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Objective: This study aims to evaluate the efficacy of two free-breathing magnetic resonance imaging (MRI) sequences—spiral ultrashort echo time (spiral UTE) and radial volumetric interpolated breath-hold examination (radial VIBE). Methods: Patients were prospectively enrolled between February 2021 and September 2022. All participants underwent both 3T MRI scanning, utilizing the radial VIBE sequence and spiral UTE sequence, as well as standard chest CT imaging. The CT and MRI examinations were conducted within a 7-day interval. Two radiologists assessed the image quality using a visual 5-point ordinal Likert scale, and pulmonary nodules identified on MRI were evaluated through comparison with CT as the reference standard. Results: A total of 52 patients participated in this study, during which 82 pulmonary nodules were detected via CT imaging. The image quality scores for depicting pulmonary vasculature and airways using the spiral UTE sequence (4.61 ± 0.63; 4.76 ± 0.48) were significantly higher than those for the radial VIBE sequence (4.27 ± 0.87; 4.14 ± 0.82) (P < 0.05). However, for nodules smaller than 6 mm, the detection rate for the spiral UTE sequence (82.61%) was notably higher than that of the radial VIBE sequence (39.13%) (P < 0.05). Additionally, the detection rate for ground-glass nodules was higher with the spiral UTE sequence (75.00%) compared to the radial VIBE sequence (17.86%) (P < 0.05). The Pearson correlation coefficient (r) between radial VIBE and CT was 0.99 (P < 0.001), and the Pearson correlation coefficient (r) between spiral UTE and CT was also 0.99 (P < 0.001). Conclusion: The spiral UTE sequence demonstrates superior capability in visualizing ground glass nodules, blood vessels, and airways. In cases where patients present with ground glass nodules, the spiral UTE sequence is the preferred choice. Conversely, when the nodules are solid or partially solid, it is advisable to opt for radial VIBE sequences that are time-efficient and exhibit fewer artifacts. [ABSTRACT FROM AUTHOR]

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]

Abstract Objective The study aimed to evaluate the application value of computed tomography (CT) three-dimensional (3D) reconstruction technology in identifying benign and malignant lung nodules and characterizing the distribution of the nodules. Methods CT 3D reconstruction was performed for lung nodules. Pathological results were used as the gold standard to compare the detection rates of various lung nodule signs between conventional chest CT scanning and CT 3D reconstruction techniques. Additionally, the differences in mean diffusion coefficient values and partial anisotropy index values between male and female patients were analyzed. Results Pathologic confirmation identified 30 patients with benign lesions and 45 patients with malignant lesions. CT 3D reconstruction demonstrated higher diagnostic accuracy for lung nodule imaging signs compared to conventional CT scanning (P

Abstract Objective This study aims to evaluate the efficacy of two free-breathing magnetic resonance imaging (MRI) sequences—spiral ultrashort echo time (spiral UTE) and radial volumetric interpolated breath-hold examination (radial VIBE). Methods Patients were prospectively enrolled between February 2021 and September 2022. All participants underwent both 3T MRI scanning, utilizing the radial VIBE sequence and spiral UTE sequence, as well as standard chest CT imaging. The CT and MRI examinations were conducted within a 7-day interval. Two radiologists assessed the image quality using a visual 5-point ordinal Likert scale, and pulmonary nodules identified on MRI were evaluated through comparison with CT as the reference standard. Results A total of 52 patients participated in this study, during which 82 pulmonary nodules were detected via CT imaging. The image quality scores for depicting pulmonary vasculature and airways using the spiral UTE sequence (4.61 ± 0.63; 4.76 ± 0.48) were significantly higher than those for the radial VIBE sequence (4.27 ± 0.87; 4.14 ± 0.82) (P

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.

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.

Respiratory diseases have a significant global impact, and assessing these conditions is crucial for improving patient outcomes. Chest X-ray is widely used for diagnosis, but expert evaluation can be challenging. Automatic computer-aided diagnosis methods can provide support for clinicians in these tasks. Deep learning has emerged as a set of algorithms with exceptional potential in such tasks. However, these algorithms require a vast amount of data, often scarce in medical imaging domains. In this work, a new data augmentation methodology based on adapted generative latent diffusion models is proposed to improve the performance of an automatic pathological screening in two high-impact scenarios: tuberculosis and lung nodules. The methodology is evaluated using three publicly available datasets, representative of real-world settings. An ablation study obtained the highest-performing image generation model configuration regarding the number of training steps. The results demonstrate that the novel set of generated images can improve the performance of the screening of these two highly relevant pathologies, obtaining an accuracy of 97.09%, 92.14% in each dataset of tuberculosis screening, respectively, and 82.19% in lung nodules. The proposal notably improves on previous image generation methods for data augmentation, highlighting the importance of the contribution in these critical public health challenges. [ABSTRACT FROM AUTHOR]

This review article provides an overview of recent research on deep learning (DL) methods for identifying and classifying lung nodules in medical images, with a focus on X-ray and CT scans. It encompasses a thorough analysis of studies published in reputed/peer-reviewed journals and international conferences. The review explores various aspects, including the development and implementation of DL models, the use of data augmentation techniques to enhance model performance and the application of transfer learning to adapt existing models to new datasets. The findings highlight the effectiveness of DL techniques in improving accuracy and efficiency in lung nodule detection and classification. Furthermore, these methodologies can be employed to cultivate automated systems that have the potential to aid radiologists in the processes of diagnosis and treatment planning. This review underscores the importance of continued research and development into the present state of DL research about detecting and classifying lung nodules. [ABSTRACT FROM AUTHOR]

Background: Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic‐based score (Rad‐score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. Methods: Retrospective data from two institutions (July 10, 2006—September 24, 2015) was used to develop and validate the Rad‐score for distinguishing lung nodule malignancy. The prognostic value of the Rad‐score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli‐Rad‐score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. Results: The Rad‐score demonstrated great performance in distinguishing malignancy, with C‐index values of 0.793 [95% CI: 0.729–0.856] in the training set and 0.730 [95% CI: 0.666–0.874] in the validation set. In independent LARC cohorts, Rad‐score validation achieved C‐index values of 0.794 [95% CI: 0.737–0.851] and 0.747 [95% CI: 0.615–0.879]. Regarding prognostic prediction, Rad‐score effectively stratified patients. Cli‐Rad‐score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C‐index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). Conclusions: CT‐based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad‐score predicts prognosis independently. Incorporation of Cli‐Rad‐score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules. [ABSTRACT FROM AUTHOR]

Pulmonary nodules are commonly encountered in pulmonary practice. Etiologies could include infectious, inflammatory, and malignant. Placental transmogrification of the lung is an extremely rare etiology of pulmonary nodules. Such condition often presents as unilateral lesions in asymptomatic men. In general, such nodules are generally stable and grow extremely slowly. We highlight an unusual case of placental transmogrification of the lung (PLC) identified in a young female. The patient's bilateral nodules were larger than what has been previously cited in the literature and exhibited growth over an 8-year follow-up period. [ABSTRACT FROM AUTHOR]

Lung nodules occupy fewer pixels in CT (Computed Tomography), which brings great difficulty to detection. In view of small target detection of lung nodule, this study proposes YOLOV3 (You Only Look Once version 3) lung nodule detection algorithm based on coordinate attention. The backbone network adopts the improved YOLOv3, reduces the number of residual blocks and introduces the dilated convolution module to sense context information around the target. In the feature utilization, coordinate attention is introduced to capture the position, direction and cross channel information, so as to locate lung nodules accurately. The loss function of YOLOv3 is improved, the boundary box is modeled as Gaussian distribution. Wasserstein distance is used to calculate the similarity between the two distributions instead of IoU (Intersection over Union), so as to improve the sensitivity of the target scale. The results on LUNA16 show that the average precision is 89.96% and the sensitivity is 95.37%. Compared with mainstream target detection algorithms, the precision and sensitivity of the proposed method are improved by 11.33% and 9.03%, respectively. [ABSTRACT FROM AUTHOR]

The objective of this study was to demonstrate the performance characteristics and potential utility of a novel tomosynthesis device as applied to imaging the chest, specifically relating to lung nodules. The imaging characteristics and quality of a novel digital tomosynthesis prototype system was assessed by scanning, a healthy volunteer, and an andromorphic lung phantom with different configurations of simulated pulmonary nodules. The adequacy of nodule detection on the phantoms was rated by chest radiologists using a standardized scale. Results from using this tomosynthesis device demonstrate in plane resolution of 16lp/cm, with estimated effective radiation doses of 90% less than low dose CT. Nodule detection was adequate across various anatomic locations on a phantom. These proof-of-concept tests showed this novel tomosynthesis device can detect lung nodules with low radiation dose to the patient. This technique has potential as an alternative to low dose chest CT for lung nodule screening and tracking. [ABSTRACT FROM AUTHOR]

In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL networks, especially convolutional neural networks (CNNs) in elevating sensitivity, accuracy, and specificity as well as overcoming false positives in the initial stages of lung cancer detection. This paper further presented the integral nodule classification stage, which stressed the importance of differentiating between benign and malignant nodules for initial cancer diagnosis. Moreover, the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification, highlighting the evolution of methodologies from conventional machine learning (ML) classifiers to transfer learning and integrated CNNs. Interestingly, while accepting the strides formed by CAD systems, the review addressed persistent challenges. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the accuracy of GPT-3.5, GPT-4, and a fine-tuned GPT-3.5 model in applying Fleischner Society recommendations to lung nodules. Methods: We generated 10 lung nodule descriptions for each of the 12 nodule categories from the Fleischner Society guidelines, incorporating them into a single fictitious report (n = 120). GPT-3.5 and GPT-4 were prompted to make follow-up recommendations based on the reports. We then incorporated the full guidelines into the prompts and re-submitted them. Finally, we re-submitted the prompts to a fine-tuned GPT-3.5 model. Results were analyzed using binary accuracy analysis in R. Results: GPT-3.5 accuracy in applying Fleischner Society guidelines was 0.058 (95% CI: 0.02, 0.12). GPT-4 accuracy was improved at 0.15 (95% CI: 0.09, 0.23; P =.02 for accuracy comparison). In recommending PET-CT and/or biopsy, both GPT-3.5 and GPT-4 had an F-score of 0.00. After explicitly including the Fleischner Society guidelines in the prompt, GPT-3.5 and GPT-4 significantly improved their accuracy to 0.42 (95% CI: 0.33, 0.51; P <.001) and to 0.66 (95% CI: 0.57, 0.74; P <.001), respectively. GPT-4 remained significantly better than GPT-3.5 (P <.001). The fine-tuned GPT-3.5 model accuracy was 0.46 (95% CI: 0.37, 0.55), not different from the GPT-3.5 model with guidelines included (P =.53). Conclusion: GPT-3.5 and GPT-4 performed poorly in applying widely known guidelines and never correctly recommended biopsy. Flawed knowledge and reasoning both contributed to their poor performance. While GPT-4 was more accurate than GPT-3.5, its inaccuracy rate was unacceptable for clinical practice. These results underscore the limitations of large language models for knowledge and reasoning-based tasks. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ObjectivesWe 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.Methods167 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.ResultsMedian 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.ConclusionsCTCs identified by the Labyrinth system are a promising biomarker for early-stage LC detection in clinical practice.

The article comments on the implementation of centralized and decentralized lung cancer screening (LCS) programs. It identifies advantages of centralized LCS programs that include high quality shared decision-making, low-dose computed tomography scans, multidisciplinary and standardized approach and centralized data infrastructure. Advantages of decentralized programs include greater access to screening across health care facilities, while challenges include quality control and standardization.

This study aimed to 3D print a patient-specific chest phantom simulating multiple lung nodules to optimise low-dose Computed Tomography (CT) protocols for lung cancer screening. The chest phantom, which was developed from a single patient’s chest CT images, was fabricated using a variety of materials, including polylactic acid (PLA), Glow-PLA, acrylonitrile butadiene styrene (ABS), and polyurethane resin. The phantom was scanned under different low-dose (LDCT) and ultra-low-dose CT (ULDCT) protocols by varying the kilovoltage peak (kVp) and milliampere-seconds (mAs). Subjective image quality of each scan (656 images) was evaluated by three radiologists using a five-point Likert scale, while objective image quality was assessed using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Anatomical conformance was assessed by comparing tissue diameters of the phantom and patient scans using Bland–Altman analysis. The phantom’s lung tissue, lung nodules, and diaphragm demonstrated radiation attenuation comparable to patient tissue, as measured in Hounsfield Units (HU). However, significant variations in HU were observed for the skin, subcutaneous fat, muscle, bone, heart, lung vessels, and blood vessels compared to patient tissues, with values ranging from 93.9 HU to −196 HU (p < 0.05). Both SNR and CNR decreased as the effective dose was reduced, with a strong positive linear correlation (r = 0.927 and r = 0.931, respectively, p < 0.001, Jamovi, version 2.3.28). The median subjective image quality score from radiologists was 4, indicating good diagnostic confidence across all CT protocols (κ = −0.398, 95% CI [−0.644 to −0.152], p < 0.002, SPSS Statistics, version 30). An optimal protocol of 80 kVp and 30 mAs was identified for lung nodule detection, delivering a dose of only 0.23 mSv, which represents a 96% reduction compared to standard CT protocols. The measurement error between patient and phantom scans was −0.03 ± 0.14 cm. These findings highlight the potential for significant dose reductions in lung cancer screening programs. Further studies are recommended to improve the phantom by selecting more tissue-equivalent materials.

Purpose: We aimed to compare the diagnostic yield and procedure-related complications of two different types of systems for percutaneous CT-guided lung biopsy. Material and methods: All patients with a lung lesion who underwent a CT-guided lung biopsy at our institution, between January 2019 and 2021, were retrospectively analyzed. The inclusion criteria were: (a) Procedures performed using either a fully automated tru-cut or a semi-automated full-core biopsy needle, (b) CT images demonstrating the position of the needles within the lesion, (c) histopathological result of the biopsy and (d) clinical follow-up for at least 12 months and\or surgical histopathological results. A total of 400 biopsy fulfilling the inclusion criteria were selected and enrolled in the study. Results: Overall technical success was 100% and diagnostic accuracy was 84%. Tru-cut needles showed a significantly higher diagnostic accuracy when compared to full-core needles (91% vs. 77%, p = 0.0004) and a lower rate of pneumothorax (31% vs. 41%, p = 0.047). Due to the statistically significant different of nodules size between the two groups, we reiterated the statistical analysis splitting our population around the 20 mm cut-off for nodule size. We still observed a significant difference in diagnostic accuracy between tru-cut and full-core needles favoring the former for both smaller and larger lesions (81% vs. 71%, p = 0.025; and 92% vs. 81%; p = 0.01, respectively). Conclusion: Our results demonstrated that the use of automated tru-cut needles is associated with higher histopathological diagnostic accuracy compared to semi-automated full-core needles for CTLB. [ABSTRACT FROM AUTHOR]

Introduction: A minimally invasive approach in the diagnosis of peripheral pulmonary lesions may pose a challenge for thoracic surgeons when selecting the most appropriate technique. Electromagnetic navigation bronchoscopy (ENB) is an innovative technique used in the diagnosis of lung tumors. Aim: Evaluation of the merits of ENB in patient manifesting undiagnosed pulmonary and mediastinal lesions, while concurrently investigating its influence on the determination of the ultimate therapeutic strategy. Case study: The 73-year-old male patient with a suspicious lung mass was admitted to the hospital for diagnosis and subsequent treatment. The patient underwent an invasive diagnosis of lung and mediastinum using ENB with cryobiopsy. The histopathological examination revealed adenocarcinoma and lymphoma cells, ultimately guiding the initiation of appropriate therapeutic measures. Results and discussion: Among the available methods, those with the highest diagnostic yield and safety should be chosen. Precise diagnosis of pulmonary lesions holds paramount importance for guiding appropriate treatment in cases of suspected malignancy. Conclusions: ENB with cryobiopsy proves effective diagnosis in case of peripheral lesions that are difficult to access for classical techniques. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic‐based score (Rad‐score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. Methods Retrospective data from two institutions (July 10, 2006—September 24, 2015) was used to develop and validate the Rad‐score for distinguishing lung nodule malignancy. The prognostic value of the Rad‐score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli‐Rad‐score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. Results The Rad‐score demonstrated great performance in distinguishing malignancy, with C‐index values of 0.793 [95% CI: 0.729–0.856] in the training set and 0.730 [95% CI: 0.666–0.874] in the validation set. In independent LARC cohorts, Rad‐score validation achieved C‐index values of 0.794 [95% CI: 0.737–0.851] and 0.747 [95% CI: 0.615–0.879]. Regarding prognostic prediction, Rad‐score effectively stratified patients. Cli‐Rad‐score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C‐index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). Conclusions CT‐based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad‐score predicts prognosis independently. Incorporation of Cli‐Rad‐score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

Lung nodule detection in computed tomography (CT) imaging is crucial for the early diagnosis and treatment of lung disorders. This study explores the application of deep learning algori thms, specifically YOLOv5, Faster R-CNN, and a proposed improved YOLOv5 (iYOLOv5) for detecting lung nodules. Using the LIDC-IDRI dataset, models are trained to recognize lung nodules accurately. The proposed iYOLOv5 model consistently outperformed the baseline models, achieving an impressive mean Average Precision (mAP) of 0.853 at a learning rate of 0.01 and a batch size of 32, compared to YOLOv5’s mAP of 0.793. Additionally, iYOLOv5 achieved a mAP of 0.864 with a learning rate of 0.001 and a batch size of 32, surpassing YOLOv5’s 0.858. Proposed iYOLOv5 also demonstrated better convergence during training, with lower loss values across various configurations, such as 0.0177 at a learning rate of 0.1 and a batch size of 32, compared to YOLOv5’s 0.0217. Additionally, lung nodules were classified into well-circumscribed, juxta-vascular, and juxta-pleural categories using CNN and CNN-SVM, with CNN-SVM showing promising results with an accuracy of 98.17%. These findings underscore the potential of the iYOLOv5 model in enhancing lung nodule detection and classification, contributing to improved diagnostic accuracy in medical imaging.

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 Background The safety and efficacy of 17-gauge needles used in CT-guided percutaneous cryoablation for lung nodules were explored in this study. The purpose of the study was to compare the findings with earlier research and multi-center clinical trials that used various needle sizes. Methods Between 2016 and 2020, a retrospective study was conducted with approval from the institutional review board. A total of 41 patients were enrolled, and 71 lung nodules were treated in 63 cryoablation procedures using local anesthesia. Complication rates were recorded, and overall survival rates as well as tumor progression-free rates were calculated using the Kaplan-Meier method. Results Self-limited hemoptysis was caused by 12.9% of the procedures, and drainage was required for pneumothoraces resulting from 11.3% of them. The overall survival rates at one, two, three, and four years were 97%, 94%, 82%, and 67%, respectively. The tumor progression-free rates at one, two, three, and four years were 86.2%, 77%, 74%, and 65%, respectively. Conclusion Cryoablation for lung nodules using 17-Gauge needles can achieve similar rates of survival and tumor control rates, similar or even lower complication rates as compared with other studies and multi-center trials using mixed sized needles.

Abstract Background Video‐assisted thoracoscopic surgery (VATS) resection of deep‐seated lung nodules smaller than 1 cm is extremely challenging. Several methods have been proposed to overcome this limitation but with not neglectable complications. Intraoperative lung ultrasound (ILU) is the latest minimally invasive proposed technique. The aim of the current study was to analyze the accuracy and efficacy of ILU associated with VATS to visualize solitary and deep‐seated pulmonary nodules smaller than 1 cm. Methods Patients with subcentimetric solitary and deep‐seated pulmonary nodules were included in this retrospective study from November 2020 to December 2022. Patients who received VATS aided with ILU were considered as group A and patients who received conventional VATS as group B (control group). The rate of nodule identification and the time for localization with VATS alone and with VATS aided with ILU in each group were analyzed. Results A total of 43 patients received VATS aided with ILU (group A) and 31 patients received conventional VATS (group B). Mean operative time was lower in group A (p

Abstract Objective To evaluate the detection rate and performance of 18F-FDG PET alone (PET), the combination of PET and low-dose thick-slice CT (PET/lCT), PET and diagnostic thin-slice CT (PET/dCT), and additional computer-aided detection (PET/dCT/CAD) for lung nodules (LN)/metastases in tumor patients. Along with this, assessment of inter-reader agreement and time requirement for different techniques were evaluated as well. Methods In 100 tumor patients (56 male, 44 female; age range: 22–93 years, mean age: 60 years) 18F-FDG PET images, low-dose CT with shallow breathing (5 mm slice thickness), and diagnostic thin-slice CT (1 mm slice thickness) in full inspiration were retrospectively evaluated by three readers with variable experience (junior, mid-level, and senior) for the presence of lung nodules/metastases and additionally analyzed with CAD. Time taken for each analysis and number of the nodules detected were assessed. Sensitivity, specificity, positive and negative predictive value, accuracy, and Receiver operating characteristic (ROC) analysis of each technique was calculated. Histopathology and/or imaging follow-up served as reference standard for the diagnosis of metastases. Results Three readers, on an average, detected 40 LN in 17 patients with PET only, 121 LN in 37 patients using ICT, 283 LN in 60 patients with dCT, and 282 LN in 53 patients with CAD. On average, CAD detected 49 extra LN, missed by the three readers without CAD, whereas CAD overall missed 53 LN. There was very good inter-reader agreement regarding the diagnosis of metastases for all four techniques (kappa: 0.84–0.93). The average time required for the evaluation of LN in PET, lCT, dCT, and CAD was 25, 31, 60, and 40 s, respectively; the assistance of CAD lead to average 33% reduction in time requirement for evaluation of lung nodules compared to dCT. The time-saving effect was highest in the less experienced reader. Regarding the diagnosis of metastases, sensitivity and specificity combined of all readers were 47.8%/96.2% for PET, 80.0%/81.9% for PET/lCT, 100%/56.7% for PET/dCT, and 95.6%/64.3% for PET/CAD. No significant difference was observed regarding the ROC AUC (area under the curve) between the imaging methods. Conclusion Implementation of CAD for the detection of lung nodules/metastases in routine 18F-FDG PET/CT read-out is feasible. The combination of diagnostic thin-slice CT and CAD significantly increases the detection rate of lung nodules in tumor patients compared to the standard PET/CT read-out. PET combined with low-dose CT showed the best balance between sensitivity and specificity regarding the diagnosis of metastases per patient. CAD reduces the time required for lung nodule/metastasis detection, especially for less experienced readers.

Introduction: Effective classification of lung cancers plays a vital role in lung tumor diagnosis and subsequent treatments. However, classification of benign and malignant lung nodules remains inaccurate. Methods: This study proposes a novel multimodal attention-based 3D convolutional neural network (CNN) which combines computed tomography (CT) imaging features and clinical information to classify benign and malignant nodules. Results: An average diagnostic sensitivity of 96.2% for malignant nodules and an average accuracy of 81.6% for classification of benign and malignant nodules were achieved in our algorithm, exceeding results achieved from traditional ResNet network (sensitivity of 89% and accuracy of 80%) and VGG network (sensitivity of 78% and accuracy of 73.1%). Discussion: The proposed deep learning (DL) model could effectively distinguish benign and malignant nodules with higher precision. [ABSTRACT FROM AUTHOR]

Machine learning and deep neural networks are improving various industries, including healthcare, which improves daily life. Deep neural networks, including Convolutional Neural Networks (CNNs), provide valuable insights and support in improving daily activities. In particular, CNNs enable the recognition and classification of images from CT and MRI scans and other tasks. However, training a CNN requires many datasets to attain optimal accuracy and performance, which is challenging in the medical field due to ethical worries, the lack of descriptive notes from experts and labeled data, and the overall scarcity of disease images. To overcome these challenges, this work proposes a hybrid CNN with transfer learning and a random forest algorithm for classifying lung cancer and non-cancer from CT scan images. This research aims include preprocessing lung nodular data, developing the proposed algorithm, and comparing its effectiveness with other methods. The findings indicate that the proposed hybrid CNN with transfer learning and random forest performs better than standard CNNs without transfer learning. This research demonstrates the potential of using machine learning algorithms in the healthcare industry, especially in disease detection and classification. [ABSTRACT FROM AUTHOR]

Detection of malignant lung nodules at an early stage may allow for clinical interventions that increase the survival rate of lung cancer patients. Using 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 lung nodule detection and classification with CT images based on hybrid deep learning (LNDC-HDL) techniques. First, we introduce a chaotic bird swarm optimization (CBSO) algorithm for lung nodule segmentation using statistical information. Second, we illustrate an improved Fish Bee (IFB) algorithm for feature extraction and selection. Third, we develop a 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. [ABSTRACT FROM AUTHOR]

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Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules. [ABSTRACT FROM AUTHOR]

The combination of low dose CT and AI performance in the pediatric population has not been explored. Understanding this relationship is relevant for pediatric patients given the potential radiation risks. Here, the objective was to determine the diagnostic performance of commercially available Computer Aided Detection (CAD) for pulmonary nodules in pediatric patients at simulated lower radiation doses. Retrospective chart review of 30 sequential patients between 12–18 years old who underwent a chest CT on the Siemens SOMATOM Force from December 20, 2021, to April 12, 2022. Simulated lower doses at 75%, 50%, and 25% were reconstructed in lung kernel at 3 mm slice thickness using ReconCT and imported to Syngo CT Lung CAD software for analysis. Two pediatric radiologists reviewed the full dose CTs to determine the reference read. Two other pediatric radiologists compared the Lung CAD results at 100% dose and each simulated lower dose level to the reference on a nodule by nodule basis. The sensitivity (Sn), positive predictive value (PPV), and McNemar test were used for comparison of Lung CAD performance based on dose. As reference standard, 109 nodules were identified by the two radiologists. At 100%, and simulated 75%, 50%, and 25% doses, lung CAD detected 60, 62, 58, and 62 nodules, respectively; 28, 28, 29, and 26 were true positive (Sn = 26%, 26%, 27%, 24%), 30, 32, 27, and 34 were false positive (PPV = 48%, 47%, 52%, 43%). No statistically significance difference of Lung CAD performance at different doses was found, with p-values of 1.0, 1.0, and 0.7 at simulated 75%, 50%, and 25% doses compared to standard dose. Conclusion: The Lung CAD shows low sensitivity at all simulated lower doses for the detection of pulmonary nodules in this pediatric population. However, radiation dose may be reduced from standard without further compromise to the Lung CAD performance. What is Known: • High diagnostic performance of Lung CAD for detection of pulmonary nodules in adults. • Several imaging techniques are applied to reduce pediatric radiation dose. What is New: • Low sensitivity at all simulated lower doses for the detection of pulmonary nodules in our pediatric population. • Radiation dose may be reduced from standard without further compromise to the Lung CAD performance. [ABSTRACT FROM AUTHOR]

PurposeRecent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel.MethodA cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC 50% of radiomic features.ConclusionWe systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.