Detection Of Pulmonary Nodules Research Articles

Small pulmonary nodule localization techniques in the era of lung cancer screening: a narrative review.

The widespread adoption of high-resolution computed tomography (CT) screening has led to increased detection of small pulmonary nodules, necessitating accurate localization techniques for surgical resection. This review examines the evolution, efficacy, and safety of various localization methods for small pulmonary nodules. Studies focusing on localization techniques for pulmonary nodules ≤30 mm in diameter were included, with emphasis on technical success rates and complication profiles. Preoperative CT-guided techniques, including hook-wire (success rate 94-98%) and anchored needle localization (success rate >99%, dislodgement rate 0%) demonstrate high technical success rates, though with varying complication profiles. Microcoil localization (97-98% success) shows comparable efficacy with lower complication rates. Dye-based methods offer simplicity but can be limited by rapid diffusion. Newer techniques like medical adhesive localization (success rate up to 100%) and electromagnetic navigation bronchoscopy (97.2% success) show promise in reducing complications and improving accuracy. Intraoperative methods such as ultrasound and hybrid operating room approaches provide real-time guidance but may be limited by nodule characteristics and available expertise. This review presents a radar chart analysis comparing techniques across key parameters and introduces an innovative decision-making algorithm that considers nodule characteristics, patient factors, and institutional resources, providing practical guidance and serving as a reference for clinicians. While no single method is universally superior, the trend towards minimally invasive, precise, and flexible approaches is evident. Future research should focus on large-scale comparative studies and the integration of artificial intelligence for optimized technique selection and improved patient outcomes.

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR. A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses. A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001). DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT. Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

Comparison of diagnostic performance for pulmonary nodule detection between free-breathing spiral ultrashort echo time and free-breathing radial volumetric interpolated breath-hold examination

ObjectiveThis 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).MethodsPatients 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.ResultsA 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).ConclusionThe 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.

Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution.

Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications. The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results. In this study, based on YOLOv5s, a concentrated-comprehensive convolution (C3_ODC) module with multidimensional attention is designed in the convolutional layer of the original backbone network for enhancing the feature-extraction capabilities of the model. Moreover, lightweight convolution is combined with weighted bidirectional feature pyramid networks (BiFPNs) to form a GS-BiFPN structure that enhances the fusion of multiscale features while reducing the number of model parameters. Finally, Focal Loss is combined with the normalized Wasserstein distance (NWD) to optimize the loss function. Focal loss focuses on carcinoma-positive samples to mitigate class imbalance, whereas the NWD enhances the detection performance of small lung nodules. In comparison experiments against the YOLOv5s, the proposed model improved the average precision by 8.7% and reduced the number of parameters and floating-point operations by 5.4% and 8.2%, respectively, while achieving 116.7 frames per second. The proposed model balances high detection accuracy against real-time requirements.

Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study.

Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored. Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. A total of 267 participants were enrolled, including 210 with low-risk and 57 with moderate-risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate-risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611-0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974-1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data. The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability. Number ChiCTR2400081283.

Comparative performance of marking of small-diameter peripheral lung neoplasms by preoperative transthoracic use of methylene blue preparations and placement of an anchor marker system

The study compared marking of small-diameter peripheral lung neoplasms by preoperative transthoracic use of 1% aqueous methylene blue solution and placement of an anchor marker system. The study evaluated the results of treatment of 36 patients with pulmonary nodules, including 27 men and 9 women aged 52 to 76 years, who were examined and treated at the Surgical Clinic of the S.M. Kirov Military Medical Academy in 2020-2023. Marking of small-diameter peripheral lung neoplasms by transthoracic use of 1% aqueous methylene blue solution made possible to detect abnormalities and lesions and perform a biopsy in 73.3% of cases. The duration of the diagnostic procedures was 30 [20-40] minutes. No postoperative complications were reported. The mean length of stay in this group of patients was 8 [6; 12] patient days. Placement of an anchor marker system allowed detection and verification of lung lesions in 95% of cases. In addition, the duration of videothoracoscopic biopsies was also 30 [20-40] minutes. No complications or deaths were reported. The mean length of stay was 7 [5; 11] patient days. The use of anchor markers to label small peripheral pulmonary nodules in the preoperative phase has some significant advantages compared with dye injection techniques, such as shorter duration of lesion mapping (p = 0.046) and less manipulation complications (p = 0.04), as well as a higher frequency of lesion detection during minimally invasive procedures. When comparing various techniques for marking pulmonary lesions, it was found that mapping of small-diameter peripheral pulmonary infiltrates using anchor markers is characterized by high performance, greater safety, facilitates intraoperative navigation for biopsy of small-diameter peripheral pulmonary neoplasms, and is superior than transthoracic use of 1% aqueous methylene blue in terms of rates of detection of pulmonary nodules during minimally invasive procedures, duration of manipulation, and complication rates.

WITHDRAWN: Genomic landscape features of minimally invasive adenocarcinoma and invasive lung adenocarcinoma

BackgroundThe widespread implementation of computed tomography has significantly increased the detection of small pulmonary nodules, including atypical adenomatous hyperplasia, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). Few studies have focused on the genomic differences between MIA and IAC. MethodsWe retrospectively analyzed patients with lung adenocarcinoma (LUAD) who underwent surgery from January 2020 to December 2023. Patients were categorized into MIA and IAC groups. Mutation status of common driver genes was assessed using next-generation sequencing. ResultsA total of 422 LUAD patients were included in the study, comprising 119 MIA cases and 303 IAC cases. MIA patients were younger and predominantly female compared to IAC patients. EGFR mutations were detected in 251 patients (59.5%), with the frequency of EGFR mutations increasing from 37.0% in MIA to 68.3% in IAC (p < 0.001). TP53 mutations were found in 108 patients (25.6%), with 7 cases (5.9%) in MIA and 101 cases (33.3%) in IAC (p < 0.001). ERBB2 mutations were identified in 23 MIA patients (19.3%) and 20 IAC patients (6.6%) (p < 0.001). Additionally, CDKN2A mutations were detected in 23 IAC patients (7.6%), while no mutations in this gene were found in the MIA group. Moreover, ALK and RET gene fusions were identified in 11 cases, respectively. ConclusionsERBB2 mutations and RET fusions are early genomic events in lung adenocarcinoma, while TP53 and CDKN2A mutations and ALK fusions occur later. Genomic intratumor heterogeneity likely arises early, before invasive characteristics develop.

Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs.

The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist's diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

BackgroundTo retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).MethodsIn the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared.ResultsDuring the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0–2) AI mark per ULDCT.ConclusionThe use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×).Relevance statementAI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities.Trial registrationThe OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl).Key PointsAn AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED.AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives).AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities.AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives.Graphical

Comparison of AI software tools for automated detection, quantification and categorization of pulmonary nodules in the HANSE LCS trial

Participant management in a lung cancer screening (LCS) depends on the assigned Lung Imaging Reporting and Data System (Lung-RADS) category, which is based on reliable detection and measurement of pulmonary nodules. The aim of this study was to compare the agreement of two AI-based software tools for detection, quantification and categorization of pulmonary nodules in an LCS program in Northern Germany (HANSE-trial). 946 low-dose baseline CT-examinations were analyzed by two AI software tools regarding lung nodule detection, quantification and categorization and compared to the final radiologist read. The relationship between detected nodule volumes by both software tools was assessed by Pearson correlation (r) and tested for significance using Wilcoxon signed-rank test. The consistency of Lung-RADS classifications between Software tool 1 (S1, Aview v2.5, Coreline Soft, Seoul, Korea) and Software tool 2 (S2, Prototype ‘’ChestCTExplore’’, software version ToDo, Siemens Healthineers, Forchheim, Germany) was evaluated by Cohen’s kappa (κ) and percentual agreement (PA).The derived volumes of true positive nodules were strongly correlated (r > 0.95), the volume derived by S2 was significantly higher than by S1 (P < 0.0001, mean difference: 6mm3). Moderate PA (62%) between S1 and S2 was found in the assignment of Lung-RADS classification (κ = 0.45). The PA of Lung-RADS classification to final read was 75% and 55% for S1 and S2, but the incorporation of S1 into the initial nodule detection and segmentation must be considered here. Significant nodule volume differences between AI software tools lead to different Lung-RADS scores in 38% of cases, which may result in altered participant management. Therefore, high performance and agreement of accredited AI software tools are necessary for a future national LCS program.

CSSANet: A channel shuffle slice-aware network for pulmonary nodule detection

Lung cancer stands as the leading cause of cancer related mortality worldwide. Precise and automated identification of lung nodules through 3D Computed Tomography (CT) scans is an essential part of screening for lung cancer effectively. Due to the small size of pulmonary nodules and the close correlation between neighboring slices of 3D CT images, most of the existing methods only consider the characteristics of a single slice, thus easily lead to insufficient detection accuracy of pulmonary nodules. To solve this problem, this paper proposes a Channel Shuffle Slice-Aware Network (CSSANet), which aims to fully exploit the spatial correlation between slices and effectively utilize the intra-slice features and inter-slice contextual information to achieve accurate detection of lung nodules. Specifically, we design a Group Shuffle Attention module (GSA module) to fuse the inter-slice feature in order to enhance the discrimination and extraction of corresponding shape information of distinct nodules in the same group of slices. Experiments and ablation study on a publicly available LUNA16 dataset demonstrate that the proposed method can enhance the detection sensitivity effectively. The Competition Performance Metric (CPM) score of 89.8 % is superior over other representative detection models.

Diagnostic accuracy and image quality evaluation of ultrashort echo time MRI in the lungs.

This study evaluates the diagnostic accuracy of ultrashort echo time (UTE)-MRI for detecting pulmonary nodules and image quality. A total of 46 patients at our hospital underwent unenhanced computed tomography (CT) and UTE-MRI. The image quality and number of nodules detected using CT were used as the gold standards. Three diagnostic radiologists independently recorded the image quality (visibility and sharpness of normal anatomical structures) of the CT and UTE images and the number of pulmonary nodules detected. The diagnostic accuracy, subjective image quality, and consistency between observations were statistically analyzed. Among 46 patients, 36 (78.2%) had pulmonary nodules on CT images, whereas 10 patients (21.7%) had no pulmonary nodules. A total of 48 lung nodules were detected, 3 of which were ground-glass opacities. UTE-MRI revealed 46 lung nodules. Compared with CT, the sensitivity of all MRI readers for detecting lung lesions was 95.8%, and the 3-observer agreement was nearly perfect (P < .001, Kendall Wa [Kender Harmonious Coefficient] = 0.913). The overall image quality score of the observers was high, ranging from good to excellent, and the consistency of the subjective UTE-MRI image quality was good (Kendall Wa = 0.877, P < .001). For tracheal display, the subsegment of the bronchus was displayed, and the wall of the tube was clearly displayed. The difference in the Wa values between the observers was 0.804 (P < .001), indicating strong consistency. For blood vessels, subsegment blood vessels could also be displayed with clear walls and uniform signals (Kendal Wa = 0.823, P < .001), indicating strong consistency. Compared to CT, UTE-MRI can detect pulmonary nodules with a high detection rate, relatively good image quality, and strong consistency between observers. The development of UTE-MRI can provide a novel imaging method for the detection and follow-up of pulmonary nodules and diagnosis of pneumonia by reducing ionizing radiation.

Full dimensional dynamic 3D convolution and point cloud in pulmonary nodule detection

Lung cancer is a leading cause of death worldwide, making early and accurate diagnosis essential for improving patient outcomes. Recently, deep learning (DL) has proven to be a powerful tool, significantly enhancing the accuracy of computer-aided pulmonary nodule detection (PND). In this study, we introduce a novel approach called the Omni-dimension Dynamic Residual 3D Net (ODR3DNet) for PND, which utilizes full-dimensional dynamic 3D convolution, along with a specialized machine learning algorithm for detecting lung nodules in 3D point clouds. The primary goal of ODR3DNet is to overcome the limitations of conventional 3D Convolutional Neural Networks (CNNs), which often struggle with adaptability and have limited feature extraction capabilities. Our ODR3DNet algorithm achieves a high CPM (Competition Performance Metric) score of 0.885, outperforming existing mainstream PND algorithms and demonstrating its effectiveness. Through detailed ablation experiments, we confirm that the OD3D module plays a crucial role in this performance boost and identify the optimal configuration for the algorithm. Moreover, we developed a dedicated machine learning detection algorithm tailored for lung 3D point cloud data. We outline the key steps for reconstructing the lungs in 3D and establish a comprehensive process for building a lung point cloud dataset, including data preprocessing, 3D point cloud conversion, and 3D volumetric box annotation. Experimental results validate the feasibility and effectiveness of our proposed approach.

A Data Augmentation Method and the Embedding Mechanism for Detection of Pulmonary Nodules on Small Samples

Abstract Lung Computed Tomography (CT) screening for pulmonary nodules provides an effective method for early diagnosis. The deep-learning-based computer-aided detection (CAD) system effectively identifies and precisely localizes suspicious pulmonary nodules in CT images, thereby significantly enhancing the accuracy and efficiency of CT diagnosis. In the medical field, the availability of medical data is limited, and research using small samples is of practical significance. By studying the data augmentation technology based on the generative model under the condition of small samples, and refining the model structure through the embedding mechanism, the accuracy and robustness of the deep learning model are explored. A 3D pixel-level statistical algorithm is proposed for the generation of pulmonary nodules. By combining simulated pulmonary nodules with healthy lung tissue, we can generate new samples of pulmonary nodules. The embedding mechanism is designed to enhance the comprehension of pixel meanings in pulmonary nodule samples by introducing latent variables. The results of the 3DVNET model with the augmentation method for pulmonary nodule detection under small sample conditions demonstrate that the proposed data augmentation method outperforms the method based on a generative adversarial network (GAN) framework, and the embedding mechanism for pulmonary nodules detection shows a significant improvement in accuracy. Conclusion: the proposed data augmentation method and embedding mechanism demonstrate significant potential in enhancing the accuracy and robustness of the model, thereby facilitating their application to various common imaging diagnostic tasks, and research using small samples is of practical significance.

Expressive feature representation pyramid network for pulmonary nodule detection

Expressive feature representation pyramid network for pulmonary nodule detection

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images

BackgroundThis study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.MethodsNon-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group.ResultsWhen distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).ConclusionThe AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Virtual thoracoscopic imaging for accurate pulmonary nodule localization: clinical experience.

The increasing detection of small pulmonary nodules on computed tomography (CT) warrants simple and effective nodule localization methods. We describe our clinical experience using an experimental computer that displays virtual thoracoscopic images. This device constructs three-dimensional images from preoperative CT scans and simulates the deflated lung parenchyma in the lateral decubitus position. Five patients underwent lung resection using this technology. The device provided images that closely resembled actual thoracoscopic images in all cases. This method addresses the limitations of other localization techniques such as allergic reactions and mechanical marker-related complications. The method only requires preoperative CT images, and the process is semi-automatically performed by specifying the nodule location, thoracoscopic camera insertion site, and camera angle. This study is still in the preliminary phase and has several limitations. However, this method has the potential to accurately predict nodule locations and eliminate the many risks associated with other techniques.

NAS FD Lung: A novel lung assist diagnostic system based on neural architecture search

In the detection and recognition of lung nodules, pulmonary nodules vary in size and shape and contain many similar tissues and organs around them, leading to the problems of both missed detection and false detection in existing detection algorithms. Designing proprietary detection and recognition networks manually requires substantial professional expertise. This process is time-consuming and labour-intensive and leads to issues like parameter redundancy and improper feature selection. Therefore, this paper proposes a new pulmonary CAD (computer-aided diagnosis) system for pulmonary nodules, NAS FD Lung (Using the NAS approach to search deep FPN and DPN networks), that can automatically learn and generate a deep learning network tailored to pulmonary nodule detection and recognition task requirements. NAS FD Lung aims to use automatic search to generate deep learning networks in the auxiliary diagnosis of pulmonary nodules to replace the manual design of deep learning networks. NAS FD Lung comprises two automatic search networks: BM NAS-FPN (Using NAS methods to search for deep FPN structures with Binary operation and Matrix multiplication fusion methods) network for nodule detection and NAS-A-DPN (Using the NAS approach to search deep DPN networks with attention mechanism) for nodule identification. The proposed technique is tested on the LUNA16 dataset, and the experimental results show that the model is superior to many existing state-of-the-art approaches. The detection accuracy of lung nodules is 98.23%. Regarding the lung nodules classification, the accuracy, specificity, sensitivity, and AUC values achieved 96.32%,97.14%,95.82%, and 98.33%, respectively.

EP.04B.03 The Diagnostic Efficacy of Seven Lung Cancer Autoantibodies in Early Detection of Pulmonary Ground-Glass Nodules

EP.04B.03 The Diagnostic Efficacy of Seven Lung Cancer Autoantibodies in Early Detection of Pulmonary Ground-Glass Nodules

Leveraging artificial intelligence for timely detection and referral of incidental pulmonary nodules at a safety-net hospital.

422 Background: Incidental pulmonary nodules (IPNs) are commonly reported through a multitude of radiologic exams. A small number of these IPNs can harbor lung cancer (LC). To aide LC detection rates, hospitals must identify and validate the completion of appropriate follow-up regarding IPNs in a timely manner. The problem with timely referral and completion of work up is found to be amplified in safety-net settings with limited resources. John Peter Smith (JPS) hospital, a safety-net hospital, has developed a method to identify, track, and refer patients presenting with IPNs. Methods: JPS has created an algorithm to classify all IPNs by means of Thynk Health, an Artificial Intelligence (AI) platform that uses Natural Language Processing (NLP) to identify and track incidental findings and lung cancer screening patients (LCS). The algorithm created a scoring system by combining Fleischner Society (2017) and the American College of Radiology (ACR) LCS (Lung-RADS) guideline recommendations. The scoring system was designed to mimic Lung-RADS established by the ACR (1, 2, 3, 4A, and 4B). The assigned score creates a risk stratification to assist providers in the patient’s care plan. Using NLP, the project queried radiologic exams performed at JPS searching for key terminology relating to IPNs. Findings were then reviewed by radiology staff for validity and assigned a score based on the algorithm. Program exclusions, when identified in the radiology report, included: patient &lt; 35 years, patient has known cancer or under surveillance of oncology team, abnormalities favoring infectious process, LCS patients and when nodule size is not reported in the radiologist’s dictation. Results: A current state analysis was completed from 3/25/24 to 4/25/24. The AI platform analyzed 5,276 CT exams (2049 abdomen, 1347 head/neck, and 1880 chest) involving partial or complete imaging of the lungs. A total of 727 (13.8%) IPNs were flagged for review. After excluding exams falling outside of the set program parameters, a total of 178 (3.4%) IPNs were identified. 21 (11.8%) of the IPNs identified received a score of 4A or 4B. Patients found to have a score of 4A (11, 6.2%) and 4B (10, 5.6%) underwent additional review. Conclusions: Our team has created a unique system that can be used as a model for other large health systems. By leveraging technology, we are able to successfully identify key factors associated with IPNs to help ensure patients receive the recommended care in a timely manner. Results of JPS incidental pulmonary nodule program: first monthly analysis. JPS Pulmonary Nodule Score 4A 4B Number of Patients (% of IPNs) 11 (6.2%) 10 (5.6%) Median Age (years) 70 68.5 JPS Assigned PCP 1 7 (63.6%) 7 (70%) Median Smoking History (PY) 2 6.4 24.5 Average Nodule Size (mm) 11.5 34.3 Pulmonary Referral 3/11 (27.3%) 7/10 (70%) Positive for Cancer 0 3/4 (75%) 1 Primary care provider. 2 Pack years.