Estimating dynamic vascular perfusion based on Er-based lanthanide nanoprobes with enhanced down-conversion emission beyond 1500 nm.

Peripheral artery disease (PAD) is a common, yet serious, circulatory condition that can increase the risk of amputation, heart attack or stroke. Accurate identification of PAD and dynamic monitoring of the treatment efficacy of PAD in real time are crucial for optimizing therapeutic outcomes. However, current imaging techniques do not enable these requirements.

Incremental Value of Left Ventricular Mechanical Dyssynchrony Assessment by Nitrogen-13 Ammonia ECG-Gated PET in Patients With Coronary Artery Disease.

Phase analysis is a technique used to assess left ventricular mechanical dyssynchrony (LVMD) in nuclear myocardial imaging. Previous studies have found an association between LVMD and myocardial ischemia. We aim to assess the potential diagnostic value of LVMD in terms of myocardial viability, and ability to predict major adverse cardiac events (MACE), using Nitrogen-13 ammonia ECG-gated positron emission tomography (gPET).

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

Background: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC.

Methods: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.

Multi-Omics and Its Clinical Application in Hepatocellular Carcinoma: Current Progress and Future Opportunities.

Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide. China covers over half of cases, leading HCC to be a vital threaten to public health. Despite advances in diagnosis and treatments, high recurrence rate remains a major obstacle in HCC management.

Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy.

Background: This study evaluated the predictive value of gene signatures for biochemical recurrence (BCR) in primary prostate cancer (PCa) patients.

Methods: Clinical features and gene expression profiles of PCa patients were attained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, which were further classified into a training set (n = 419), a validation set (n = 403). The least absolute shrinkage and selection operator Cox (LASSO-Cox) method was used to select discriminative gene signatures in training set for biochemical recurrence-free survival (BCRFS).

Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.

Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images.

Specific Borrmann classification in advanced gastric cancer by an ensemble multilayer perceptron network: a multicenter research.

Purpose: Borrmann classification in advanced gastric cancer (AGC) is necessarily associated with personalized surgical strategy and prognosis. But few radiomics research studies have focused on specific Borrmann classification, and there is yet no consensus regarding what machine learning methods should be the most effective.

Methods: A combined size of 889 AGC patients was retrospectively enrolled from two centers.

3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279).

Purpose: To develop and validate a pretreatment computed tomography (CT)-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiation therapy (CCRT) among patients with locally advanced thoracic esophageal squamous cell carcinoma (TESCC).

Methods And Materials: We conducted a prospective, multicenter study on the therapeutic efficacy of CCRT among TESCC patients across 9 hospitals in China (ChiCTR2000039279). A total of 306 patients with locally advanced TESCC diagnosed by histopathology from August 2015 to May 2020 were included in this study.

Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study.

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain.

Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.

Background: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics.

Methods: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals.

Visual experience modulates whole-brain connectivity dynamics: A resting-state fMRI study using the model of radiologists.

Visual expertise refers to proficiency in visual recognition. It is attributed to accumulated visual experience in a specific domain and manifests in widespread neural activities that extend well beyond the visual cortex to multiple high-level brain areas. An extensive body of studies has centered on the neural mechanisms underlying a distinctive domain of visual expertise, while few studies elucidated how visual experience modulates resting-state whole-brain connectivity dynamics.

A Thrombin-Responsive Nanoprobe for Visualization of Thrombus Formation through Three-Dimensional Optical/Computed Tomography Hybrid Imaging.

Early spontaneous detection of thrombin activation benefits precise theranostics for thrombotic vascular disease. Herein, a thrombin-responsive nanoprobe conjugated by a FITC dye, PEGylated FeO nanoparticles, and a thrombin-sensitive peptide (LASG) was constructed to visualize thrombin activation and subsequent thrombosis . The FITC dye was linked to the LASG coated on the FeO nanoparticles for sensing the thrombin activity via the Förster resonance energy transfer effect.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Deep Learning: A Multi-Center and Prospective Validation Study.

Microvascular invasion (MVI) is a critical risk factor for postoperative recurrence of hepatocellular carcinoma (HCC). Preknowledge of MVI would assist tailored surgery planning in HCC management. In this multicenter study, we aimed to explore the validity of deep learning (DL) in MVI prediction using two imaging modalities-contrast-enhanced computed tomography (CE-CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI).

Recent progress in drug delivery and cancer theranostic built from metal-organic framework.

With the improvement of living standards, cancer has become a great challenge around the world during last decades, meanwhile, abundant nanomaterials have been developed as drug delivery system (DDS) or cancer theranostic agents (CTAs) with their outstanding properties. However, low multifunctional efficiency and time-consuming synthesis limit their further applications. Nowadays, green chemistry, in particular, the concept of atom economy, has defined new criteria for the simplicity and efficient production of biomaterials for nanomedicine, which not only owns the property of spatio-temporal precision imaging, but also possess the ability to treat cancer.

Real-time intraoperative glioma diagnosis using fluorescence imaging and deep convolutional neural networks.

Purpose: Surgery is the predominant treatment modality of human glioma but suffers difficulty on clearly identifying tumor boundaries in clinic. Conventional practice involves neurosurgeon's visual evaluation and intraoperative histological examination of dissected tissues using frozen section, which is time-consuming and complex. The aim of this study was to develop fluorescent imaging coupled with artificial intelligence technique to quickly and accurately determine glioma in real-time during surgery.

Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer.

Background: This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients.

Methods: A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79).

Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP ) light propagation model for quantitative photoacoustic tomography.

When using quantitative photoacoustic tomography (q-PAT) reconstruction to recover the optical absorption coefficients of tissue, the commonly used diffusion equation has several limitations in the case of the objects that have small geometries and high-absorption or low-scattering areas. Furthermore, the conventional perturbation reconstruction strategy is unsatisfactory when the target tissue containing large heterogeneous features. We herein present a modified q-PAT implementation that employs the higher-order photon migration model achieving the tradeoff between mathematical rigidity and computational efficiency.

A narrative review of near-infrared fluorescence imaging in hepatectomy for hepatocellular carcinoma.

Hepatectomy is a main therapeutic strategy for hepatocellular carcinoma (HCC), which requires removal of primary and disseminated tumors and maximum preservation of normal liver tissue. However, in a clinical operation, it is difficult to recognize the tumor tissue and its boundary with the naked eye and palpation, which often leads to insufficient or excessive resection. Near-infrared fluorescence (NIRF) imaging, a non-invasive, real-time, low-cost, and highly sensitive imaging technique has been extensively studied in surgical navigation.

Nonconvex Laplacian Manifold Joint Method for Morphological Reconstruction of Fluorescence Molecular Tomography.

Purpose: Fluorescence molecular tomography (FMT) is a promising technique for three-dimensional (3D) visualization of biomarkers in small animals. Morphological reconstruction is valuable and necessary for further applications of FMT owing to its innate requirement for knowledge of the molecular probe distributions.

Procedures: In this study, a Laplacian manifold regularization joint ℓ-norm model is proposed for morphological reconstruction and solved by a nonconvex algorithm commonly referred to as the half-threshold algorithm.

Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images.

Objectives: The high variability of hypertrophic cardiomyopathy (HCM) genetic phenotypes has prompted the establishment of risk-stratification systems that predict the risk of a positive genetic mutation based on clinical and echocardiographic profiles. This study aims to improve mutation-risk prediction by extracting cardiovascular magnetic resonance (CMR) morphological features using a deep learning algorithm.

Methods: We recruited 198 HCM patients (48% men, aged 47 ± 13 years) and divided them into training (147 cases) and test (51 cases) sets based on different genetic testing institutions and CMR scan dates (2012, 2013, respectively).

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer.

Background: Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC.

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

Purpose: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM).

Methods: In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST).

Preoperative prediction of peritoneal metastasis in colorectal cancer using a clinical-radiomics model.

Purpose: To establish and validate a combined clinical-radiomics model for preoperative prediction of synchronous peritoneal metastasis (PM) in patients with colorectal cancer (CRC).

Method: We enrolled 779 patients (585 in the training set: 553 with nonmetastasis (NM) and 32 with PM; 194 in the validation set: 184 with NM and 10 with PM) with clinicopathologically confirmed CRC. The significant clinical risk factors were used to build the clinical model; the least absolute shrinkage and selection operator (LASSO) algorithm was adopted to construct a radiomics signature, which included imaging features of the primary lesion and the largest peripheral lymph node, and stepwise logistic regression was applied to select the significant variables to develop the clinical-radiomics model.