Cancer Imaging Research Articles

89Zr-Labeled DFO@Durvalumab-HSA nanoparticles: In vitro potential for triple-negative breast cancer.

This study presents the development and evaluation of a DFO@mAb-NP (DFO@Durvalumab-HSA-DTX nanoparticle) nanoplatform for imaging in triple-negative breast cancer (TNBC). The nanoplatform demonstrated significant changes postconjugation with DFO, evidenced by increased particle size from 178.1 ± 5 nm to 311 ± 26 nm and zeta potential alteration from -31.9 ± 3 mV to -40.5 ± 0.8 mV. Fourier-transform infrared spectroscopy and ultraviolet spectral analyses confirmed successful DFO conjugation, with notable shifts in peak wavelengths. High labeling efficiency was achieved with 89Zr, as indicated by thin layer radio chromatography and high-performance liquid radio chromatography results, with labeling efficiencies of 98 ± 2% for 89Zr-DFO@mAb and 96 ± 3% for 89Zr-DFO@mAb-NP. The nanoplatforms maintained stability over 24 h, showing less than 5% degradation. Lipophilicity assays revealed logP values of 0.5 ± 0.03 for 89Zr-DFO@mAb-NP and 0.98 ± 0.2 for 89Zr-DFO@mAb, indicating a higher lipophilic tendency in the radiolabeled Durvalumab. Cell uptake experiments showed an initial high uptake in MDA-MB-468 cells (45.1 ± 3.2%), which decreased over time, highlighting receptor-specific interactions. These comprehensive findings suggest the promising potential of the DFO@mAb-NP nanoplatform for targeted imaging in TNBC, with implications for improved diagnostic accuracy and treatment strategies.

Identification of a Novel Vascular Endothelial Growth Factor Receptor-3-Targeting Peptide for Molecular Imaging of Metastatic Lymph Nodes.

Because of the insidious nature of lymphatic metastatic cancer, accurate imaging tracing is very difficult to achieve in the clinic. Previous studies have developed the LARGR peptide (named TMVP1) as a radiotracer for vascular endothelial growth factor receptor-3 (VEGFR-3) imaging in cancer. However, its affinity for the target remains insufficient, resulting in low imaging sensitivity. In this study, we identified a high-affinity VEGFR-3 targeting peptide, named TMVP1446, using a multiplex screening platform. TMVP1446 demonstrated a dissociation constant of 8.97 × 10-8 M. Both in vitro and in vivo assays confirmed that fluorescently labeled TMVP1446 specifically bound to VEGFR-3. In a 4T1-luciferase tumor mouse model, cyanine 7-labeled TMVP1446 effectively discriminated between contralateral normal lymph nodes (c-LN) and cancer-metastatic sentinel lymph nodes (m-SLN). To evaluate the potential of TMVP1446, we developed a novel VEGFR-3 positron emission tomography radiotracer ([68Ga]Ga-DOTA-TMVP1446) for cancer-m-SLN imaging. [68Ga]Ga-DOTA-TMVP1446 accurately detected and assessed the status of lymph node metastasis, even in micrometastatic tumors, in the B16-F10 mouse tumor model. These findings suggest that TMVP1446 has great potential for advancing VEGFR-3 molecular imaging and metastatic sentinel lymph node imaging.

Integrating molecular imaging and transcriptomic profiling in advanced HER2-positive breast cancer receiving trastuzumab emtansine (T-DM1): an analysis of the ZEPHIR clinical trial.

The ZEPHIR clinical trial evaluated the role of [89Zr]trastuzumab-PET/CT (HER2-PET/CT) and 2-[18F]fluoro-2-deoxy-D-glucose PET/CT ([18F]FDG-PET/CT) in predicting outcomes in patients with advanced HER2-positive breast cancer treated with trastuzumab emtansine (T-DM1). Here, we combined molecular/metabolic imaging and transcriptomic data to investigate the biological processes associated with [89Zr]trastuzumab and [18F]FDG uptake, and to dissect the mechanisms involved in T-DM1 resistance. RNA was extracted from metastasis biopsies obtained in the ZEPHIR trial. HER2-PET/CT and [18F]FDG-PET/CT imaging data of biopsied lesions were integrated with transcriptomic data. Lesions were compared based on the level of [89Zr]trastuzumab uptake as well as on the presence/absence of metabolic response, defined comparing baseline and on-treatment [18F]FDG-PET/CT. We analyzed matched transcriptomic and molecular/metabolic imaging data for 24 metastases. Genes and pathways involved in extracellular matrix (ECM) organization and glycosylphosphatidylinositol synthesis were enriched in lesions presenting low [89Zr]trastuzumab uptake. [18F]FDG uptake at baseline correlated with proliferation and immune-related processes. Hypoxia and ECM-related processes were enriched in lesions showing no metabolic response to T-DM1, while immune-related processes were associated with high [89Zr]trastuzumab uptake and metabolic response. Gene signatures including differentially expressed genes according to [89Zr]trastuzumab uptake and metabolic response showed predictive value in an external cohort. To our knowledge, this study represents the first correlative analysis between [89Zr]trastuzumab tumor uptake and gene expression profiling in humans. Our findings suggest a role of ECM in impairing [89Zr]trastuzumab tumor uptake and T-DM1 metabolic response in advanced HER2-positive breast cancer, highlighting the potential of molecular imaging to depict tumor microenvironment features.

Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review

Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures. Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024. Methodology: With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique. Results: Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer. Conclusion: Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

The peritumoral edema index and related mechanisms influence the prognosis of GBM patients

BackgroundPeritumoral brain edema (PTBE) represents a characteristic phenotype of intracranial gliomas. However, there is a lack of consensus regarding the prognosis and mechanism of PTBE. In this study, clinical imaging data, along with publicly available imaging data, were utilized to assess the prognosis of PTBE in glioblastoma (GBM) patients, and the associated mechanisms were preliminarily analyzed.MethodsWe investigated relevant imaging features, including edema, in GBM patients using ITK-SNAP imaging segmentation software. Risk factors affecting progression-free survival (PFS) and overall survival (OS) were assessed using a Cox proportional hazard regression model. In addition, the impact of PTBE on PFS and OS was analyzed in clinical GBM patients using the Kaplan–Meier survival analysis method, and the results further validated by combining data from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). Finally, functional enrichment analysis based on TCIA and TCGA datasets identified several pathways potentially involved in the mechanism of edema formation.ResultsThis study included a total of 32 clinical GBM patients and 132 GBM patients from public databases. Univariate and multivariate analyses indicated that age and edema index (EI) are independent risk factors for PFS, but not for OS. Kaplan–Meier curves revealed consistent survival analysis results between IE groups among both clinical patients and TCIA and TCGA patients, suggesting a significant effect of PTBE on PFS but not on OS. Furthermore, functional enrichment analysis predicted the involvement of several pathways related mainly to cellular bioenergetics and vasculogenic processes in the mechanism of PTBE formation. While these novel results warrant confirmation in a larger patient cohort, they support good prognostic value for PTBE assessment in GBM.ConclusionsOur results indicate that a low EI positively impacts disease control in GBM patients, but this does not entirely translate into an improvement in OS. Multiple genes, signaling pathways, and biological processes may contribute to the formation of peritumoral edema in GBM through cytotoxic and vascular mechanisms.

Quantitative magnetic resonance imaging in prostate cancer: A review of current technology

Quantitative magnetic resonance imaging in prostate cancer: A review of current technology

Same-Day Positron Emission Tomography/Computed Tomography with 68Ga-Radiolabeled Fibroblast Activation Protein Inhibitors and 18F-Fluorodeoxyglucose Imaging for Gastrointestinal Cancers.

Objective: We investigated the clinical practicability of same-day 68Ga-radiolabeled fibroblast activation protein inhibitors (68Ga-FAPI)-first and 18F-fluorodeoxyglucose (18F-FDG) imaging and compared it with same-day 18F-FDG-first or 2-day procedures in diagnosing gastrointestinal cancers. Materials and Methods: Sixty-five patients with confirmed gastrointestinal cancers were divided into same-day 68Ga-FAPI-first group (Group A), same-day 18F-FDG-first group (Group B), and 2-day group (Group C). Low-dose CT and low injection activity were performed on 68Ga-FAPI positron emission tomography/computed tomography (PET/CT). Interval times, radiation dose, diagnostic performance, and detectability were assessed among groups. Additionally, the uptake, tumor-to-liver ratio (TLR), diagnostic efficacy, and TNM stage were compared between the two modalities. Results: The total waiting time for Group C was significantly longer than that for Group A or B (both p < 0.001). The total dose-length product and effective dose decreased in all groups. There were comparable detectability and diagnostic performance among groups (all p > 0.05). The within-group analysis in Group B indicated that 68Ga-FAPI PET/CT had higher uptake in the primary and recurrent lesions than 18F-FDG without differences in TLR, due to higher liver background on 68Ga-FAPI PET than Group A or C (both p < 0.001).68Ga-FAPI PET/CT possessed higher accuracy than 18F-FDG and changed staging in 14 patients (14/65, 21.54%). Conclusions: The same-day 68Ga-FAPI-first and 18F-FDG imaging reduced examination waiting time without increased radiation dose, simultaneously achieving excellent diagnostic performance and improving clinical staging in diagnosing gastrointestinal cancers.

Benefits of cross-border access to human genomes at scale for research and healthcare

Abstract Genomics data will soon be routinely generated and integrated into national healthcare systems. To maximise the potential of genomic medicine, data should be accessible for research where possible. This is the remit of ELIXIR, an intergovernmental organisation that brings together life science resources from across Europe. Innovative solutions are needed to ensure the validated research findings for disease or preventative medicine are then integrated into healthcare. To tackle this, the 1+MG initiative, a joint initiative of 25 EU countries, the UK, and Norway, aims to enable secure access to genomics and the corresponding clinical data across Europe for better research, personalised healthcare and health policy making. In the design and scale-up phase (B1MG project), recommendations and guidelines to advance towards the deployment of personalised medicine at a European scale have been produced, adopted by 1+MG and developed into a 1+MG framework. This includes guidance on data governance, standards, quality and infrastructure, recommendations on how to approach citizen engagement and a tool for countries to self-assess implementation into healthcare. The European Genomic Data Infrastructure (GDI) project supports the scale-up and sustainability phase of the 1+MG initiative, to deploy infrastructure across 24 countries to support the overall ambition. Recommendations are being used to promote governance and technical interoperability across European initiatives including the European Health Data Space (EHDS), and European Cancer Image Initiative (EUCAIM). The 1+MG will be established as a European Data Infrastructure Consortia in 2025 and will act as an Authorised Participant in the EHDS providing access to a permanent high-quality federated data collection of genomic and health data that will accelerate research, innovation and policymaking facilitating the deployment of genomic medicine across Europe.

An update on pancreatic cancer imaging, staging and use of the PACT-UK radiology template pre- and post-neoadjuvant treatment.

Pancreatic ductal adenocarcinoma continues to have a poor prognosis, although recent advances in neoadjuvant treatments have provided some hope. Imaging assessment of suspected tumours can be challenging and requires a specific approach, with pancreas protocol CT being the primary imaging modality for staging with other modalities used as problem-solving tools to facilitate appropriate management. Imaging assessment post neoadjuvant treatment can be particularly difficult due to a current lack of robust radiological criteria to predict response and differentiate treatment induced fibrosis/inflammation from residual tumour. This review aims to provide an update of pancreatic ductal adenocarcinoma with particular focus on three points: tumour staging pre- and post-neoadjuvant treatment including vascular assessment, structured reporting with introduction of the PACT-UK radiology template (PAncreatic Cancer reporting Template-UK), and the potential future role of artificial intelligence in the diagnosis and staging of pancreatic cancer.

Recurrence prediction of gastric cancer based on multi-resolution feature fusion and context information

Pathological images of gastric cancer serve as the gold standard for diagnosing this malignancy. However, the recurrence prediction task often encounters challenges such as insignificant morphological features of the lesions, insufficient fusion of multi-resolution features, and inability to leverage contextual information effectively. To address these issues, a three-stage recurrence prediction method based on pathological images of gastric cancer is proposed. In the first stage, the self-supervised learning framework SimCLR was adopted to train low-resolution patch images, aiming to diminish the interdependence among diverse tissue images and yield decoupled enhanced features. In the second stage, the obtained low-resolution enhanced features were fused with the corresponding high-resolution unenhanced features to achieve feature complementation across multiple resolutions. In the third stage, to address the position encoding difficulty caused by the large difference in the number of patch images, we performed position encoding based on multi-scale local neighborhoods and employed self-attention mechanism to obtain features with contextual information. The resulting contextual features were further combined with the local features extracted by the convolutional neural network. The evaluation results on clinically collected data showed that, compared with the best performance of traditional methods, the proposed network provided the best accuracy and area under curve (AUC), which were improved by 7.63% and 4.51%, respectively. These results have effectively validated the usefulness of this method in predicting gastric cancer recurrence.

Methodological aspects of correlative, multimodal, multiparametric breast cancer imaging: from data acquisition to image processing for AI-based radioproteomics in a preclinical setting

Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI (mpMRI) platforms for studying numerous biological parameters. mpMRI platforms prove particularly indispensable when investigating tumors that exhibit profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of new targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities, such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at a molecular level and microscopic scale. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as the physiological limitations of the laboratory mice of the investigation. Standardization efforts in image acquisition and processing are required to increase the reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialog regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice with breast cancer xenografts.

Synthetic double inversion recovery imaging for rectal cancer T staging evaluation: imaging quality and added value to T2-weighted imaging

ObjectiveTo assess the image quality of synthetic double inversion recovery (SyDIR) imaging and enhance the value of T2-weighted imaging (T2WI) in evaluating T stage for rectal cancer patients.MethodsA total of 112 pathologically confirmed rectal cancer patients were retrospectively selected after undergoing MRI, including synthetic MRI. The image quality of T2WI and SyDIR imaging was compared based on signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall picture quality, presence of motion artifacts, lesion edge sharpness, and conspicuity. The concordance between MRI and pathological staging results, using T2WI alone and the combination of T2WI and SyDIR for junior and senior radiologists, was assessed using the Kappa test. The area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic efficacy of extramural infiltration in rectal cancer patients.ResultsNo significant differences in imaging quality were observed between conventional T2WI and SyDIR (p = 0.07–0.53). The combination of T2WI and SyDIR notably improved the staging concordance between MRI and pathology for both junior (kappa value from 0.547 to 0.780) and senior radiologists (kappa value from 0.738 to 0.834). In addition, the integration of T2WI and SyDIR increased the AUC for diagnosing extramural infiltration for both junior (from 0.842 to 0.918) and senior radiologists (from 0.917 to 0.938).ConclusionThe combination of T2WI and SyDIR increased the consistency of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration, which would benefit treatment selection.Critical relevance statementSyDIR sequence provides additional diagnostic value for T2WI in the T staging of rectal cancer, improving the agreement of T staging between MRI and pathology, as well as the diagnostic performance of extramural infiltration.Key PointsSynthetic double inversion recovery (SyDIR) and T2WI have comparable image quality.SyDIR provides rectal cancer anatomical features for extramural infiltration detections.The combination of T2WI and SyDIR improves the accuracy of T staging in rectal cancer.Graphical

Preparation and identification of a fluorescent probe with CsPbBr3perovskite quantum dots and CD44v6 specific peptide for gastric cancer imaging.

Since the sensitivity and accuracy of traditional detection for early gastric cancer diagnosis are still insufficient, it is significant to continuously optimize the optical molecular imaging detection technology based on an endoscopic platform. The signal intensity and stability of traditional chemical fluorescent dyes are low, which hinders the clinical application of molecular imaging detection technology. This work developed a probe based on perovskite quantum dots (PQDs) and peptide ligands. By utilizing CsPbBr3perovskite PQDs modified by azithromycin (AZI), combined with the specific polypeptide ligand of CD44v6, a gastric cancer biomarker, the perovskite-based probe (AZI-PQDs probe) which can specifically identify gastric cancer tumor was prepared. Owing to the high photoluminescence quantum yield of CsPbBr3PQDs, the naked eye can observe the imaging under the excitation of the hand-held ultraviolet light source. AZI-PQDs probe can accurately identify gastric cancer cells, tissues, and xenograft models with experiments ofex vivoandin vivofluorescence imaging detection. It also exhibited low toxicity and immunogenicity, indicating the safety of the probe. This work provides a probe combined with cancer specificity and a reliable fluorescent signal that has the potential for application in gastric cancer optical molecular imaging.

Optical Biosensors for Detection of Cancer Biomarkers: Current and Future Perspectives.

Optical biosensors are emerging as a promising technique for the sensitive and accurate detection of cancer biomarkers, enabling significant advancements in the field of early diagnosis. This study elaborates on the latest developments in optical biosensors designed for detecting cancer biomarkers, highlighting their vital significance in early cancer diagnosis. When combined with targeted nanoparticles, the bio-fluids can help in the molecular stage diagnosis of cancer. This enhances the discrimination of disease from the normal subjects drastically. The optical sensor methods that are involved in the disease diagnosis and imaging of cancer taken for the present review are surface plasmon resonance, localized surface plasmon resonance, fluorescence resonance energy transfer, surface-enhanced Raman spectroscopy and colorimetric sensing. The article meticulously describes the specific biomarkers and analytes that optical biosensors target. Beyond elucidating the underlying principles and applications, this article furnishes an overview of recent breakthroughs and emerging trends in the field. This encompasses the evolution of innovative nanomaterials and nanostructures designed to augment sensitivity and the incorporation of microfluidics for facilitating point-of-care testing, thereby charting a course towards prospective advancements.

Long-Term Outcomes of Prostate-Specific Membrane Antigen–PET Imaging of Recurrent Prostate Cancer

Although prostate-specific membrane antigen positron emission tomography (PSMA-PET) has shown improved sensitivity and specificity compared with conventional imaging for the detection of biochemical recurrent (BCR) prostate cancer, the long-term outcomes of a widespread shift in imaging are unknown. To estimate long-term outcomes of integrating PSMA-PET into the staging pathway for recurrent prostate cancer. This decision analytic modeling study simulated outcomes for patients with BCR following initial definitive local therapy. Inputs used were from the literature and a retrospective cohort study conducted at 2 institutions. The base case analysis assumed modest benefits of earlier detection and treatment, and scenario analyses considered prostate-specific antigen (PSA) level at imaging and different outcomes of earlier vs delayed treatment. The analysis was performed between April 1, 2023, and May 1, 2024. (1) Immediate PSMA-PET imaging, (2) conventional imaging (computed tomography and bone scan [CTBS]) followed by PSMA-PET if CTBS findings were negative or equivocal, and (3) CTBS alone. The main outcomes were detection of metastases, deaths from prostate cancer, and life-years and quality-adjusted life-years (QALYs) gained. The model estimated that per 1000 simulated patients with BCR (assumed median age, 66 years), PSMA-PET is expected to diagnose 611 (95% uncertainty interval [UI], 565-656) patients with metastasis compared with 630 (95% UI, 586-675) patients diagnosed using CTBS followed by PSMA-PET and 297 (95% UI, 202-410) patients diagnosed using CTBS alone. Moreover, the estimated number of prostate cancer deaths was 512 (95% UI, 472-552 deaths) with PSMA-PET, 520 (95% UI, 480-559 deaths) with CTBS followed by PSMA-PET, and 587 (95% UI, 538-632 deaths) with CTBS alone. Imaging with PSMA-PET yielded the highest number of QALYs, which were 824 (95% UI, 698-885) higher than CTBS. These results differed by PSA level at the time of testing, with the highest incremental life-years and QALYs and lowest number of deaths from prostate cancer among patients with PSA levels of at least 5.0 ng/mL. Finally, the estimates were sensitive to the expected benefit of initiating therapy for recurrent prostate cancer earlier in the disease course. The results of this decision-analytic model suggest that upfront PSMA-PET imaging for the evaluation of BCR is expected to be associated with reduced cancer mortality and gains in life-years and QALYs compared with the conventional imaging strategy, assuming modest benefits of earlier detection and treatment.

A scoping review on cancer and body image research

The purpose of this research is to explore previous studies that investigated the body image of cancer patients and survivors by identifying the variables that have been examined and the gaps in the current research through a scoping review. The researchers conducted extensive and methodical searches in several databases using relevant keywords. After analyzing the full text of 89 papers, 45 studies were included in this review, published between 1995 and 2024. Most of the studies were conducted solely with female participants, and breast cancer was the most commonly researched cancer type. The researchers identified variables that affect the body image perspectives of this population, with the surgery method being the most frequently tested variable, followed by education level, age, cancer state, and mental health. This scoping review highlights the need to pay particular attention to a vulnerable group of under-educated female cancer patients who must undergo invasive cancer treatment.

Recent Progress and Current Status of Artificial Intelligence in Skin Cancer Diagnosis: A Systematic Review—Where do we Stand?

Introduction: Skin cancer is one of the most prevalent forms worldwide, with a significant increase in recent decades. Real-time and accurate detection can reduce the burdens of invasive treatments. The advent of Artificial Intelligence (AI) and Machine learning (ML) has introduced multiple tools to aid accurate and early detection, categorizing dermatological images and proving especially valuable in regions with a shortage of specialists. However, the adoption of these AI-based tools requires consideration of efficacy, safety, and ethical implications. Objective: The systematic review aims to evaluate existing research on the detection, categorization, and assessment of skin cancer images. Methods: The systematic literature review is conducted based on studies published from 2018 to 2023 in PubMed, Scopus, Embase, Web of Science, IEEE Xplore, ACM DL, and Ovid MEDLINE. Study selection, data extraction, and inclusion are carried out after a proper evaluation of the studies. Results are presented in tables and figures using a narrative synthesis. Results: The search identified 687 studies from the database. However, after three phases of identification, screening, and evaluation, only 16 studies were chosen, focusing on developing and validating AI tools to detect, diagnose, and categorize skin cancer. This systematic review covers the selected studies in multiple dimensions. Conclusion: The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.

Sensing and Imaging Agents for Cyclooxygenase Enzyme.

In this concept, we present a comprehensive study on the development and application of COX-2-specific fluorescent probes for cancer imaging and diagnosis. Targetting cancer cells and measuring cancer-related activities in specific organelles quickly and accurately are crucial factors for early diagnosis and research on cancer pathology and treatment. This concept explores a variety of probes which include BDIMC1-2, TPI-IMC, YZP1, IQ-1, ANQ-IMC-6, Niblue-C6-IMC, AZB-IMC2, CMP, CF3-Fluorocoxib, IBPC1, CoxFluor, etc. and each one demonstrates unique mechanisms and high selectivity towards COX-2 enzymes. These probes were designed to enhance fluorescence upon binding to COX-2 which enable precise visualization of tumor and inflamed tissues. The research emphasizes the importance of COX-2 as a biomarker in cancer diagnostics, particularly in identifying cancer stem cells and inflamed tissues. This concept highlights the potentiality of these probes in non-invasive imaging techniques which offering significant advancements in cancer diagnosis and monitoring. The in vivo and in vitro experiments, including applications in mouse models and human tissue samples, confirm the efficacy of these probes in providing detailed imaging for clinical and research applications.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Application of nanoliposome as contrast agents for magnetic resonance imaging technique

AbstractLiposomes, nano‐sized vesicles primarily comprising phospholipids and cholesterol, have emerged as pivotal tools in medical imaging, notably in magnetic resonance imaging (MRI), due to their biocompatibility and ability to encapsulate diverse molecules. Tailorable properties like size, surface charge, and encapsulation capacity make liposomes ideal for targeted delivery of imaging agents and drugs to specific tissues, improving pharmacokinetics. As MRI contrast agent (CA) carriers, liposomes encapsulate gadolinium, mitigating toxicity and boosting relaxivity and circulation times. Functionalization with targeting ligands and stimuli‐responsive designs enhances their controlled release and targeted delivery capabilities, crucial for cancer imaging and therapy. Benefits include reduced toxicity, prolonged circulation, targeted delivery, enhanced bioavailability, and potential for multimodal imaging. Challenges remain, such as stability, clearance, and manufacturing intricacies, requiring further research. Nonetheless, liposomal MRI CAs hold promise for enhancing diagnostic precision and therapeutic effectiveness in oncology and neurology, offering a robust pathway for future biomedical advancements. Addressing existing limitations could unlock their full potential in improving patient care and outcomes.