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While numerous methods exist for diagnosing tumors through the detection of miRNA within tumor cells, few can simultaneously achieve both tumor diagnosis and treatment. In this study, a novel graphene oxide (GO)-based DNA nanodevice (DND), initiated by miRNA, was developed for fluorescence signal amplification imaging and photodynamic therapy in tumor cells. After entering the cells, tumor-associated miRNA drives DND to Catalyzed hairpin self-assembly (CHA). The CHA reaction generated a multitude of DNA Y-type structures, resulting in a substantial amplification of Ce6 fluorescence release and the generation of numerous singlet oxygen (1O2) species induced by laser irradiation, consequently inducing cell apoptosis. In solution, DND exhibited high selectivity and sensitivity to miRNA-21, with a detection limit of 11.47 pM. Furthermore, DND discriminated between normal and tumor cells via fluorescence imaging and specifically generated O21 species in tumor cells upon laser irradiation, resulting in tumor cells apoptosis. The DND offer a new approach for the early diagnosis and timely treatment of malignant tumors.

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Diagnosing and then treating disease defines theranostics. The approach holds promise by facilitating targeted disease outcomes. The simultaneous analysis of finding the presence of disease pathophysiology while providing a parallel in treatment is a novel and effective strategy for seeking improved medical care. We discuss how theranostics improves disease outcomes is discussed. The chapter reviews the delivery of targeted therapies. Bioimaging techniques are highlighted as early detection and tracking systems for microbial infections, degenerative diseases, and cancers.

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Increasing evidence emphasizes the pivotal role of CD4+ T cells in orchestrating cancer immunity. Noninvasive in vivo imaging of the temporal dynamics of CD4+ T cells and their distribution patterns might provide novel insights into their effector and regulator cell functions during cancer immunotherapy (CIT). Methods: We conducted a comparative analysis of 89Zr-labeled anti-mouse (m) and anti-human (h) CD4-targeting minibodies (Mbs) for in vivo positron emission tomography (PET)/magnetic resonance imaging (MRI) of CD4+ T cells in human xenografts, syngeneic tumor-bearing wild-type (WT), and human CD4+ knock-in (hCD4-KI) mouse models. Results: Both 89Zr-CD4-Mbs yielded high radiolabeling efficiencies of >90%, immunoreactivities of >70%, and specific in vitro binding to their target antigens. The specificity of in vivo targeting of 89Zr-hCD4-Mb was confirmed by PET/MRI, revealing ~4-fold greater 89Zr-hCD4-Mb uptake in subcutaneous hCD4+ hematopoietic peripheral blood acute lymphoblastic leukemia tumors (HPB-ALL) than in solid hCD4- diffuse histiocytic lymphomas (DHL) and 89Zr-mCD4-Mb uptake in hCD4+ HPB-ALL tumors. In a comparative cross-validation study in anti-programmed death ligand (αPD-L1)/anti-4-1BB-treated orthotopic PyMT mammary carcinoma-bearing hCD4-KI and WT mice, we detected 2- to 3-fold enhanced species-specific 89Zr-hCD4-Mb or 89Zr-mCD4-Mb uptake within CD4+ cell-enriched secondary lymphatic organs (lymph nodes and spleens). The 89Zr-hCD4-Mb uptake in the PyMT tumors was more pronounced in hCD4-KI mice compared to the WT control littermates. Most importantly, MC38 adenocarcinoma-bearing mice treated with a combination of αPD-L1 and anti-lymphocyte-activation gene 3 (αLag-3) antibodies exhibited ~1.4-fold higher 89Zr-mCD4-Mb uptake than mice that were not responsive to therapy or sham-treated mice. Conclusion: CD4 PET/MRI enabled monitoring of the CD4+ cell distribution in secondary lymphatic organs and the tumor microenvironment, capable of predicting sensitivity to CIT. Our imaging approach will provide deeper insights into the underlying molecular mechanisms of CD4-directed cancer immunotherapies in preclinical mouse models and is applicable for clinical translation.

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Rationale: Theranostic nanoplatforms exert a vital role in facilitating concurrent real-time diagnosis and on-demand treatment of diseases, thereby making contributions to the improvement of therapeutic efficacy. Nevertheless, the structural intricacy and the absence of well-defined integration of dual functionality persist as challenges in the development of theranostic nanoplatforms. Methods: We develop an atomically precise theranostic nanoplatform based on metal-organic cage (MOC) to provide magnetic resonance imaging (MRI) guided chemodynamic therapy (CDT) for cancer therapy and assess the theranostic performance both in vitro and in vivo. Through UV-vis spectroscopy, electron paramagnetic resonance (EPR), confocal microscopy, flow cytometry, immunofluorescence staining, and western blotting, the ability of MOC-Mn to generate •OH and the subsequent inhibition of HeLa cells was confirmed. Results: The MOC-Mn composed of manganese and calixarene was successfully synthesized and comprehensively characterized. The catalytic activity of manganese within MOC-Mn facilitated the efficient generation of hydroxyl radicals (•OH) through a Fenton-like reaction, leveraging the high concentrations of hydrogen peroxide in the tumor microenvironment (TME). Additionally, its capacity to prolong the T1 relaxation time and augment the MR signal was observed. The theranostic efficacy was verified via rigorous in vitro and in vivo experiments, indicating that MOC-Mn offered clearer visualization of tumor particulars and substantial suppression of tumor growth. Conclusion: This study showcases a precise MRI-guided CDT theranostic nanoplatform for cancer therapy, thereby promoting the advancement of precise nanomedicine and structure-function research.

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Tumour detection with high selectivity and sensitivity is crucial for delineating tumour margins and identifying metastatic foci during image-guided surgery. Optical nanoprobes with preferential tumour accumulation is often limited by inefficient amplification of biological signals. Here, we report the design of a library of hydrophobic core-tunable ultra-pH-sensitive nanoprobes (HUNPs) for orthogonally amplifying tumour microenvironmental signals on multiple tumour models. We find that tuning the hydrophobicity of nanoparticle core composition with non-ionizable monomers can enhance cellular association of HUNPs by more than ten-fold, resulting in a high cellular internalization efficiency of HUNPs with up to 50% in tumours. Combining high tumour accumulation and high cell internalization efficiency, HUNPs show orthogonally amplified fluorescence signals, permitting the precise locating and delineating margins between malignant lesions and normal tissues with high contrast-to-noise ratio and resolution. Our study provides key strategies to design nanomedicines with high intracellular bioavailability for cancer detection, drug/gene delivery, and therapy.

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Immune checkpoint inhibitors have shown remarkable benefits in the treatment of solid tumors,while the occurrence of atypical response patterns and immune-related adverse events during treatment challenges the accuracy of therapeutic evaluation.Medical imaging is crucial for the evaluation of immunotherapy.It enables the assessment of treatment efficacy via both morphological and functional ways and offers unique a predictive value when being combined with artificial intelligence.Here we review the recent research progress in imaging-based evaluation of solid tumors treated with immune checkpoint inhibitors.

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Generative AI is revolutionizing oncological imaging, enhancing cancer detection and diagnosis. This editorial explores its impact on expanding datasets, improving image quality, and enabling predictive oncology. We discuss ethical considerations and introduce a unique perspective on personalized cancer screening using AI-generated digital twins. This approach could optimize screening protocols, improve early detection, and tailor treatment plans. While challenges remain, generative AI in oncological imaging offers unprecedented opportunities to advance cancer care and improve patient outcomes.

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The primary goal during cancer removal surgery is to completely excise the malignant tumor. Because the color of the tumor and surrounding tissues is very similar, it is difficult to observe with the naked eye, posing a risk of damaging surrounding blood vessels during the tumor removal process. Therefore, fluorescence emission is induced using a fluorescent contrast agent, and color classification is monitored through camera imaging. LEDs must be irradiated to generate the fluorescent emission electromotive force. However, the power and beam width of the LED are insufficient to generate this force effectively, so the beam width and intensity must be increased to irradiate the entire lesion. Additionally, there should be no shaded areas in the beam irradiation range. This paper proposes a method to enhance the beam width and intensity while eliminating shadow areas. A total reflection beam mirror was used to increase beam width and intensity. However, when the beam width increased, a shadow area appeared at the edge, limiting irradiation of the entire lesion. To compensate for this shadow area, a concave lens was combined with the beam mirror, resulting in an increase in beam width and intensity by more than 1.42 times and 18.6 times, respectively. Consequently, the beam width reached 111.8°, and the beam power was 13.6 mW. The proposed method is expected to be useful for observing tumors through the induction of fluorescence emission during cancer removal surgery or for pathological examination in the pathology department.

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64Cu is gaining recognition not only for its diagnostic capabilities in nuclear medical imaging but also for its therapeutic and theranostic potential. The simultaneous ß- and Auger emissions of 64Cu can be utilized to induce a therapeutic effect on cancerous lesions. The finding of the exceptional biodistribution characteristics of the radionuclide 64Cu, when administered as basic copper ions, has highlighted its potential therapeutic application in cancer treatment. Preclinical and clinical research on the effectiveness of [64Cu]CuCl2 as a theranostic radiopharmaceutical has commenced only in the past decade. Current clinical studies are increasingly demonstrating the high specificity and uptake of [64Cu]Cu2+ by malignant tissues during early cancer progression, indicating its potential for early cancer diagnosis across various organs. This short review aims to present the latest preclinical studies involving [64Cu]CuCl2, offering valuable insights for researchers planning new in vitro and in vivo studies to explore the theranostic potential of [64Cu]Cu2+.

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Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.

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The fusion of cutting-edge imaging technologies with radiation therapy (RT) has catalyzed transformative breakthroughs in cancer treatment in recent decades. It is critical for us to review our achievements and preview into the next phase for future synergy between imaging and RT. This paper serves as a review and preview for fostering collaboration between these two domains in the forthcoming decade. Firstly, it delineates ten prospective directions ranging from technological innovations to leveraging imaging data in RT planning, execution, and preclinical research. Secondly, it presents major directions for infrastructure and team development in facilitating interdisciplinary synergy and clinical translation. We envision a future where seamless integration of imaging technologies into RT will not only meet the demands of RT but also unlock novel functionalities, enhancing accuracy, efficiency, safety, and ultimately, the standard of care for patients worldwide.

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Despite that the segment anything model (SAM) achieved impressive results on general-purpose semantic segmentation with strong generalization ability on daily images, its demonstrated performance on medical image segmentation is less precise and unstable, especially when dealing with tumor segmentation tasks that involve objects of small sizes, irregular shapes, and low contrast. Notably, the original SAM architecture is designed for 2D natural images and, therefore would not be able to extract the 3D spatial information from volumetric medical data effectively. In this paper, we propose a novel adaptation method for transferring SAM from 2D to 3D for promptable medical image segmentation. Through a holistically designed scheme for architecture modification, we transfer the SAM to support volumetric inputs while retaining the majority of its pre-trained parameters for reuse. The fine-tuning process is conducted in a parameter-efficient manner, wherein most of the pre-trained parameters remain frozen, and only a few lightweight spatial adapters are introduced and tuned. Regardless of the domain gap between natural and medical data and the disparity in the spatial arrangement between 2D and 3D, the transformer trained on natural images can effectively capture the spatial patterns present in volumetric medical images with only lightweight adaptations. We conduct experiments on four open-source tumor segmentation datasets, and with a single click prompt, our model can outperform domain state-of-the-art medical image segmentation models and interactive segmentation models. We also compared our adaptation method with existing popular adapters and observed significant performance improvement on most datasets. Our code and models are available at: https://github.com/med-air/3DSAM-adapter.

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This study aimed to evaluate the dose in different protocols of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) procedure. The retrospective study involves 207 patients with confirmed malignancies who underwent PET/CT. Effective dose (E) from PET was estimated based on injected activity and dose coefficient as per International Commission on Radiation Protection (ICRP) 128. Estimation of E from CT was done utilizing the dose length product (DLP) method and conversion factors as per ICRP 102. There was a significant statistical difference observed in E between different PET/CT protocols (P < .001). E of PET in the whole body (WB) was found to be 4.9 ± 0.9 mSv, whereas mean volume computed tomography dose indexvol, DLP, and E of CT in WB were 7.0 ± 0.2 mGy, 674.3 ± 80.7 mGy.cm, and 10.1 ± 1.2 mSv, respectively. No linear correlation was seen between the size-specific dose estimate and E of CT (r = -0.003; P = .978). The total mean E in WB PET/CT was 17.0 ± 1.7 mSv. CT dose was contributing more than PET dose in all protocols except brain PET/CT. Optimization strategies can be evaluated only if monitored periodically.

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Metastasis driven by cancer cell migration is the leading cause of cancer-related deaths. It involves significant changes in the organization of the cytoskeleton, which includes the actin microfilaments and the vimentin intermediate filaments. Understanding how these filament change cells from normal to invasive offers insights that can be used to improve cancer diagnosis and therapy. We have developed a computational, transparent, large-scale and imaging-based pipeline, that can distinguish between normal human cells and their isogenically matched, oncogenically transformed, invasive and metastasizing counterparts, based on the spatial organization of actin and vimentin filaments in the cell cytoplasm. Due to the intricacy of these subcellular structures, manual annotation is not trivial to automate. We used established deep learning methods and our new multi-attention channel architecture. To ensure a high level of interpretability of the network, which is crucial for the application area, we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The methods offer detailed, objective and measurable understanding of how different components of the cytoskeleton contribute to metastasis, insights that can be used for future development of novel diagnostic tools, such as a nanometer level, vimentin filament-based biomarker for digital pathology, and for new treatments that significantly can increase patient survival.

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Upconversion nanoparticles (UCNPs) are a class of nanomaterials composed of lanthanide ions with great potential for paraclinical applications, especially in laboratory and imaging sciences. UCNPs have tunable optical properties and the ability to convert long-wavelength (low energy) excitation light into short-wavelength (high energy) emission in the ultraviolet (UV)-visible and near-infrared (NIR) spectral regions. The core-shell structure of UCNPs can be customized through chemical synthesis to meet the needs of different applications. The surface of UCNPs can also be tailored by conjugating small molecules and/or targeting ligands to achieve high specificity and selectivity, which are indispensable elements in biomedical applications. Specifically, coatings can enhance the water dispersion, biocompatibility, and efficiency of UCNPs, thereby optimizing their functionality and boosting their performance. In this context, multimodal imaging can provide more accurate in vivo information when combined with nuclear imaging. This article intends to provide a comprehensive review of the core structure, structure optimization, surface modification, and various recent applications of UCNPs in biomolecular detection, cell imaging, tumor diagnosis, and deep tissue imaging. We also present and discuss some of their critical challenges, limitations, and potential future directions.

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Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.

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Naphthalocyanine-based agents exhibit huge potential in photodynamic therapy, yet their photodynamic performance is restricted by the penetration depth of the external laser. Herein, we employed 18F-FDG as an internal light source to excite silicon naphthalocyanine nanoparticles to simultaneously circumvent radiative transition and boost 1O2 generation for tumor suppression.

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The burgeoning field of cancer theranostics has witnessed advancements through the development of targeted molecular agents, particularly peptides. These agents exploit the overexpression or mutations of specific receptors, such as the Epidermal Growth Factor receptor (EGFR) and αVß3 integrin, which are pivotal in tumor growth, angiogenesis, and metastasis. Despite the extensive research into and promising outcomes associated with antibody-based therapies, peptides offer a compelling alternative due to their smaller size, ease of modification, and rapid bioavailability, factors which potentially enhance tumor penetration and reduce systemic toxicity. However, the application of peptides in clinical settings has challenges. Their lower binding affinity and rapid clearance from the bloodstream compared to antibodies often limit their therapeutic efficacy and diagnostic accuracy. This overview sets the stage for a comprehensive review of the current research landscape as it relates to EGFR- and integrin αVß3-targeting peptides. We aim to delve into their synthesis, radiolabeling techniques, and preclinical and clinical evaluations, highlighting their potential and limitations in cancer theranostics. This review not only synthesizes the extant literature to outline the advancements in peptide-based agents targeting EGFR and integrin αVß3 but also identifies critical gaps that could inform future research directions. By addressing these gaps, we contribute to the broader discourse on enhancing the diagnostic precision and therapeutic outcomes of cancer treatments.

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Hypoxia is an important feature of the tumor microenvironment (TME) of most solid tumors, and it is closely linked to cancer cell proliferation, therapy resistance, and the tumor immune response. Herein, we describe a method for hypoxia-induced heterogeneous oxygen distribution in xenograft tumors based on phosphorescence imaging microscopy (PLIM) using intravascular and intracellular oxygen probes. We synthesized Ir(III) complexes with polyethylene glycol (PEG) units of different molecular weights into the ligand as intravascular oxygen probes, BTP-PEGm (m = 2000, 5000, 10000, 20000). BTP-PEGm showed red emission with relatively high emission quantum yield and high oxygen sensitivity in saline. Cellular and in vivo experiments using these complexes revealed that BTP-PEG10000 was the most suitable probe in terms of blood retention and ease of intravenous administration in mice. PLIM measurements of xenograft tumors in mice treated with BTP-PEG10000 allowed simultaneous imaging of the tumor microvasculature and quantification of oxygen partial pressures. From lifetime images using the red-emitting intracellular oxygen probe BTPDM1 and the green-emitting intravascular fluorescent probe FITC-dextran, we demonstrated hypoxic heterogeneity in the TME with a sparse vascular network and showed that the oxygen levels of tumor cells gradually decreased with vascular distance.

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