Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous condition characterized by diverse pulmonary and extrapulmonary manifestations. Efforts to quantify its various components using CT imaging have advanced, aiming for more precise, objective, and reproducible assessment and management. Beyond emphysema and small airway disease, the two major components of COPD, CT quantification enables the evaluation of pulmonary vascular alteration, ventilation-perfusion mismatches, fissure completeness, and extrapulmonary features such as altered body composition, osteoporosis, and atherosclerosis. Recent advancements, including the application of deep learning techniques, have facilitated fully automated segmentation and quantification of CT parameters, while innovations such as image standardization hold promise for enhancing clinical applicability. Numerous studies have reported associations between quantitative CT parameters and clinical or physiologic outcomes in patients with COPD. However, barriers remain to the routine implementation of these technologies in clinical practice. This review highlights recent research on COPD quantification, explores advances in technology, and also discusses current challenges and potential solutions for improving quantification methods.

The analysis of medical imaging reports is labour-intensive but crucial for accurate diagnosis and effective patient screening. Often presented as unstructured text, these reports require systematic organisation for efficient interpretation. This study applies Natural Language Processing (NLP) techniques tailored for European Portuguese to automate the analysis of cardiology reports, streamlining patient screening. Using a methodology involving tokenization, part-of-speech tagging and manual annotation, the MediAlbertina PT-PT language model was fine-tuned, achieving 96.13% accuracy in entity recognition. The system enables rapid identification of conditions such as aortic stenosis through an interactive interface, substantially reducing the time and effort required for manual review. It also facilitates patient monitoring and disease quantification, optimising healthcare resource allocation. This research highlights the potential of NLP tools in Portuguese healthcare contexts, demonstrating their applicability to medical report analysis and their broader relevance in improving efficiency and decision-making in diverse clinical environments.

Generalizable foundation models for computed tomographic (CT) medical imaging data are emerging AI tools anticipated to vastly improve clinical workflow efficiency. However, existing models are typically trained within narrow imaging contexts, including limited anatomical coverage, contrast settings, and clinical indications. These constraints reduce their ability to generalize across the broad spectrum of real-world presentations encountered in volumetric CT imaging data. We introduce Percival, a vision-language foundation model trained on over 400,000 CT volumes and paired radiology reports from more than 50,000 participants enrolled in the Penn Medicine BioBank. Percival employs a dual-encoder architecture with a transformer-based image encoder and a BERT-style language encoder, aligned via symmetric contrastive learning. Percival was validated on over 20,000 participants imaging data encompassing over 100,000 CT volumes. In image-text recall tasks, Percival outperforms models trained on limited anatomical windows. To assess Percival's clinical knowledge, we evaluated the biologic, phenotypic and prognostic relevance using laboratory-wide, phenome-wide association studies and survival analyses, uncovering a rich latent structure aligned with physiological measurements and disease phenotypes.

Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering both metabolic and anatomical insights into tumor behavior. Most differentiated thyroid carcinomas (DTC) are indolent and therefore FDG PET/CT is not routinely incorporated into management. However, in biologically aggressive DTCs, FDG PET/CT plays a crucial role in detecting recurrence and metastases. This narrative review with articles from the last 25 years from PubMed database, explores the evolving role of FDG PET/CT, focusing on its utility in recurrence detection, staging, and follow-up of radioactive iodine (RAI)-refractory cases. Current guidelines recommend FDG PET/CT primarily for high-risk patients with elevated thyroglobulin levels and negative RAI scans (TENIS syndrome). We also examine advancements in PET imaging, novel radiotracers and theragnostic approaches that enhance diagnostic accuracy and treatment monitoring. While FDG PET/CT has proven valuable in biologically aggressive DTC, its routine use remains limited by cost, accessibility, and concerns regarding radiation exposure in younger patients requiring repeated imaging studies. Future developments in molecular imaging, including novel tracers and artificial intelligence-driven analysis, are expected to refine its role, leading to more personalized and effective management, though economic and reimbursement challenges remain important considerations for broader adoption.

This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC). Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clinical-radiological model and deep learning models aimed at MVI prediction. The models were developed on a dataset of 263 cases consisting of data from three centers, internally validated on a set of 66 patients, and externally tested on two independent sets. An adversarial network-based deep learning (AD-DL) model was developed to learn domain-invariant features from multiple centers within the training set. The area under the receiver operating characteristic curve (AUC) was calculated using pathological MVI status. With the best-performed model, early recurrence-free survival (ERFS) stratification was validated on the external test set by the log-rank test, and the differentially expressed genes (DEGs) associated with MVI status were tested on the RNA sequencing analysis of the Cancer Imaging Archive. The AD-DL model demonstrated the highest diagnostic performance and generalizability with an AUC of 0.793 in the internal test set, 0.801 in external test set 1, and 0.773 in external test set 2. The model's prediction of MVI status also demonstrated a significant correlation with ERFS (p = 0.048). DEGs associated with MVI status were primarily enriched in the metabolic processes and the Wnt signaling pathway, and the epithelial-mesenchymal transition process. The AD-DL model allows preoperative MVI prediction and ERFS stratification in HCC patients, which has a good generalizability and biological interpretability. The adversarial network-based deep learning model predicts MVI status well in HCC patients and demonstrates good generalizability. By integrating bioinformatics analysis of the model's predictions, it achieves biological interpretability, facilitating its clinical translation. Current MVI assessment models for HCC lack interpretability and generalizability. The adversarial network-based model's performance surpassed clinical radiology and squeeze-and-excitation network-based models. Biological function analysis was employed to enhance the interpretability and clinical translatability of the adversarial network-based model.

Artificial intelligence (AI) can advance medical diagnostics, but interpretability limits its clinical use. This work links standardized quantitative Radiomics features (RF) extracted from medical images with clinical frameworks like PI-RADS, ensuring AI models are understandable and aligned with clinical practice. We investigate the connection between visual semantic features defined in PI-RADS and associated risk factors, moving beyond abnormal imaging findings, and establishing a shared framework between medical and AI professionals by creating a standardized radiological/biological RF dictionary. Six interpretable and seven complex classifiers, combined with nine interpretable feature selection algorithms (FSA), were applied to RFs extracted from segmented lesions in T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) multiparametric MRI sequences to predict TCIA-UCLA scores, grouped as low-risk (scores 1-3) and high-risk (scores 4-5). We then utilized the created dictionary to interpret the best predictive models. Combining sequences with FSAs including ANOVA F-test, Correlation Coefficient, and Fisher Score, and utilizing logistic regression, identified key features: The 90th percentile from T2WI, (reflecting hypo-intensity related to prostate cancer risk; Variance from T2WI (lesion heterogeneity; shape metrics including Least Axis Length and Surface Area to Volume ratio from ADC, describing lesion shape and compactness; and Run Entropy from ADC (texture consistency). This approach achieved the highest average accuracy of 0.78 ± 0.01, significantly outperforming single-sequence methods (p-value < 0.05). The developed dictionary for Prostate-MRI (PM1.0) serves as a common language and fosters collaboration between clinical professionals and AI developers to advance trustworthy AI solutions that support reliable/interpretable clinical decisions.

Radiology reports are essential in medical imaging, providing critical insights for diagnosis, treatment, and patient management by bridging the gap between radiologists and referring physicians. However, the manual generation of radiology reports is time-consuming and labor-intensive, leading to inefficiencies and delays in clinical workflows, particularly as case volumes increase. Although deep learning approaches have shown promise in automating radiology report generation, existing methods, particularly those based on the encoder-decoder framework, suffer from significant limitations. These include a lack of explainability due to black-box features generated by encoder and limited adaptability to diverse clinical settings. In this study, we address these challenges by proposing a novel deep learning framework for radiology report generation that enhances explainability, accuracy, and adaptability. Our approach replaces traditional black-box features in computer vision with transparent keyword lists, improving the interpretability of the feature extraction process. To generate these keyword lists, we apply a multi-label classification technique, which is further enhanced by an automatic keyword adaptation mechanism. This adaptation dynamically configures the multi-label classification to better adapt specific clinical environments, reducing the reliance on manually curated reference keyword lists and improving model adaptability across diverse datasets. We also introduce a frequency-based multi-label classification strategy to address the issue of keyword imbalance, ensuring that rare but clinically significant terms are accurately identified. Finally, we leverage a pre-trained text-to-text large language model (LLM) to generate human-like, clinically relevant radiology reports from the extracted keyword lists, ensuring linguistic quality and clinical coherence. We evaluate our method using two public datasets, IU-XRay and MIMIC-CXR, demonstrating superior performance over state-of-the-art methods. Our framework not only improves the accuracy and reliability of radiology report generation but also enhances the explainability of the process, fostering greater trust and adoption of AI-driven solutions in clinical practice. Comprehensive ablation studies confirm the robustness and effectiveness of each component, highlighting the significant contributions of our framework to advancing automated radiology reporting. In conclusion, we developed a novel deep-learning based radiology report generation method for preparing high-quality and explainable radiology report for chest X-ray images using the multi-label classification and a text-to-text large language model. Our method could address the lack of explainability in the current workflow and provide a clear and flexible automated pipeline to reduce the workload of radiologists and support the further applications related to Human-AI interactive communications.

The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques provide standardized assistance to clinicians, in which many diagnostic and predictive tasks are manually conducted, causing low reproducibility. These AI methods can additionally provide explainability to help clinicians make the best decisions for patient care. This review explores state-of-the-art AI methods, focusing on their application in image classification, image segmentation, multiple instance learning, generative models, and self-supervised learning. In radiology, AI enhances tumor detection, diagnosis, and treatment planning through advanced imaging modalities and real-time applications. In pathology, AI-driven image analysis improves cancer detection, biomarker discovery, and diagnostic consistency. Multimodal AI approaches can integrate data from radiology, pathology, and genomics to provide comprehensive diagnostic insights. Emerging trends, challenges, and future directions in AI-driven cancer research are discussed, emphasizing the transformative potential of these technologies in improving patient outcomes and advancing cancer care. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external validation using University of California, San Francisco (UCSF) data. Woollie surpasses ChatGPT in medical benchmarks and excels in eight non-medical benchmarks. Analyzing 39,319 radiology impression notes from 4002 patients, it achieved an overall area under the receiver operating characteristic curve (AUROC) of 0.97 for cancer progression prediction on MSK data, including a notable 0.98 AUROC for pancreatic cancer. On UCSF data, it achieved an overall AUROC of 0.88, excelling in lung cancer detection with an AUROC of 0.95. As the first oncology specific LLM validated across institutions, Woollie demonstrates high accuracy and consistency across cancer types, underscoring its potential to enhance cancer progression analysis.

<b>Background:</b> Chest radiographs play a crucial role in tuberculosis screening in high-prevalence regions, although widespread radiographic screening requires expertise that may be unavailable in settings with limited medical resources. <b>Objectives:</b> To evaluate a multimodal generative artificial intelligence (AI) model for detecting tuberculosis-associated abnormalities on chest radiography in patients undergoing tuberculosis screening. <b>Methods:</b> This retrospective study evaluated 800 chest radiographs obtained from two public datasets originating from tuberculosis screening programs. A generative AI model was used to create free-text reports for the radiographs. AI-generated reports were classified in terms of presence versus absence and laterality of tuberculosis-related abnormalities. Two radiologists independently reviewed the radiographs for tuberculosis presence and laterality in separate sessions, without and with use of AI-generated reports and recorded if they would accept the report without modification. Two additional radiologists reviewed radiographs and clinical readings from the datasets to determine the reference standard. <b>Results:</b> By the reference standard, 422/800 radiographs were positive for tuberculosis-related abnormalities. For detection of tuberculosis-related abnormalities, sensitivity, specificity, and accuracy were 95.2%, 86.7%, and 90.8% for AI-generated reports; 93.1%, 93.6%, and 93.4% for reader 1 without AI-generated reports; 93.1%, 95.0%, and 94.1% for reader 1 with AI-generated reports; 95.8%, 87.2%, and 91.3% for reader 2 without AI-generated reports; and 95.8%, 91.5%, and 93.5% for reader 2 with AI-generated reports. Accuracy was significantly lower for AI-generated reports than for both readers alone (p<.001), but significantly higher with than without AI-generated reports for one reader (reader 1: p=.47; reader 2: p=.47). Localization performance was significantly lower (p<.001) for AI-generated reports (63.3%) than for reader 1 (79.9%) and reader 2 (77.9%) without AI-generated reports and did not significantly change for either reader with AI-generated reports (reader 1: 78.7%, p=.71; reader 2: 81.5%, p=.23). Among normal and abnormal radiographs, reader 1 accepted 91.7% and 52.4%, while reader 2 accepted 83.2% and 37.0%, respectively, of AI-generated reports. <b>Conclusion:</b> While AI-generated reports may augment radiologists' diagnostic assessments, the current model requires human oversight given inferior standalone performance. <b>Clinical Impact:</b> The generative AI model could have potential application to aid tuberculosis screening programs in medically underserved regions, although technical improvements remain required.