction algorithms (P<0.001) and between reconstruction matrices (P=0.022). Sensitivities were 94.2% and 88.3% for TrueX-336; 88.3% and 85.9% for TrueX-168; 67.8% and 66.3% for OSEM-336; and 67.0% and 67.9% for OSEM-168; for rater 1 and rater 2, respectively. SUV_max and SUV_mean were significantly higher for images reconstructed with 336×336 matrices than for those reconstructed with 168×168 matrices (P<0.001).

CONCLUSION

Our results demonstrate that PSF-based PET reconstruction, and, to a lesser degree, higher matrix size, improve detection of metabolically active lung lesions. However, PSF-based PET reconstructions and larger matrix sizes lead to higher SUVs, which may be a concern when PET data from different institutions are compared.

Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier

Improving the performance of computer-aided detection (CAD) system for pulmonary nodules is still an important issue for its future clinical applications. This study aims to develop a new CAD scheme for pulmonary nodule detection based on dynamic self-adaptive template matching and Fisher linear discriminant analysis (FLDA) classifier. We first segment and repair lung volume by using OTSU algorithm and three-dimensional (3D) region growing. Next, the suspicious regions of interest (ROIs) are extracted and filtered by applying 3D dot filtering and thresholding method. Then, pulmonary nodule candidates are roughly detected with 3D dynamic self-adaptive template matching. Finally, we optimally select 11 image features and apply FLDA classifier to reduce false positive detections. The performance of the new method is validated by comparing with other methods through experiments using two groups of public datasets from Lung Image Database Consortium (LIDC) and ANODE09. By a 10-fold cross-validation experiment, the new CAD scheme finally has achieved a sensitivity of 90.24% and a false-positive (FP) of 4.54 FP/scan on average for the former dataset, and a sensitivity of 84.1% with 5.59 FP/scan for the latter. By comparing with other previously reported CAD schemes tested on the same datasets, the study proves that this new scheme can yield higher and more robust results in detecting pulmonary nodules.

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.

Normal postoperative appearances of lung cancer

The major lung resections are the pneumonectomies and lobectomies. The sublobar resections are segmentectomies and wedge resections. These are performed either through open surgery through a thoracotomy or by video-assisted mini-invasive surgery for lobectomies and sublobar resections. Understanding the procedures involved allows the normal postoperative appearances to be interpreted and these normal anatomical changes to be distinguished from potential postoperative complications. Surgery results in a more or less extensive physiological adaptation of the chest cavity depending on the lung volume, which has been resected. This adaptation evolves during the initial months postoperatively. Chest radiography and computed tomography can show narrowing of the intercostal spaces, a rise of the diaphragm and shift of the mediastinum on the side concerned following major resections.

Automatic 3D pulmonary nodule detection in CT images: A survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks.

Prognosis and status of lymph node involvement in patients with adenocarcinoma in situ and minimally invasive adenocarcinoma-a systematic literature review and pooled-data analysis

BACKGROUND

Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have been brought up that substitute for bronchioloalveolar carcinoma (BAC), according to the new classification of lung adenocarcinoma. There has been increasing opinions that argues for the adjustment of lymph node disposition in patients with such early stage tumors. Therefore, we sought to overview the prognosis and status of lymph node involvement in AIS/MIA patients.

METHODS

PubMed, Springer and Ovid databases were searched for relevant studies. Data was extracted and results summarized to demonstrate the disposition of lymph nodes in AIS/MIA.

RESULTS

Twenty-three studies consisting of 6,137 lung adenocarcinoma were included. AIS/MIA accounted for 821 of the total 6,137. All included patients received curative surgery. After a review of the summarized data we found that only one patient (with MIA) had N1 node metastasis, N2 disease was not found in any of the included patients. In concordance with this, studies that reported 5-year disease free survival (5-year DFS) have almost 100% rate.

CONCLUSIONS

Our findings indicated that patients with AIS/MIA have good survival prognosis after surgical resection, and that recurrence and lymph node metastasis in these patients is rare. Therefore, we strongly encouraged further studies to determine the role of different lymph node disposition strategies.

Diagnostic Performance of Plasma DNA Methylation Profiles in Lung Cancer, Pulmonary Fibrosis and COPD

Disease-specific alterations of the cell-free DNA methylation status are frequently found in serum samples and are currently considered to be suitable biomarkers.

Candidate markers were identified by bisulfite conversion-based genome-wide methylation screening of lung tissue from lung cancer, fibrotic ILD, and COPD. cfDNA from 400 μl serum (n = 204) served to test the diagnostic performance of these markers. Following methylation-sensitive restriction enzyme digestion and enrichment of methylated DNA via targeted amplification (multiplexed MSRE enrichment), a total of 96 markers were addressed by highly parallel qPCR.

Lung cancer was efficiently separated from non-cancer and controls with a sensitivity of 87.8%, (95%CI: 0.67–0.97) and specificity 90.2%, (95%CI: 0.65–0.98). Cancer was distinguished from ILD with a specificity of 88%, (95%CI: 0.57–1), and COPD from cancer with a specificity of 88% (95%CI: 0.64–0.97). Separation of ILD from COPD and controls was possible with a sensitivity of 63.1% (95%CI: 0.4–0.78) and a specificity of 70% (95%CI: 0.54–0.81). The results were confirmed using an independent sample set (n = 46) by use of the four top markers discovered in the study (HOXD10, PAX9, PTPRN2, and STAG3) yielding an AUC of 0.85 (95%CI: 0.72–0.95).

This technique was capable of distinguishing interrelated complex pulmonary diseases suggesting that multiplexed MSRE enrichment might be useful for simple and reliable diagnosis of diverse multifactorial disease states.

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The multiplexed MSRE enrichment strategy allowed a highly parallel assessment the cfDNA methylation status based on 400 μl of patient serum.

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The multivariate classification of the discovered biomarkers, thus, stabilized the prediction and allowed for differential diagnosis.

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This method including the biomarker set may be used to monitor the lung cancer risk in COPD and fibrotic ILD patients.

Research in Context

We developed a multiplexed DNA methylation profiling strategy to track disease specific DNA methylation changes prevailing in 400 μl serum of lung cancer, fibrotic ILD and COPD patients. Plasma or serum samples, often referred to as ‘liquid biopsies’ have several advantages over tissue sampling: (a) they are easily accessible, (b) are not subject to biopsy bias and (c) can be repeatedly drawn from the same patient. The differential diagnosis attempt performed in the study on patients suffering from different lung diseases suggests that multiplexed cfDNA methylation profiling allows for the capture of these interconnected disease phenotypes, stabilizes the prediction and enhances diagnostic accuracy.

Role of imaging in diagnosis, staging and follow-up of lung cancer

PURPOSE OF REVIEW

Primary lung cancer is still the number one cause of cancer death worldwide. Screening, detection and staging of lung cancer are important because the only potentially curative therapy today is surgical resection of early-stage lung cancer.

RECENT FINDINGS

Different imaging techniques can be used in these different processes. Recent advances in computed tomography (CT) technology have allowed investigation of novel methods for the evaluation of lung cancer. Recent advances in magnetic resonance technology and administration of contrast media have further improved the image quality and diagnostic capability of magnetic resonance. Positron emission tomography (PET)/CT has been shown to be superior to stand-alone PET or CT in the evaluation of lymph nodes and in the detection of distant metastases.

SUMMARY

The current recommended imaging required for lung cancer staging is CT of the thorax and PET/CT from skull base to mid-thigh. However, with the recent developments in the armamentarium of imaging techniques, the choice of one of these techniques can be directed by the presence of a technique in a local hospital and/or by the presence of an experienced person at that time.

The IASLC/ATS/ERS classification of lung adenocarcinoma-a surgical point of view

Adenocarcinoma has become the most common histologic type of lung cancers. Ground glass nodules (GGN), most of them early stage noninvasive or minimally invasive adenocarcinomas (MIA), have been encountered more frequently with the application of computed tomography (CT) screening. The International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic lung adenocarcinoma classification combines radiologic, histologic, clinic, and molecular features to form a diagnostic approach for different subgroups of diseases. One of the major focuses of this new classification is the introduction of adenocarcinoma in situ (AIS) and MIA, to replace the old term of bronchioloalveolar carcinoma (BAC). Not all GGNs are malignant lesions that should be surgically resected upon first presentation. A management approach different to solid nodules has been suggested based on the understanding that these lesions tend to have a more indolent nature. Hasty intervention should be avoided and potential surgical risks, radiation exposure, patient psychology, and socio-economical burden must be balanced comprehensively before surgery is decided upon. In the mean time, surgical issues concerning extent of resection and lymphadenectomy should also be carefully contemplated once intervention is deemed necessary. Extremely good prognosis with a near 100% disease-free survival could be expected when a pure GGN is completely resected. This has led to re-evaluation of sublobar resections, including both segmentectomy and big wedge resection, for small (≤2 cm) less invasive histology (AIS or MIA) appearing as GGN on CT scan. Evidences are accumulating that these limited resections are oncologically equivalent to standard lobectomy. And extensive lymph node dissection may not have additional staging or prognostic benefit. These would add new meaning to the contemporary definition of minimally invasive surgery for lung cancers. Overall, joint effort from a multiple disciplinary team is imperative, and decision making should be based on both anatomical and biological nature of the disease.