Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learning-based predictive models rely primarily on pre-treatment imaging to predict categorical response outcomes, limiting their ability to capture the complex morphological and textural transformations induced by immunotherapy. This study introduces ImmunoDiff, an anatomy-aware diffusion model designed to synthesize post-treatment CT scans from baseline imaging while incorporating clinically relevant constraints. The proposed framework integrates anatomical priors, specifically lobar and vascular structures, to enhance fidelity in CT synthesis. Additionally, we introduce a novel cbi-Adapter, a conditioning module that ensures pairwise-consistent multimodal integration of imaging and clinical data embeddings, to refine the generative process. Additionally, a clinical variable conditioning mechanism is introduced, leveraging demographic data, blood-based biomarkers, and PD-L1 expression to refine the generative process. Evaluations on an in-house NSCLC cohort treated with immune checkpoint inhibitors demonstrate a 21.24% improvement in balanced accuracy for response prediction and a 0.03 increase in c-index for survival prediction. Code will be released soon.

Artificial intelligence (AI)-based chest X-ray (CXR) interpretation assistants have demonstrated significant progress and are increasingly being applied in clinical settings. However, contemporary medical AI models often adhere to a simplistic input-to-output paradigm, directly processing an image and an instruction to generate a result, where the instructions may be integral to the model's architecture. This approach overlooks the modeling of the inherent diagnostic reasoning in chest X-ray interpretation. Such reasoning is typically sequential, where each interpretive stage considers the images, the current task, and the contextual information from previous stages. This oversight leads to several shortcomings, including misalignment with clinical scenarios, contextless reasoning, and untraceable errors. To fill this gap, we construct CXRTrek, a new multi-stage visual question answering (VQA) dataset for CXR interpretation. The dataset is designed to explicitly simulate the diagnostic reasoning process employed by radiologists in real-world clinical settings for the first time. CXRTrek covers 8 sequential diagnostic stages, comprising 428,966 samples and over 11 million question-answer (Q&A) pairs, with an average of 26.29 Q&A pairs per sample. Building on the CXRTrek dataset, we propose a new vision-language large model (VLLM), CXRTrekNet, specifically designed to incorporate the clinical reasoning flow into the VLLM framework. CXRTrekNet effectively models the dependencies between diagnostic stages and captures reasoning patterns within the radiological context. Trained on our dataset, the model consistently outperforms existing medical VLLMs on the CXRTrek benchmarks and demonstrates superior generalization across multiple tasks on five diverse external datasets. The dataset and model can be found in our repository (https://github.com/guanjinquan/CXRTrek).

While Multi-Task Learning (MTL) offers inherent advantages in complex domains such as medical imaging by enabling shared representation learning, effectively balancing task contributions remains a significant challenge. This paper addresses this critical issue by introducing DeepChest, a novel, computationally efficient and effective dynamic task-weighting framework specifically designed for multi-label chest X-ray (CXR) classification. Unlike existing heuristic or gradient-based methods that often incur substantial overhead, DeepChest leverages a performance-driven weighting mechanism based on effective analysis of task-specific loss trends. Given a network architecture (e.g., ResNet18), our model-agnostic approach adaptively adjusts task importance without requiring gradient access, thereby significantly reducing memory usage and achieving a threefold increase in training speed. It can be easily applied to improve various state-of-the-art methods. Extensive experiments on a large-scale CXR dataset demonstrate that DeepChest not only outperforms state-of-the-art MTL methods by 7% in overall accuracy but also yields substantial reductions in individual task losses, indicating improved generalization and effective mitigation of negative transfer. The efficiency and performance gains of DeepChest pave the way for more practical and robust deployment of deep learning in critical medical diagnostic applications. The code is publicly available at https://github.com/youssefkhalil320/DeepChest-MTL

The efficacy of immunotherapy in non-small cell lung cancer (NSCLC) is intricately associated with baseline PD-L1 expression rates. The standard method for measuring PD-L1 is immunohistochemistry, which is invasive and may not capture tumor heterogeneity. The primary aim of the current study is to assess whether CT-based radiomics models can accurately predict PD-L1 expression status in NSCLC and evaluate their quality and potential gaps in their design. Scopus, PubMed, Web of Science, Embase, and IEEE databases were systematically searched up until February 14, 2025, to retrieve relevant studies. Data from validation cohorts of models that classified patients by tumor proportion score (TPS) of 1% (TPS1) and 50% (TPS50) were extracted and analyzed separately. Quality assessment was performed through METRICS and QUADAS-2 tools. Diagnostic test accuracy meta-analysis was conducted using a bivariate random-effects approach to pool values of performance metrics. The qualitative synthesis included twenty-two studies, and the meta-analysis analyzed 11 studies with 997 individual subjects. The pooled AUC, sensitivity, and specificity of TPS1 models were 0.85, 0.76, and 0.79, respectively. The pooled AUC, sensitivity, and specificity of TPS50 models were 0.88, 0.72, and 0.86, accordingly. The QUADAS-2 tool identified a substantial risk of bias regarding the flow and timing and index test domains. Certain methodological limitations were highlighted by the METRICS score, which averaged 58.1% and ranged from 24% to 83.4%. CT-based radiomics demonstrates strong potential as a non-invasive method for predicting PD-L1 expression in NSCLC. While promising, significant methodological gaps must be addressed to achieve the generalizability and reliability required for clinical application.

Discrete wavelet transforms have been applied in many machine learning models for the analysis of COVID-19; however, little is known about the impact of combined multilevel wavelet decompositions for the disease identification. This study proposes a computer-aided diagnosis system for addressing the combined multilevel effects of multiscale radiomic features on multiclass COVID-19 classification using chest X-ray images. A two-level discrete wavelet transform was applied to an optimal region of interest to obtain multiscale decompositions. Both approximation and detail coefficients were extensively investigated in varying frequency bands through 1240 experimental models. High dimensionality in the feature space was managed using a proposed filter- and wrapper-based feature selection approach. A comprehensive comparison was conducted between the bands and features to explore best-performing ensemble algorithm models. The results indicated that incorporating multilevel decompositions could lead to improved model performance. An inclusive region of interest, encompassing both lungs and the mediastinal regions, was identified to enhance feature representation. The light gradient-boosting machine, applied on combined bands with the features of basic, gray-level, Gabor, histogram of oriented gradients and local binary patterns, achieved the highest weighted precision, sensitivity, specificity, and accuracy of 97.50 %, 97.50 %, 98.75 %, and 97.50 %, respectively. The COVID-19-versus-the-rest receiver operating characteristic area under the curve was 0.9979. These results underscore the potential of combining decomposition levels with the original signals and employing an inclusive region of interest for effective COVID-19 detection, while the feature selection and training processes remain efficient within a practical computational time.

Despite the established association between chronic obstructive pulmonary disease (COPD) severity and risk of osteoporosis, even after accounting for the known shared confounding variables (e.g., age, smoking, history of exacerbations, steroid use), there is paucity of data on bone loss among mild to moderate COPD, which is more prevalent in the general population. We conducted a longitudinal analysis using data from the Multi-Ethnic Study of Atherosclerosis. Participants with chest CT at Exam 5 (2010-2012) and Exam 6 (2016-2018) were included. Mild to moderate COPD was defined as forced expiratory volume in 1 s (FEV₁) to forced vital capacity ratio of <0.70 and FEV₁ of 50 % or higher. Vitamin D deficiency was defined as serum vitamin D < 20 ng/mL. We utilized a validated deep learning algorithm to perform automated multilevel segmentation of vertebral bodies (T1-T10) from chest CT and derive 3D volumetric thoracic vertebral BMD measurements at Exam 5 and 6. Of the 1226 participants, 173 had known mild to moderate COPD at baseline, while 1053 had no known COPD. After adjusting for age, race/ethnicity, sex, body mass, index, bisphosphonate use, alcohol consumption, smoking, diabetes, physical activity, C-reactive protein and vitamin D deficiency, mild to moderate COPD was associated with faster decline in BMD (estimated difference, β = -0.38 g/cm³/year; 95 % CI: -0.74, -0.02). A significant interaction between COPD and vitamin D deficiency (p = 0.001) prompted stratified analyses. Among participants with vitamin D deficiency (47 % of participants), COPD was associated with faster decline in BMD (-0.64 g/cm³/year; 95 % CI: -1.17 to -0.12), whereas no significant association was observed among those with normal vitamin D in both crude and adjusted models. Mild to moderate COPD is associated with longitudinal declines in vertebral BMD exclusively in participants with vitamin D deficiency over 6-year follow-up. Vitamin D deficiency may play a crucial role in bone loss among patients with mild to moderate COPD.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a potentially life-threatening condition, classified as group 4 pulmonary hypertension (PH), caused by stenosis or occlusion of the pulmonary arteries due to unresolved thromboembolic material. The prognosis for untreated CTEPH patients is poor because it leads to elevated pulmonary artery pressure and right heart failure. Early and accurate diagnosis of CTEPH is crucial because it remains the only form of PH that is potentially curable. However, diagnosing CTEPH is often challenging and frequently delayed or misdiagnosed. This review discusses the current role of multimodal imaging in diagnosing CTEPH, guiding clinical decision-making, and monitoring post-treatment outcomes. The characteristic findings, strengths, and limitations of various imaging modalities, such as computed tomography, ventilation-perfusion lung scintigraphy, digital subtraction pulmonary angiography, and magnetic resonance imaging, are evaluated. Additionally, the role of artificial intelligence in improving the diagnosis and treatment outcomes of CTEPH is explored. Optimal patient assessment and therapeutic decision-making should ideally be conducted in specialized centers by a multidisciplinary team, utilizing data from imaging, pulmonary hemodynamics, and patient comorbidities.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To estimate the total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level using lung thickness maps generated by a U-Net deep learning model. Materials and Methods This retrospective study included 5,959 chest CT scans from two public datasets: the lung nodule analysis 2016 (<i>n</i> = 656) and the Radiological Society of North America (RSNA) pulmonary embolism detection challenge 2020 (<i>n</i> = 5,303). Additionally, 72 participants were selected from the Klinikum Rechts der Isar dataset (October 2018 to December 2019), each with a corresponding chest radiograph taken within seven days. Synthetic radiographs and lung thickness maps were generated using forward projection of CT scans and their lung segmentations. A U-Net model was trained on synthetic radiographs to predict lung thickness maps and estimate TLV. Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient <b>(r)</b>, and two-sided Student's t-distribution. Results The study included 72 participants (45 male, 27 female, 33 healthy: mean age 62 years [range 34-80]; 39 with chronic obstructive pulmonary disease: mean age 69 years [range 47-91]). TLV predictions showed low error rates (MSEPublic-Synthetic = 0.16 L², MSEKRI-Synthetic = 0.20 L², MSEKRI-Real = 0.35 L²) and strong correlations with CT-derived reference standard TLV (nPublic-Synthetic = 1,191, r = 0.99, <i>P</i> < .001; nKRI-Synthetic = 72, r = 0.97, <i>P</i> < .001; nKRI-Real = 72, r = 0.91, <i>P</i> < .001). When evaluated on different datasets, the U-Net model achieved the highest performance for TLV estimation on the Luna16 test dataset, with the lowest mean squared error (MSE = 0.09 L²) and strongest correlation (<i>r</i> = 0.99, <i>P</i> <.001) compared with CT-derived TLV. Conclusion The U-Net-generated pixel-level lung thickness maps successfully estimated TLV for both synthetic and real radiographs. ©RSNA, 2025.

To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion models in identifying visceral pleural invasion (VPI) in lung adenocarcinoma. A total of 449 patients (female:male, 263:186; 59.8 ± 10.5 years) diagnosed with clinical IA stage lung adenocarcinoma (LAC) from two distinct hospitals were enrolled in the study and divided into a training cohort (n = 289) and an external test cohort (n = 160). The fusion models were constructed from the feature level and the decision level respectively. A comprehensive analysis was conducted to assess the prediction ability and prognostic value of radiomics, deep learning, and fusion models. The diagnostic performance of radiologists of varying seniority with and without the assistance of the optimal model was compared. The late fusion model demonstrated superior diagnostic performance (AUC = 0.812) compared to clinical (AUC = 0.650), radiomics (AUC = 0.710), deep learning (AUC = 0.770), and the early fusion models (AUC = 0.586) in the external test cohort. The multivariate Cox regression analysis showed that the VPI status predicted by the late fusion model were independently associated with patient disease-free survival (DFS) (p = 0.044). Furthermore, model assistance significantly improved radiologist performance, particularly for junior radiologists; the AUC increased by 0.133 (p < 0.001) reaching levels comparable to the senior radiologist without model assistance (AUC: 0.745 vs. 0.730, p = 0.790). The proposed decision-level (late fusion) model significantly reducing the risk of overfitting and demonstrating excellent robustness in multicenter external validation, which can predict VPI status in LAC, aid in prognostic stratification, and assist radiologists in achieving higher diagnostic performance.

This work aimed to develop an automated method for quantifying the distribution and severity of perfusion changes on CT pulmonary angiography (CTPA) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and to assess their associations with clinical parameters and expert annotations. Following automated segmentation of the chest, a machine-learning model assuming three distributions of attenuation in the pulmonary parenchyma (hyperemic, normal, and oligemic) was fitted to the attenuation histogram of CTPA images using Bayesian analysis. The proportion of each component, its spatial heterogeneity (entropy), and center-to-periphery distribution of the attenuation were calculated and correlated with the findings on CTPA semi-quantitatively evaluated by radiologists and with clinical function tests. CTPA scans from 52 patients (mean age, 65.2 ± 13.0 years; 27 men) diagnosed with CTEPH were analyzed. An inverse correlation was observed between the proportion of normal parenchyma and brain natriuretic propeptide (proBNP, ρ = -0.485, p = 0.001), mean pulmonary arterial pressure (ρ = -0.417, p = 0.002) and pulmonary vascular resistance (ρ = -0.556, p < 0.0001), mosaic attenuation (ρ = -0.527, p < 0.0001), perfusion centralization (ρ = -0.489, p = < 0.0001), and right ventricular diameter (ρ = -0.451, p = 0.001). The entropy of hyperemic parenchyma showed a positive correlation with the pulmonary wedge pressure (ρ = 0.402, p = 0.003). The slope of center-to-periphery attenuation distribution correlated with centralization (ρ = -0.477, p < 0.0001), and with proBNP (ρ = -0.463, p = 0.002). This study validates an automated system that leverages Bayesian analysis to quantify the severity and distribution of perfusion changes in CTPA. The results show the potential of this method to support clinical evaluations of CTEPH by providing reproducible and objective measures. Question This study introduces an automated method for quantifying the extent and spatial distribution of pulmonary perfusion abnormalities in CTEPH using variational Bayesian estimation. Findings Quantitative measures describing the extent, heterogeneity, and distribution of perfusion changes demonstrate strong correlations with key clinical hemodynamic indicators. Clinical relevance The automated quantification of perfusion changes aligns closely with radiologists' evaluations, delivering a standardized, reproducible measure with clinical relevance.