Dual-Modality Imaging-Guided Manganese-Based Nanotransformer for Enhanced Gas-Photothermal Therapy Combined Immunotherapeutic Strategy Against Triple-Negative Breast Cancer.

Activating the stimulator of the interferon gene (STING) is a promising immunotherapeutic strategy for converting "cold" tumor microenvironment into "hot" one to achieve better immunotherapy for malignant tumors. Herein, a manganese-based nanotransformer is presented, consisting of manganese carbonyl and cyanine dye, for MRI/NIR-II dual-modality imaging-guided multifunctional carbon monoxide (CO) gas treatment and photothermal therapy, along with triggering cGAS-STING immune pathway against triple-negative breast cancer. This nanosystem is able to transfer its amorphous morphology into a crystallographic-like formation in response to the tumor microenvironment, achieved by breaking metal-carbon bonds and forming coordination bonds, which enhances the sensitivity of magnetic resonance imaging. Moreover, the generated CO and photothermal effect under irradiation of this nanotransformer induce immunogenic death of tumor cells and release damage-associated molecular patterns. Simultaneously, the Mn acts as an immunoactivator, potentially stimulating the cGAS-STING pathway to augment adaptive immunity, resulting in promoting the secretion of type I interferon, the proliferation of cytotoxic T lymphocytes and M2-macrophages repolarization. This nanosystem-based gas-photothermal treatment and immunoactivating therapy synergistic effect exhibit excellent antitumor efficacy both in vitro and in vivo, reducing the risk of triple-negative breast cancer recurrence and metastasis; thus, this strategy presents great potential as multifunctional immunotherapeutic agents for cancer treatment.

Fibroblast activation protein-sensitive polymeric nanobeacon for early diagnosis of renal fibrosis.

Early diagnosis and treatment of renal fibrosis (RF) significantly affect the clinical outcomes of chronic kidney diseases (CKDs). As the typical fibrotic ailment, RF is characterized by remodeling of the extracellular matrix, and the activation of fibroblast activation protein (FAP) plays a crucial role in the mediation of extracellular matrix protein degradation. Therefore, FAP can serve as a biomarker for RF. However, up to now, no effective tools have been reported to diagnose early-stage RF via detecting FAP. In this work, a polymeric nanobeacon integrating an FAP-sensitive amphiphilic polymer and fluorophores was proposed, which was used to diagnose early RF by sensing FAP. The FAP can be detected in the range of 0 to 200 ng/mL with a detection limit of 0.132 ng/mL. Furthermore, the fluorescence imaging results demonstrate that the polymeric nanobeacon can sensitively image fibrotic kidneys in mice with unilateral ureteral occlusion (UUO), suggesting its potential for early RF diagnosis and guidance of FAP-targeted treatments. Importantly, when employed alongside with non-invasive diagnostic techniques like magnetic resonance imaging (MRI) and serological tests, this nanobeacon exhibits excellent biocompatibility, low biological toxicity, and sustained imaging capabilities, making it a suitable fluorescent tool for diagnosing various FAP-related fibrotic conditions. To our knowledge, this study represents the first attempt to image RF in early stage by detecting FAP, offering a promising fluorescent molecular tool for diagnosing various FAP-associated diseases in the future.

Albumin-based nanosystem for dual-modality imaging-guided chem-phototherapy against immune-cold triple-negative breast cancer.

Triple-negative breast cancer is a highly aggressive and metastatic tumor; diagnosing it in the early stages is still difficult, and the prognosis for conventional radio-chemotherapy and immunotreatment is not promising due to cancer's immunosuppressive microenvironment. The utilization of protein-based nanosystem has proven to be effective in delivering agents with limited adverse effects, yet the combination of diagnosis and treatment remains a difficult challenge. This research took advantage of natural albumin and organic molecules to construct a self-assemble core-shell nanostructure combining with superparamagnetic iron oxide nanocrystals and heptamethine cyanine dye IR780 through non-covalent interactions. This nanocomposite successfully decreased the transverse relaxation time of the magnetic resonance hydrogen nucleus, resulting in outstanding T2 imaging, as well as emitting near-infrared II fluorescence, thereby the resulting dual-modality imaging tool was applied to improve diagnostic competency. It is noteworthy that the nanocomposites exhibited impressive enzyme-like catalytic and photothermal capabilities, resulting in a successful activation of the immune system to efficiently suppress distant metastatic lesions in vivo. Consequently, this nano-drug-based therapy could be an advantageous asset in reinforcing the immune system and hindering the growth and reappearance of the immune-cold breast cancer.

A Cascaded Multi-Task Generative Framework for Detecting Aortic Dissection on 3-D Non-Contrast-Enhanced Computed Tomography.

Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing aortic dissection (AD). However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non-contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, we propose a novel cascaded multi-task generative framework for AD detection using NCE-CT volumes. The framework includes a 3D nnU-Net and a 3D multi-task generative architecture (3D MTGA). Specifically, the 3D nnU-Net was employed to segment aortas from NCE-CT volumes. The 3D MTGA was then employed to simultaneously synthesize CE-CT volumes, segment true & false lumen, and classify the patient as AD or non-AD. A theoretical formulation demonstrated that the 3D MTGA could increase the Jensen-Shannon Divergence (JSD) between AD and non-AD for each NCE-CT volume, thus indirectly improving the AD detection performance. Experiments also showed that the proposed framework could achieve an average accuracy of 0.831, a sensitivity of 0.938, and an F1-score of 0.847 in comparison with seven state-of-the-art classification models used by three radiologists with junior, intermediate, and senior experiences, respectively. The experimental results indicate that the proposed framework obtains superior performance to state-of-the-art models in AD detection. Thus, it has great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice. The source codes and supplementary materials for our framework are available at https://github.com/yXiangXiong/CMTGF.

A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography.

Aortic dissection (AD) is a rare but potentially fatal disease with high mortality. The aim of this study is to synthesize contrast enhanced computed tomography (CE-CT) images from non-contrast CT (NCE-CT) images for detecting aortic dissection. In this paper, a cascaded deep learning framework containing a 3D segmentation network and a synthetic network was proposed and evaluated. A 3D segmentation network was firstly used to segment aorta from NCE-CT images and CE-CT images. A conditional generative adversarial network (CGAN) was subsequently employed to map the NCE-CT images to the CE-CT images non-linearly for the region of aorta. The results of the experiment suggest that the cascaded deep learning framework can be used for detecting the AD and outperforms CGAN alone.

Deep Learning for Accurate Segmentation of Venous Thrombus from Black-Blood Magnetic Resonance Images: A Multicenter Study.

OBJECTIVE: Deep vein thrombosis (DVT) is the third-largest cardiovascular disease, and accurate segmentation of venous thrombus from the black-blood magnetic resonance (MR) images can provide additional information for personalized DVT treatment planning. Therefore, a deep learning network is proposed to automatically segment venous thrombus with high accuracy and reliability. METHODS: In order to train, test, and external test the developed network, total images of 110 subjects are obtained from three different centers with two different black-blood MR techniques (i.e., DANTE-SPACE and DANTE-FLASH). Two experienced radiologists manually contoured each venous thrombus, followed by reediting, to create the ground truth. 5-fold cross-validation strategy is applied for training and testing. The segmentation performance is measured on pixel and vessel segment levels. For the pixel level, the dice similarity coefficient (DSC), average Hausdorff distance (AHD), and absolute volume difference (AVD) of segmented thrombus are calculated. For the vessel segment level, the sensitivity (SE), specificity (SP), accuracy (ACC), and positive and negative predictive values (PPV and NPV) are used. RESULTS: The proposed network generates segmentation results in good agreement with the ground truth. Based on the pixel level, the proposed network achieves excellent results on testing and the other two external testing sets, DSC are 0.76, 0.76, and 0.73, AHD (mm) are 4.11, 6.45, and 6.49, and AVD are 0.16, 0.18, and 0.22. On the vessel segment level, SE are 0.95, 0.93, and 0.81, SP are 0.97, 0.92, and 0.97, ACC are 0.96, 0.94, and 0.95, PPV are 0.97, 0.82, and 0.96, and NPV are 0.97, 0.96, and 0.94. CONCLUSIONS: The proposed deep learning network is effective and stable for fully automatic segmentation of venous thrombus on black blood MR images.

Time-efficient and contrast-free magnetic resonance imaging approach to the diagnosis of deep vein thrombosis on black-blood gradient-echo sequence: a pilot study.

BACKGROUND: Black-blood thrombus imaging (BTI) has shown to be advantageous for the diagnosis of deep vein thrombosis (DVT). However, previous techniques using fast spin echo have a high specific absorption rate. As DANTE (delay alternating with nutation for tailored excitation) black-blood preparation can suppress blood flows over a broad range of velocities, we hypothesized that a DANTE black-blood preparation combined with a fast low-angle shot (FLASH) gradient-echo readout-DANTE-FLASH could be used to diagnose DVT. METHODS: Eleven healthy volunteers and 30 suspected DVT patients were recruited to undergo DANTE-FLASH and magnetic resonance direct thrombus imaging (MRDTI). The suspected DVT patients were also examined by ultrasound (US). For the segment level, a total of 1,066 venous vessel segments were analyzed. Using US and MRDTI as the references, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and accuracy (ACC) of DANTE-FLASH were calculated. To quantitatively compare image quality between DANTE-FLASH and MRDTI, image signal-to-noise ratio (SNR), apparent contrast-to-noise ratio (CNR) between muscle and the venous lumen, and the apparent CNR between the thrombus and venous lumen were measured. Additionally, diagnostic confidence, image quality, and clot burden were also evaluated. RESULTS: Using the consensus results of US and MRDTI as a standard reference, the diagnostic SE, SP, PPV, NPV, and ACC of DANTE-FLASH for the 2 readers were 97.0% and 93.2%, 99.0% and 98.2%, 93.4% and 87.9%, 99.6% and 99.0%, and 98.8% and 97.6%, respectively. According to the image quantitative analysis results, DANTE-FLASH demonstrated higher image SNR and CNR than MRDTI. The image quality and diagnostic confidence scores of DANTE-FLASH were higher than MRDTI (3.66±0.44 vs. 3.52±0.52, P<0.001, and 3.84±0.36 vs. 3.76±0.41, P<0.001). There was excellent agreement between DANTE-FLASH and MRDTI on clot burden evaluation. CONCLUSIONS: DANTE-FLASH provided better image quality than MRDTI and accurately detected thrombi. It may, therefore, serve as a safe and convenient alternative for the diagnosis of DVT.