To describe patient-reported media exposure, misinformation, advocacy awareness and support, and cancer-care policy priorities among patients with cancer in India, and to develop an exploratory patient-centred analytical framework incorporating formative composite measures of media influence and advocacy. A cross-sectional questionnaire-based survey of 1,189 unique patients with cancer was analysed. This includes demographic and clinical characteristics, sources and frequency of cancer-related information, perceived positive and negative effects of media, exposure to misleading information, advocacy awareness and support, and policy priorities. Composite indices included the Positive Media Score (PMS), Negative Media Score (NMS), Media Influence Index (MII), Advocacy Awareness Score (AAS), and Advocacy Support Score (ASS). Data analysis was performed using Python-based statistical coding. Clinician-led communication remained dominant, with doctors and hospital staff as the most common information sources. The Positive Media Score was 3.45 ± 0.70, the Negative Media Score was 2.68 ± 0.63, and the Media Influence Index was 0.77 ± 0.84, indicating higher positive-media composite scores than negative-media composite scores at the cohort level. Among participants reporting misinformation exposure, 45.7
To improve survival-related multimodal fusion for head and neck cancer (HNC) prognosis, this study proposes a Multiscale Sparse Cross-Attention Network (MSCN) for PET/CT-based survival prediction. FDG-PET/CT images and follow-up data from 296 HNC patients across four centers in The Cancer Imaging Archive (TCIA) were retrospectively analyzed. Data from three centers (n = 255) were used for training and internal validation, and the remaining center (n = 41) for independent testing. MSCN employs a 3D ResNeXt encoder with multiscale context aggregation to extract modality-specific features and introduces a sparse cross-modal cross-attention mechanism to constrain token-level interactions. Case-level deep features were selected via univariate Cox regression, correlation filtering, and LASSO, and incorporated into a multivariate Cox proportional hazards model for survival prediction. In the independent test set, MSCN achieved the best performance under the PET + CT setting (C-index = 0.848, 95
This study performed a bibliometric analysis of the top 100 most cited papers on artificial intelligence (AI) and its applications in health and medicine. Data was retrieved from the Scopus database in December 2024, focusing on papers published between 2001 and 2024. The search string included terms such as "artificial intelligence," "machine learning," "deep learning," and "electronic health records," combined with "health" or "medicine." To ensure specificity, only documents with these terms in their titles were included. The analysis was conducted in two stages: (1) exploring publications to identify a focused dataset and (2) selecting the top 100 most cited papers for in-depth evaluation. A total of 9,219 documents were analyzed, comprising 7,959 articles and 1,260 reviews. D.W. Bates emerged as the top author with 103 publications, Harvard Medical School was the leading institution, with 560 publications, while the United States dominated geographically, contributing 5,413 papers. The publication details, top authors, universities, and countries are presented in supplementary tables. Figures illustrate the dynamics of authors, universities, and countries, as well as collaboration networks for authors, departments, and countries. Additionally, uni-, bi-, and tri-gram analyses and the thematic evolution of the top 100 most cited papers were explored. This study provides a detailed analysis of AI applications in health and medicine, highlighting influential authors, institutions, and countries, as well as funding dynamics and publication trends. The findings offer valuable insights into the evolution of research in this domain and its key contributors, serving as a foundation for guiding future investigations and identifying emerging trends.
Nuclear theranostics involves the use of radioactive materials to image biological processes of expressing specific disease targets, such as membrane transporters or cell surface receptors, and designing these agents to deliver ionizing radiation to tissues that express these targets.With the increasing focus on precision medicine and sustainable healthcare, theranostic approaches have emerged as promising strategies for improving disease diagnosis, chemoraditaion therapy, treatment planning, therapeutic monitoring, and toxicity prediction, thereby enhancing patient outcomes and reducing the healthcare burden. This review summarizes recent advances in targeted theranostic radiopharmaceuticals, including monoclonal antibody-based systems, peptide receptor radionuclide therapy, nanotechnology-assisted theranostics, and pre-targeting strategies. Important considerations related to radionuclide selection, dosimetry, radiolabeling stability, and clinical translation are also discussed. Recent developments in novel radionuclides and radiopharmaceutical platforms have expanded the clinical applications of theranostics to neuroendocrine tumors, prostate cancer, and other malignant and non-malignant diseases. Advances in molecular imaging, personalized dosimetry, and targeted drug delivery have accelerated the clinical translation of theranostic agents while supporting innovation in the nuclear medicine field. However, challenges related to radionuclide production, regulatory approval, accessibility, and large-scale manufacturing continue to limit its widespread clinical implementation. Nuclear theranostics is a rapidly evolving field in modern nuclear medicine that supports precision healthcare through integrated diagnosis and targeted therapy. Continued advancements in radiopharmaceutical development and translational research are expected to improve the safety, effectiveness, and accessibility of theranostic approaches, contributing to sustainable healthcare delivery.
To develop and demonstrate a tool for efficient extraction, management, and visualization of DICOM metadata for facilitating retrospective protocol auditing and longitudinal trend analysis in diagnostic radiology. We developed the DICOM Tags Database as an extension module of IndoQCT platform using Python, PyQt5, and SQLite. The software features a dynamic workflow allowing users to configure custom database schemas by selecting tags directly from a DICOM viewer. The system includes integrated visualization tools for generating trend lines and frequency histograms. To evaluate the tool, anonymized thoracic CT datasets from three different hospitals using diverse scanner vendors (Toshiba, Siemens, and GE) were analyzed. The software successfully extracted and archived key dosimetric and mechanical parameters, including CTDIvol, tube voltage, exposure time, slice thickness, and table height. Longitudinal visualization shows specific protocol trends, thereby identifying outliers in slice thickness settings and highlighting vendor-specific variations in coordinate system definitions. The DICOM Tags Database bridges the gap between manual data recording and complex command-line utilities.
