Explore the vast range of titles available.

Introduction The sensitivity of suspicious lung nodules biopsied by currently available techniques is suboptimal. Robotic-assisted navigation bronchoscopy (RANB) is a novel method for biopsying lung nodules. Our study objective was to determine the sensitivity for malignancy and overall diagnostic accuracy for RANB when combined with cone beam CT (CBCT) for secondary confirmation. Methods 52 consecutive patients were prospectively enrolled. Demographic data, nodule characteristics, procedural information, and follow-up results were obtained. Results Mean patient age was 66, with the majority Caucasian (73%) females (65%) with a similar number of never (46%) and former (46%) smokers. 15 patients had a history of cancer and 3 had a prior thoracic surgery. 59 total nodules were included as 7 patients had two nodules biopsied. Mean nodule diameter was < 2 cm in all dimension with the majority solid (41, 70%) and located in the upper lobes (left: 22, 37%; right: 17, 29%). Bronchus sign was absent (32, 54%) or present (27, 46%) in a similar number. All nodules were successfully reached with nine (15%) requiring minor directional changes after initial cone beam CT. A tissue diagnosis was obtained in 83% (49/59) of biopsied nodules, with malignancy (31, 65%) most common. Including all biopsy results and follow-up imaging, we obtained an 84% (31/37) procedural sensitivity for malignancy and an overall 86% (51/59) diagnostic yield. Conclusion RANB with CBCT increases sensitivity for malignancy and diagnostic accuracy of lung nodule biopsies. Combining these modalities has the potential to shift the diagnostic approach to pulmonary nodules.

Background Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules. Results 3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907. Conclusion The 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules.

Lung cancer has one of the worst morbidity and fatality rates of any malignant tumour. Most lung cancers are discovered in the middle and late stages of the disease, when treatment choices are limited, and patients’ survival rate is low. The aim of lung cancer screening is the identification of lung malignancies in the early stage of the disease, when more options for effective treatments are available, to improve the patients’ outcomes. The desire to improve the efficacy and efficiency of clinical care continues to drive multiple innovations into practice for better patient management, and in this context, artificial intelligence (AI) plays a key role. AI may have a role in each process of the lung cancer screening workflow. First, in the acquisition of low-dose computed tomography for screening programs, AI-based reconstruction allows a further dose reduction, while still maintaining an optimal image quality. AI can help the personalization of screening programs through risk stratification based on the collection and analysis of a huge amount of imaging and clinical data. A computer-aided detection (CAD) system provides automatic detection of potential lung nodules with high sensitivity, working as a concurrent or second reader and reducing the time needed for image interpretation. Once a nodule has been detected, it should be characterized as benign or malignant. Two AI-based approaches are available to perform this task: the first one is represented by automatic segmentation with a consequent assessment of the lesion size, volume, and densitometric features; the second consists of segmentation first, followed by radiomic features extraction to characterize the whole abnormalities providing the so-called “virtual biopsy”. This narrative review aims to provide an overview of all possible AI applications in lung cancer screening.

Background To assess relative safety and diagnostic performance of low- and standard-dose computed tomography (CT)-guided biopsy for pulmonary nodules (PNs). Materials and methods This was a single-center prospective randomized controlled trial (RCT). From June 2020 to December 2020, consecutive patients with PNs were randomly assigned into low- or standard-dose groups. The primary outcome was diagnosis accuracy. The secondary outcomes included technical success, diagnostic yield, operation time, radiation dose, and biopsy-related complications. This RCT was registered on 3 January 2020 and listed within ClinicalTrials.gov (NCT04217655). Results Two hundred patients were randomly assigned to low-dose (n = 100) and standard-dose (n = 100) groups. All patients achieved the technical success of CT-guided biopsy and definite final diagnoses. No significant difference was found in operation time (n = 0.231) between the two groups. The mean dose-length product was markedly reduced within the low-dose group compared to the standard-dose group (31.5 vs. 333.5 mGy-cm, P < 0.001). The diagnostic yield, sensitivity, specificity, and accuracy of the low-dose group were 68%, 91.5%, 100%, and 94%, respectively. The diagnostic yield, sensitivity, specificity, and accuracy were 65%, 88.6%, 100%, and 92% in the standard-dose group. There was no significant difference observed in diagnostic yield (P = 0.653), diagnostic accuracy (P = 0.579), rates of pneumothorax (P = 0.836), and lung hemorrhage (P = 0.744) between the two groups. Conclusions Compared with standard-dose CT-guided biopsy for PNs, low-dose CT can significantly reduce the radiation dose, while yielding comparable safety and diagnostic accuracy.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of their benign or malignant nature is important for the long-term survival of patients. With the development of artificial intelligence, deep learning has become the main method for lung nodule classification. However, successful deep learning models usually require large number of parameters and carefully annotated data. In the field of medical images, the availability of such data is usually limited, which makes deep networks often perform poorly on new test data. In addition, the model based on the linear stacked single branch structure hinders the extraction of multi-scale features and reduces the classification performance. In this paper, to address this problem, we propose a lightweight interleaved fusion integration network with multi-scale feature learning modules, called MIFNet. The MIFNet consists of a series of MIF blocks that efficiently combine multiple convolutional layers containing 1 × 1 and 3 × 3 convolutional kernels with shortcut links to extract multiscale features at different levels and preserving them throughout the block. The model has only 0.7 M parameters and requires low computational cost and memory space compared to many ImageNet pretrained CNN architectures. The proposed MIFNet conducted exhaustive experiments on the reconstructed LUNA16 dataset, achieving impressive results with 94.82% accuracy, 97.34% F1 value, 96.74% precision, 97.10% sensitivity, and 84.75% specificity. The results show that our proposed deep integrated network achieves higher performance than pre-trained deep networks and state-of-the-art methods. This provides an objective and efficient auxiliary method for accurately classifying the type of lung nodule in medical images.

Background Thoracoscopic segmentectomy is the main surgical method for the treatment of earlylung cancer. With the promotion of technology and increasingly accurate criteria for lung subsegments, lung nodules with complex positions involving intersegmental and multisegments have become technical bottlenecks. This study aimed to verify whether seeking anatomical conditions for creating a fissure by tunneling techniques with precise resection of lung segments could solve this bottleneck problem. Methods The clinical data of patients with lung nodules ≤ 2 cm located in the complex position in the Department of Thoracic Surgery of Jiangsu Provincial People's Hospital from January 2019 to August 2023 were collected. Date analyzed the characteristics of patients who underwent seeking anatomical conditions for creating a fissure by tunneling techniques with precise resection of lung segments (segment group) at complex setting and compared the surgical outcomes and complications between these lobectomy patients (lobectomy group) at similar locations. Results A total of 22 patients were included segment group and 47 patients were included lobectomy group. Except for the depth ratio or tumor size or consolidation tumor ratio (CTR), there were no significant differences in the other baseline data between the two groups. All patients in segment group received a satisfactory surgical margin. Compared to the lobectomy group, surgical outcomes were better in segment group ( p  < 0.05 for postoperative hospital stay and the counts of resected subsegments). Conclusion Seeking anatomical conditions for creating a fissure by tunneling techniques is a promising technique for performing precise resection of lung segments with a safe resection margin for patients with lung nodules at complex positions involving multiple segments. It can be used as a precise resection of lung segments technique.

With advancements in technology, lung nodule detection has significantly improved in both speed and accuracy. However, challenges remain in deploying these methods in complex real-world scenarios. This paper introduces an enhanced lung nodule detection algorithm base on RT-DETR, called LN-DETR. First, we designed a Deep and Shallow Detail Fusion layer that effectively fuses cross-scale features from both shallow and deep layers. Second, we optimized the computational load of the backbone network, effectively reducing the overall scale of the model. Finally, an efficient downsampling is designed to enhance the detection of lung nodules by re-weighting contextual information. Experiments conducted on the public LUNA16 dataset demonstrate that the proposed method, with a reduced number of parameters and computational overhead, achieves 83.7% mAP@0.5 and 36.3% mAP@0.5:0.95, outperforming RT-DETR in both model size and accuracy. These results highlight the superior detection accuracy of the proposed network while maintaining computational efficiency.

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the need for the accurate and efficient detection and classification of lung nodules. This study introduces an advanced multi-stage framework designed to address the challenges of precision, scalability, and adaptability in clinical diagnostics. This study presents a comprehensive framework for the detection, segmentation, and classification of lung nodules utilizing advanced preprocessing, segmentation, classification, and optimization techniques. The framework employs Sparse Edge-Preserving Enhancement (SEPE) for pre-processing, ensuring that critical nodule-specific features are retained while reducing noise. For segmentation, an enhanced DeepLabv3 + architecture integrates Atrous Spatial Pyramid Pooling (ASPP) and Refined Boundary Decoder (RBD) modules, supported by pretrained backbones, such as EfficientNetV2, DenseNet-201, ResNet-101, and InceptionV3. The classification phase leverages a Multiscale Adaptive Swin Transformer (MA-SwinT) with a Multi-Scale Embedding Attention Mechanism (MEAM) to accurately distinguish between benign and malignant nodules. Optimization using the Fossa Optimization Algorithm (FOA) fine-tunes the hyperparameters to ensure robust performance. The experimental results demonstrate the superiority of the framework on both the LUNA16 and LIDC-IDRI datasets. On the LUNA16 dataset, segmentation achieved a Dice Coefficient of 98.75%, IoU of 97.88%, Jaccard Index of 89.62%, and Hausdorff Distance of 2.025 mm, with an accuracy of 99.15%, precision of 98.50%, recall of 99.00%, F1 score of 98.75%, and specificity of 99.20%. For the LIDC-IDRI dataset, segmentation achieved a Dice Coefficient of 98.92%, IoU of 98.21%, Jaccard Index of 90.15%, and Hausdorff Distance of 2.010 mm, while the classification metrics achieved an accuracy of 99.40%, precision of 99.00%, recall of 99.20%, F1 score of 99.10%, and specificity of 99.55%. These results underline the ability of the framework to achieve high precision, recall, and overall accuracy, making it a reliable tool for lung nodule diagnosis in clinical applications.

The importance of lung cancer as a complication of lung transplantation is increasingly recognised. It may become an important survival-limiting factor in lung transplant patients as management of other complications continues to improve and utilisation of extended criteria donors grows. Radiology can play a key role in tackling this issue at multiple stages in the transplantation pathway and follow-up process. Routine chest CT as part of pre-transplant recipient assessment (and donor assessment if available) can identify suspicious lung lesions with high sensitivity and detect chronic structural lung diseases such as pulmonary fibrosis associated with an increased risk of malignancy post-transplant. Pre-transplant CT also provides a comparison for later CT studies in the assessment of nodules or masses. The potential role of regular chest CT for lung cancer screening after transplantation is less certain due to limited available evidence on its efficacy. Radiologists should be cognisant of how the causes of pulmonary nodules in lung transplant patients may differ from the general population, vary with time since transplantation and require specific recommendations for further investigation/follow-up as general guidelines are not applicable. As part of the multidisciplinary team, radiology is involved in an aggressive diagnostic and therapeutic management approach for nodular lung lesions after transplant both through follow-up imaging and image-guided tissue sampling. This review provides a comprehensive overview of available clinical data and evidence on lung cancer in lung transplant recipients, and in particular an assessment of the current and potential roles of pre- and post-transplant imaging. Key Points • Lung cancer after lung transplantation may become an increasingly important survival-limiting factor as mortality from other complications declines. • There are a number of important roles for radiology in tackling the issue which include pre-transplant CT and supporting an aggressive multidisciplinary management strategy where lung nodules are detected in transplant patients. • The introduction of routine surveillance chest CT after transplant in addition to standard clinical follow-up as a means of lung cancer screening should be considered.

The diverse types and sizes, proximity to non-nodule structures, identical shape characteristics, and varying sizes of nodules make them challenging for segmentation methods. Although many efforts have been made in automatic lung nodule segmentation, most of them have not sufficiently addressed the challenges related to the type and size of nodules, such as juxta-pleural and juxta-vascular nodules. The current research introduces a Squeeze-Excitation Dilated Attention-based Residual U-Net (SEDARU-Net) with a robust intensity normalization technique to address the challenges related to different types and sizes of lung nodules and to achieve an improved lung nodule segmentation. After preprocessing the images with the intensity normalization method and extracting the Regions of Interest by YOLOv3, they are fed into the SEDARU-Net with dilated convolutions in the encoder part. Then, the extracted features are given to the decoder part, which involves transposed convolutions, Squeeze-Excitation Dilated Residual blocks, and skip connections equipped with an Attention Gate, to decode the feature maps and construct the segmentation mask. The proposed model was evaluated using the publicly available Lung Nodule Analysis 2016 (LUNA16) dataset, achieving a Dice Similarity Coefficient of 97.86%, IoU of 96.40%, sensitivity of 96.54%, and precision of 98.84%. Finally, it was shown that each added component to the U-Net’s structure and the intensity normalization technique increased the Dice Similarity Coefficient by more than 2%. The proposed method suggests a potential clinical tool to address challenges related to the segmentation of lung nodules with different types located in the proximity of non-nodule structures.