Critically ill children admitted to the intensive care unit frequently need respiratory care to support the lung function. Mechanical ventilation is a complex field with multiples parameters to set. The development of precision medicine will allow clinicians to personalize respiratory care and improve patients' outcomes. Lung and diaphragmatic ultrasound, electrical impedance tomography, neurally adjusted ventilatory assist ventilation, as well as the use of monitoring data in machine learning models are increasingly used to tailor care. Each modality offers insights into different aspects of the patient's respiratory system function and enables the adjustment of treatment to better support the patient's physiology. Precision medicine in respiratory care has been associated with decreased ventilation time, increased extubation and ventilation wean success and increased ability to identify phenotypes to guide treatment and predict outcomes. This review will focus on the use of precision medicine in the setting of pediatric acute respiratory distress syndrome, asthma, bronchiolitis, extubation readiness trials and ventilation weaning, ventilation acquired pneumonia and other respiratory tract infections. Precision medicine is revolutionizing respiratory care and will decrease complications associated with ventilation. More research is needed to standardize its use and better evaluate its impact on patient outcomes.

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages where curative treatment options are limited. Low-dose computed tomography (LDCT) for lung cancer screening (LCS) of individuals selected based on age and smoking history has shown a significant reduction in lung cancer-specific mortality. The number needed to screen to prevent one death from lung cancer is lower than that for breast cancer, cervical cancer, and colorectal cancer. Despite the substantial impact on reducing lung cancer-related mortality and proof that LCS with LDCT is effective, uptake of LCS has been low and LCS eligibility criteria remain imperfect. While LCS programs have historically faced patient recruitment challenges, research suggests that there are novel opportunities to both identify and improve screening for at-risk populations. In this review, we discuss the global obstacles to implementing LCS programs and strategies to overcome barriers in resource-limited settings. We explore successful approaches to promote LCS through robust engagement with community partners. Finally, we examine opportunities to enhance LCS in at-risk populations not captured by current eligibility criteria, including never smokers and individuals with a family history of lung cancer, with a focus on early detection through novel artificial intelligence technologies.

Bone metastasis is a common and serious complication in lung cancer patients, leading to severe pain, pathological fractures, and reduced quality of life. Early prediction of bone metastasis can enable timely interventions and improve patient outcomes. In this study, we developed a multimodal Swin Transformer-based deep learning model for predicting bone metastasis risk in lung cancer patients by integrating CT imaging and pathological data. A total of 215 patients with confirmed lung cancer diagnoses, including those with and without bone metastasis, were included. The model was designed to process high-resolution CT images and digitized histopathological images, with the features extracted independently by two Swin Transformer networks. These features were then fused using decision-level fusion techniques to improve classification accuracy. The Swin-Dual Fusion Model achieved superior performance compared to single-modality models and conventional architectures such as ResNet50, with an AUC of 0.966 on the test data and 0.967 on the training data. This integrated model demonstrated high accuracy, sensitivity, and specificity, making it a promising tool for clinical application in predicting bone metastasis risk. The study emphasizes the potential of transformer-based models to revolutionize bone oncology through advanced multimodal analysis and early prediction of metastasis, ultimately improving patient care and treatment outcomes.

To evaluate the performance of large language models (LLMs) in generating clinically follow-up recommendations for pulmonary nodules by leveraging radiological report findings and management guidelines. This retrospective study included CT follow-up reports of pulmonary nodules documented by senior radiologists from September 1st, 2023, to April 30th, 2024. Sixty reports were collected for prompting engineering additionally, based on few-shot learning and the Chain of Thought methodology. Radiological findings of pulmonary nodules, along with finally prompt, were input into GPT-4o-mini or ERNIE-4.0-Turbo-8K to generate follow-up recommendations. The AI-generated recommendations were evaluated against radiologist-defined guideline-based standards through binary classification, assessing nodule risk classifications, follow-up intervals, and harmfulness. Performance metrics included sensitivity, specificity, positive/negative predictive values, and F1 score. On 1009 reports from 996 patients (median age, 50.0 years, IQR, 39.0-60.0 years; 511 male patients), ERNIE-4.0-Turbo-8K and GPT-4o-mini demonstrated comparable performance in both accuracy of follow-up recommendations (94.6 % vs 92.8 %, P = 0.07) and harmfulness rates (2.9 % vs 3.5 %, P = 0.48). In nodules classification, ERNIE-4.0-Turbo-8K and GPT-4o-mini performed similarly with accuracy rates of 99.8 % vs 99.9 % sensitivity of 96.9 % vs 100.0 %, specificity of 99.9 % vs 99.9 %, positive predictive value of 96.9 % vs 96.9 %, negative predictive value of 100.0 % vs 99.9 %, f1-score of 96.9 % vs 98.4 %, respectively. LLMs show promise in providing guideline-based follow-up recommendations for pulmonary nodules, but require rigorous validation and supervision to mitigate potential clinical risks. This study offers insights into their potential role in automated radiological decision support.

Echo features of lymph nodes (LNs) influence target selection during endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). This study evaluates deep learning's diagnostic capabilities on EBUS images for detecting mediastinal LN metastasis in lung cancer, emphasising the added value of integrating a region of interest (ROI), LN size on CT, and PET-CT findings. We analysed 2901 EBUS images from 2055 mediastinal LN stations in 1454 lung cancer patients. ResNet18-based deep learning models were developed to classify images of true positive malignant and true negative benign LNs diagnosed by EBUS-TBNA using different inputs: original images, ROI images, and CT size and PET-CT data. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and other diagnostic metrics. The model using only original EBUS images showed the lowest AUROC (0.870) and accuracy (80.7%) in classifying LN images. Adding ROI information slightly increased the AUROC (0.896) without a significant difference (p = 0.110). Further adding CT size resulted in a minimal change in AUROC (0.897), while adding PET-CT (original + ROI + PET-CT) showed a significant improvement (0.912, p = 0.008 vs. original; p = 0.002 vs. original + ROI + CT size). The model combining original and ROI EBUS images with CT size and PET-CT findings achieved the highest AUROC (0.914, p = 0.005 vs. original; p = 0.018 vs. original + ROI + PET-CT) and accuracy (82.3%). Integrating an ROI, LN size on CT, and PET-CT findings into the deep learning analysis of EBUS images significantly enhances the diagnostic capability of models for detecting mediastinal LN metastasis in lung cancer, with the integration of PET-CT data having a substantial impact.

Accurate preoperative prediction of major pathological response or pathological complete response after neoadjuvant chemo-immunotherapy remains a critical unmet need in resectable non-small-cell lung cancer (NSCLC). Conventional size-based imaging criteria offer limited reliability, while biopsy confirmation is available only post-surgery. We retrospectively assembled 509 consecutive NSCLC cases from four Chinese thoracic-oncology centers (March 2018 to March 2023) and prospectively enrolled 50 additional patients. Three 3-dimensional convolutional neural networks (pre-treatment CT, pre-surgical CT, dual-phase CT) were developed; the best-performing dual-phase model (NeoPred) optionally integrated clinical variables. Model performance was measured by area under the receiver-operating-characteristic curve (AUC) and compared with nine board-certified radiologists. In an external validation set (n=59), NeoPred achieved an AUC of 0.772 (95% CI: 0.650 to 0.895), sensitivity 0.591, specificity 0.733, and accuracy 0.627; incorporating clinical data increased the AUC to 0.787. In a prospective cohort (n=50), NeoPred reached an AUC of 0.760 (95% CI: 0.628 to 0.891), surpassing the experts' mean AUC of 0.720 (95% CI: 0.574 to 0.865). Model assistance raised the pooled expert AUC to 0.829 (95% CI: 0.707 to 0.951) and accuracy to 0.820. Marked performance persisted within radiological stable-disease subgroups (external AUC 0.742, 95% CI: 0.468 to 1.000; prospective AUC 0.833, 95% CI: 0.497 to 1.000). Combining dual-phase CT and clinical variables, NeoPred reliably and non-invasively predicts pathological response to neoadjuvant chemo-immunotherapy in NSCLC, outperforms unaided expert assessment, and significantly enhances radiologist performance. Further multinational trials are needed to confirm generalizability and support surgical decision-making.

Lung cancer remains a leading cause of cancer-related mortality worldwide, emphasizing the critical need for accurate and early diagnostic solutions. This study introduces a novel multimodal artificial intelligence (AI) framework that integrates Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) to improve lung cancer classification and severity assessment. The CNN model, trained on 1019 preprocessed CT images, classified lung tissue into four histological categories, adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal, with a weighted accuracy of 92 %. Interpretability is enhanced using Gradient-weighted Class Activation Mapping (Grad-CAM), which highlights the salient image regions influencing the model's predictions. In parallel, an ANN trained on clinical data from 999 patients-spanning 24 key features such as demographic, symptomatic, and genetic factors-achieves 99 % accuracy in predicting cancer severity (low, medium, high). SHapley Additive exPlanations (SHAP) are employed to provide both global and local interpretability of the ANN model, enabling transparent decision-making. Both models were rigorously validated using k-fold cross-validation to ensure robustness and reduce overfitting. This hybrid approach effectively combines spatial imaging data and structured clinical information, demonstrating strong predictive performance and offering an interpretable and comprehensive AI-based solution for lung cancer diagnosis and management.

To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT). Participants who underwent chest standard-dose CT (SDCT) followed by ULDCT from October 2020 to January 2022 were prospectively included. ULDCT images reconstructed with HIR and DLR were compared with SDCT images to evaluate image quality, nodule detection rate, and measurement accuracy using a commercially available deep learning-based nodule evaluation system. Wilcoxon signed-rank test was used to evaluate the percentage errors of nodule size and nodule volume between HIR and DLR images. Eighty-four participants (54 ± 13 years; 26 men) were finally enrolled. The effective radiation doses of ULDCT and SDCT were 0.16 ± 0.02 mSv and 1.77 ± 0.67 mSv, respectively (P < 0.001). The mean ± standard deviation of the lung tissue noises was 61.4 ± 3.0 HU for SDCT, 61.5 ± 2.8 HU and 55.1 ± 3.4 HU for ULDCT reconstructed with HIR-Strong setting (HIR-Str) and DLR-Strong setting (DLR-Str), respectively (P < 0.001). A total of 535 nodules were detected. The nodule detection rates of ULDCT HIR-Str and ULDCT DLR-Str were 74.0% and 83.4%, respectively (P < 0.001). The absolute percentage error in nodule volume from that of SDCT was 19.5% in ULDCT HIR-Str versus 17.9% in ULDCT DLR-Str (P < 0.001). Compared with HIR, DLR reduced image noise, increased nodule detection rate, and improved measurement accuracy of nodule volume at chest ULDCT. Not applicable.

Pulmonary hypertension (pH) is a malignant pulmonary circulation disease. Right heart catheterization (RHC) is the gold standard procedure for quantitative evaluation of pulmonary hemodynamics. Accurate and noninvasive quantitative evaluation of pulmonary hemodynamics is challenging due to the limitations of currently available assessment methods. Patients who underwent computed tomographic pulmonary angiography (CTPA) and RHC examinations within 2 weeks were included. The dataset was randomly divided into a training set and a test set at an 8:2 ratio. A radiomic feature model and another two-dimensional (2D) feature model aimed to quantitatively evaluate of pulmonary hemodynamics were constructed. The performance of models was determined by calculating the mean squared error, the intraclass correlation coefficient (ICC) and the area under the precision-recall curve (AUC-PR) and performing Bland-Altman analyses. 345 patients: 271 patients with PH (mean age 50 ± 17 years, 93 men) and 74 without PH (mean age 55 ± 16 years, 26 men) were identified. The predictive results of pulmonary hemodynamics of radiomic feature model integrating 5 2D features and other 30 radiomic features were consistent with the results from RHC, and outperformed another 2D feature model. The radiomic feature model exhibited moderate to good reproducibility to predict pulmonary hemodynamic parameters (ICC reached 0.87). In addition, pH can be accurately identified based on a classification model (AUC-PR =0.99). This study provides a noninvasive method for comprehensively and quantitatively evaluating pulmonary hemodynamics using CTPA images, which has the potential to serve as an alternative to RHC, pending further validation.

Due to imbalanced data values and high-dimensional features of lung cancer from CT scans images creates significant challenges in clinical research. The improper classification of these images leads towards higher complexity in classification process. These critical issues compromise the extraction of biomedical traits and also design incomplete classification of lung cancer. As the conventional approaches are partially successful in dealing with the complex nature of high-dimensional and imbalanced biomedical data for lung cancer classification. Thus, there is a crucial need to develop a robust classification technique which can address these major concerns in the classification of lung cancer images. In this paper, we propose a novel structural formation of the oblique decision tree (OBT) using a swarm intelligence technique, namely, the Binary Bat Swarm Algorithm (BBSA). The application of BBSA enables a competitive recognition rate to make structural reforms while building OBT. Such integration improves the ability of the machine learning swarm classifier (MLSC) to handle high-dimensional features and imbalanced biomedical datasets. The adaptive feature selection using BBSA allows for the exploration and selection of relevant features required for classification from ODT. The ODT classifier introduces flexibility in decision boundaries, which enables it to capture complex linkages between biomedical data. The proposed MLSC model effectively handles high-dimensional, imbalanced lung cancer datasets using TCGA_LUSC_2016 and TCGA_LUAD_2016 modalities, achieving superior precision, recall, F-measure, and execution efficiency. The experiments are conducted in Python to evaluate the performance metrics that consistently demonstrate enhanced classification accuracy and reduced misclassification rates compared to existing methods. The MLSC is assessed in terms of both qualitative and quantitative measurements to study the capability of the proposed MLSC in classifying the instances more effectively than the conventional state-of-the-art methods.