DCDiff: Dual-Granularity Cooperative Diffusion Models for Pathology Image Analysis.

Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information.

Multi-contrast learning-guided lightweight few-shot learning scheme for predicting breast cancer molecular subtypes.

Invasive gene expression profiling studies have exposed prognostically significant breast cancer subtypes: normal-like, luminal, HER-2 enriched, and basal-like, which is defined in large part by human epidermal growth factor receptor 2 (HER-2), progesterone receptor (PR), and estrogen receptor (ER). However, while dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been generally employed in the screening and therapy of breast cancer, there is a challenging problem to noninvasively predict breast cancer molecular subtypes, which have extremely low-data regimes. In this paper, a novel few-shot learning scheme, which combines lightweight contrastive convolutional neural network (LC-CNN) and multi-contrast learning strategy (MCLS), is worthwhile to be developed for predicting molecular subtype of breast cancer in DCE-MRI.

AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer.

Background And Objectives: Tumor microenvironment (TME) is a determining factor in decision-making and personalized treatment for breast cancer, which is highly intra-tumor heterogeneous (ITH). However, the noninvasive imaging phenotypes of TME are poorly understood, even invasive genotypes have been largely known in breast cancer.

Methods: Here, we develop an artificial intelligence (AI)-driven approach for noninvasively characterizing TME by integrating the predictive power of deep learning with the explainability of human-interpretable imaging phenotypes (IMPs) derived from 4D dynamic imaging (DCE-MRI) of 342 breast tumors linked to genomic and clinical data, which connect cancer phenotypes to genotypes.

MEAI: an artificial intelligence platform for predicting distant and lymph node metastases directly from primary breast cancer.

Purpose: Breast cancer patients typically have decent prognoses, with a 5-year survival rate of more than 90%, but when the disease metastases to lymph node or distant, the prognosis drastically declines. Therefore, it is essential for future treatment and patient survival to quickly and accurately identify tumor metastasis in patients. An artificial intelligence system was developed to recognize lymph node and distant tumor metastases on whole-slide images (WSIs) of primary breast cancer.

A Mutation Found in a Newly Established Ovarian Cancer Cell Line (FDOVL) Promotes Lymph Node Metastasis in Ovarian Cancer.

Peritoneal implantation and lymph node metastasis have different driving mechanisms in ovarian cancer. Elucidating the underlying mechanism of lymph node metastasis is important for treatment outcomes. A new cell line, FDOVL, was established from a metastatic lymph node of a patient with primary platinum-resistant ovarian cancer and was then characterized.

Enhanced antitumor immune responses via a new agent [I]-labeled dual-target immunosuppressant.

Unlabelled: Radionuclides theranostic are ideal "partners" for bispecific antibodies to explore the immune response of patients and synergistic treatment. A bispecific single-domain antibody-Fc fusion protein, KN046, exhibits a good treatment effect by binding to programmed cell death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). An ionizing-radiation stimulus mediated by a low-dose of [I] may be used for immunopotentiation.

Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-Ray.

Chest X-ray (CXR) is commonly performed as an initial investigation in COVID-19, whose fast and accurate diagnosis is critical. Recently, deep learning has a great potential in detecting people who are suspected to be infected with COVID-19. However, deep learning resulting with black-box models, which often breaks down when forced to make predictions about data for which limited supervised information is available and lack inter-pretability, still is a major barrier for clinical integration.

F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

Background: Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.

Methods: In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20).

Intelligent design of polymer nanogels for full-process sensitized radiotherapy and dual-mode computed tomography/magnetic resonance imaging of tumors.

Development of intelligent radiosensitization nanoplatforms for imaging-guided tumor radiotherapy (RT) remains challenging. We report here the construction of an intelligent nanoplatform based on poly(-vinylcaprolactam) (PVCL) nanogels (NGs) co-loaded with gold (Au) and manganese dioxide (MnO) nanoparticles (NPs) for dual-mode computed tomography (CT)/magnetic resonance (MR) imaging-guided "full-process" sensitized RT of tumors. PVCL NGs were synthesized precipitation polymerization and loaded with Au and MnO NPs.

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical applications. Deep convolutional neural networks have become the state-of-the-art in PET tumor segmentation.

Fibronectin-Coated Metal-Phenolic Networks for Cooperative Tumor Chemo-/Chemodynamic/Immune Therapy via Enhanced Ferroptosis-Mediated Immunogenic Cell Death.

The development of nanomedicine formulations to overcome the disadvantages of traditional chemotherapeutic drugs and integrate cooperative theranostic modes still remains challenging. Herein, we report the facile construction of a multifunctional theranostic nanoplatform based on doxorubicin (DOX)-loaded tannic acid (TA)-iron (Fe) networks (for short, TAF) coated with fibronectin (FN) for combination tumor chemo-/chemodynamic/immune therapy under the guidance of magnetic resonance (MR) imaging. We show that the DOX-TAF@FN nanocomplexes created through coordination of TA and Fe(III) and physical coating with FN have a mean particle size of 45.

A core-shell Au@CuSe heterogeneous metal nanocomposite for photoacoustic and computed tomography dual-imaging-guided photothermal boosted chemodynamic therapy.

Chemodynamic therapy (CDT) has aroused extensive attention for conquering cancers because of its high specificity and low invasiveness. Quick generation of hydroxyl radicals (·OH) during CDT could induce more irreparable damage to cancer cells. The generation rate of ·OH could be magnified via the selection of suitable nanocatalysts or under the assistance of exogenous thermal energy from photothermal therapy (PTT).

Engineering a Smart Agent for Enhanced Immunotherapy Effect by Simultaneously Blocking PD-L1 and CTLA-4.

Combinations of immune checkpoint therapies show encouraging results in the treatment of many human cancers. However, the higher costs and greater side effects of such combinations compared with single-agent immunotherapies limit their further applications. In this work, a novel smart agent, KN046@ F-ZIF-8, is developed to overcome these limitations.

The value of preoperative 18F-FDG PET metabolic and volumetric parameters in predicting microvascular invasion and postoperative recurrence of hepatocellular carcinoma.

Background: Microvascular invasion (MVI) is very important in the evaluation of hepatocellular carcinoma (HCC), but diagnosis is determined by postoperative pathology; thus, preoperative noninvasive methods will play an active role. The purpose of the study was to assess the performance of metabolic parameters of preoperative 18F-fluorodeoxyglucose PET/computerized tomography (18F-FDG PET/CT) in the prediction of MVI and postoperative recurrence in primary hepatocellular carcinoma.

Methods: We retrospectively collected 72 patients with HCC who have performed 18F-FDG PET/CT scan before partial hepatectomy between 2016 and 2019.

Overcoming T Cell Exhaustion via Immune Checkpoint Modulation with a Dendrimer-Based Hybrid Nanocomplex.

T cell exhaustion, in which dysfunctional T cells are limited in cytokine release and constrained in immune response, leads to immune escape of cancer cells and decreased efficiency of cancer immunotherapy. Direct regulation or blocking of programmed death 1 (PD-1) represents a promising strategy to overcome T cell exhaustion for reinvigorating anticancer immunity. Here, the construction of a 1,3-propanesultone (1,3-PS)-grafted zwitterionic dendrimer-entrapped gold nanoparticle platform chelated with Gd(III) (Gd-Au DENP-PS) for immune checkpoint modulation is reported.

68Ga-FAPI and 18F-FDG PET/CT in Perineum Extramammary Paget Disease.

Extramammary Paget disease is a rare skin intraepithelial adenocarcinoma. Our case presents 68Ga-fibroblast activation protein inhibitor and 18F-FDG PET/CT imaging in a patient with perineum extramammary Paget disease. Compared with 18F-FDG PET/CT, the primary tumor, enlarged pelvic lymph nodes and right maxillofacial lesion showed higher uptake in 68Ga-fibroblast activation protein inhibitor PET/CT.

A pH-responsive photoacoustic imaging probe for tumor pH imaging in vivo based on polyaniline-bovine serum albumin.

Precise pH detection in tumors can guide the design of pH-responsive drugs and theranostic agents to improve treatment efficacy. However, most reported pH-responsive probes are fluorescent probes, for which in vivo application is limited by low probe penetration depth. In this study, a pH-responsive polyaniline-bovine serum albumin (BSA) probe was constructed for precise pH detection in tumors using photoacoustic imaging.

Diffusion-weighted magnetic resonance imaging reflects activation of signal transducer and activator of transcription 3 during focal cerebral ischemia/reperfusion.

Signal transducer and activator of transcription (STAT) is a unique protein family that binds to DNA, coupled with tyrosine phosphorylation signaling pathways, acting as a transcriptional regulator to mediate a variety of biological effects. Cerebral ischemia and reperfusion can activate STATs signaling pathway, but no studies have confirmed whether STAT activation can be verified by diffusion-weighted magnetic resonance imaging (DWI) in rats after cerebral ischemia/reperfusion. Here, we established a rat model of focal cerebral ischemia injury using the modified Longa method.

Erythropoietin reduces apoptosis of brain tissue cells in rats after cerebral ischemia/reperfusion injury: a characteristic analysis using magnetic resonance imaging.

Some experiments have shown that erythropoietin (EPO) increases resistance to apoptosis and facilitates neuronal survival following cerebral ischemia. However, results from studies are rarely reported. Perfusion-weighted imaging (PWI) and diffusion-weighted imaging (DWI) have been applied successfully to distinguish acute cerebral ischemic necrosis and penumbra in living animals; therefore, we hypothesized that PWI and DWI could be used to provide imaging evidence for the conclusion that EPO could reduce apoptosis in brain areas injured by cerebral ischemia/reperfusion.

Prediction of Vascular Invasion Using a 3-Point Scale Computed Tomography Grading System in Pancreatic Ductal Adenocarcinoma: Correlation With Surgery.

Objective: The aim of this study was to evaluate the correlation between a 3-point scale multidetector computed tomography (MDCT) grading system and surgical exploration in predicting vascular invasion and resectability in patients with pancreatic ductal adenocarcinoma (PDA).

Methods: Fifty-five patients with surgical and pathologic confirmation of PDA were retrospectively analyzed by 3 radiologists independently. All patients had MDCT examination with multiplanar reformatted images, computed tomography (CT) angiography, and negative-contrast CT cholangiopancreatography (nCTCP).

Presurgical Evaluation of Pancreatic Cancer: A Comprehensive Imaging Comparison of CT Versus MRI.

Objective: The purpose of this study was to compare comprehensive CT and MRI in the presurgical evaluation of pancreatic cancer.

Materials And Methods: Thirty-eight patients with pathologically proven pancreatic cancer were included in a retrospective study. CT with negative-contrast CT cholangiopancreatography and CT angiography (CTA) (CT image set) versus MRI with MRCP and MR angiography (MRI image set) were analyzed independently by two reviewers for tumor detection, extension, metastasis, vascular invasion, and resectability.

Differentiation of noncalculous periampullary obstruction: comparison of CT with negative-contrast CT cholangiopancreatography versus MRI with MR cholangiopancreatography.

Objectives: The purpose of this study was to compare CT with negative-contrast CT cholangiopancreatography (nCTCP) using subvolume minimum intensity projection (MinIP) versus MRI with MRCP in differentiating noncalculous periampullary obstruction.

Methods: Sixty-four patients with clinically proven noncalculous periampullary obstructions who had undergone both MDCT and MR examinations before operation were reviewed retrospectively. Two reviewers independently interpreted the two image sets (the CT with nCTCP set [CT set] vs.

The effects of fludarabine on rat cerebral ischemia.

Cerebral ischemic injury involves a variety of cellular and molecular events. Signal transducers and activators of transcription-1 (STAT-1) activation is associated with neuronal cell death and contributes to ischemic injury. The effects of fludarabine, a specific inhibitor of STAT1 protein, on cerebral ischemic/reperfusion (I/R) injury were studied in a rat model.

Effects of erythropoietin on STAT1 and STAT3 levels following cerebral ischemia-reperfusion in rats.

Background And Purpose: Cerebral ischemia-reperfusion injury can activate signal transducers and activators of transcription (STAT). STAT1 initiates neuronal apoptosis following cerebral ischemia-reperfusion, while STAT3 is neuroprotective. Erythropoietin (EPO) promotes regeneration through STAT3 and facilitates neuronal survival following ischemia.