Cancer Biotherapy and Radiopharmaceuticals

Oncologic emergencies in critically ill cancer patients frequently require rapid, real-time assessment of tumor responses to therapeutic interventions. However, conventional imaging modalities such as computed tomography and magnetic resonance imaging are often impractical in intensive care units (ICUs) due to logistical constraints and patient instability. Super-resolution ultrasound (SR-US) imaging has emerged as a promising noninvasive alternative, facilitating bedside evaluation of tumor microvascular dynamics with exceptional spatial resolution. This study assessed the clinical utility of real-time SR-US imaging in monitoring tumor perfusion changes during emergency management in oncological ICU settings.

In this prospective observational study, critically ill patients with oncologic emergencies underwent bedside SR-US imaging before and after the initiation of emergency therapy (e.g., corticosteroids, decompression, or chemotherapy). SR-US was employed to quantify microvascular parameters, including perfusion density and flow heterogeneity. Data processing incorporated artificial intelligence for real-time vessel segmentation and quantitative analysis.

SR-US imaging successfully detected perfusion changes within hours of therapy initiation. A significant correlation was observed between reduced tumor perfusion and clinical improvement, including symptom relief and shorter ICU stay. This technology enables visualization of microvessels as small as 30 µm, surpassing conventional ultrasound limits. No adverse events were reported with the use of contrast microbubbles. In addition, SR-US imaging reduces the need for transportation to radiology departments, thereby optimizing ICU workflow.

Real-time SR-US imaging offers a novel, bedside-compatible method for evaluating tumor vascular response during the acute phase of oncological emergencies. Its integration into ICU care pathways could enhance timely decision-making, reduce reliance on static imaging, and support personalized cancer management. Further multicenter validation is required.

In this single-center retrospective study, the authors evaluated whether real-time ultrasound-guided positioning of an implantable venous access port catheter tip at the superior vena cava-right atrial junction (SVC-RAJ) reduces the risk of catheter-related thrombosis (CRT) in adult patients with cancer and developed a multivariable risk prediction model to support individualized prevention.

Clinical data from 600 consecutive patients who underwent port implantation at Zhongshan People’s Hospital were analyzed. Patients were grouped according to final catheter tip position (SVC-RAJ versus non-SVC-RAJ), and CRT incidence was compared between groups.

The overall incidence of CRT was 30.33% (182/600) and was significantly lower in the SVC-RAJ group than in the non-SVC-RAJ group (22.42% vs. 38.73%, p < 0.001). In multivariable analysis, catheter tip positioning at the SVC-RAJ remained an independent protective factor (odds ratio = 0.517, 95% confidence interval [CI]: 0.353–0.756). Age, body mass index (BMI), tumor stage, neutrophil-to-lymphocyte ratio, D-dimer level, catheterization duration, and prophylactic anticoagulation status were also independently associated with CRT. A nomogram integrating these variables demonstrated good discrimination (area under the curve = 0.866, 95% CI: 0.837–0.895), with a sensitivity of 70.33% and a specificity of 85.89%. Performance across specific age or BMI strata was not separately evaluated in this study, and further stratified validation in larger datasets is needed to assess model consistency across demographic subgroups.

These findings support ultrasound-guided SVC-RAJ positioning as a clinically relevant strategy for reducing CRT risk and maintaining reliable venous access in contemporary oncology care pathways.

Ultrasound (US) is widely used in oncology, yet prospective clinical evidence for integrating US into radiopharmaceutical positron emission tomography/computed tomography (PET/CT) workflows remains limited. We assessed whether a standardized periprocedural US step under anesthesia improves lesion detectability and quantitative conspicuity without compromising safety.

In a prospective single-arm trial, 39 patients were enrolled and 38 completed US-assisted PET/CT. Under general anesthesia with continuous monitoring, US was applied to the target region immediately before radiotracer administration, followed by whole-body PET/CT under uninterrupted anesthesia. Two blinded nuclear medicine physicians interpreted paired standard versus US-assisted datasets, scoring lesion detection and 5-point confidence. Primary endpoints were within-patient change in detection and confidence. Secondary endpoints included standardized uptake value/tumor-to-background ratio (TBR) metrics, management change, and Common Terminology Criteria for Adverse Events (CTCAE)-graded safety. Paired McNemar and Wilcoxon signed-rank tests were used; κ quantified interreader agreement.

US assistance increased lesion detection and improved reader confidence, with higher paired TBR and most patients showing clinically meaningful TBR gains. Management changed in 12/38 (31.6%). No CTCAE grade ≥3 events occurred; vitals remained stable, and stop criteria were not triggered.

US integration into PET/CT under anesthesia was feasible and safe, improving imaging performance and clinical decision impact, aligning with clinical trial and risk-focused US oncology priorities.

Early diagnosis and accurate prediction of treatment response in esophageal squamous cell carcinoma (ESCC) remain major clinical challenges due to the lack of reliable and noninvasive biomarkers. Recently, artificial intelligence-driven endoscopic ultrasound image analysis has shown great promise in revealing genomic features associated with imaging phenotypes.

A prospective study of 115 patients with ESCC was conducted. Deep features were extracted from endoscopic ultrasound using a ResNet50 convolutional neural network. Important features shared across three machine learning models (NN, GLM, DT) were used to construct an image-derived signature. Plasma levels of leukotriene B4 (LTB4) and other inflammatory markers were measured using enzyme-linked immunosorbent assay. Correlations between signature and inflammation markers were analyzed, followed by logistic regression and subgroup analyses.

The endoscopic ultrasound image-derived signature, generated using deep learning algorithms, effectively distinguished esophageal cancer from normal esophageal tissue. Among all inflammatory markers, LTB4 exhibited the strongest negative correlation with the image signature and showed significantly higher expression in the healthy control group. Multivariate logistic regression analysis identified LTB4 as an independent risk factor for ESCC (odds ratio = 1.74, p = 0.037). Furthermore, LTB4 expression was significantly associated with patient sex, age, and chemotherapy response. Notably, higher LTB4 levels were linked to an increased likelihood of achieving a favorable therapeutic response.

This study demonstrates that deep learning-derived endoscopic ultrasound image features can effectively distinguish ESCC from normal esophageal tissue. By integrating image features with serological data, the authors identified LTB4 as a key inflammation-related biomarker with significant diagnostic and therapeutic predictive value.

Wilms tumor (nephroblastoma) is the most frequent malignant tumor of the urinary system in children; however, current treatment choices are restricted due to inadequate drug targeting, systemic toxicity, and multidrug resistance. Chlorogenic acid (CGA), a natural polyphenolic molecule, has promise as an anti-Wilms tumor action but has limited water solubility and bioavailability. To address these constraints, the authors created an ultrasound-responsive folate-CGA nanocarrier (1,2-distearoyl-sn-glycero-3-phosphoethanolamine-polyethylene glycol-folic acid-CGA [DSPE-PEG-FA/CGA]) that enables targeted and regulated drug administration.

The nanocarrier was created utilizing DSPE as the structural backbone, with PEG modification for increased stability and FA conjugation for tumor-specific targeting. Following low-intensity ultrasonic treatment, the nanocarrier increased permeability and induced CGA release via acoustic cavitation effects, greatly enhancing local drug accumulation inside tumor tissues.

Comprehensive characterization demonstrated good encapsulation efficiency, consistent particle size, and acceptable ultrasonic responsiveness. In vitro experiments indicated increased cellular absorption via folate receptor-mediated endocytosis and ultrasound stimulation, leading to more cytotoxicity against Wilms tumor cells than nonultrasound-treated controls. In vivo, the DSPE-PEG-FA/CGA system demonstrated selective tumor accumulation, dose-dependent tumor growth suppression, and good biosafety in nude mice, with no changes in body weight or organ damage. Mechanistic research revealed mixed-lineage kinase 1 (MLK1) as a direct molecular target of CGA, since it binds to MLK1’s active pocket (specifically residues L303, I228, and A249) and modifies the MAPK and PI3K-Akt signaling pathways to induce apoptosis and decrease proliferation.

Overall, this work shows that ultrasound-responsive folate-CGA nanotherapy is an effective, safe, and focused method for treating Wilms tumors by combining molecular suppression of MLK1 with ultrasound-enhanced delivery accuracy.

Curcumin exhibits strong gene-regulatory, epigenetic-modulating, and oncogene-suppressive properties, but its poor aqueous solubility and low intracellular bioavailability limit therapeutic application. Gene-targeted nanodelivery platforms combined with ultrasound-mediated permeabilization provide a promising approach to enhance the precision and potency of curcumin-based gene therapy. This study evaluates CRISPR-supported, curcumin-loaded polymeric nanoparticles (CRISPR-CuNPs) designed to silence the KRAS-G12D oncogene while enhancing tumor-suppressor gene expression (TP53, PTEN) in metastatic pulmonary cancer cells, using experimentally optimized ultrasound conditions validated for delivery efficiency and cellular safety.

Curcumin-loaded polymeric nanoparticles were co-formulated with plasmid DNA encoding CRISPR/Cas9 elements targeting KRAS-G12D and delivered to cancer cells with or without experimentally optimized low-intensity pulsed ultrasound (1 MHz, 0.5 W/cm²). Nanoparticle characterization included size, zeta potential, polydispersity index, and encapsulation effectiveness. Nanoparticle stability, morphology, and plasmid retention were evaluated under physiological temperature and serum conditions before and after ultrasound exposure. Intracellular uptake, endosomal escape, and nuclear localization were quantified using luminescence microscopy and flow cytometry. Gene silencing and gene improvement were analyzed using qPCR, Western blotting, and chromatin immunoprecipitation assays for epigenetic profiling. The MTT, Annexin V, and three-dimensional tumor models were used to assess cytotoxicity, apoptosis, and spheroid loss, respectively.

After ultrasound exposure, CRISPR-CuNPs showed a 2.7-fold increase in nuclear-associated nanoparticle fluorescence, suggesting improved endosomal escape and nuclear entry for effective gene delivery and modulation. A low polydispersity index (0.18 ± 0.02) determined by dynamic light scattering minimized variability in uptake across replicate organs. KRAS mRNA and protein levels were reduced by 72 ± 4%, with greater suppression observed in CRISPR-CuNP-treated cells compared with curcumin-only controls, confirming CRISPR-mediated gene disruption. Curcumin-mediated chromatin remodeling was evidenced by enrichment of H3K27ac at TP53 and PTEN promoters, resulting in upregulation of TP53 (3.5-fold) and PTEN (2.8-fold). Ultrasound-enhanced CRISPR-CuNPs further increased KRAS protein repression to 90 and elevated PTEN expression 46-fold. Apoptosis induction reached 87 ± 3, synergistic interaction between KRAS silencing and epigenetic activation of tumor-suppressor pathways, while metastatic lung cancer spheroids showed an 80% reduction in viable tumor volume. Minimal γ-H2AX induction indicated low acute DNA damage, supporting short-term genomic safety.

Ultrasound-stimulated CRISPR-CuNPs provide a potent, non-radiotoxic platform for targeted gene therapy. By integrating experimentally validated ultrasound parameters with KRAS gene silencing and curcumin-driven epigenetic activation of TP53 and PTEN, this approach achieves synergistic suppression of metastatic pulmonary cancer phenotypes. While sequencing-based genome-editing confirmation and long-term genomic stability studies remain necessary, this system holds significant promise for gene-enhancing, minimally invasive therapeutic strategies for the unborn.

Traditional Chinese Medicine (TCM) and ultrasound-based therapy techniques have emerged as viable complementary approaches to cancer treatment, since both have modulatory effects on the tumor microenvironment (TME). TCM is distinguished by its multicomponent and multitarget processes, whereas ultrasound treatments provide noninvasive biophysical modification to improve medication transport, immunological activation, and vascular permeability. Despite growing recognition of the synergistic potential of these modalities, no comprehensive bibliometric examination of their confluence in TME research has yet been done.

CiteSpace and VOSviewer were used to evaluate publications from the Web of Science Core Collection spanning from 2014 to 2024. A total of 771 relevant publications were used to create visual knowledge maps, highlight research hotspots, collaborative networks, and emerging trends, with a particular emphasis on studies that combined TCM and ultrasonography in cancer-related TME regulation.

Over the last decade, research at the interface of TCM and ultrasonic therapy has expanded rapidly. China has dominated this sector in terms of publication volume and worldwide influence, with strong partnerships with the United States and the United Kingdom. The core institutions include the Shanghai University of Traditional Chinese Medicine, Guangzhou University of Chinese Medicine, Zhejiang University, Shanghai Jiao Tong University, and the Chinese Academy of Sciences. The research field has shifted from basic studies on “cell proliferation” and “apoptosis” to more sophisticated inquiries into “immune microenvironment regulation,” “ultrasound-assisted drug delivery,” “nanomedicine,” and “synergistic therapy.”

The combination of TCM and ultrasonic therapy in TME research represents a new multidisciplinary frontier that combines molecular biology, materials science, and clinical oncology. This bibliometric and knowledge atlas study emphasizes the expanding body of data supporting ultrasound-enhanced TCM therapies as a promising paradigm in cancer therapy. To fully realize their combined promise in precision oncology, future initiatives should focus on mechanistic validation, standardized clinical evaluation, and worldwide collaboration.

Accurate and noninvasive breast cancer grading and therapy monitoring remain critical challenges in oncology. Traditional methods often rely on invasive histopathological assessments or imaging-only techniques, which may not fully capture the molecular and morphological intricacies of tumor response.

This article presents a novel, noninvasive framework for breast cancer analysis and therapy monitoring that combines two parallel mechanisms: (1) a dual-stream convolutional neural network (CNN) processing high-intensity ultrasound images, and (2) a biomarker-aware CNN stream utilizing patient-specific breast cancer biomarkers, including carbohydrate antigen 15-3, carcinoembryonic antigen, and human epidermal growth factor receptor 2 levels. The imaging stream extracts spatial and morphological features, while the biomarker stream encodes quantitative molecular indicators, enabling a multimodal understanding of tumor characteristics. The outputs from both streams are fused to predict the cancer grade (G1–G3) with high reliability.

Experimental evaluation on a cohort of pre- and postchemotherapy patients demonstrated the effectiveness of the proposed approach, achieving an overall grading accuracy of 97.8%, with an area under the curve of 0.981 for malignancy classification. The model also enables quantitative post-therapy analysis, revealing an average tumor response improvement of 41.3% across the test set, as measured by predicted regression in grade and changes in biomarker-imaging correlation.

This dual-parallel artificial intelligence strategy offers a promising noninvasive alternative to traditional histopathological and imaging-alone methods, supporting real-time cancer monitoring and personalized treatment evaluation. The integration of high-resolution imaging with biomolecular data significantly enhances diagnostic depth, paving the way for intelligent, patient-specific breast cancer management.