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Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice. [ABSTRACT FROM AUTHOR]

Background: Gastrointestinal cancer cells display both morphology and physiology diversity, thus posing a significant challenge for precise representation by a single data model. We conducted an in-depth study of gastrointestinal cancer heterogeneity by integrating and analyzing data from multiple modalities. Methods: We used a modified Canny algorithm to identify edges from tumor images, capturing intricate nonlinear interactions between pixels. These edge features were then combined with differentially expressed mRNA, miRNA, and immune cell data. Before data integration, we used the K-medoids algorithm to pre-cluster individual data types. The results of pre-clustering were used to construct the kernel matrix. Finally, we applied spectral clustering to the fusion matrix to identify different tumor subtypes. Furthermore, we identified hub genes linked to these subtypes and their biological roles through the application of Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Ontology (GO) enrichment analysis. Results: Our investigation categorized patients into three distinct tumor subtypes and pinpointed hub genes associated with each. Genes MAGI2-AS3, MALAT1, and SPARC were identified as having a differential impact on the metastatic and invasive capabilities of cancer cells. Conclusion: By harnessing multimodal features, our study enhances the understanding of gastrointestinal tumor heterogeneity and identifies biomarkers for personalized medicine and targeted treatments. [ABSTRACT FROM AUTHOR]

Robin Schmidt,1,2 Charlie Alexander Hamm,1,3 Christopher Rueger,1 Han Xu,1 Yubei He,1,2 Luzie Alexandra Gottwald,4 Bernhard Gebauer,1 Lynn Jeanette Savic1– 3 1Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany; 2Experimental Clinical Research Center (ECRC) at Charité - Universitätsmedizin Berlin and Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, 13125, Germany; 3Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany; 4Vivantes Humboldt-Klinikum, Berlin, 13509, GermanyCorrespondence: Lynn Jeanette Savic, Charité Universitätsmedizin Berlin, Campus Virchow Klinikum, Klinik für Radiologie, Augustenburger Platz 1, Berlin, D-13353, Germany, Email lynn-jeanette.savic@charite.dePurpose: Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE).Patients and Methods: Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher’s exact and t-test, Kaplan–Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson’s correlation.Results: OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed.Conclusion: The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.Keywords: cancer imaging, hepatocellular carcinoma, microvascular invasion, magnetic resonance tomography, predictive imaging biomarkers

[123I]β-methyl-p-iodophenyl-pentadecanoic acid ([123I]BMIPP), which is used for nuclear medicine imaging of myocardial fatty acid metabolism, accumulates in cancer cells. However, the mechanism of accumulation remains unknown. Therefore, this study aimed to elucidate the accumulation and accumulation mechanism of [123I]BMIPP in cancer cells. We compared the accumulation of [123I]BMIPP in cancer cells with that of [18F]FDG and found that [123I]BMIPP was a much higher accumulation than [18F]FDG. The accumulation of [123I]BMIPP was evaluated in the presence of sulfosuccinimidyl oleate (SSO), a CD36 inhibitor, and lipofermata, a fatty acid transport protein (FATP) inhibitor, under low-temperature conditions and in the presence of etomoxir, a carnitine palmitoyl transferase I (CPT1) inhibitor. The results showed that [123I]BMIPP accumulation was decreased in the presence of SSO and lipofermata in H441, LS180, and DLD-1 cells, suggesting that FATPs and CD36 are involved in [123I]BMIPP uptake in cancer cells. [123I]BMIPP accumulation in all cancer cell lines was significantly decreased at 4 °C compared to that at 37 °C and increased in the presence of etomoxir in all cancer cell lines, suggesting that the accumulation of [123I]BMIPP in cancer cells is metabolically dependent. In a biological distribution study conducted using tumor-bearing mice transplanted with LS180 cells, [123I]BMIPP highly accumulated in not only LS180 cells but also normal tissues and organs (including blood and muscle). The tumor-to-intestine or large intestine ratios of [123I]BMIPP were similar to those of [18F]FDG, and the tumor-to-large-intestine ratios exceeded 1.0 during 30 min after [123I]BMIPP administration in the in vivo study. [123I]BMIPP is taken up by cancer cells via CD36 and FATP and incorporated into mitochondria via CPT1. Therefore, [123I]BMIPP may be useful for imaging cancers with activated fatty acid metabolism, such as colon cancer. However, the development of novel imaging radiotracers based on the chemical structure analog of [123I]BMIPP is needed. [ABSTRACT FROM AUTHOR]

Protein-based nanoparticles have garnered significant attention in theranostic applications due to their superior biocompatibility, exceptional biodegradability and ease of functionality. Compared to other nanocarriers, protein-based nanoparticles offer additional advantages, including biofunctionality and precise molecular recognition abilities, which make them highly effective in navigating complex biological environments. Moreover, proteins can serve as powerful tools with self-assembling structures and reagents that enhance cell penetration. And their derivation from abundant renewable sources and ability to degrade into harmless amino acids further enhance their suitability for biomedical applications. However, protein-based nanoparticles have so far not realized their full potential. In this review, we summarize recent advances in the use of protein nanoparticles in tumor diagnosis and treatment and outline typical methods for preparing protein nanoparticles. The review of protein nanoparticles may provide useful new insights into the development of biomaterial fabrication. [Display omitted] • The nano-platforms for theranostics by utilizing protein nanoparticles are reviewed. • Protein nanoparticles-based theranostics has good and tunable imaging and therapeutic modalities • Current defects and prospects of protein nanoparticles are also discussed. [ABSTRACT FROM AUTHOR]

PurposeThe aim of this study was to develop and validate a prediction model for classification of pulmonary nodules based on preoperative CT imaging.Materials and methodsA data set of Centers 1 (training set: 2633; internal testing set: 1129); Center 2 and Center 3 (external testing set: 218) of patients with pulmonary nodule cases was retrospectively collected. Handcrafted features were extracted from noncontrast chest CT scans by three senior radiologists. A total of 22 clinically handcrafted parameters (age, gender, L-RADS, and PNI-GARS et al.) were used to construct machine learning models (random forest, gradient boosting, and explainable boosting) for the classification of preoperative pulmonary nodules, and the parameters of the model were adjusted to achieve optimal performance. To evaluate the prediction capacity of each model. Both 5-fold cross-validation and 10-fold cross-validation were used to test the robustness of the models.ResultsThe explainable boosting model had the best performance on our constructed data. The model achieves an accuracy of 89.9%, a precision of 97.48%, a specificity of 89.5%, a sensitivity of 91.1%, and an AUC of 90.3%. In human-machine comparison, the AUC of machine learning models (90.4%, 95% CI: 85.5%–94.8%) was significantly improved compared to radiologists (60%, 95% CI: 50%–71.4%).ConclusionsThe explainable boosting model exhibited superior performance on our dataset, achieving high accuracy and precision in the diagnosis of pulmonary nodules compared to experienced radiologists.

The SINFONIA project’s main objective is to develop novel methodologies and tools that will provide a comprehensive risk appraisal for detrimental effects of radiation exposure on patients, workers, caretakers, and comforters, the public, and the environment during the management of patients suspected or diagnosed with lymphoma, brain tumors, and breast cancers. The project plan defines a series of key objectives to be achieved on the way to the main objective. One of these objectives is to develop and operate a repository to collect, pool, and share data from imaging and non-imaging examinations and radiation therapy sessions, histological results, and demographic information related to individual patients with lymphoma, brain tumors, and breast cancers. This paper presents the final version of that repository, a cloud-based platform for imaging and non-imaging data. It results from the implementation and integration of several software tools and programming frameworks under an evolutive architecture according to the project partners’ needs and the constraints of the General Data Protection Regulation. It provides, among other services, data uploading and downloading, data sharing, file decompression, data searching, DICOM previsualization, and an infrastructure for submitting and running Artificial Intelligence models.

The clinical application of cancer immunotherapy is unsatisfied due to low response rates and systemic immune-related adverse events. Microwave hyperthermia can be used as a synergistic immunotherapy to amplify the antitumor effect. Herein, we designed a Gd-based metal-organic framework (Gd-MOF) nanosystem for MRI-guided thermotherapy and synergistic immunotherapy, which featured high performance in drug loading and tumor tissue penetration. The PD-1 inhibitor (aPD-1) was initially loaded in the porous Gd-MOF (Gd/M) nanosystem. Then, the phase change material (PCM) and the cancer cell membrane were further sequentially modified on the surface of Gd/MP to obtain Gd-MOF@aPD-1@CM (Gd/MPC). When entering the tumor microenvironment (TME), Gd/MPC induces immunogenic death of tumor cells through microwave thermal responsiveness, improves tumor suppressive immune microenvironment and further enhances anti-tumor ability of T cells by releasing aPD-1. Meanwhile, Gd/MPC can be used for contrast-enhanced MRI. Transcriptomics data revealed that the downregulation of MSK2 in cancer cells leads to the downregulation of c-fos and c-jun, and ultimately leads to the apoptosis of cancer cells after treatment. In general, Gd/MPC nanosystem not only solves the problem of system side effect, but also achieves the controlled drug release via PCM, providing a promising theranostic nanoplatform for development of cancer combination immunotherapy.

Tumor associated fibroblasts (TAFs) play a key role in tumor growth and metastatization. TAFs overexpress different biomarkers that are usually expressed at low levels in physiological conditions. Among them are the fibroblast growth factor receptors (FGFRs) that bind the fibroblast growth factors (FGFs). In particular, the overexpression of FGFR-2c in tumors has been associated with advanced clinical stages and increased metastatization. Here, we developed a non-invasive tool to evaluate, in vivo, the expression of FGFR-2c in metastatic cancer. This is based on 99mTc-labelled FGF-2. Methods: 99mTc-FGF-2 was tested in vitro and in vivo in mice bearing allografts of sarcoma cells. Images of 99mTc-FGF-2 were acquired using a new portable high-resolution ultra-sensitive gamma camera for small animal imaging. Results: FGF-2 was labeled with high specific activity but low labelling efficiency, thus requiring post-labeling purification by gel-filtration chromatography. In vitro binding to 2C human keratinocytes showed a Kd of 3.36 × 10−9 M. In mice bearing J774A.1 cell allografts, we observed high and rapid tumor uptake of 99mTc-FGF-2 with a high Tumor/Blood ratio at 24 h post-injection (26.1 %ID/g and 12.9 %ID) with low kidney activity and moderate liver activity. Conclusions: we labeled FGF-2 with 99mTc and showed nanomolar Kd in vitro with human keratinocytes expressing FGF-2 receptors. In mice, 99mTc-FGF-2 rapidly and efficiently accumulated in tumors expressing FGF-2 receptors. This new radiopharmaceutical could be used in humans to image TAFs. [ABSTRACT FROM AUTHOR]

Background: Incidental findings (IFs) are commonly seen in staging rectal magnetic resonance imaging (MRI) scans. Their prevalence and clinical significance have not been previously documented. Purpose: To assess the prevalence, clinical significance, and outcomes of incidental findings in MRI scans performed for the staging of rectal cancer. Material and Methods: A retrospective study was performed at a tertiary colorectal imaging institution. Consecutive MRI rectal staging scans with correlative pathology confirmed primary rectal cancer between March 2014 and March 2021 were identified. The respective imaging reports were reviewed for IFs, which were classified as high, moderate, and low, according to their clinical significance. Medical records were reviewed to assess the outcomes of the highly significant IFs. Results: There were 266 eligible patients (97 women; mean age = 64.2 years) during the study period. A total of 120 (45%) patients did not have any IFs. A total of 238 IFs in 146 (55%) patients were found. There were 21 (9%) IFs of high clinical significance, 122 (51%) of moderate clinical significance, and 95 (40%) of low clinical significance. The prostate and uterus had the most IFs of high clinical significance, two of which were subsequently pathology confirmed as prostate adenocarcinomas. Conclusion: IFs were seen in more than half of the staging MRI scans in rectal cancer but less than 10% of these were of high clinical significance. The results of this study highlight the range of potential IFs and can guide future research assessing the potential impact of these IFs on patients and the healthcare system. [ABSTRACT FROM AUTHOR]

Radioimmunotherapy (RIT) is a promising alternative to conventional treatment options. Here, we present experimental work on the synthesis, radiochemistry, and in vivo performance of a lanthanoid‐selective nonadentate bispidine ligand suitable for 177Lu3+ ion complexation. The ligand (bisp,1) was derivatised with a photoactivatable aryl azide (ArN3) group as a bioconjugation handle for light‐induced labelling of proteins. Quantitative radiosynthesis of [177Lu]Lu‐1+ was accomplished in 10 minutes at 40 °C. Subsequent incubation of [177Lu]Lu‐1+ with trastuzumab, followed by irradiation with light at 365 nm for 15 min, at room temperature and pH 8.0–8.3, gave the radiolabelled mAb, [177Lu]Lu‐1‐azepin‐trastuzumab ([177Lu]Lu‐1‐mAb) in a decay‐corrected radiochemical yield of 14 %, and radiochemical purity (RCP)>90 %. Stability studies and cellular binding assays in vitro using the SK‐OV‐3 human ovarian cancer cells confirmed that [177Lu]Lu‐1‐mAb remained biological active and displayed specific binding to HER2/neu. Experiments in immunocompromised female athymic nude mice bearing subcutaneous xenograft models of SK‐OV‐3 tumours revealed significantly higher tumour uptake in the normal group compared with the control block group (29.8±11.4 %ID g−1 vs. 14.8±6.1 %ID g−1, respectively; P‐value=0.037). The data indicate that bispidine‐based ligand systems are suitable starting points for constructing novel, high‐denticity chelators for specific complexation of larger radiotheranostic metal ion nuclides. [ABSTRACT FROM AUTHOR]

Background: Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy‐induced cellular changes. Purpose: To investigate time‐dependent diffusion imaging with the short‐time‐limit random walk with barriers model (STL‐RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy. Study Type: Prospective. Population: Twenty‐seven patients (median age of 58 years and 7.4% of females) with p16+/p16− oropharyngeal/oral cavity squamous cell carcinomas (OPSCC/OCSCC) underwent MRI scans before therapy, of which 16 patients had second scans at 2 weeks of the 7‐weeks chemoradiation therapy (CRT). Field Strength/Sequence: 3‐T, diffusion sequence with oscillating gradient spine echo (OGSE) and pulse gradient spin echo (PGSE). Assessment: Diffusion weighted images were acquired using OGSE and PGSE. Effective diffusion times were derived for the STL‐RWBM to estimate free diffusion coefficient D0, volume‐to‐surface area ratio of cellular membranes V/S, and cell membrane permeability κ. Mean values of these parameters were calculated in tumor volumes. Statistical Tests: Tumor microstructure parameters were compared with clinical stages of p16+ I‐II OPSCC, p16+ III OPSCC, and p16− IV OCSCC by Spearman's rank correlation and with digital pathological analysis of a resected tissue sample. Tumor microstructure parameter responses during CRT in the 16 patients were assessed by paired t‐tests. A P‐value of <0.05 was considered statistically significant. Results: The derived effective diffusion times affected estimated values of V/S and κ by 40%. The tumor V/S values were significantly correlated with clinical stages (r = 0.47) as an increase from low to high clinical stages. The in vivo estimated cell size agreed with one from pathological analysis of a tissue sample. Early tumor cellular responses showed a significant increase in D0 (14%, P = 0.03) and non‐significant increases in κ (56%, P = 0.6) and V/S (10%, P = 0.1). Data Conclusion: Effective diffusion time estimation might impact microstructure parameter estimation. The tumor V/S was correlated with OPSCC/OCSCC clinical stages. Level of Evidence: 1 Technical Efficacy Stage: 1 [ABSTRACT FROM AUTHOR]

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods: Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results: The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions: Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.

The present case report is aimed to highlight the difficulty and the reason for the delayed diagnosis of phosphaturic mesenchymal tumors, emphasizing the need of standardized protocols for diagnosis, surgery and follow-up in high-volume hospitals. The clinical signs and symptoms, diagnostic and therapeutic procedures, immunohistological features were analyzed. Delayed diagnosis of phosphaturic mesenchymal tumor was primarily due to non-specific clinical symptoms such as fatigue, muscular and bone pain, and multiple fractures. This cryptic clinical picture made the diagnosis tricky that led to treatment of patient for non-specific pain and stress fractures before to consider the tumor-induced osteomalacia syndrome. Some well-documented studies were found in the literature in which the history of trauma is a critical trigger of glomus tumors. Extra-subungual tumors most frequently occur in the knee and ankle regions, particularly among young adults, and the diagnosis is typically made approximately 7.2 years after initial symptom onset. The difficult tumor localization represented an additional obstacle to the prompt treatment, leading to delayed curative surgery.

Cancer is a disease where abnormal cells grow uncontrollably and spread to other body parts. It can originate anywhere in the human body, which consists of trillions of cells. These cells continually divide, replenishing the body’s needs. As cells age or sustain damage, they naturally undergo apoptosis, allowing new cells to take their place. Our research uses a secondary dataset from Kaggle, comprising over 130,000 images representing various cancer types. We have developed a novel Deep-learning model capable of detecting and classifying cancer at early stages with remarkable accuracy. The model classifies eight primary cancer types and 26 subtypes, each represented by 5,000 images. Our approach combines various computational tools, including pre-trained Convolutional Neural Networks, Machine learning, and Deep learning classifiers such as KNN and SVM, and innovative multimodal architectures of merged CNN-LSTM hybrids. We applied two distinct classification strategies. In our first approach, the main class and subclass are classified together. In the second approach, the model first predicts the main eight classes and then 26 subclasses concerning the main class classification, where the KNN model achieved higher accuracy for the Lymphoma class than CNNs. Finally, the X-OR gate-based fusion technique applied after prediction significantly reduces misclassifications and enhances the certainty of cancer types. Our findings reveal great accuracy levels of 99.25% for primary cancer classifications and 97.80% for subclass classifications. The introduction of novel models, Vception (VGG + Inception) and Vmobilnet (VGG + MobileNet), integrated with LSTM, further advances diagnostic capabilities. Again, By utilizing an X-OR gate post-prediction from Vmobilenet and Vception models, we achieved a main class accuracy of 99.95% and a subclass accuracy of 99.13%, significantly boosting model confidence. Moreover, individually, KNN achieved 97.14% accuracy for the Lymphoma class using PCA. This study not only sets a new benchmark in cancer detection but also promises to improve patient care and treatment outcomes significantly.

Green fluorescent protein (GFP) Firstly discovered from the jellyfish Aequorea victoriaand it was widely used in biological science as a universal fluorescent tag. Many scientists have focused their efforts on the identification and development of fluorescent proteins with novel characteristics and enhanced properties, to be used for visualization of structural organization and dynamic processes in living cells. The discovery of green fluorescent protein (GFP) followed by purification and elucidation of its molecular structure introduced a new and promising marker for biological studies. GFP is intrinsically fluorescent, has low toxicity, pH and heat stability and is easily imaged and quantified. This review article focuses on the discovery, diversity of fluorescent patterns and colors, other fluorescent protein, purification, structure, properties, and numerous applications of GFP in biological research such as in vivo imaging of cancer, promoter and protein fusion constructs, biosensors, Ex vivo imaging, and imaging the efficacy of therapeutics.

Abstract Objectives Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. Methods We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. Results We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. Conclusion RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. Critical relevance statement This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. Key points - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction. Graphical Abstract

Boronic acid‐containing molecules are substantially popularized in chemical biology and medicinal chemistry due to the broad spectrum of covalent conjugations as well as interaction modules offered by the versatile boron atom. Apparently, the WGA peptide (wheat germ agglutinin, 62–73), which shows a considerably low binding affinity to sialic acid, turned into a selective and >5 folds potent binder with the aid of a suitable boronic acid probe installed chemoselectively. In silico studies prompted us to install BA probes on the cysteine residue, supposedly located in close proximity to the bound sialic acid. In vitro studies revealed that the tailored boronopeptides show enhanced binding ability due to the synergistic recognition governed by selective non‐covalent interactions and cis‐diol boronic acid conjugation. The intense binding is observed even in 10 % serum, thus enabling profiling of sialyl‐glycan on cancer cells, as compared with the widely used lectin, Sambucus nigra. The synergistic binding mode between the best boronopeptide (P3) binder and sialic acid was analyzed via 1H and 11B NMR. [ABSTRACT FROM AUTHOR]

Breast cancer stands as the most prevalent cancer globally, necessitating comprehensive care. A multidisciplinary approach proves crucial for precise diagnosis and treatment, ultimately leading to effective disease management. While surgical interventions continue to evolve and remain integral for curative treatment, imaging assumes a fundamental role in breast cancer detection. Advanced imaging techniques not only facilitate improved diagnosis but also contribute significantly to the overall enhancement of breast cancer management. This review article aims to provide an overview of innovative technologies such as virtual reality, augmented reality, and three-dimensional imaging, utilized in the medical field to elevate the diagnosis and treatment of breast cancer. Additionally, the article delves into an emerging technology known as the metaverse, still under development. Through the analysis of impactful research and comparison of their findings, this study offers valuable insights into the advantages of each innovative technique. The goal is to provide physicians, surgeons, and radiologists with information on how to enhance breast cancer management. [ABSTRACT FROM AUTHOR]

Introduction: Hypophysitis is reported in 8.5%-14% of patients receiving combination immune checkpoint inhibition (cICI) but can be a diagnostic challenge. This study aimed to assess the role of routine diagnostic imaging performed during therapeutic monitoring of combination anti-CTLA-4/anti-PD-1 treatment in the identification of hypophysitis and the relationship of imaging findings to clinical diagnostic criteria. Methods: This retrospective cohort study identified patients treated with cICI between January 2016 and January 2019 at a quaternary melanoma service. Medical records were reviewed to identify patients with a documented diagnosis of hypophysitis based on clinical criteria. Available structural brain imaging with magnetic resonance imaging (MRI) or computed tomography (CT) of the brain and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography with computed tomography (FDG-PET/CT) were assessed retrospectively. The main radiological outcome measures were a relative change in pituitary size or FDG uptake temporally attributed to cICI. Results: There were 162 patients (median age 60 years, 30% female) included. A total of 100 and 134 had serial CT/MRI of the brain and FDG-PET/CT, respectively. There were 31 patients who had a documented diagnosis of hypophysitis and an additional 20 who had isolated pituitary imaging findings. The pituitary gland enlargement was mild, and the largest absolute gland size was 13 mm, with a relative increase of 7 mm from baseline. There were no cases of optic chiasm compression. Pituitary enlargement and increased FDG uptake were universally transient. High-dose glucocorticoid treatment for concurrent irAEs prevented assessment of the pituitary-adrenal axis in 90% of patients with isolated imaging findings. Conclusion: Careful review of changes in pituitary characteristics on imaging performed for assessment of therapeutic response to iICI may lead to increased identification and more prompt management of cICI-induced hypophysitis. [ABSTRACT FROM AUTHOR]

Objectives: Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. Methods: We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. Results: We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. Conclusion: RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. Critical relevance statement: This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. Key points: - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction. [ABSTRACT FROM AUTHOR]

Radiological diagnosis of oral squamous cell carcinoma (OSCC) is one of the main steps in treatment planning. T (tumor size), DOI (depth of invasion) (AJCC 8th edition), and nodal metastases (N+) were evaluated using CT and MRI to assess the most effective imaging method. The effectiveness of the radiological imaging methods was compared with histopathological results. Imaging diagnostic studies were performed and retrospectively analyzed in 125 patients with OSCC (CT n = 54 and MRI n = 71). Histopathology evaluated T, DOI, and N+. The radiological T results of CT in comparison with histopathological examination showed agreement in 62.5% of cases for T1, 56.25% for T2, 25% for T3, and 42.8% for T4 (p-value = 0.07), and regarding MRI, 52.2% for T1, 36.4% for T2, 33.3% in T3, and 33.3% for T4. The DOI results of CT and MRI juxtaposed against the histopathological findings were as follows: for CT, n = 18 for DOI ≤ 10 mm and n = 36 for >10 mm; for MRI, n = 29 for DOI ≤ 10 mm and n = 42 for >10 mm (DOI CT vs. DOI hist. pat. p-value = 0.23; DOI MRI vs. DOI hist. pat. p-value = 0.006). Regarding nodal metastasis, n = 21 for N0 and n = 32 for N+ for CT (p-value = 0.02), and n = 49 for N0 and n = 22 for N+ for MRI (p-value = 0.1). In the radiological N+ group, the histopathological findings coincided with the results of MRI and CT in 27% and 62.5% of cases, respectively (N0: 83.6% for MRI; 85.7% for CT). Upon evaluating T, a decreasing percentage of overlapping results with an increasing tumor size was observed. The accuracy of both imaging studies was at a similar level, with a slight advantage for MRI. Among the patients on whom CT was performed, DOI analysis did not show statistically significant differences. This led to the conclusion that, in most cases, the DOI results based on CT overlapped with those described via histopathological examination. However, among the group of patients with MRI as the imaging method of choice, the differences proved to be statistically significant (p-value = 0.006). The results of this study indicate that CT is a more accurate method for DOI assessment. The results of the radiologic metastasis evaluation (N+ group) overlapped more in the CT group, while the percentage of corresponding results in the radiologic N0 vs. hist. pat. N0 group was high and similar in both groups. These results indicate that it is easier to confirm the absence of a metastasis than its presence. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose. The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64 ± 12.7 years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined. iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P <.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P <.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P <.05), reaching a maximum with iMAR 5. iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths. [ABSTRACT FROM AUTHOR]

Nanomaterials possess unusual physicochemical properties including unique optical, magnetic, electronic properties, and large surface-to-volume ratio. However, nanomaterials face some challenges when they were applied in the field of biomedicine. For example, some nanomaterials suffer from the limitations such as poor selectivity and biocompatibility, low stability, and solubility. To address the above-mentioned obstacles, functional nucleic acid has been widely served as a powerful and versatile ligand for modifying nanomaterials because of their unique characteristics, such as ease of modification, excellent biocompatibility, high stability, predictable intermolecular interaction and recognition ability. The functionally integrating functional nucleic acid with nanomaterials has produced various kinds of nanocomposites and recent advances in applications of functional nucleic acid decorated nanomaterials for cancer imaging and therapy were summarized in this review. Further, we offer an insight into the future challenges and perspectives of functional nucleic acid decorated nanomaterials.

Targeted therapies for prostate, breast, and ovarian cancers are based on their activity against primary tumors rather than their anti‐metastatic activity. Consequently, there is an urgent need for new agents targeting the metastatic process. Emerging evidence correlates in vitro and in vivo cancer invasion and metastasis with increased activity of the proteases mesotrypsin (prostate and breast cancer) and kallikrein 6 (KLK6; ovarian cancer). Thus, mesotrypsin and KLK6 are attractive putative targets for therapeutic intervention. As potential therapeutics for advanced metastatic prostate, breast, and ovarian cancers, we report novel mesotrypsin‐ and KLK6‐based therapies, based on our previously developed mutants of the human amyloid β‐protein precursor Kunitz protease inhibitor domain (APPI). These mutants, designated APPI‐3M (prostate and breast cancer) and APPI‐4M (ovarian cancer), demonstrated significant accumulation in tumors and therapeutic efficacy in orthotopic preclinical models, with the advantages of long retention times in vivo, high affinity and favorable pharmacokinetic properties. The applicability of the APPIs, as a novel therapy and for imaging purposes, is supported by their good safety profile and their controlled and scalable manufacturability in bioreactors.

Treatment response assessment by imaging plays a vital role in evaluating changes in solid tumors during oncology therapeutic clinical trials. Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 is the reference standard imaging response criteria and provides details regarding image acquisition, image interpretation and categorical response classification. While RECIST 1.1 is applied for the majority of clinical trials in solid tumors, other criteria and modifications have been introduced when RECIST 1.1 outcomes may be incomplete. Available criteria beyond RECIST 1.1 can be explored in an algorithmic fashion dependent on imaging modality, tumor type and method of treatment. Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) is available for use with PET/CT. Modifications to RECIST 1.1 can be tumor specific, including mRECIST for hepatocellular carcinoma and mesothelioma. Choi criteria for gastrointestinal stromal tumors incorporate tumor density with alterations to categorical response thresholds. Prostate Cancer Working Group 3 (PCWG3) imaging criteria combine RECIST 1.1 findings with those of bone scans. In addition, multiple response criteria have been created to address atypical imaging responses in immunotherapy.

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues. [ABSTRACT FROM AUTHOR]

Targeted therapies for prostate, breast, and ovarian cancers are based on their activity against primary tumors rather than their anti‐metastatic activity. Consequently, there is an urgent need for new agents targeting the metastatic process. Emerging evidence correlates in vitro and in vivo cancer invasion and metastasis with increased activity of the proteases mesotrypsin (prostate and breast cancer) and kallikrein 6 (KLK6; ovarian cancer). Thus, mesotrypsin and KLK6 are attractive putative targets for therapeutic intervention. As potential therapeutics for advanced metastatic prostate, breast, and ovarian cancers, we report novel mesotrypsin‐ and KLK6‐based therapies, based on our previously developed mutants of the human amyloid β‐protein precursor Kunitz protease inhibitor domain (APPI). These mutants, designated APPI‐3M (prostate and breast cancer) and APPI‐4M (ovarian cancer), demonstrated significant accumulation in tumors and therapeutic efficacy in orthotopic preclinical models, with the advantages of long retention times in vivo, high affinity and favorable pharmacokinetic properties. The applicability of the APPIs, as a novel therapy and for imaging purposes, is supported by their good safety profile and their controlled and scalable manufacturability in bioreactors. [ABSTRACT FROM AUTHOR]

Purposes: To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines. Materials and methods: A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21 years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring. Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients. Results: Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%–73%) CLAIM items reported. Adherence has improved over time based on publication year. Conclusion: The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies. [ABSTRACT FROM AUTHOR]

Fluorescence image-guided surgery (FIGS) can serve as a tool to achieve successful resection of tumour tissues during surgery, serving as a surgical navigator for surgeons. FIGS relies on the use of fluorescent molecules that can specifically interact with cancer cells. In this work, we developed a new model of fluorescent probe based on benzothiazole-phenylamide moiety featuring the visible fluorophore nitrobenzoxadiazole (NBD), namely BPN-01. This compound was designed and synthesised for potential applications in the tissue biopsy examination and ex-vivo imaging during FIGS of solid cancers. The probe BPN-01 exhibited favourable spectroscopic properties, particularly in nonpolar and alkaline solvents. Moreover, in vitro fluorescence imaging revealed that the probe appeared to recognise and be internalised in the prostate (DU-145) and melanoma (B16-F10) cancer cells, but not in the normal cells (myoblast C2C12). The cytotoxicity studies revealed that probe BPN-01 was not toxic to the B16 cells, suggesting excellent biocompatibility. Furthermore, the computational analysis showed that the calculated binding affinity of the probe to both translocator protein 18 kDa (TSPO) and human epidermal growth factor receptor 2 (HER2) was considerably high. Hence, probe BPN-01 displays promising properties and may be valuable for visualising cancer cells in vitro. Furthermore, ligand 5 can potentially be labelled with NIR fluorophore and radionuclide, and serves as a dual imaging agent for in vivo applications. [ABSTRACT FROM AUTHOR]

Abstract The advent of nanotechnology has opened new possibilities for bioimaging. Metal nanoparticles (such as gold, silver, iron, copper, etc.) hold tremendous potential and offer enormous opportunities for imaging and diagnostics due to their broad optical characteristics, ease of manufacturing technique, and simple surface modification. The arginine-glycine-aspartate (RGD) peptide is a three-amino acid sequence that seems to have a considerably greater ability to adhere to integrin adhesion molecules that exclusively express on tumour cells. RGD peptides act as the efficient tailoring ligand with a variety of benefits including non-toxicity, greater precision, rapid clearance, etc. This review focuses on the possibility of non-invasive cancer imaging using metal nanoparticles with RGD assistance. Graphical abstract

The multifactorial nature of cancer still classifies the disease as one of the leading causes of death worldwide. Modern medical sciences are following an interdisciplinary approach that has been fueled by the nanoscale revolution of the past years. The exploitation of high-Z materials, in combination with ionizing or non-ionizing radiation, promises to overcome restrictions in medical imaging and to augment the efficacy of current therapeutic modalities. Gold nanoparticles (AuNPs) have proven their value among the scientific community in various therapeutic and diagnostic techniques. However, the high level of multiparametric demands of AuNP experiments in combination with their biocompatibility and cytotoxicity levels remain crucial issues. Gadolinium NPs (GdNPs), have presented high biocompatibility, low cytotoxicity, and excellent hemocompatibility, and have been utilized in MRI-guided radiotherapy, photodynamic and photothermal therapy, etc. Τhe utilization of gadolinium bound to AuNPs may be a promising alternative that would reduce phenomena, such as toxicity, aggregation, etc., and could create a multimodal in vivo contrast and therapeutic agent. This review highlights multi-functionalization strategies against cancer where gold and gadolinium NPs are implicated. Their experimental applications and limitations of the past 5 years will be analyzed in the hope of enlightening the benefits and drawbacks of their proper combination.

Artificial intelligence (AI) represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight.

PurposeWith the initiation of human hyperpolarized 13 C (HP-13 C) trials at multiple sites and the development of improved acquisition methods, there is an imminent need to maximally extract diagnostic information to facilitate clinical interpretation. This study aims to improve human HP-13 C MR spectroscopic imaging through means of Tensor Rank truncation-Image enhancement (TRI) and optimal receiver combination (ORC).MethodsA data-driven processing framework for dynamic HP 13 C MR spectroscopic imaging (MRSI) was developed. Using patient data sets acquired with both multichannel arrays and single-element receivers from the brain, abdomen, and pelvis, we examined the theory and application of TRI, as well as 2 ORC techniques: whitened singular value decomposition (WSVD) and first-point phasing. Optimal conditions for TRI were derived based on bias-variance trade-off.ResultsTRI and ORC techniques together provided a 63-fold mean apparent signal-to-noise ratio (aSNR) gain for receiver arrays and a 31-fold gain for single-element configurations, which particularly improved quantification of the lower-SNR-[13 C]bicarbonate and [1-13 C]alanine signals that were otherwise not detectable in many cases. Substantial SNR enhancements were observed for data sets that were acquired even with suboptimal experimental conditions, including delayed (114 s) injection (8× aSNR gain solely by TRI), or from challenging anatomy or geometry, as in the case of a pediatric patient with brainstem tumor (597× using combined TRI and WSVD). Improved correlation between elevated pyruvate-to-lactate conversion, biopsy-confirmed cancer, and mp-MRI lesions demonstrated that TRI recovered quantitative diagnostic information.ConclusionOverall, this combined approach was effective across imaging targets and receiver configurations and could greatly benefit ongoing and future HP 13 C MRI research through major aSNR improvements.

Tumor associated fibroblasts (TAFs) play a key role in tumor growth and metastatization. TAFs overexpress different biomarkers that are usually expressed at low levels in physiological conditions. Among them are the fibroblast growth factor receptors (FGFRs) that bind the fibroblast growth factors (FGFs). In particular, the overexpression of FGFR-2c in tumors has been associated with advanced clinical stages and increased metastatization. Here, we developed a non-invasive tool to evaluate, in vivo, the expression of FGFR-2c in metastatic cancer. This is based on 99mTc-labelled FGF-2. Methods: 99mTc-FGF-2 was tested in vitro and in vivo in mice bearing allografts of sarcoma cells. Images of 99mTc-FGF-2 were acquired using a new portable high-resolution ultra-sensitive gamma camera for small animal imaging. Results: FGF-2 was labeled with high specific activity but low labelling efficiency, thus requiring post-labeling purification by gel-filtration chromatography. In vitro binding to 2C human keratinocytes showed a Kd of 3.36 × 10−9 M. In mice bearing J774A.1 cell allografts, we observed high and rapid tumor uptake of 99mTc-FGF-2 with a high Tumor/Blood ratio at 24 h post-injection (26.1 %ID/g and 12.9 %ID) with low kidney activity and moderate liver activity. Conclusions: we labeled FGF-2 with 99mTc and showed nanomolar Kd in vitro with human keratinocytes expressing FGF-2 receptors. In mice, 99mTc-FGF-2 rapidly and efficiently accumulated in tumors expressing FGF-2 receptors. This new radiopharmaceutical could be used in humans to image TAFs.

IntroductionHypophysitis is reported in 8.5%–14% of patients receiving combination immune checkpoint inhibition (cICI) but can be a diagnostic challenge. This study aimed to assess the role of routine diagnostic imaging performed during therapeutic monitoring of combination anti-CTLA-4/anti-PD-1 treatment in the identification of hypophysitis and the relationship of imaging findings to clinical diagnostic criteria.MethodsThis retrospective cohort study identified patients treated with cICI between January 2016 and January 2019 at a quaternary melanoma service. Medical records were reviewed to identify patients with a documented diagnosis of hypophysitis based on clinical criteria. Available structural brain imaging with magnetic resonance imaging (MRI) or computed tomography (CT) of the brain and 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography with computed tomography (FDG-PET/CT) were assessed retrospectively. The main radiological outcome measures were a relative change in pituitary size or FDG uptake temporally attributed to cICI.ResultsThere were 162 patients (median age 60 years, 30% female) included. A total of 100 and 134 had serial CT/MRI of the brain and FDG-PET/CT, respectively. There were 31 patients who had a documented diagnosis of hypophysitis and an additional 20 who had isolated pituitary imaging findings. The pituitary gland enlargement was mild, and the largest absolute gland size was 13 mm, with a relative increase of 7 mm from baseline. There were no cases of optic chiasm compression. Pituitary enlargement and increased FDG uptake were universally transient. High-dose glucocorticoid treatment for concurrent irAEs prevented assessment of the pituitary–adrenal axis in 90% of patients with isolated imaging findings.ConclusionCareful review of changes in pituitary characteristics on imaging performed for assessment of therapeutic response to iICI may lead to increased identification and more prompt management of cICI-induced hypophysitis.

Traditional chemotherapeutic drugs have limitations due to their non-targeted ability toward cancer cells. Stimuli-activatable prodrugs are designed to overcome these obstacles. However, the real-time monitoring of stimuli-activatable theranostic prodrugs still poses challenges. Herein, a prodrug (Fe–SS–HCy), consisting of a ferrocene-modified hemicyanine linked via a disulfide bond, is synthesized for anticancer imaging and therapy. Before activation, the toxicity of Fe–SS–HCy is low. The fluorescence of Fe–SS–HCy is quenched by ferrocene due to photoinduced electron transfer. After being taken up by cancer cells, the intracellular GSH activates Fe–SS–HCy, which releases HCy. The fluorescence of HCy is restored and selectively accumulates in the mitochondria, which further produce reactive oxygen species (ROS) to induce cancer cell death. Thus, this "off-on" fluorogenic HCy presents a new strategy for monitoring prodrug activation in real-time and for enhancing therapeutic efficacy with reduced side effects. [ABSTRACT FROM AUTHOR]

Purpose: (S)-4-(3-[18F]Fluoropropyl)-L-glutamic acid ([18F]FSPG) is an L-glutamate derivative used as a PET biomarker to assess intracellular redox status in vivo through targeting of the cystine/glutamate antiporter protein, xc− transporter. In this report, we describe a radiosynthesis of [18F]FSPG for use in PET studies that address specific challenges in relation to the radiotracer purity, molar activity, and quality control testing methods. Procedures: The radiosynthesis of [18F]FSPG was performed using a customised RNPlus Research automated radiosynthesis system (Synthra GmbH, Hamburg, Germany). [18F]FSPG was labelled in the 3-fluoropropylmoiety at the 4-position of the glutamic acid backbone with fluorine-18 via substitution of nucleophilic [18F]fluoride with a protected naphthylsulfonyloxy-propyl-L-glutamate derivative. Radiochemical purity of the final product was determined by radio HPLC using a new method of direct analysis using a Hypercarb C18 column. Results: The average radioactivity yield of [18F]FSPG was 4.2 GBq (range, 3.4–4.8 GBq) at the end of synthesis, starting from 16 GBq of [18F]fluoride at the end of bombardment (n = 10) in a synthesis time of 50 min. The average molar activity and radioactivity volumetric concentration at the end of synthesis were 66 GBq µmol−1 (range, 48–73 GBq µmol−1) and 343–400 MBq mL−1, respectively. Conclusion: Stability tests using a 4.6 GBq dose with a radioactivity volumetric concentration of 369 MBq mL−1 at the end of synthesis showed no observable radiolysis 3 h after production. The formulated product is of high radiochemical purity (> 95%) and higher molar activity compared to previous methods and is safe to inject into mice up to 3 h after production. [ABSTRACT FROM AUTHOR]

The multifactorial nature of cancer still classifies the disease as one of the leading causes of death worldwide. Modern medical sciences are following an interdisciplinary approach that has been fueled by the nanoscale revolution of the past years. The exploitation of high-Z materials, in combination with ionizing or non-ionizing radiation, promises to overcome restrictions in medical imaging and to augment the efficacy of current therapeutic modalities. Gold nanoparticles (AuNPs) have proven their value among the scientific community in various therapeutic and diagnostic techniques. However, the high level of multiparametric demands of AuNP experiments in combination with their biocompatibility and cytotoxicity levels remain crucial issues. Gadolinium NPs (GdNPs), have presented high biocompatibility, low cytotoxicity, and excellent hemocompatibility, and have been utilized in MRI-guided radiotherapy, photodynamic and photothermal therapy, etc. Τhe utilization of gadolinium bound to AuNPs may be a promising alternative that would reduce phenomena, such as toxicity, aggregation, etc., and could create a multimodal in vivo contrast and therapeutic agent. This review highlights multi-functionalization strategies against cancer where gold and gadolinium NPs are implicated. Their experimental applications and limitations of the past 5 years will be analyzed in the hope of enlightening the benefits and drawbacks of their proper combination. [ABSTRACT FROM AUTHOR]