Cone beam CT augmented fluoroscopy allows safe and efficient diagnosis of a difficult lung nodule

Abstract Background Detection of small peripheral lung nodules is constantly increasing with the development of low dose computed tomography lung cancer screening programs. A tissue diagnosis is often required to confirm malignity, with endobronchial biopsies being associated with a lower pneumothorax rate than percutaneous approaches. Endoscopic diagnosis of peripheral small size lung nodules is however often challenging using traditional bronchoscopy and endobronchial ultrasound alone. New virtual bronchoscopic navigation techniques such as electromagnetic navigational bronchoscopy (ENB) have developed to improve peripheral navigation, with diagnostic yield however remaining in the 30–50% range for small lesions. Recent studies have shown the benefits of combining Cone beam computed tomography (CBCT) with ENB to improve diagnostic yield to up to 83%. The use of ENB however remains limited by disposable cost, bronchus sign dependency and inaccuracies due to CT to body divergence. Case presentation This case report highlights the feasibility and usefulness of CBCT-guided bronchoscopy for the sampling of lung nodules difficult to reach through traditional bronchoscopy because of nodule size and peripheral position. Procedure was scheduled in a mobile robotic hybrid operating room with patient under general anaesthesia. CBCT acquisition was performed to localize the target lesion and plan the best path to reach it into bronchial tree. A dedicated software was used to segment the lesion and the bronchial path which 3D outlines were automatically fused in real time on the fluoroscopic images to augment live guidance. Navigation to the lesion was guided with bronchoscopy and augmented fluoroscopy alone. Before the sampling, CBCT imaging was repeated to confirm the proper position of the instrument into the lesion. Four transbronchial needle aspirations (TBNA) were performed and the tissue analysis showed a primary lung adenocarcinoma. Conclusions CBCT and augmented fluoroscopy technique is a safe and effective and has potential to improve early stage peripheral lesions endobronchial diagnostic yield without ENB. Additional studies are warranted to confirm its safety, efficacy and technical benefits, both for diagnosis of oncological and non-oncological disease and for endobronchial treatment of inoperable patients.

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Cone-Beam Computed Tomography-Guided Electromagnetic Navigation for Peripheral Lung Nodules

Respiration ◽

10.1159/000510763 ◽

2021 ◽

pp. 1-8

Author(s):

Fayez Kheir ◽

Sanket R. Thakore ◽

Juan Pablo Uribe Becerra ◽

Mohammad Tahboub ◽

Rahul Kamat ◽

...

Keyword(s):

Computed Tomography ◽

Real Time ◽

Cone Beam Computed Tomography ◽

Diagnostic Yield ◽

Univariate Analysis ◽

Endobronchial Ultrasound ◽

Cone Beam ◽

Lung Nodules ◽

Electromagnetic Navigation ◽

Peripheral Lung

Background: Electromagnetic navigation bronchoscopy (ENB) is a minimally invasive technology for the diagnosis of peripheral pulmonary nodules. However, ENB is limited by the lack of real-time confirmation of various biopsy devices. Cone-beam computed tomography (CBCT) could increase diagnostic yield by allowing real-time confirmation to overcome the inherent divergence of nodule location. Objectives: The aim of this study was to assess the diagnostic yield of ENB plus CBCT as compared with ENB alone for biopsy of peripheral lung nodules. Method: We conducted a retrospective study of patients undergoing ENB before and after the implementation of CBCT. Data from 62 consecutive patients with lung nodules located in the outer two-thirds of the lung who underwent ENB and combined ENB-CBCT were collected. Radial endobronchial ultrasound was used during all procedures as well. Diagnostic yield was defined as the presence of malignancy or benign histological findings that lead to a specific diagnosis. Results: Thirty-one patients had ENB-CBCT, and 31 patients had only ENB for peripheral lung lesions. The median size of the lesion for the ENB-CBCT group was 16 (interquartile range (IQR) 12.6–25.5) mm as compared to 21.5 (IQR 16–27) mm in the ENB group (p = 0.2). In the univariate analysis, the diagnostic yield of ENB-CBCT was 74.2% and ENB 51.6% (p = 0.05). Following multivariate regression analysis adjusting for the size of the lesion, distance from the pleura, and presence of bronchus sign, the odds ratio for the diagnostic yield was 3.4 (95% CI 1.03–11.26, p = 0.04) in the ENB-CBCT group as compared with ENB alone. The median time for the procedure was shorter in patients in the ENB-CBCT group (74 min) than in those in the ENB group (90 min) (p = 0.02). The rate of adverse events was similar in both groups (6.5%, p = 0.7). Conclusions: The use of CBCT might increase the diagnostic yield in ENB-guided peripheral lung nodule biopsies. Future randomized clinical trials are needed to confirm such findings.

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Efficacy and Safety of Cone-Beam Computed Tomography-Derived Augmented Fluoroscopy Combined with Endobronchial Ultrasound in Peripheral Pulmonary Lesions

Respiration ◽

10.1159/000515181 ◽

2021 ◽

pp. 1-9

Author(s):

Kai-Lun Yu ◽

Shun-Mao Yang ◽

Huan-Jang Ko ◽

Hui-Yu Tsai ◽

Jen-Chung Ko ◽

...

Keyword(s):

Computed Tomography ◽

Propensity Score ◽

Cone Beam Computed Tomography ◽

Diagnostic Yield ◽

Endobronchial Ultrasound ◽

Cone Beam ◽

Complication Rates ◽

Efficacy And Safety ◽

Pulmonary Lesions ◽

Peripheral Pulmonary Lesions

Background: The diagnostic yield of peripheral pulmonary lesions (PPLs) using radial endobronchial ultrasound (EBUS) remains challenging without navigation systems. Cone-beam computed tomography-derived augmented fluoroscopy (CBCT-AF) represents a recently developed technique, and its clinical utility remains to be investigated. Objectives: The aim of this study was to investigate the diagnostic yield of transbronchial biopsy (TBB) using a combination of CBCT-AF and radial EBUS. Methods: We recruited consecutive patients with PPLs who underwent radial EBUS-guided TBB, with or without AF, between October 2018 and July 2019. Following propensity score 1:1 matching, we recorded the procedure-related data and measured their efficacy and safety. Results: While 72 patients received EBUS-plus-AF, 235 patients received EBUS only. We included 53 paired patients following propensity score matching. The median size of lesions was 2.8 and 2.9 cm in the EBUS-plus-AF group and EBUS-only group, respectively. Diagnostic yield was higher in the former group (75.5 vs. 52.8%; p = 0.015). The diagnostic yield for the EBUS-plus-AF group was significantly higher for lesions ≤30 mm (73.5 vs. 36.1%; p = 0.002). Moreover, there was no significant difference in the complication rates (3.8 vs. 5.7%; p = 1.000). Twenty-four nodules (45.3%) were invisible by fluoroscopy in the EBUS-plus-AF group. All of them were identifiable on CBCT images and successfully annotated for AF. The mean radiation dose of total procedure, CBCT, and fluoroscopy was 19.59, 16.4, and 3.17 Gy cm2, respectively. Conclusions: TBB using a combination of CBCT-AF and EBUS resulted in a satisfactory diagnostic yield and safety.

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Cone-Beam Computed Tomography-Derived Augmented Fluoroscopy Improves the Diagnostic Yield of Endobronchial Ultrasound-Guided Transbronchial Biopsy for Peripheral Pulmonary Lesions

Diagnostics ◽

10.3390/diagnostics12010041 ◽

2021 ◽

Vol 12 (1) ◽

pp. 41

Author(s):

Ching-Kai Lin ◽

Hung-Jen Fan ◽

Zong-Han Yao ◽

Yen-Ting Lin ◽

Yueh-Feng Wen ◽

...

Keyword(s):

Computed Tomography ◽

Success Rate ◽

Diagnostic Yield ◽

Study Groups ◽

Transbronchial Biopsy ◽

Endobronchial Ultrasound ◽

Cone Beam ◽

Ultrasound Guided ◽

Pulmonary Lesions ◽

Peripheral Pulmonary Lesions

Background: Endobronchial ultrasound-guided transbronchial biopsy (EBUS-TBB) is used for the diagnosis of peripheral pulmonary lesions (PPLs), but the diagnostic yield is not adequate. Cone-beam computed tomography-derived augmented fluoroscopy (CBCT-AF) can be utilized to assess the location of PPLs and biopsy devices, and has the potential to improve the diagnostic accuracy of bronchoscopic techniques. The purpose of this study was to verify the contribution of CBCT-AF to EBUS-TBB. Methods: Patients who underwent EBUS-TBB for diagnosis of PPLs were enrolled. The navigation success rate and diagnostic yield were used to evaluate the effectiveness of CBCT-AF in EBUS-TBB. Results: In this study, 236 patients who underwent EBUS-TBB for PPL diagnosis were enrolled. One hundred fifteen patients were in CBCT-AF group and 121 were in non-AF group. The navigation success rate was significantly higher in the CBCT-AF group (96.5% vs. 86.8%, p = 0.006). The diagnostic yield was even better in the CBCT-AF group when the target lesion was small in size (68.8% vs. 0%, p = 0.026 for lesions ≤10 mm and 77.5% vs. 46.4%, p = 0.016 for lesions 10–20 mm, respectively). The diagnostic yield of the two study groups became similar when the procedures with a failure of navigation were excluded. The procedure-related complication rate was similar between the two study groups. Conclusion: CBCT-AF is safe, and effectively enhances the navigation success rate, thereby increasing the diagnostic yield of EBUS-TBB for PPLs.

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

10.21203/rs.3.rs-1190013/v1 ◽

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.

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Development and outcomes of a comprehensive multidisciplinary incidental lung nodule and lung cancer screening program

10.21203/rs.2.18446/v1 ◽

2019 ◽

Author(s):

Gregory LeMense ◽

Ernest A. Waller ◽

Cheryl Campbell ◽

Tyler Bowen

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Cancer Diagnosis ◽

Screening Program ◽

Early Stage ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Lung Nodules ◽

Hospital System ◽

Number Of Patients

Abstract BackgroundAppropriate management of lung nodules detected incidentally or through lung cancer screening can increase the rate of early-stage diagnoses and potentially improve treatment outcomes. However, the implementation and management of comprehensive lung nodule programs is challenging. MethodsA single-center, retrospective study was conducted to describe the development and outcomes of a lung nodule program at a community practice in Tennessee.ResultsThe number of patients with lung nodules referred to the program increased over 2 years, with 665 patients in Year 1 and 745 patients in Year 2. Most nodules were incidental (60% Year 1, 65% Year 2). In Year 1, 17% of nodules were symptomatic and 12% were identified through screening. Of the 665 nodules in Year 1, 182 underwent a diagnostic intervention and 121 (18%) received a cancer diagnosis. Most diagnostic interventions were image-guided bronchoscopy (88%) or percutaneous biopsy (9%). The proportion of Stage I-II cancer diagnoses increased from 23% prior to program implementation to 36% in Year 1 and 38% in Year 2. Among screening cases, follow-up scans were conducted within 18 months in 71%. Only 2% of patients under watchful waiting required a diagnostic intervention, of which 1% received a cancer diagnosis.ConclusionsThe current study reports outcomes over the first two years of a lung cancer screening and incidental nodule program. The program was successful and manageable, given the appropriate level of data management and oversight. Comprehensive lung nodule programs have the potential to benefit the patient, physician, and hospital system.

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

Problems in oncology ◽

10.37469/0507-3758-2019-65-2-224-233 ◽

2019 ◽

Vol 65 (2) ◽

pp. 224-233

Author(s):

Sergey Morozov ◽

Viktor Gombolevskiy ◽

Anton Vladzimirskiy ◽

Albina Laypan ◽

Pavel Kononets ◽

...

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Cancer Screening ◽

Low Dose ◽

Early Stage ◽

Lung Cancer Screening ◽

Stage I ◽

Malignant Neoplasms ◽

Number Needed To Screen ◽

Low Dose Computed Tomography

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

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽

10.3390/math9131457 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1457

Author(s):

Muazzam Maqsood ◽

Sadaf Yasmin ◽

Irfan Mehmood ◽

Maryam Bukhari ◽

Mucheol Kim

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Search Space ◽

Lung Nodules ◽

Suggested Model ◽

Lung Cancer Diagnosis ◽

Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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Cone-beam computed tomography guidance with navigational overlay for percutaneous lung nodule biopsy

Pediatric Radiology ◽

10.1007/s00247-018-4296-4 ◽

2018 ◽

Vol 49 (3) ◽

pp. 327-331 ◽

Cited By ~ 1

Author(s):

Giridhar M. Shivaram ◽

Anne Elizabeth Gill ◽

Eric J. Monroe ◽

Kevin S. H. Koo ◽

C. Matthew Hawkins

Keyword(s):

Computed Tomography ◽

Cone Beam Computed Tomography ◽

Lung Nodule ◽

Cone Beam ◽

Computed Tomography Guidance

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Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190206153321 ◽

2020 ◽

Vol 16 (1) ◽

pp. 16-26 ◽

Cited By ~ 1

Author(s):

Shabana Rasheed Ziyad ◽

Venkatachalam Radha ◽

Thavavel Vayyapuri

Keyword(s):

Computed Tomography ◽

Review Paper ◽

Lung Nodule ◽

Lung Nodules ◽

Computer Aided Detection ◽

Lung Nodule Detection ◽

Nodule Detection ◽

Computer Aided ◽

Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

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Novel use of the endobronchial ultrasound through esophagus (EUS-B): Diagnosis of thyroid lesions

Monaldi Archives for Chest Disease ◽

10.4081/monaldi.2020.1275 ◽

2020 ◽

Vol 90 (2) ◽

Author(s):

Mario Tamburrini ◽

Parikshit Thakare ◽

Umberto Zuccon

Keyword(s):

Computed Tomography ◽

Thyroid Gland ◽

Incidental Finding ◽

Lung Nodule ◽

Endobronchial Ultrasound ◽

X Ray ◽

First Case ◽

Chest X Ray ◽

Thyroid Lesions

There is paucity in literature on the use of endobronchial ultrasound through esophagus (EUS-B) for the diagnosing thyroid gland lesions. We report the first case of colloid goiter diagnosed using EUS-B- FNA technique. A 77-year-old man presented with ophthalmic symptoms and an incidental finding of lung nodule on chest x-ray. The computed tomography of thorax revealed a left upper lobe nodule and an oval shaped left paratracheal lesion near left pole of thyroid gland. EUS-B- FNAC was performed which lead to the diagnosis of colloid goiter.

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