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Lung nodules are frequently detected on low‐dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline‐discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening‐detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. Plain Language Summary: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening‐detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules).Although the vast majority of lung nodules are not lung cancer, it is important to have evidence‐based, standardized approaches to the evaluation and management of a lung nodule.The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms. Given the need to balance the benefits of early‐detection of lung cancer and the risks of diagnostic tests, it is imperative to develop evidence‐based strategies for lung nodule management. This report identifies six gaps in knowledge about lung nodule management and proposes six recommendations to improve the management approaches described in clinical practice guidelines. [ABSTRACT FROM AUTHOR]

Objectives: We focus on utilizing the Labyrinth system for the detection of circulating tumor cells (CTCs) in patients with lung nodules. Our aim is to evaluate CTCs isolated through the Labyrinth system as a biomarker for early-stage lung cancer (LC) detection. Methods: 167 patients with low dose computed tomography (LDCT) diagnostic results for lung nodules and 31 healthy volunteers (HV) were enrolled. Blood samples were processed for CTC detection. LDCT positive (LDCT+) patients underwent surgery and were categorized into those with LC and those with benign lung diseases (BLD) based on their biopsy results. BLD Patients, LDCT negative (LDCT-) patients and HV served as controls. The correlation of CTC counts with LC, BLD, LDCT- and HV was investigated. Receiver operating characteristic (ROC) curves were used to assess the Labyrinth system's diagnostic potential for early-stage LC. Results: Median CTC counts for LC, BLD, LDCT- and HV were 2.7 CTC/mL, 0.6 CTC/mL, 0.4 CTC/mL, 0 CTC/mL, respectively. Statistical analysis indicated CTC counts could distinguish LC from BLD, LDCT- and HV (p-Values < 0.001). Using a cut-off of 1 CTC/mL, the study showed 84.4% sensitivity and 82.4% specificity for LDCT+ patients. Specificity increased to 85.9% for patients with lung nodules and 88.2% for all participants. In conclusion, CTCs detected by the Labyrinth system can serve as a biomarker for early-stage LC detection for patients with lung nodules. Conclusions: CTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice. [ABSTRACT FROM AUTHOR]

Background: anatomic lung segmentectomies allow accurate resection of pulmonary lesions, maximizing healthy tissue preservation, and reducing unnecessary loss of lung function. In this setting, accurate preoperative planning is crucial. We present our early experience, detailing the successful use of 3D-CT models in tailoring therapeutic strategies for three patients undergoing complex anatomical lung resections due to neoplastic diseases. Case Presentation: (1) 60-year-old male patient with significant pulmonary functional impairment underwent successful right lower lobe bi-segmentectomy (S7–S8) for carcinoid, stage IA1. (2) 65-year-old female patient with previous left lung resection and functional impairment underwent uneventful right upper lobe bi-segmentectomy (S1–S2) for double lung adenocarcinoma, stage IIb. (3) 67-year-old male with previous ipsilateral lung resection underwent left lower lobe segmentectomy (S8) for metastatic colic adenocarcinoma without any complications. Conclusion: 3D-CT imaging, particularly through VPTM platform, enhances the safety and precision of complex lung segmentectomy, providing a valuable surgical map for improved outcomes.

Abstract Background Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant. Methods Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods. Results Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year). Conclusions In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

Background: anatomic lung segmentectomies allow accurate resection of pulmonary lesions, maximizing healthy tissue preservation, and reducing unnecessary loss of lung function. In this setting, accurate preoperative planning is crucial. We present our early experience, detailing the successful use of 3D-CT models in tailoring therapeutic strategies for three patients undergoing complex anatomical lung resections due to neoplastic diseases. Case Presentation: (1) 60-year-old male patient with significant pulmonary functional impairment underwent successful right lower lobe bi-segmentectomy (S7–S8) for carcinoid, stage IA1. (2) 65-year-old female patient with previous left lung resection and functional impairment underwent uneventful right upper lobe bi-segmentectomy (S1–S2) for double lung adenocarcinoma, stage IIb. (3) 67-year-old male with previous ipsilateral lung resection underwent left lower lobe segmentectomy (S8) for metastatic colic adenocarcinoma without any complications. Conclusion: 3D-CT imaging, particularly through VPTM platform, enhances the safety and precision of complex lung segmentectomy, providing a valuable surgical map for improved outcomes. [ABSTRACT FROM AUTHOR]

Background: Computed tomography to body divergence (CTBD) is one of the main barriers to bronchoscopic techniques for the diagnosis of peripherally located lung nodules. Cone-beam CT (CBCT) guidance is being rapidly adopted to correct for this phenomenon and to potentially increase diagnostic outcomes. In this trial, we hypothesized that the addition of mobile CBCT (m-CBCT) could improve the rate of tool in lesion (TIL) and the diagnostic yield of shape-sensing robotic-assisted bronchoscopy (SS-RAB). Methods: This was a prospective, single-arm study, which enrolled patients with peripheral lung nodules of 1–3 cm and compared the rate of TIL and the diagnostic yield of SS-RAB alone and combined with mCBCT. Results: A total of 67 subjects were enrolled, the median nodule size was 1.7 cm (range, 0.9–3 cm). TIL was achieved in 23 patients (34.3%) with SS-RAB alone, and 66 patients (98.6%) with the addition of mCBCT (p < 0.0001). The diagnostic yield of SS-RAB alone was 29.9% (95% CI, 29.3–42.3%) and it was 86.6% (95% CI, 76–93.7%) with the addition of mCBCT (p < 0.0001). There were no pneumothoraxes or any bronchoscopy-related complications, and the median total dose–area product (DAP) was 50.5 Gy-cm2. Conclusions: The addition of mCBCT guidance to SS-RAB allows bronchoscopists to compensate for CTBD, leading to an increase in TIL and diagnostic yield, with acceptable radiation exposure. [ABSTRACT FROM AUTHOR]

Background: Lung nodules are commonly detected in high-risk populations, necessitating reliable screening methods. Low-dose computed tomography (CT) is considered effective for early detection and follow-up. Objective: To evaluate the utility of low-dose CT in screening and follow-up of lung nodules over one year. Methods: A longitudinal study was conducted with 100 participants aged 50-75 years, including 60 males and 40 females. Participants were categorized based on smoking status: 40% current smokers, 50% former smokers, and 10% never smokers. Baseline and follow-up CT scans were performed at 3, 6, 9, and 12 months to detect and monitor lung nodules. Results: At baseline, 35 participants (35%) had detected nodules. Follow-up scans showed 40 nodules (88.9%) remained stable, 4 (8.9%) increased in size, and 1 (2.2%) decreased. New nodules were detected in 10 participants (10%). Statistical analysis revealed a sensitivity of 95%, specificity of 90%, positive predictive value of 85%, and negative predictive value of 97%. Clinical outcomes indicated 4 malignant nodules, with 2 classified as Stage I, 1 as Stage II, and 1 as Stage III. Treatments included surgical resection, chemotherapy, and radiation therapy. The cost-effectiveness analysis showed an average cost per low-dose CT scan of ₹3,250, with a total annual screening cost of ₹1,300,000 and a cost per detected malignant nodule of ₹3,25,000. Patient satisfaction was high at 90%, and overall survival rate was 100% at one year. Conclusion: Low-dose CT is an effective, sensitive, and cost-efficient method for screening and monitoring lung nodules, with significant potential for early detection and improved clinical outcomes. [ABSTRACT FROM AUTHOR]

While artificial intelligence has achieved considerable maturity in lung nodule detection, research on growth prediction remains limited. Accurate growth prediction aids clinical decision-making, informing patient follow-up strategies. This paper proposes a novel nodule growth prediction network model that generates high-quality lung nodule images at specific time intervals. The model employs a two-branch structure for feature extraction. One branch, leveraging a displacement field prediction mechanism, models the shape transformation of pulmonary nodules through voxel-level future displacement estimation. The other branch, empowered by a three-dimensional U-Net, focused on learning texture changes within the nodules. A coordinate attention mechanism that emphasizes informative features within the extracted high-dimensional feature map. Subsequently, the outputs of both branches are fused and fed into the feature reconstruction module to generate the final lung nodule growth prediction image. Furthermore, a time interval coding module is introduced to incorporate the desired time interval into the network, enabling the generation of prediction images for different future time points.