Pulmonary nodules and the psychological harm they can cause: A scoping review.

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

Ultrathin Bronchoscopy Without Virtual Navigation for Diagnosis of Peripheral Lung Lesions.

BACKGROUND: The increasing incidence of encountering lung nodules necessitates an ongoing search for improved diagnostic procedures. Various bronchoscopic technologies have been introduced or are in development, but further studies are needed to define a method that fits best in clinical practice and health care systems. RESEARCH QUESTION: How do basic bronchoscopic tools including a combination of thin (outer diameter 4.2 mm) and ultrathin bronchoscopes (outer diameter 3.0 mm), radial endobronchial ultrasound (rEBUS) and fluoroscopy perform in peripheral pulmonary lesion diagnosis? STUDY DESIGN AND METHODS: This is a retrospective review of the performance of peripheral bronchoscopy using thin and ultrathin bronchoscopy with rEBUS and 2D fluoroscopy without a navigational system for evaluating peripheral lung lesions in a single academic medical center from 11/2015 to 1/2021. We used a strict definition for diagnostic yield and assessed the impact of different variables on diagnostic yield, specifically after employment of the ultrathin bronchoscope. Logistic regression models were employed to assess the independent associations of the most impactful variables. RESULTS: A total of 322 patients were included in this study. The median of the long axis diameter was 2.2 cm and the median distance of the center of the lesion from the visceral pleural surface was 1.9 cm. Overall diagnostic yield was 81.3% after employment of the ultrathin bronchoscope, with more detection of concentric rEBUS views (93% vs. 78%, p < 0.001). Sensitivity for detecting malignancy also increased from 60.5% to 74.7% (p = 0.033) after incorporating the ultrathin scope into practice, while bronchus sign and peripheral location of the lesion were not found to affect diagnostic yield. Concentric rEBUS view, solid appearance, upper/middle lobe location and larger size of the nodules were found to be independent predictors of successful achievement of diagnosis at bronchoscopy. INTERPRETATION: This study demonstrates a high diagnostic yield of biopsy of lung lesions achieved by utilization of thin and ultrathin bronchoscopes. Direct visualization of small peripheral airways with simultaneous rEBUS confirmation increased localization rate of small lesions in a conventional bronchoscopy setting without virtual navigational planning.

Clinical application of CT-assisted body surface localization combined with intraoperative stereotactic anatomical localization in thoracoscopic lung nodule resection: a single-centre retrospective study.

BACKGROUND: Today, the detection rate of lung nodules is increasing. Some of these nodules may become malignant. Thus, timely resection of potentially malignant nodules is essential. However, Identifying the location of nonsurface or soft-textured nodules during surgery is challenging. Various localization techniques have been developed to accurately identify lung nodules. Common methods include preoperative CT-guided percutaneous placement of hook wires and microcoils. Nonetheless, these procedures may cause complications such as pneumothorax and haemothorax. Other methods regarding localization of pulmonary nodules have their own drawbacks. We conducted a clinical study which was retrospective to identify a safe, accurate and suitable method for determining lung nodule localization. To evaluate the clinical value of CT-assisted body surface localization combined with intraoperative stereotactic anatomical localization in thoracoscopic lung nodule resection. METHODS: We retrospectively collected the clinical data of 120 patients who underwent lung nodule localization and resection surgery at the Department of Thoracic Surgery, First Affiliated Hospital of Bengbu Medical College, from January 2020 to January 2022. Among them, 30 patients underwent CT-assisted body surface localization combined with intraoperative stereotactic anatomical localization, 30 patients underwent only CT-assisted body surface localization, 30 patients underwent only intraoperative stereotactic anatomical localization, and 30 patients underwent CT-guided percutaneous microcoil localization. The success rates, complication rates, and localization times of the four lung nodule localization methods were statistically analysed. RESULTS: The success rates of CT-assisted body surface localization combined with intraoperative stereotactic anatomical localization and CT-guided percutaneous microcoil localization were both 96.7%, which were significantly higher than the 70.0% success rate in the CT-assisted body surface localization group (P < 0.05). The complication rate in the combined group was 0%, which was significantly lower than the 60% in the microcoil localization group (P < 0.05). The localization time for the combined group was 17.73 ± 2.52 min, which was significantly less than that (27.27 ± 7.61 min) for the microcoil localization group (P < 0.05). CONCLUSIONS: CT-assisted body surface localization combined with intraoperative stereotactic anatomical localization is a safe, painless, accurate, and reliable method for lung nodule localization.

The 2023 American Association for Thoracic Surgery (AATS) Expert Consensus Document: Management of subsolid lung nodules.

OBJECTIVE: Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules. METHODS: The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel. RESULTS: Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules. CONCLUSIONS: Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.

Solitary pulmonary capillary hemangioma - An underrecognized rare tumor. Report of 32 new cases with literature review.

OBJECTIVE: To explore the clinical, imaging, pathologic characteristics and differential diagnosis of solitary pulmonary capillary hemangioma (SPCH). METHODS: Thirty two cases of SPCH were collected and studied, with literature review. RESULTS: This study included 13 males and 19 females, with a male-to-female ratio of 1:1.5. The age ranged from 26 to 70 years (median age of 43 years). All patients were asymptomatic at presentation. Lung nodules were incidentally discovered during chest computed tomography (CT). Imaging features included 21 cases with partial solid nodules (PSN), 7 cases with ground-glass nodules (GGN), and 4 cases with solid nodules (SN). Eleven cases were in the left lung lower basal segment, 11 cases in the right lung lower basal segment, 6 cases in the right lung upper anterior segment, and 4 cases in the right lung middle lateral segment. The lower basal segments of the lungs were involved in 22 (11 in each lung) cases (22/32, 68 %). The tumors ranged from 6 to 18 mm (average 10 mm). Macroscopically, 16 cases had clear boundaries, while 16 cases had unclear boundaries, and gray-red or dark brown on cut surfaces. Intraoperative frozen section was performed in 27 cases, with diagnosis of SPCH in 12 and pneumonia or inflammatory lesion in 15. Microscopically, the nodules were composed of densely proliferated and dilated capillaries. The capillary walls were lined with a single layer of flat endothelial cells, without atypical features. Collapsed alveolar septa were replaced by a large number of capillaries. All cases showed proliferating capillaries spreading into the walls of small veins/arteries and bronchi, with 3 cases showing dilated capillaries protruding into the bronchiolar lumens as polyp-like structures. Twenty-six cases (26/32, 81 %) showed proliferating capillaries passed over the interlobular septa. Twenty-six cases (26/32, 81 %) showed irregular intimal thickening of small muscular arteries in the peripheral areas of the lesions, with the thickened intima being cellular or fibrous. In twenty-seven cases (27/32, 84 %) the lesions were located in the subpleura, with 6 cases involving the pleura. CONCLUSION: SPCH is a rare benign lung tumor that mostly occurs in the lung lower basal segments with predominance in females. It usually appears as a ground-glass nodule on CT and is very similar to early-stage lung cancer. Accurate diagnosis requires collaboration of radiologists, surgeons, and pathologists. SPCH should be regarded as an important differential diagnosis of small incidental lung nodules.

First-in-human use of a new robotic electromagnetic navigation bronchoscopic platform with integrated Tool-in-Lesion Tomosynthesis (TiLT) technology for peripheral pulmonary lesions: The FRONTIER study.

BACKGROUND AND OBJECTIVE: As the presentation of pulmonary nodules increases, the importance of a safe and accurate method of sampling peripheral pulmonary nodules is highlighted. First-generation robotic bronchoscopy has successfully assisted navigation and improved peripheral reach during bronchoscopy. Integrating tool-in-lesion tomosynthesis (TiLT) may further improve yield. METHODS: We performed a first-in-human clinical trial of a new robotic electromagnetic navigation bronchoscopy system with integrated digital tomosynthesis technology (Galaxy System, Noah Medical). Patients with moderate-risk peripheral pulmonary nodules were enrolled in the study. Robotic bronchoscopy was performed using electromagnetic navigation with TiLT-assisted lesion guidance. Non-specific results were followed up until either a clear diagnosis was achieved or repeat radiology at 6 months demonstrated stability. RESULTS: Eighteen patients (19 nodules) were enrolled. The average lesion size was 20 mm, and the average distance from the pleura was 11.6 mm. The target was successfully reached in 100% of nodules, and the biopsy tool was visualized inside the target lesion in all cases. A confirmed specific diagnosis was achieved in 17 nodules, 13 of which were malignant. In one patient, radiological monitoring confirmed a true non-malignant result. This translates to a yield of 89.5% (strict) to 94.7% (intermediate). Complications included one pneumothorax requiring observation only and another requiring an overnight chest drain. There was one case of severe pneumonia following the procedure. CONCLUSION: In this first-in-human study, second-generation robotic bronchoscopy using electromagnetic navigation combined with integrated digital tomosynthesis was feasible with an acceptable safety profile and demonstrated a high diagnostic yield for small peripheral lung nodules.

Imaging modalities during navigational bronchoscopy.

INTRODUCTION: Lung nodules are commonly encountered in clinical practice. Technological advances in navigational bronchoscopy and imaging modalities have led to paradigm shift from nodule screening or follow-up to early lung cancer detection. This is due to improved nodule localization and biopsy confirmation with combined modalities of navigational platforms and imaging tools. To conduct this article, relevant literature was reviewed via PubMed from January 2014 until January 2024. AREAS COVERED: This article highlights the literature on different imaging modalities combined with commonly used navigational platforms for diagnosis of peripheral lung nodules. Current limitations and future perspectives of imaging modalities will be discussed. EXPERT OPINION: The development of navigational platforms improved localization of targets. However, published diagnostic yield remains lower compared to percutaneous-guided biopsy. The discordance between the actual location of lung nodule during the procedure and preprocedural CT chest is the main factor impacting accurate biopsies. The utilization of advanced imaging tools with navigation-based bronchoscopy has been shown to assist with localizing targets in real-time and improving biopsy success. However, it is important for interventional bronchoscopists to understand the strengths and limitations of these advanced imaging technologies.

Use of fractals in determining the malignancy degree of lung nodules.

Introduction: A Computer-Assisted Detection (CAD) System for classification into malignant-benign classes using CT images is proposed. Methods: Two methods that use the fractal dimension (FD) as a measure of the lung nodule contour irregularities (Box counting and Power spectrum) were implemented. The LIDC-IDRI database was used for this study. Of these, 100 slices belonging to 100 patients were analyzed with both methods. Results: The performance between both methods was similar with an accuracy higher than 90%. Little overlap was obtained between FD ranges for the different malignancy grades with both methods, being slightly better in Power spectrum. Box counting had one more false positive than Power spectrum. Discussion: Both methods are able to establish a boundary between the high and low malignancy degree. To further validate these results and enhance the performance of the CAD system, additional studies will be necessary.

Robust deep learning from incomplete annotation for accurate lung nodule detection.

Deep learning plays a significant role in the detection of pulmonary nodules in low-dose computed tomography (LDCT) scans, contributing to the diagnosis and treatment of lung cancer. Nevertheless, its effectiveness often relies on the availability of extensive, meticulously annotated dataset. In this paper, we explore the utilization of an incompletely annotated dataset for pulmonary nodules detection and introduce the FULFIL (Forecasting Uncompleted Labels For Inexpensive Lung nodule detection) algorithm as an innovative approach. By instructing annotators to label only the nodules they are most confident about, without requiring complete coverage, we can substantially reduce annotation costs. Nevertheless, this approach results in an incompletely annotated dataset, which presents challenges when training deep learning models. Within the FULFIL algorithm, we employ Graph Convolution Network (GCN) to discover the relationships between annotated and unannotated nodules for self-adaptively completing the annotation. Meanwhile, a teacher-student framework is employed for self-adaptive learning using the completed annotation dataset. Furthermore, we have designed a Dual-Views loss to leverage different data perspectives, aiding the model in acquiring robust features and enhancing generalization. We carried out experiments using the LUng Nodule Analysis (LUNA) dataset, achieving a sensitivity of 0.574 at a False positives per scan (FPs/scan) of 0.125 with only 10% instance-level annotations for nodules. This performance outperformed comparative methods by 7.00%. Experimental comparisons were conducted to evaluate the performance of our model and human experts on test dataset. The results demonstrate that our model can achieve a comparable level of performance to that of human experts. The comprehensive experimental results demonstrate that FULFIL can effectively leverage an incomplete pulmonary nodule dataset to develop a robust deep learning model, making it a promising tool for assisting in lung nodule detection.

[The role of endoscopy in the management of peripheral pulmonary nodules, part 2: Treatment]. / Place de l'endoscopie dans la gestion des nodules pulmonaires périphériques, partie 2 : traitement.

The management of peripheral lung nodules is challenging, requiring specialized skills and sophisticated technologies. The diagnosis now appears accessible to advanced endoscopy (see Part 1), which can also guide treatment of these nodules; this second part provides an overview of endoscopy techniques that can enhance surgical treatment through preoperative marking, and stereotactic radiotherapy treatment through fiduciary marker placement. Finally, we will discuss how, in the near future, these advanced endoscopic techniques will help to implement ablation strategy.

Diagnostic Performance of MRI for the Detection of Pulmonary Nodules: A Systematic Review and Meta-Analysis.

Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Keywords: Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT Supplemental material is available for this article. © RSNA, 2024.

MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.

Obtaining an accurate segmentation of the pulmonary nodules in computed tomography (CT) images is challenging. This is due to: (1) the heterogeneous nature of the lung nodules; (2) comparable visual characteristics between the nodules and their surroundings. A robust multi-scale feature extraction mechanism that can effectively obtain multi-scale representations at a granular level can improve segmentation accuracy. As the most commonly used network in lung nodule segmentation, UNet, its variants, and other image segmentation methods lack this robust feature extraction mechanism. In this study, we propose a multi-stride residual 3D UNet (MRUNet-3D) to improve the segmentation accuracy of lung nodules in CT images. It incorporates a multi-slide Res2Net block (MSR), which replaces the simple sequence of convolution layers in each encoder stage to effectively extract multi-scale features at a granular level from different receptive fields and resolutions while conserving the strengths of 3D UNet. The proposed method has been extensively evaluated on the publicly available LUNA16 dataset. Experimental results show that it achieves competitive segmentation performance with an average dice similarity coefficient of 83.47 % and an average surface distance of 0.35 mm on the dataset. More notably, our method has proven to be robust to the heterogeneity of lung nodules. It has also proven to perform better at segmenting small lung nodules. Ablation studies have shown that the proposed MSR and RFIA modules are fundamental to improving the performance of the proposed model.

Squamous Cell Carcinoma Arising in a Zenker's Diverticulum.

Squamous cell carcinoma (SCC) developing in a Zenker's diverticulum (ZD) is an uncommon condition. The preferred treatment for SCC in the pharyngeal pouch is complete diverticulum resection. Only histopathological evaluation of the pouch can rule out SCC. Here, we present a case of a 62-year-old male patient, who was evaluated for repeated episodes of aspiration and dysphagia, and diagnosed to have a large ZD, the patient underwent Zenker's diverticulectomy with cricopharyngeal myotomy with wide margins due to clinically suspicious specimen. Histopathological examination revealed well-differentiated SCC arising within ZD, involving the whole thickness of the wall and almost touching the serosa (1 mm). The patient developed metastatic lung nodule on PET-CT, so metastatic lung nodule was excised with video-assisted thoracoscopic surgery (VATS), and chemotherapy and immunotherapy were given. On follow-up imaging patient is tumor-free to date, two years after the surgery. The occurrence of synchronous or metachronous lung cancer makes it one of the rarest cases.

Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.

Cancer detection and accurate classification pose significant challenges for medical professionals, as it is described as a lethal illness. Diagnosing the malignant lung nodules in its initial stage significantly enhances the recovery and survival rates. Therefore, a novel model named convolutional vision Elman bidirectional-based crossover boosted grey wolf optimization (CViEBi-CBGWO) has been proposed to enhance classification accuracy. CT images selected for further preprocessing are obtained from the LUNA16 dataset and LIDC-IDRI dataset. The data undergoes preprocessing phases involving normalization, data augmentation, and filtering to improve the generalization ability as well as image quality. The local features within the preprocessed images are extracted by implementing the convolutional neural network (CNN). For extracting the global features within the preprocessed images, the vision transformer (ViT) model consists of five encoder blocks. The attained local and global features are combined to generate the feature map. The Elman bidirectional long short-term memory (EBiLSTM) model is applied to categorize the generated feature map as benign and malignant. The crossover operation is integrated with the grey wolf optimization (GWO) algorithm, and the combined form of CBGWO fine-tunes the parameters of the CViEBi model, eliminating the problem of local optima. Experimental validation is conducted using various evaluation measures to assess effectiveness. Comparative analysis demonstrates a superior classification accuracy of 98.72% in the proposed method compared to existing methods.

Pulmonary coccidioidomycosis mimicking malignancy associated with Sweet's syndrome (acute febrile neutrophilic dermatosis).

A suspicious malignant lung nodule with cutaneous reaction is not always cancer, especially in low risk for malignancy patients. A lung biopsy should be taken into consideration. The associated cause of Sweet's syndrome directs the treatment in each patient.

Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung, which are either cancerous or non-cancerous. Accurate segmentation of these nodules is crucial for early detection and diagnosis of lung cancer. However, lung nodules can have various shapes, sizes, and densities, making their accurate segmentation a difficult task. Moreover, they can be easily confused with other structures in the lung, including blood vessels and airways, further complicating the segmentation process. To address this challenge, this paper proposes a novel multi-crop convolutional neural network (multi-crop CNN) model that utilizes different sized cropped regions of CT scan images for accurate segmentation of lung nodules. The model consists of three modules, namely the feature representation module, boundary refinement module, and segmentation module. The feature representation module captures features from the lung CT scan image using cropped regions of different sizes, while the boundary refinement module combines the boundary maps and feature maps to generate a final feature map for the segmentation process. The segmentation module produces a high-resolution segmentation map that shows improved accuracy in segmenting cancerous lung nodules. The proposed multi-crop CNN model is evaluated on two segmentation datasets namely LUNA 16 and LIDC-IDRI with an accuracy of 98.3% and 98.5%, respectively. The performances are measured in terms of accuracy, recall, precision, dice coefficient, specificity, AUC/ROC, Hausdorff distance, Jaccard index, and average Hausdorff. Overall, the proposed multi-crop CNN model demonstrates the potential to enhance the lung nodule segmentation accuracy, which could lead to earlier detection and diagnosis of lung cancer and ultimately reduce mortality rates associated with the disease.

Program-Based Lung Cancer Care: A Prospective Observational Tumor Registry Linkage Study.

Introduction: Low-dose computed tomography screening (LDCT) and lung nodule programs (LNP) promote early lung cancer detection, improve survival; Multidisciplinary Care Programs (MDC) promote guideline-concordant care. The impact of such program-based care on "real-world" lung cancer survival is unquantified. We evaluated outcomes of lung cancer care delivered through structured programs in a community health care system. Methods: We conducted a cohort study linking institutional prospective observational LDCT, LNP and MDC databases with Tumor Registry of Baptist Cancer Center facilities. We categorized all patients diagnosed with lung cancer between 2011 and 2021 into program-based care versus non-program-based care cohorts. We compared patient characteristics, stage distribution, treatment modalities, survival and mortality in each pathway of care. Results: Of 12,148 patients, 237, 1,165, 1,140 and 9,606 were diagnosed through the LDCT, LNP, MDC or no program, respectively; non-program-based care sequentially diminished from 96.3% to 66.5%, diagnosis through LDCT increased from 0.5% to 7.1%, LNP from 3.5% to 20.8%; and MDC alone decreased from a high of 12.8% in 2014 to 5.6% in 2021. Program-based care was associated with earlier stage (p < 0.001), higher surgical resection rates (p < 0.001), greater use of adjuvant therapy (p < 0.001), better aggregate and stage-stratified survival (p < 0.001), and lower all-cause and lung cancer-specific mortality (p < 0.001). Recipients of non-program-based care were considerably less likely to receive lung cancer treatment; results remained consistent when patients receiving no treatment were excluded. Conclusions: Program-based care was associated with substantially better survival. Increasing access to program-based care should be explored as a matter of urgent public policy.