scholarly journals Advanced Bronchoscopic Technologies for Biopsy of the Pulmonary Nodule: A 2021 Review

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2304
Author(s):  
Micah Z. Levine ◽  
Sam Goodman ◽  
Robert J. Lentz ◽  
Fabien Maldonado ◽  
Otis B. Rickman ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Cost Effective ◽  
Tissue Sampling ◽  
Interventional Pulmonology ◽  
Novel Technologies ◽  
Medical Centers ◽  
Standard Component

The field of interventional pulmonology (IP) has grown from a fringe subspecialty utilized in only a few centers worldwide to a standard component in advanced medical centers. IP is increasingly recognized for its value in patient care and its ability to deliver minimally invasive and cost-effective diagnostics and treatments. This article will provide an in-depth review of advanced bronchoscopic technologies used by IP physicians focusing on pulmonary nodules. While most pulmonary nodules are benign, malignant nodules represent the earliest detectable manifestation of lung cancer. Lung cancer is the second most common and the deadliest cancer worldwide. Differentiating benign from malignant nodules is clinically challenging as these entities are often indistinguishable radiographically. Tissue biopsy is often required to discriminate benign from malignant nodule etiologies. A safe and accurate means of definitively differentiating benign from malignant nodules would be highly valuable for patients, and the medical system at large. This would translate into a greater number of early-stage cancer detections while reducing the burden of surgical resections for benign disease. There is little high-grade evidence to guide clinicians on optimal lung nodule tissue sampling modalities. The number of novel technologies available for this purpose has rapidly expanded over the last decade, making it difficult for clinicians to assess their efficacy. Unfortunately, there is a wide variety of methods used to determine the accuracy of these technologies, making comparisons across studies impossible. This paper will provide an in-depth review of available data regarding advanced bronchoscopic technologies.

scholarly journals Effect of a pulmonary nodule fact sheet on patient anxiety and knowledge: a quality improvement initiative

2018 ◽  
Vol 7 (3) ◽  
pp. e000437 ◽  
Author(s):  
Matthew T Koroscil ◽  
Mitchell H Bowman ◽  
Michael J Morris ◽  
Andrew J Skabelund ◽  
Andrew M Hersh
Keyword(s):  
Lung Cancer ◽  
Quality Improvement ◽  
Pulmonary Nodule ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Low Risk ◽  
Quality Improvement Initiative ◽  
Patient Anxiety ◽  
Patient Understanding ◽  
Fact Sheet

IntroductionThe utilisation of chest CT for the evaluation of pulmonary disorders, including low-dose CT for lung cancer screening, is increasing in the USA. As a result, the discovery of both screening-detected and incidental pulmonary nodules has become more frequent. Despite an overall low risk of malignancy, pulmonary nodules are a common cause of emotional distress among adult patients.MethodsWe conducted a multi-institutional quality improvement (QI) initiative involving 101 participants to determine the effect of a pulmonary nodule fact sheet on patient knowledge and anxiety. Males and females aged 35 years or older, who had a history of either screening-detected or incidental solid pulmonary nodule(s) sized 3–8 mm, were included. Prior to an internal medicine or pulmonary medicine clinic visit, participants were given a packet containing a pre-fact sheet survey, a pulmonary nodule fact sheet and a post-fact sheet survey.ResultsOf 101 patients, 61 (60.4%) worried about their pulmonary nodule at least once per month with 18 (17.8%) worrying daily. The majority 67/101 (66.3%) selected chemotherapy, chemotherapy and radiation, or radiation as the best method to cure early-stage lung cancer. Despite ongoing radiographic surveillance, 16/101 (15.8%) stated they would not be interested in an intervention if lung cancer was diagnosed. Following review of the pulmonary nodule fact sheet, 84/101 (83.2%) reported improved anxiety and 96/101 (95.0%) reported an improved understanding of their health situation. Patient understanding significantly improved from 4.2/10.0 to 8.1/10.0 (p<0.01).ConclusionThe incorporation of a standardised fact sheet for subcentimeter solid pulmonary nodules improves patient understanding and alleviates anxiety. We plan to implement pulmonary nodule fact sheets into the care of our patients with low-risk subcentimeter pulmonary nodules.

scholarly journals Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinglun Liang ◽  
Guoliang Ye ◽  
Jianwen Guo ◽  
Qifan Huang ◽  
Shaohui Zhang
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
Ct Images ◽  
False Positives ◽  
Classification Systems ◽  
Cad System ◽  
Nodule Detection ◽  
Diagnosis Of Lung Cancer ◽  
Lung Ct

Malignant pulmonary nodules are one of the main manifestations of lung cancer in early CT image screening. Since lung cancer may have no early obvious symptoms, it is important to develop a computer-aided detection (CAD) system to assist doctors to detect the malignant pulmonary nodules in the early stage of lung cancer CT diagnosis. Due to the recent successful applications of deep learning in image processing, more and more researchers have been trying to apply it to the diagnosis of pulmonary nodules. However, due to the ratio of nodules and non-nodules samples used in the training and testing datasets usually being different from the practical ratio of lung cancer, the CAD classification systems may easily produce higher false-positives while using this imbalanced dataset. This work introduces a filtering step to remove the irrelevant images from the dataset, and the results show that the false-positives can be reduced and the accuracy can be above 98%. There are two steps in nodule detection. Firstly, the images with pulmonary nodules are screened from the whole lung CT images of the patients. Secondly, the exact locations of pulmonary nodules will be detected using Faster R-CNN. Final results show that this method can effectively detect the pulmonary nodules in the CT images and hence potentially assist doctors in the early diagnosis of lung cancer.

Non-surgical management of early-stage lung cancer

2017 ◽  
Author(s):  
Jim Brown ◽  
Neal Navani
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Pulmonary Nodules ◽  
The Elderly ◽  
Multidisciplinary Teams ◽  
Solitary Pulmonary Nodules ◽  
Early Stage Lung Cancer ◽  
Mortality And Morbidity ◽  
Inoperable Patient ◽  
Ablative Techniques

As low-dose computed tomography screening of ‘high-risk’ smokers is occurring with increasing frequency, the incidental discovery of solitary pulmonary nodules is becoming more frequent, and lung cancer multidisciplinary teams are now often faced with balancing risk and benefit when making decisions regarding the radical treatment of patients with a clinical diagnosis of early lung cancer but borderline fitness. Surgery offers the best prospect of cure but is associated with significant mortality and morbidity; the elderly and frail experience more toxicity and a greater impact on the quality of life. This chapter reviews the criteria for assessing surgical fitness and examines the evidence for minimally invasive and ablative techniques for the treatment of early peripheral lung cancer in the medically inoperable patient.

scholarly journals Diagnosis and outcome of resected solitary pulmonary nodules after liver transplantation

2019 ◽  
Author(s):  
Mari Tone ◽  
Nobuyasu Awano ◽  
Takehiro Izumo ◽  
Hanako Yoshimura ◽  
Tatsunori Jo ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Liver Transplantation ◽  
Early Stage ◽  
Lung Resection ◽  
Invasive Pulmonary Aspergillosis ◽  
Primary Lung Cancer ◽  
Pulmonary Nodules ◽  
Solitary Pulmonary Nodules ◽  
Computed Tomography Scans

Abstract Objective Solitary pulmonary nodules after liver transplantation are challenging clinical problems. Herein, we report the causes and clinical courses of resected solitary pulmonary nodules in patients who underwent liver transplantation. Methods We retrospectively obtained medical records of 68 patients who underwent liver transplantation between March 2009 and June 2016. This study mainly focused on patients with solitary pulmonary nodules observed on computed tomography scans during follow-ups that were conducted until their deaths or February 2019. Results Computed tomography scans revealed solitary pulmonary nodules in 7 of the 68 patients. Definitive diagnoses were obtained using video-assisted lung resection in all seven patients. None experienced major postoperative complications. The final pathologic diagnoses were primary lung cancer in three patients, pulmonary metastases from hepatocellular carcinoma in one patient, invasive pulmonary aspergillosis in one patient, post-transplant lymphoproliferative disorder in one patient, and hemorrhagic infarction in one patient. The three patients with lung cancer were subsequently treated with standard curative resection. Conclusions Solitary pulmonary nodules present in several serious but potentially curable diseases, such as early-stage lung cancer. Patients who present with solitary pulmonary nodules after liver transplantation should be evaluated by standard diagnostic procedures, including surgical biopsy if necessary.

scholarly journals Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fukui Liang ◽  
Caiqin Li ◽  
Xiaoqin Fu
Keyword(s):  
Artificial Intelligence ◽  
Lung Cancer ◽  
Deep Learning ◽  
Medical Imaging ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Chest Ct ◽  
Detection Sensitivity ◽  
Lung Nodule Detection ◽  
Nodule Detection

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient’s survival rate. Therefore, early diagnosis of lung cancer is very important. Early-stage lung cancer usually appears as a solitary lung nodule on medical imaging. It usually appears as a round or nearly round dense shadow in the chest radiograph. It is difficult to distinguish lung nodules and lung soft tissues with the naked eye. Therefore, this article proposes a deep learning-based artificial intelligence chest CT lung nodule detection performance evaluation study, aiming to evaluate the value of chest CT imaging technology in the detection of noncalcified nodules and provide help for the detection and treatment of lung cancer. In this article, the Lung Medical Imaging Database Consortium (LIDC) was selected to obtain 536 usable cases based on inclusion criteria; 80 cases were selected for examination, artificial intelligence software, radiologists, and thoracic imaging specialists. Using 80 pulmonary nodules detection in each case, the pathological type of pulmonary nodules, nonlime tuberculous test results, detection sensitivity, false negative rate, false positive rate, and CT findings were individually analyzed, and the detection efficiency software of artificial intelligence was evaluated. Experiments have proved that the sensitivity of artificial intelligence software to detect noncalcified nodules in the pleural, peripheral, central, and hilar areas is higher than that of radiologists, indicating that the method proposed in this article has achieved good detection results. It has a better nodule detection sensitivity than a radiologist, reducing the complexity of the detection process.

Segmentation of Lung Nodules in CT Scan Data

2017 ◽  
pp. 601-613 ◽  
Author(s):  
Shehzad Khalid ◽  
Anwar C. Shaukat ◽  
Amina Jameel ◽  
Imran Fareed
Keyword(s):  
Lung Cancer ◽  
Lung Nodule ◽  
Clinical Importance ◽  
Pulmonary Nodules ◽  
Cad System ◽  
Nodule Detection ◽  
Scan Data ◽  
Effective Domain ◽  
Aided Diagnosis

Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient's chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. Several studies have shown the feasibility and robustness of automated matching of corresponding nodule pairs between follow up examinations. Different image pre-processing and segmentation techniques are used in various research sides to segment different tumors or ulcers from different images. This paper aims to make a review on the existing segmentation algorithms used for CT images of pulmonary nodules and presents a study of the existing methods on automated lung nodule detection. It provides a comparison of the performance of the existing approaches in regards to effective domain results.

An Early Prediction and Classification of Lung Nodule Diagnosis on CT Images Based on Hybrid Deep Learning Techniques

2022 ◽  
Author(s):  
Vijay Kumar Gugulothu ◽  
Savadam Balaji
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Deep Learning ◽  
Early Stage ◽  
Selection Process ◽  
Lung Nodule ◽  
Lung Cancer Screening ◽  
Ct Images ◽  
Learning Techniques ◽  
Feature Extraction And Selection

Abstract Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. The use of hybrid deep learning techniques to detect nodules will improve the sensitivity of lung cancer screening and the interpretation speed of lung scans.Accurate detection of lung nodes is an important step in computed tomography (CT) imaging to detect lung cancer. However, it is very difficult to identify strong nodes due to the diversity of lung nodes and the complexity of the surrounding environment.Here, we proposed alung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce achaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate anImproved Fish Bee (IFB) algorithm for feature extraction and selection process. Third, we develop hybrid classifier i.e. hybrid differential evolution based neural network (HDE-NN) for tumor prediction and classification.Experimental results have shown that the use of computed tomography, which demonstrates the efficiency and importance of the HDE-NN specific structure for detecting lung nodes on CT scans, increases sensitivity and reduces the number of false positives. The proposed method shows that the benefits of HDE-NN node detection can be reaped by combining clinical practice.

Early detection of lung cancer with an incidental lung nodule program (ILNP).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8553-8553
Author(s):  
Matthew Smeltzer ◽  
Wei Liao ◽  
Meghan Meadows-Taylor ◽  
Nicholas Faris ◽  
Carrie Fehnel ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Detection ◽  
Cancer Patients ◽  
Task Force ◽  
Early Stage ◽  
Survival Rates ◽  
Lung Nodule ◽  
Tube Placement ◽  
Ct Guided ◽  
Group 2

8553 Background: Lung cancer early detection improves survival, but risk-based low-dose CT screening (LDCT) only identifies a minority of patients. We implemented an ILNP in a community healthcare system, and evaluated its risks and benefits. Methods: Patients with lung lesions on routinely-performed radiologic studies were flagged by radiologists and triaged using evidence-based guidelines. We tracked demographics, clinical characteristics, procedures, complications, and health outcomes. We analyzed ILNP subjects’ eligibility for LDCT by National Lung Screening Trial (NLST), Center for Medicaid Services (CMS), NEderlands Leuvens Screening ONderzoek (NELSON), National Comprehensive Cancer Network (NCCN) Risk Groups 1 and 2 (screening recommended), NCCN Risk Group 3 (screening not currently recommended), and US Preventive Services Task Force (USPSTF) criteria from 2013 and 2020. Statistical analysis used the chi-square test and Kaplan Meier method. Results: From 2015-2020, 13,710 patients were evaluated in the ILNP program: median age, 64 years; 42% male; 65% White, 29% Black; 667 (4.9%) were diagnosed with lung cancer. Lung cancers diagnosed from ILNP were 39% adenocarcinoma / 20% Squamous Cell with clinical stage distribution 49% I, 8% II, 17% III, and 16% IV. 832 (6.1%) had invasive diagnostic testing- CT-guided biopsy (50%), bronchoscopy (30%), and/or EBUS (26%); 11% of the 832 had >1 invasive diagnostic test. The most common complications from invasive testing were pneumothorax and chest tube placement. Only 11%-20% of all ILNP patients would have been eligible for LDCT. In ILNP patients diagnosed with lung cancer, only 33% were eligible for screening by NLST criteria; the proportion increased substantially when USPSTF 2020 or NCCN Group 2 criteria were applied (Table). Compared to NLST, NCCN Group 2 criteria increased screening eligibility among cancer patients by 22% (from 33% to 55%), while only increasing screening eligibility by 6% (from 8% to 14%) in non-cancer patients. Aggregate 1-year and 3-year survival rates for lung cancer patient diagnosed through ILNP were 76% (95% CI: 73, 80) and 64% (95% CI: 59, 69). Conclusions: The ILNP identified early-stage lung cancer more frequently than most LDCT programs, with promising survival rates. The majority of subjects with lung cancer were not eligible for LDCT, we still need to optimize risk-based screening criteria. Even with new, expanded criteria for LDCT, structured ILNP is necessary to expand early detection of lung cancer.[Table: see text]

Serum-based protein biomarkers for detection of lung cancer

2014 ◽  
Vol 9 (4) ◽  
pp. 341-358
Author(s):  
Shilpa Bhatnagar ◽  
Deepshikha Katare ◽  
Swatantra Jain
Keyword(s):  
Lung Cancer ◽  
Cancer Biology ◽  
Early Stage ◽  
Expression Patterns ◽  
Cost Effective ◽  
Diagnostic Tools ◽  
Protein Biomarkers ◽  
Functional Studies ◽  
Molecular Tests ◽  
Effective Manner

AbstractLung cancer is one of the most common cancers in terms of both incidence and mortality.The major reasons for the increasing number of deaths from lung cancer are late detection and lack of effective therapies. To improve our understanding of lung cancer biology, there is urgent need for blood-based, non-invasive molecular tests to assist in its detection in a cost-effective manner at an early stage when curative interventions are still possible. Recent advances in proteomic technology have provided extensive, high throughput analytical tools for identification, characterization and functional studies of proteomes. Changes in protein expression patterns in response to stimuli can serve as indicators or biomarkers of biological and pathological processes as well as physiological and pharmacological responses to drug treatment, thus aiding in early diagnosis and prognosis of disease. However, only a few biomarkers have been approved by the FDA to date for screening and diagnostic purposes. This review provides a brief overview of currently available proteomic techniques, their applications and limitations and the current state of knowledge about important serum biomarkers in lung cancer and their potential value as prognostic and diagnostic tools.

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