The PRECISE framework, defined as Patient-Focused Radiology Reports with Enhanced Clarity and Informative Summaries for Effective Communication, leverages GPT-4 to create patient-friendly summaries of radiology reports at a sixth-grade reading level. The purpose of the study was to evaluate the effectiveness of the PRECISE framework in improving the readability, reliability, and understandability of radiology reports. We hypothesized that the PRECISE framework improves the readability and patient understanding of radiology reports compared to the original versions. The PRECISE framework was assessed using 500 chest X-ray reports. Readability was evaluated using the Flesch Reading Ease, Gunning Fog Index, and Automated Readability Index. Reliability was gauged by clinical volunteers, while understandability was assessed by non-medical volunteers. Statistical analyses including t-tests, regression analyses, and Mann-Whitney U tests were conducted to determine the significance of the differences in readability scores between the original and PRECISE-generated reports. Readability scores significantly improved, with the mean Flesch Reading Ease score increasing from 38.28 to 80.82 (p-value < 0.001), the Gunning Fog Index decreasing from 13.04 to 6.99 (p-value < 0.001), and the ARI score improving from 13.33 to 5.86 (p-value < 0.001). Clinical volunteer assessments found 95 % of the summaries reliable, and non-medical volunteers rated 97 % of the PRECISE-generated summaries as fully understandable. The application of the PRECISE approach demonstrates promise in enhancing patient understanding and communication without adding significant burden to radiologists. With improved reliability and patient-friendly summaries, this approach holds promise for fostering patient engagement and understanding in healthcare decision-making. The PRECISE framework represents a pivotal step towards more inclusive and patient-centric care delivery.

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-19 pneumonia could help clinical physicians describe the reasons for their decisions. We aimed to construct predictive models of SVD in patients with COVID-19 pneumonia at an early stage on computed tomography (CT) images using SVD-specific features that can be visualized on accumulated Betti number (BN) maps. BN maps (b0 and b1 maps) were generated by calculating the BNs within a shifting kernel in a manner similar to a convolution. Accumulated BN maps were constructed by summing BN maps (b0 and b1 maps) derived from a range of multiple-threshold values. Topological features were computed as intrinsic topological properties of COVID-19 pneumonia from the accumulated BN maps. Predictive models of SVD were constructed with two feature selection methods and three machine learning models using nested fivefold cross-validation. The proposed model achieved an area under the receiver-operating characteristic curve of 0.854 and a sensitivity of 0.908 in a test fold. These results suggested that topological image features could characterize COVID-19 pneumonia at an early stage as SVD.

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.

To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients. This retrospective study included 291 COVID-19 patients with complete clinical data between December 2022 and March 2024, and some (n=82) of them developed secondary aspergillus infection after admission. Patients were divided into training (n=162), internal validation (n=69) and external validation (n=60) cohorts. The least absolute shrinkage and selection operator regression was applied to select the most significant image features extracted from chest CT. Univariate and multivariate logistic regression analyses were performed to develop a multifactorial model, which integrated chest CT with clinical risk factors, to predict secondary aspergillus infection in hospitalized COVID-19 patients. The performance of the constructed models was assessed with the receiver operating characteristic curve and the area under the curve (AUC). The clinical application value of the models was comprehensively evaluated using decision curve analysis (DCA). Eleven radiomics features and seven clinical risk factors were selected to develop prediction models. The multifactorial model demonstrated a favorable predictive performance with the highest AUC values of 0.98 (95% CI, 0.96-1.00) in the training cohort, 0.98 (95% CI, 0.96-1.00) in the internal validation cohort, and 0.87 (95% CI, 0.75-0.99) in the external validation cohort, which was significantly superior to the models relied solely on chest CT or clinical risk factors. The calibration curves from Hosmer-Lemeshow tests showed that there were no significant differences in the training cohort (p=0.359) and internal validation cohort (p=0.941), suggesting the good performance of the multifactorial model. DCA indicated that the multifactorial model exhibited better performance than others. The multifactorial model can serve as a reliable tool for predicting the risk of COVID-19-associated pulmonary aspergillosis.

Accurately distinguishing histological subtypes and risk categorization of thymomas is difficult. To differentiate the histologic risk categories of thymomas, we developed a combined radiomics model based on non-enhanced and contrast-enhanced computed tomography (CT) radiomics, clinical, and semantic features. In total, 360 patients with pathologically-confirmed thymomas who underwent CT examinations were retrospectively recruited from three centers. Patients were classified using improved pathological classification criteria as low-risk (LRT: types A and AB) or high-risk (HRT: types B1, B2, and B3). The training and external validation sets comprised 274 (from centers 1 and 2) and 86 (center 3) patients, respectively. A clinical-semantic model was built using clinical and semantic variables. Radiomics features were filtered using intraclass correlation coefficients, correlation analysis, and univariate logistic regression. An optimal radiomics model (Rad_score) was constructed using the AutoML algorithm, while a combined model was constructed by integrating Rad_score with clinical and semantic features. The predictive and clinical performances of the models were evaluated using receiver operating characteristic/calibration curve analyses and decision-curve analysis, respectively. Radiomics and combined models (area under curve: training set, 0.867 and 0.884; external validation set, 0.792 and 0.766, respectively) exhibited performance superior to the clinical-semantic model. The combined model had higher accuracy than the radiomics model (0.79 vs. 0.78, p<0.001) in the entire cohort. The original_firstorder_median of venous phase had the highest relative importance among features in the radiomics model. Radiomics and combined radiomics models may serve as noninvasive discrimination tools to differentiate thymoma risk classifications.

Lung adenocarcinoma and squamous cell carcinoma are the two most common pathological lung cancer subtypes. Accurate diagnosis and pathological subtyping are crucial for lung cancer treatment. Solitary solid lung nodules with lobulation and spiculation signs are often indicative of lung cancer; however, in some cases, postoperative pathology finds benign solid lung nodules. It is critical to accurately identify solid lung nodules with lobulation and spiculation signs before surgery; however, traditional diagnostic imaging is prone to misdiagnosis, and studies on artificial intelligence-assisted diagnosis are few. Therefore, we introduce a volumetric SWIN Transformer-based method. It is a multi-scale, multi-task, and highly interpretable model for distinguishing between benign solid lung nodules with lobulation and spiculation signs, lung adenocarcinomas, and lung squamous cell carcinoma. The technique's effectiveness was improved by using 3-dimensional (3D) computed tomography (CT) images instead of conventional 2-dimensional (2D) images to combine as much information as possible. The model was trained using 352 of the 441 CT image sequences and validated using the rest. The experimental results showed that our model could accurately differentiate between benign lung nodules with lobulation and spiculation signs, lung adenocarcinoma, and squamous cell carcinoma. On the test set, our model achieves an accuracy of 0.9888, precision of 0.9892, recall of 0.9888, and an F1-score of 0.9888, along with a class activation mapping (CAM) visualization of the 3D model. Consequently, our method could be used as a preoperative tool to assist in diagnosing solitary solid lung nodules with lobulation and spiculation signs accurately and provide a theoretical basis for developing appropriate clinical diagnosis and treatment plans for the patients.