Outcome of incidentally detected airway nodules

Interpreting Lung Cancer Screening CTs: Practical Approach to Lung Cancer Screening and Application of Lung-RADS

Lung cancer screening via low-dose computed tomography (CT) reduces mortality from lung cancer, and eligibility criteria have recently been expanded to include patients aged 50 to 80 with at least 20 pack-years of smoking history. Lung cancer screening CTs should be interepreted with use of Lung Imaging Reporting and Data System (Lung-RADS), a reporting guideline system that accounts for nodule size, density, and growth. The revised version of Lung-RADS includes several important changes, such as expansion of the definition of juxtapleural nodules, discussion of atypical pulmonary cysts, and stepped management for suspicious nodules. By using Lung-RADS, radiologists and clinicians can adopt a uniform approach to nodules detected during CT lung cancer screening and reduce false positives.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

Comparison of Lung-RADS Version 1.1 and Lung-RADS Version 2022 in Classifying Airway Nodules Detected at Lung Cancer Screening CT

Purpose To compare the Lung Imaging Reporting and Data System (Lung-RADS) version 1.1 with version 2022 classification of airway nodules detected at lung cancer screening CT examinations. Materials and Methods This retrospective study included all patients who underwent a lung cancer screening CT examination in the authors' health care network between 2015 and 2021 with a reported airway or endobronchial nodule. A fellowship-trained cardiothoracic radiologist reviewed these CT images and characterized the airway nodules by size, location, multiplicity, morphology, dependent portions of airway, internal air, fluid attenuation, distal changes, outcome at follow-up, and final pathologic diagnosis, if malignant. Sensitivity and specificity of Lung-RADS version 1.1 in detecting malignant nodules were compared with those of Lung-RADS version 2022 using the McNemar test. Results A total of 174 patients were included. Of these, 163 (94%) had airway nodules that were deemed benign, while 11 (6%) had malignant nodules. Airway nodules in the trachea and mainstem bronchi were all benign, while lobar and segmental airway nodules had the highest risk for lung cancer (17.2% and 11.1%, respectively). Of the 12 subsegmental airway nodules that were obstructive, three (25%) were malignant and nine (75%) were benign. Nodules with nonobstructive morphologies, dependent portions of airway, internal air, or fluid attenuation were all benign. Only 10 of the 92 (10.9%) patients with positive Lung-RADS by clinical report had cancer. Lung-RADS version 2022 resulted in higher specificity than version 1.1 (82% vs 50%, P < .001), without sacrificing sensitivity (91% for both). Conclusion Compared with the previous version, Lung-RADS version 2022 reduced the number of false-positive screening CT examinations while still identifying malignant airway nodules. Keywords: CT, Lung, Primary Neoplasms, Pulmonary, Lung Cancer Screening, Lung-RADS, Nodule Risk, Airway Nodule, Endobronchial Nodule © RSNA, 2024.

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population

PURPOSE

The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RADS®) with lung cancer diagnosis.

METHODS

This retrospective study enrolled individuals undergoing LCS between January 1, 2015, and June 30, 2020. Individuals' sociodemographic characteristics, Lung-RADS scores, pathology-proven lung cancers, and tumor characteristics were determined via electronic health record and the health system's tumor registry. Associations between the outcome of lung cancer diagnosis within 1 year after LCS and covariates of sociodemographic characteristics and Lung-RADS score were determined using logistic regression.

RESULTS

Of 3,326 individuals undergoing 5,150 LCS examinations, 102 (3.1%) were diagnosed with lung cancer within 1 year of LCS; most of these cancers were screen detected (97 of 102 [95.1%]). Over the study period, there were 118 total LCS-detected cancers in 113 individuals (3.4%). Most LCS-detected cancers were adenocarcinomas (62 of 118 [52%]), 55.9% (65 of 118) were stage I, and 16.1% (19 of 118) were stage IV. The sensitivity, specificity, positive predictive value, and negative predictive value of Lung-RADS in diagnosing lung cancer within 1 year of LCS were 93.1%, 83.8%, 10.6%, and 99.8%, respectively. On multivariable analysis controlling for sociodemographic characteristics, only Lung-RADS score was associated with lung cancer (odds ratio for a one-unit increase in Lung-RADS score, 4.68; 95% confidence interval, 3.87-5.78).

CONCLUSIONS

The frequency of LCS-detected lung cancer and stage IV cancers was higher than reported in the National Lung Screening Trial. Although Lung-RADS was a significant predictor of lung cancer, the positive predictive value of Lung-RADS is relatively low, implying opportunity for improved nodule classification.

Historical Perspective on Lung Cancer Screening

Lung cancer represents a large burden on society with a staggering incidence and mortality rate that has steadily increased until recently. The impetus to design an effective screening program for the deadliest cancer in the United States and worldwide began in 1950. It has taken more than 50 years of numerous clinical trials and continued persistence to arrive at the development of modern-day screening program. As the program continues to grow, it is important for clinicians to understand its evolution, track outcomes, and continually assess the impact and bias of screening on the medical, social, and economic systems.

3D-2D image registration in the presence of soft-tissue deformation in image-guided transbronchial interventions

Purpose. Target localization in pulmonary interventions (e.g. transbronchial biopsy of a lung nodule) is challenged by deformable motion and may benefit from fluoroscopic overlay of the target to provide accurate guidance. We present and evaluate a 3D-2D image registration method for fluoroscopic overlay in the presence of tissue deformation using a multi-resolution/multi-scale (MRMS) framework with an objective function that drives registration primarily by soft-tissue image gradients.Methods. The MRMS method registers 3D cone-beam CT to 2D fluoroscopy without gating of respiratory phase by coarse-to-fine resampling and global-to-local rescaling about target regions-of-interest. A variation of the gradient orientation (GO) similarity metric (denotedGO') was developed to downweight bone gradients and drive registration via soft-tissue gradients. Performance was evaluated in terms of projection distance error at isocenter (PDE_iso). Phantom studies determined nominal algorithm parameters and capture range. Preclinical studies used a freshly deceased, ventilated porcine specimen to evaluate performance in the presence of real tissue deformation and a broad range of 3D-2D image mismatch.Results. Nominal algorithm parameters were identified that provided robust performance over a broad range of motion (0-20 mm), including an adaptive parameter selection technique to accommodate unknown mismatch in respiratory phase. TheGO'metric yielded median PDE_iso= 1.2 mm, compared to 6.2 mm for conventionalGO.Preclinical studies with real lung deformation demonstrated median PDE_iso= 1.3 mm with MRMS +GO'registration, compared to 2.2 mm with a conventional transform. Runtime was 26 s and can be reduced to 2.5 s given a prior registration within ∼5 mm as initialization.Conclusions. MRMS registration via soft-tissue gradients achieved accurate fluoroscopic overlay in the presence of deformable lung motion. By driving registration via soft-tissue image gradients, the method avoided false local minima presented by bones and was robust to a wide range of motion magnitude.

Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images-The ACDC@LungHP Challenge 2019

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354 ±0.1149 to 0.8372 ±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398 ±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.

Extensive eye-oral-bronchial mucosal nodules with eosinopgillia: a rare case report and literature review

BACKGROUND

Mucosal nodules can be caused by infection, inflammation and neoplastic disease. Many noninfectious diseases, such as eosinophilia, amyloidosis, sarcoidosis, Wegener's granuloma, langerhans cell histiocytosis etc., are associated with the formation of multisytem mucosal nodules, especially significant bronchial lesions. Detailed medical history, comprehensive metabolic profile, biopsy specimen and imaging examinations are required for differentiating among these disorders. The process of diagnosis and treatment of our patient's mucosal nodules was challenging, which could be helpful to similar cases.

CASE PRESENTATION

We represent a case of a 29-year-old woman with plentiful nodules of unknown origin on extensive mucous membranes. Biopsy specimen reports inflammatory lesions with large numbers of neutrophils, lymphocytes, and varying degrees of eosinophils. Treatment of anti-infection, anti-tussive and anti-allergic was ineffective, but glucocorticoid showed great improvement to her symptoms.

CONCLUSION

We experienced a rare case with plentiful nodules of unknown origin on extensive mucous membranes. She may be a specific phenotype of eosinophilia or may be a novel multisystem disease with respiratory system as the primary symptom. The diagnosis of our patient remains unclear, but tentative glucocorticoid therapy was beneficial.

Overcoming Obstacles in Pathological Diagnosis of Pulmonary Nodules through Circulating Tumor Cell Enrichment

With the popularity of low-dose computed tomography (LDCT) in clinical examination of the lung, the prevalence of pulmonary nodules has significantly increased, thus significantly improving the early diagnosis of lung cancer, but also potentially contributing to overtreatment. This study aims to develop a noninvasive method to assist in diagnosing the pulmonary nodules. To do so, 3798 patients are recruited from the Department of Thoracic Surgery at Shanghai Pulmonary Hospital and peripheral blood samples are collected from them before surgery. From these samples, circulating tumor cells (CTC) are isolated using folate receptor (FR) positivity, and then enriched and analyzed in relation to cancer gene expression, stage, and level of invasion. The average CTC concentration of patients with lung disease is 11.97 functional unit (FU) in a 3 mL sample of blood. FR-positive CTC levels correlate with the expression of lung cancer driver genes tumor-node-matastasis (TNM) stage, and pleura invasion. The sensitivity of CTC levels to lung cancer diagnosis is 87.05%. Results from this study demonstrate that the determination of FR-positive CTC concentration is a convenient and time-saving strategy to improve the pathological diagnosis of pulmonary nodules.