Detection Of Pulmonary Nodules Research Articles

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection on chest computed tomography: comparison of simulated lower radiation doses.

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. • High diagnostic performance of Lung CAD for detection of pulmonary nodules in adults. • Several imaging techniques are applied to reduce pediatric radiation dose. • 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.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Detection of low‐dose computed tomography pulmonary nodules based on 3D CNN‐CapsNet

AbstractLung cancer is currently one of the diseases with the highest mortality rate. Early detection of pulmonary nodules is consistently one of the most effective ways to improve the overall survival of cancer patients. However, the existing deep learning‐based pulmonary nodule detection methods still have some problems such as low sensitivity, high false positives, and difficulty in detecting small nodules. To solve the above problems, a low‐dose computed tomography pulmonary nodule detection algorithm based on 3D convolution neural network and capsule network, namely 3D CNN‐CapsNet, is proposed in this work. Combination of full CNN and capsule network reduces the dependence of CNNs on a large amount of data. In 3D CNN‐CapsNet, the convolution kernel of different sizes is applied to the features of different channels to extract richer context information. Then, fused features of different scales are fed into the capsule network for representative feature extraction and more accurate classification. The authors evaluate their proposed method on Early Lung Cancer Program dataset. The nodule detection rate is 95.19%, the sensitivity is 92.31%, the specificity is 98.08%, and the F1‐score is 0.95 which are much better than other baseline methods. These experiments demonstrate that 3D CNN‐CapsNet can effectively improve the detection accuracy of nodules, and can better meet the diagnostic needs of pulmonary nodules.

CT-guided percutaneous marking of small pulmonary nodules with [99mTc]Tc-Macrosalb is very accurate and allows minimally invasive lung-sparing resection: a single-centre quality control.

The detection of small lung nodules in thoracoscopic procedure is difficult when the lesions are not located within the outer border of the lung. In the case of ground-glass opacities, it is often impossible to palpate the lesion. Marking lung nodules using a radiotracer is a known technique. We analysed the accuracy and safety of the technique and the potential benefits of operating in a hybrid operating room. 57 patients, including 33 (58%) females with a median age of 67 years (range 21-82) were included. In 27 patients, we marked and resected the lesion in a hybrid room. In 30 patients, the lesion was marked at the department of radiology the day before resection. [99mTc]Tc-Macrosalb (Pulmocis®) was used at an activity of 1 MBq in the hybrid room and at an activity of 3 MBq the day before to get technical feasible results. Radioactivity was detected using the Neoprobe® detection system. Precise detection and resection of the nodules was possible in 95% of the lesions and in 93% of the patients. Complete thoracoscopic resection was possible in 90% of the patients. Total conversion rate was 10%, but conversion due to failure of the marking of the nodule was observed in only 5% of the patients. Histology revealed 28 (37%) primary lung cancers, 24 (32%) metastases and 21 (28%) benign lesions. In 13 (23%) patients, minor complications were observed. None of them required additional interventions. The radio-guided detection of small pulmonary nodules is very accurate and safe after CT-guided injection of [99mTc]Tc-Macrosalb. Performing the operation in a hybrid room has several logistic advantages and allows using lower technetium-99m activities. The technique allows minimally invasive lung sparing resection and prevents overtreatment of benign and metastatic lesions.

The added value of using artificial intelligence in adult chest X-rays for nodules and masses detection in daily radiology practice

BackgroundPulmonary nodule detection in CXR is challenging. Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s interpretation and to assess its impact on the reporting process. The current study included 150 patients who had CXR interpreted by radiologists and by AI software.ResultsCT detected pulmonary nodules in 99 cases (66%) while the visual model of analysis, as well as AI, detected nodules among 92 cases (61.3%) compared to 93 (62%) cases detected by combined visual/AI model. A total of 216 nodules were detected by CT (64.4% solid and 31.5% GG). Only 188 nodules were detected by the AI while 170 nodules were detected by visual analysis. As per case classification or nodule analysis, the AI showed the highest area under curve (AUC) (0.890, 95% CI) and (0.875, 95% CI), respectively, followed by the combined visual/AI model. Regarding the nodules’ texture, the AI model’s sensitivity for solid nodules was 91.4% which was greater than the combined visual/AI and visual models alone, while in GG nodules, the combined visual/AI model’s sensitivity was higher than the AI and visual models. The probability score using the combined visual/AI model was significantly higher than using the visual model alone (P value = 0.001).ConclusionsThe use of the AI model in CXR interpretation regarding nodules and masses detection helps in more accurate decision-making and increases the diagnostic performance affecting the patient’s morbidity and mortality.

Clinical application of radiological AI for pulmonary nodule evaluation: Replicability and susceptibility to the population shift caused by the COVID-19 pandemic

Purposereplicability and generalizability of medical AI are the recognized challenges that hinder a broad AI deployment in clinical practice. Pulmonary nodes detection and characterization based on chest CT images is one of the demanded use cases for automatization by means of AI, and multiple AI solutions addressing this task are becoming available. Here, we evaluated and compared the performance of several commercially available radiological AI with the same clinical task on the same external datasets acquired before and during the pandemic of COVID-19. Approach5 commercially available AI models for pulmonary nodule detection were tested on two external datasets labelled by experts according to the intended clinical task. Dataset1 was acquired before the pandemic and did not contain radiological signs of COVID-19; dataset2 was collected during the pandemic and did contain radiological signs of COVID-19. ROC-analysis was applied separately for the dataset1 and dataset2 to select probability thresholds for each dataset separately. AUROC, sensitivity and specificity metrics were used to assess and compare the results of AI performance. ResultsStatistically significant differences in AUROC values were observed between the AI models for the dataset1. Whereas for the dataset2 the differences of AUROC values became statistically insignificant. Sensitivity and specificity differed statistically significantly between the AI models for the dataset1. This difference was insignificant for the dataset2 when we applied the probability threshold initially selected for the dataset1. An update of the probability threshold based on the dataset2 created statistically significant differences of sensitivity and specificity between AI models for the dataset2. For 3 out of 5 AI models, the update of the probability threshold was valuable to compensate for the degradation of AI model performances with the population shift caused by the pandemic. ConclusionsPopulation shift in the data is able to deteriorate differences of AI models performance. Update of the probability threshold together with the population shift seems to be valuable to preserve AI models performance without retraining them.

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation

Background and objective: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules.Methods: We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality.Results: Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models.Conclusions: The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.

Multiscale self-calibrated pulmonary nodule detection network fusing dual attention mechanism

Objective. In this study, we propose a model called DEPMSCNet (a multiscale self-calibration network) that has a high sensitivity and low false positive rate for detecting pulmonary nodules. Approach. First, at the feature extraction stage, we propose to use the REPSA-MSC module instead of the traditional convolutional neural network. The module extracts multiscale information from the feature map based on the image pyramid strategy while introducing adaptive convolutional branches to detect contextual information at each position of the multiscale, thereby expanding the receptive field and improving sensitivity. At the same time, multiple branches are adaptively weighted by channel attention, and the weights of different branches are adjusted to better generate pixel-level attention. Secondly, the proposed DSAM (dual-path spatial attention module) operates at the information fusion stage. This module fully exploits the rich spatial information of CT scans, obtains receptive field information from two branches, combines low-level feature map information with high-level semantic information, and enhances location-related information to effectively improve specificity. Thirdly, the focal loss function is used to solve the problem of positive and negative sample imbalance. Main results. The proposed model has been evaluated on the public lung nodule analysis (LUNA16) challenge dataset. The technique outperforms the most recent state-of-the-art detection algorithms in terms of sensitivity and specificity, obtaining a sensitivity of 0.988 and a competitive performance metric (CPM) of 0.963. Significance. Ablation experiments show that the two modules proposed in this paper effectively reduce false positives and improve sensitivity. This model effectively reduces the number of false positive nodules that doctors see on CT scans.

Intraoperative Contrast-Enhanced Ultrasonography (Io-CEUS) in Minimally Invasive Thoracic Surgery for Characterization of Pulmonary Tumours: A Clinical Feasibility Study.

The intraoperative detection of solitary pulmonary nodules (SPNs) continues to be a major challenge, especially in minimally invasive video-assisted thoracic surgery (VATS). The location, size, and intraoperative frozen section result of SPNs are decisive regarding the extent of lung resection. This feasibility study investigates the technical applicability of intraoperative contrast-enhanced ultrasonography (Io-CEUS) in minimally invasive thoracic surgery. In this prospective, monocentric clinical feasibility study, n = 30 patients who underwent Io-CEUS during elective minimally invasive lung resection for SPNs between October 2021 and February 2023. The primary endpoint was the technical feasibility of Io-CEUS during VATS. Secondary endpoints were defined as the detection and characterization of SPNs. In all patients (female, n = 13; mean age, 63 ± 8.6 years) Io-CEUS could be performed without problems during VATS. All SPNs were detected by Io-CEUS (100%). SPNs had a mean size of 2.2 cm (0.5-4.5 cm) and a mean distance to the lung surface of 2.0 cm (0-6.4 cm). B-mode, colour-coded Doppler sonography, and contrast-enhanced ultrasound were used to characterize all tumours intraoperatively. Significant differences were found, especially in vascularization as well as in contrast agent behaviour, depending on the tumour entity. After successful lung resection, a pathologic examination confirmed the presence of lung carcinomas (n = 17), lung metastases (n = 10), and benign lung tumours (n = 3). The technical feasibility of Io-CEUS was confirmed in VATS before resection regarding the detection of suspicious SPNs. In particular, the use of Doppler sonography and contrast agent kinetics revealed intraoperative specific aspects depending on the tumour entity. Further studies on Io-CEUS and the application of an endoscopic probe for VATS will follow.

Deep learning-based automatic detection for pulmonary nodules on chest radiographs: The relationship with background lung condition, nodule characteristics, and location

PurposeComputer-aided diagnosis (CAD), which assists in the interpretation of chest radiographs, is becoming common. However, few studies have evaluated the benefits and pitfalls of CAD in the real world. This study aimed to evaluate the independent performance of commercially available deep learning-based automatic detection (DLAD) software, EIRL Chest X-ray Lung Nodule, in a cohort that included patients with background pulmonary abnormalities often encountered in clinical situations. MethodsPatients with clinically suspected lung cancer for whom chest radiography was performed within a month before or after CT scan between June 2020 and May 2022 in our institution were enrolled. The reference standard was created using a bounding box annotated by two radiologists with reference to the CT. The visibility score, characteristics, location of the pulmonary nodules, presence of overlapping structures or pulmonary disease, and background lung score were manually determined. ResultsWe included 388 patients. The DLAD software detected 222 of the 322 nodules visible on manual evaluation, with a sensitivity of 0.689 and a false-positive rate of 0.168. The detectability of the DLAD software was significantly lower for small and subsolid and nodules with overlapping structures. The visibility score and sensitivity of detection by the DLAD software were positively correlated. The relationship between the background lung score and detection by the DLAD software was unclear. ConclusionThe standalone performance of DLAD in detecting pulmonary nodules exhibited a sensitivity of 0.689 and a false-positive rate of 0.168. Understanding the characteristics of DLAD is crucial when interpreting chest radiographs with the assistance of the DLAD.

Abnormal origin of the right posterior segmental bronchus: case report and literature review

BackgroundWith the increasing availability of chest computed tomography (CT), the detection of small pulmonary nodules has become more common, facilitating the development of lung segmental resection. However, anatomical variations of the bronchi are common, particularly in the right upper lobe of the lung.Case presentationWe report a case of thoracoscopic resection of the posterior segment of the right upper lobe of the lung. Preoperatively, the nodule was believed to be located in the superior segment of the right lower lobe. However, intraoperative exploration revealed that the nodule was located in the posterior segment of the right upper lobe, further showing that the bronchi of the posterior segment of the right lung opened into the bronchus intermedius. The procedure was completed uneventfully. Postoperative retrospective three-dimensional (3D) reconstruction of the lung CT images confirmed that the bronchi of the posterior segment of the right upper lobe originated from the bronchus intermedius.ConclusionsThis rare case highlights the importance of 3D reconstruction to guide accurate segmentectomy in patients with anatomic variations.

Automated Pulmonary Nodule Classification and Detection Using Deep Learning Architectures.

Recent advancement in biomedical imaging technologies has contributed to tremendous opportunities for the health care sector and the biomedical community. However, collecting, measuring, and analyzing large volumes of health-related data like images is a laborious and time-consuming job for medical experts. Thus, in this regard, artificial intelligence applications (including machine and deep learning systems) help in the early diagnosis of various contagious/ cancerous diseases such as lung cancer. As lung or pulmonary cancer may have no apparent or clear initial symptoms, it is essential to develop and promote a Computer Aided Detection (CAD) system that can support medical experts in classifying and detecting lung nodules at early stages. Therefore, in this article, we analyze the problem of lung cancer diagnosis by classification and detecting pulmonary nodules, i.e., benign and malignant, in CT images. To achieve this objective, an automated deep learning based system is introduced for classifying and detecting lung nodules. In addition, we use novel state-of-the-art detection architectures, including, Faster-RCNN, YOLOv3, and SSD, for detection purposes. All deep learning models are evaluated using a publicly available benchmark LIDC-IDRI data set. The experimental outcomes reveal that the False Positive Rate (FPR) is reduced, and the accuracy is enhanced.

Pulmonary nodule detection on lung parenchyma images using hyber-deep algorithm

The incidence of lung cancer has seen a significant increase in recent times, leading to a rise in fatalities. The detection of pulmonary nodules from CT images has emerged as an effective method to aid in the diagnosis of lung cancer. Ensuring information security holds utmost significance in the detection of nodules, with particular attention given to safeguarding patient privacy within the context of the Internet of Things (IoT). In this regard, migration learning emerges as a potent technique for preserving the confidentiality of patient data. Firstly, we applied several data-preprocessing steps such as lung segmentation based on K-Means, denoising methods, and lung parenchyma extraction through a dedicated medical IoT network. We used the Microsoft Common Object in Context (MS-COCO) dataset to pre-train the detection framework and fine-tuned it with the Lung Nodule Analysis 16 (LUNA16) dataset to adapt to nodule detection tasks. To evaluate the effectiveness of our proposed pipeline, we conducted extensive experiments that included subjective evaluation of detection results and quantitative data analysis. The results of these experiments demonstrated the efficacy of our approach in accurately detecting pulmonary nodules. Our study provides a promising framework for trustworthy pulmonary nodule detection on lung parenchyma images using a secured hyper-deep algorithm, which has the potential to improve lung cancer diagnosis and reduce fatalities associated with it.

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma.

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection in computed tomography of the chest

PurposeTo test the performance of a commercially available adult pulmonary nodule detection artificial intelligence (AI) tool in pediatric CT chests. Methods30 consecutive chest CTs with or without contrast of patients ages 12–18 were included. Images were retrospectively reconstructed at 3 mm and 1 mm slice thickness. AI for detection of lung nodules in adults (Syngo CT Lung Computer Aided Detection (CAD)) was evaluated. 3 mm axial images were retrospectively reviewed by two pediatric radiologists (reference read) who determined the location, type, and size of nodules. Lung CAD results at 3 mm and 1 mm slice thickness were compared to reference read by two other pediatric radiologists. Sensitivity (Sn) and positive predictive value (PPV) were analyzed. ResultsThe radiologists identified 109 nodules. At 1 mm, CAD detected 70 nodules; 43 true positive (Sn = 39 %), 26 false positive (PPV = 62 %), and 1 nodule which had not been identified by radiologists. At 3 mm, CAD detected 60 nodules; 28 true positive (Sn = 26 %), 30 false positive (PPV = 48 %) and 2 nodules which had not been identified by radiologists. There were 103 solid nodules (47 measuring < 3 mm) and 6 subsolid nodules (5 measuring < 5 mm). When excluding 52 nodules (solid < 3 mm and subsolid < 5 mm) based on algorithm conditions, the Sn increased to 68 % at 1 mm and 49 % at 3 mm but there was no significant change in the PPV measuring 60 % at 1 mm and 48 % at 3 mm. ConclusionThe adult Lung CAD showed low sensitivity in pediatric patients, but better performance at thinner slice thickness and when smaller nodules were excluded.

Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets

Background: Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.Methods: The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.Findings: The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.Discussion and conclusion: Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

Cancer risk assessment in patients with persistent pulmonary nodules and its correlation with cancer-free survival.

8569 Background: With the wide use of CT for lung cancer screening and diagnosis, detection of pulmonary nodules increases drastically. Brock malignancy risk scoring is a validated risk prediction model for distinguishing malignant nodules, but it only provides a snapshot without incorporating the dynamic changes of lung nodules. Our study tested the performance of Brock malignancy risk scoring system in patients with persistent lung nodules. Methods: We prospectively studied a cohort of 304 patients with persistent lung nodules (at least 2 CT scans, 3 months apart with no evidence of shrinkage) who were under management at MD Anderson Cancer Center from 11/28/2018 to 12/14/2022. These lung nodules were assessed by radiologists with Brock full model. These patients were followed up routinely and subjected to biopsy as determined by treating physicians. The area under the receiver operating characteristic curve (AUC) and the optimal cut-off of Brock model was studied. Additionally, we studied another cohort with 130 patients with histologically confirmed lung cancer. We retrospectively reviewed the CT or PET/CT scans and assessed the corresponding persistent lung nodules as defined above prior to the cancer diagnosis. We explored the correlations among nodule characteristics, demographic factors and Brock cancer risk scores. Cox proportional hazards model was built for multivariate analysis. Results: The median follow-up time for the prospective cohort was 337 days and 40 of the 304 patients (13.16%) were diagnosed with lung cancer with a median lung cancer-free survival of 228 days. The mean risk score was 24.20% (0.12%-62.86%) for histologically confirmed malignant vs 11.01% (0.07%-61.84%) for the remaining lung nodules (P &lt; 0.001). Of note, 4 of 46 (8.70%) patients with persistent lung nodules of risk scores between 5%-10% and 8 of 134 (5.97%) patients with risk score &lt; 5% were diagnosed with lung cancer. The AUC for Brock model was 0.72 and the optimal cut-off value is 10.64% (sensitivity: 0.702, specificity:0.677). Among the retrospective cohort of 130 lung cancer patients (82.3% adenocarcinoma, 13.1% squamous cell carcinoma and 4.6% others), the predicted risk score ranged from 0.09% to 85.82%, including 33.85% of patients with risk score &lt; 10% and 26.15% of patients with predicted risk score &lt; 5%. The risk score was not correlated with age, sex, race, ethnicity, smoking history, family history of lung cancer, emphysema, nodule types or locations. The low-risk patients had a longer median cancer-free time (P = 0.001). Conclusions: Persistency is an important risk factor for malignant lung nodules. Using Brock criteria, a substantial proportion of lung nodules with true malignant potential can be overlooked because of predicted “low risk”. Improved prediction models incorporating the dynamic changes of lung nodules are warranted to guide early diagnosis of lung cancer.

Pulmonary nodule detection based on Hierarchical-Split HRNet and feature pyramid network with atrous convolution

Accurate pulmonary nodule detection is crucial to the diagnosis of lung diseases. In this work, we propose an end-to-end network for pulmonary nodule detection mainly consisting of pre-processing, detection modules for candidate prediction, and a discriminator to identify the existence of nodules. In the detection module, HS-HRNet is proposed to fulfill feature extraction on high-resolution input for pulmonary nodules that occupy tiny spaces of CT images. HS-HRNet incorporates plug-and-play Hierarchical-Split block into High-Resolution Network (HRNet) and modifies STEM with sandglass module. The main advantages of these modifications are that HS-HRNet can largely promote feature representation ability by split and concatenation operation with no significant increase in computation. In addition, a novel Feature Pyramid Network with Atrous Convolution (AC-FPN) is proposed for multi-scale feature fusion and multi-level prediction. This design allows context and spatial information in different feature levels to be leveraged and extracted under larger receptive fields. Besides, a discriminator replaces false positive reduction modules in most pulmonary detection methods. The discriminator judges the existence of nodules and back-propagates prediction proposals to detection modules through adversarial training. Experiments on publicly available datasets demonstrate competitive performance in pulmonary nodule detection.

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global search module. A multi-scale cascade module has also been introduced to enhance the model detection using attention enhancement, global information search, and contextual feature fusion. The experimental results showed that the proposed network achieved accurate detection of lung nodule information, and our method achieves a high sensitivity of 88.08% of the average FROC score on the LUNA16 dataset. In addition, ablation experiments also demonstrated the effectiveness of our method.

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.