Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.

BACKGROUND: Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS: The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS: Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION: BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Enhancing the Selectivity of Leucine Aminopeptidase Near-Infrared Fluorescent Probes for Assisting in Surgical Tumor Resection.

Selective fluorescence imaging of analytes is a challenge for monitoring diseases as homologues interfere with the imaging agents. Leucine aminopeptidase (LAP), a kind of protease, is related to tumor pathogenesis. The known LAP fluorescent probes based on leucine recognition have limited selectivity. Herein, a selective t-butyl-alanine recognition unit for LAP through the ligand regulation strategy is prepared as a new near-infrared (NIR) fluorescent probe (DCM-LAP) having a large Stokes shift of 214 nm and a high sensitivity with a detection limit of 168 mU/L. DCM-LAP has an enhanced response toward LAP with NIR fluorescence at 656 nm based on intramolecular charge transfer. The probe is selective without being interfered with by biological enzymes including the aminopeptidase N (APN). DCM-LAP can image LAP activity in living cells. It can also visualize the cell invasion and migration processes. DCM-LAP is employed in the real-time imaging of LAP in tumor-bearing nude mice and guides in the accurate resection of breast tumors. It also distinguishes tumor tissues from normal with a high tumor-to-normal ratio (9.8). The DCM-LAP probe can thus assist in the investigations of LAP-associated clinical disease.

Coronary computed tomography angiography imaging features combined with computed tomography-fractional flow reserve, pericoronary fat attenuation index, and radiomics for the prediction of myocardial ischemia.

BACKGROUND: This study aimed to predict myocardial ischemia (MIS) by constructing models with imaging features, CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics based on coronary computed tomography angiography (CCTA). METHODS AND RESULTS: This study included 96 patients who underwent CCTA and single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI). According to SPECT-MPI results, there were 72 vessels with MIS in corresponding supply area and 105 vessels with no-MIS. The conventional model [lesion length (LL), MDS (maximum stenosis diameter × 100% / reference vessel diameter), MAS (maximum stenosis area × 100% / reference vessel area) and CT value], radiomics model (radiomics features), and multi-faceted model (all features) were constructed using support vector machine. Conventional and radiomics models showed similar predictive efficacy [AUC: 0.76, CI 0.62-0.90 vs. 0.74, CI 0.61-0.88; p > 0.05]. Adding pFAI to the conventional model showed better predictive efficacy than adding CT-FFR (AUC: 0.88, CI 0.79-0.97 vs. 0.80, CI 0.68-0.92; p < 0.05). Compared with conventional and radiomics model, the multi-faceted model showed the highest predictive efficacy (AUC: 0.92, CI 0.82-0.98, p < 0.05). CONCLUSION: pFAI is more effective for predicting MIS than CT-FFR. A multi-faceted model combining imaging features, CT-FFR, pFAI, and radiomics is a potential diagnostic tool for MIS.

Is coronary artery calcium an independent risk factor for white matter hyperintensity?

BACKGROUND: Cardiovascular diseases have been considered the primary cause of disability and death worldwide. Coronary artery calcium (CAC) is an important indicator of the severity of coronary atherosclerosis. This study is aimed to investigate the relationship between CAC and white matter hyperintensity (WMH) in the context of diagnostic utility. METHODS: A retrospective analysis was conducted on 342 patients with a diagnosis of WMH on magnetic resonance images (MRI) who also underwent chest computed tomography (CT) scans. WMH volumes were automatically measured using a lesion prediction algorithm. Subjects were divided into four groups based on the CAC score obtained from chest CT scans. A multilevel mixed-effects linear regression model considering conventional vascular risk factors assessed the association between total WMH volume and CAC score. RESULTS: Overall, participants with coronary artery calcium (CAC score > 0) had larger WMH volumes than those without calcium (CAC score = 0), and WMH volumes were statistically different between the four CAC score groups, with increasing CAC scores, the volume of WMH significantly increased. In the linear regression model 1 of the high CAC score group, for every 1% increase in CAC score, the WMH volume increases by 2.96%. After including other covariates in model 2 and model 3, the ß coefficient in the high CAC group remains higher than in the low and medium CAC score groups. CONCLUSION: In elderly adults, the presence and severity of CAC is related to an increase in WMH volume. Our findings suggest an association between two different vascular bed diseases in addition to traditional vascular risk factors, possibly indicating a comorbid mechanism.

Engineering a Ratiometric Photoacoustic Probe with a Hepatocyte-Specific Targeting Ability for Liver Injury Imaging.

In situ imaging of biological indicators is imperative for pathological research by utilizing an activatable photoacoustic (PA) probe. However, precise imaging in actual applications is hampered by the inevitable poor accumulation and low sensitivity. Herein, an amphiphilic molecular probe (AP) was rationally constructed as proof of concept for in situ imaging of drug-induced liver injury, which consists of a hydrophilic target unit and a superoxide anion radical (O2•-)-sensitive small-molecule PA moiety. The probe AP successfully realizes the selectivity and sensitivity toward O2•- in vitro and in living cells. Notably, the amphiphilic probe AP can be selectively retained in the liver and activated toward endogenous O2•- through receptor-mediated endocytosis inside hepatocytes. By virtue of the highly efficient accumulation at the liver, AP was further applied to assess isoniazid-induced liver injury through desired ratiometric PA signals. In addition, based on the fluctuation of O2•-, the therapeutic efficacy of hepatoprotective medicines for hepatotoxicity was analyzed in vivo. Therefore, the O2•--specific probe could serve as a promising molecular tool for early diagnosis study of other liver diseases and analysis of new potential therapeutic agents.

Design Strategy of Fluorescent Probes for Live Drug-Induced Acute Liver Injury Imaging.

Drug-induced acute liver injury (DIALI) is increasingly recognized as a significant cause of acute liver injury (ALI), which is characterized by a rapid loss of hepatocyte function in patients without pre-existing liver diseases. Evaluation of corresponding biomarkers, including alanine transaminase and aspartate amino transferase, is available as a diagnostic tool for hepatotoxicity. However, these blood tests have certain limitations: (1) they are generally not available for early estimation; (2) it is difficult to visualize and identify hepatotoxicity unambiguously in real-time; and (3) the biomarkers are not unique and are usually influenced by a variety of diseases, leading to potential false results. It is of grave importance and burgeoning demand to develop an early diagnostic approach for such diseases, but the ideal toolkit remains an unresolved challenge.As an alternative, molecular optical probes (fluorescence, chemiluminescence, bioluminescence, etc.) display a lot of advantages, such as high sensitivity, noninvasive fast analysis, and real-time in situ detection. They have emerged as potent and promising tools for the biomedical study of DIALI in living system. Until now, a number of optical probes for DIALI have been reported with some great potential for clinical trials. However, most of the probes still suffer from false signals because of the limitations in clinical application, including poor selectivity, low sensitivity, and biocompatibility. One key challenge that probes face in the ALI environment is the excessive exposure to reactive oxygen/nitrogen species and diffusivity, which may lead to false-positive or negative signals.Our group has employed multiple rational approaches to engineer high-performance optical probes for DIALI. With such development, we have successfully achieved the accurate detection of DIALI with minimal false signals both ex vivo and in vivo. While marching firmly toward understanding the biogenesis and progression of DIALI, we ultimately aim at the early stage clinical diagnosis of the disease, as well as mechanism understanding for clinical trials. In this Account, we summarize and present our three new approaches for the development of high-fidelity optical probes: (1) a combined screening and rational design strategy, (2) a double-locked probe design strategy, and (3) in situ imaging based on the release of a precipitating fluorochrome strategy. Using these strategies, we have formulated probes for a range of biological species that are biomarkers of DIALI, including reactive nitrogen species (ONOO-), reactive sulfur species (H2S and H2Sn), and enzymes (LAP, MAO, and ALP). We have highlighted the rationale for our design and screening strategy and methods to achieve high-fidelity optical probes. Some recent examples of optical probes developed by our laboratory and collaborations are mainly illustrated herein. We anticipate the strategies summarized here to inspire future molecular optical probe design, to contribute to studies of the detailed molecular mechanisms underlying liver diseases, and to improve the efficiency of the diagnosis and treatment of these diseases in clinical settings.

Automated quality assessment of chest radiographs based on deep learning and linear regression cascade algorithms.

OBJECTIVES: Develop and evaluate the performance of deep learning and linear regression cascade algorithms for automated assessment of the image layout and position of chest radiographs. METHODS: This retrospective study used 10 quantitative indices to capture subjective perceptions of radiologists regarding image layout and position of chest radiographs, including the chest edges, field of view (FOV), clavicles, rotation, scapulae, and symmetry. An automated assessment system was developed using a training dataset consisting of 1025 adult posterior-anterior chest radiographs. The evaluation steps included: (i) use of a CNN framework based on ResNet - 34 to obtain measurement parameters for quantitative indices and (ii) analysis of quantitative indices using a multiple linear regression model to obtain predicted scores for the layout and position of chest radiograph. In the testing dataset (n = 100), the performance of the automated system was evaluated using the intraclass correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute difference (MAD), and mean absolute percentage error (MAPE). RESULTS: The stepwise regression showed a statistically significant relationship between the 10 quantitative indices and subjective scores (p < 0.05). The deep learning model showed high accuracy in predicting the quantitative indices (ICC = 0.82 to 0.99, r = 0.69 to 0.99, MAD = 0.01 to 2.75). The automatic system provided assessments similar to the mean opinion scores of radiologists regarding image layout (MAPE = 3.05%) and position (MAPE = 5.72%). CONCLUSIONS: Ten quantitative indices correlated well with the subjective perceptions of radiologists regarding the image layout and position of chest radiographs. The automated system provided high performance in measuring quantitative indices and assessing image quality. KEY POINTS: • Objective and reliable assessment for image quality of chest radiographs is important for improving image quality and diagnostic accuracy. • Deep learning can be used for automated measurements of quantitative indices from chest radiographs. • Linear regression can be used for interpretation-based quality assessment of chest radiographs.

Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram.

BACKGROUND: Coronary computed tomography angiography (CCTA) is a well-established non-invasive diagnostic test for the assessment of coronary artery diseases (CAD). CCTA not only provides information on luminal stenosis but also permits non-invasive assessment and quantitative measurement of stenosis based on radiomics. PURPOSE: This study is aimed to develop and validate a CT-based radiomics machine learning for predicting chronic myocardial ischemia (MIS). METHODS: CCTA and SPECT-myocardial perfusion imaging (MPI) of 154 patients with CAD were retrospectively analyzed and 94 patients were diagnosed with MIS. The patients were randomly divided into two sets: training (n = 107) and test (n = 47). Features were extracted for each CCTA cross-sectional image to identify myocardial segments. Multivariate logistic regression was used to establish a radiomics signature after feature dimension reduction. Finally, the radiomics nomogram was built based on a predictive model of MIS which in turn was constructed by machine learning combined with the clinically related factors. We then validated the model using data from 49 CAD patients and included 18 MIS patients from another medical center. The receiver operating characteristic curve evaluated the diagnostic accuracy of the nomogram based on the training set and was validated by the test and validation set. Decision curve analysis (DCA) was used to validate the clinical practicability of the nomogram. RESULTS: The accuracy of the nomogram for the prediction of MIS in the training, test and validation sets was 0.839, 0.832, and 0.816, respectively. The diagnosis accuracy of the nomogram, signature, and vascular stenosis were 0.824, 0.736 and 0.708, respectively. A significant difference in the number of patients with MIS between the high and low-risk groups was identified based on the nomogram (P < .05). The DCA curve demonstrated that the nomogram was clinically feasible. CONCLUSION: The radiomics nomogram constructed based on the image of CCTA act as a non-invasive tool for predicting MIS that helps to identify high-risk patients with coronary artery disease.

A novel method for determining the X-ray beam angulation of the supraspinatus outlet view.

BACKGROUND: Rapid and accurate quantification of the supraspinatus outlet view (SOV) is a clinical challenge. PURPOSE: To quantify the X-ray beam angle of the SOV using the horizontal angle of the subscapular spine line (SSSL) and to further verify the feasibility of this method. MATERIAL AND METHODS: A total of 119 patients who underwent shoulder computed tomography (CT) examination were enrolled in the retrospective study. Three-dimensional (3D) CT reconstruction was performed and manually adjusted to provide the position similar to SOV. The rotation angle of the 3D image along the long axis of the human body (marked as ß) was obtained. The horizontal angle of SSSL (marked as α) was measured on the anteroposterior localizer image of shoulder CT. Pearson correlation and linear regression correlation analysis were performed. In addition, the first-time success rate between the experience-based group and the measurement-based group were compared to verify the novel method. RESULTS: We found a linear correlation between α and ß (r = 0.962; P = 0.000). There was no significant correlation between the experience-based group and the measurement-based group in terms of age (P = 0.500), sex (P = 0.397), and side (P = 0.710), but there was a significant statistical difference in the first success rate between the two validation groups (χ2 = 5.808a, P = 0.016). CONCLUSION: This novel quantitative measurement method for determining the X-ray beam angle of SOV using the horizontal angle of SSSL is feasible.

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis. METHODS: A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots. RESULTS: At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists' measurements, the model showed similar performance to the radiologists. CONCLUSIONS: The developed model can automatically measure SIRM on flexion-neutral-extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.

Highly Selective Fluorescent Probe Design for Visualizing Hepatic Hydrogen Sulfide in the Pathological Progression of Nonalcoholic Fatty Liver.

Hydrogen sulfide (H2S), emerging as an important gaseous signal, has attracted more and more attention for its key role in chronic fatty liver diseases. However, lacking tools for H2S-specific in situ detection, the changes of endogenous hepatic H2S levels in the pathological progression of chronic liver diseases are still unclear. To this end, we adopted a strategy of combining molecular probe design and nanofunctionalization to develop a highly selective near-infrared (NIR) fluorescent probe, which allows in vivo real-time monitoring of hepatic H2S levels in the process of nonalcoholic fatty liver disease (NAFLD). As a proof of strategy demonstration, we first designed NIR molecular probes for H2S sensing through chemical design and probe screening and then loaded molecular probes into mesoporous silicon nanomaterials (MSNs) with surface encapsulation using poly(ethylene glycol) to construct a highly selective probe MSN@CSN@PEG, with significantly improved selectivity and photostability. Moreover, MSN@CSN@PEG exhibited high selectivity and sensitivity for endogenous H2S in cells and tumors in vivo, eliminating the interference of a high concentration of biothiols and sulfhydryl proteins. Furthermore, the probe was applied to in situ intravital imaging and systematic assessment of hepatic H2S levels in different stages of NAFLD for the first time, which may offer a promising tool for the future study of fatty liver diseases and other chronic liver diseases.

Dual-Stimulus Responsive Near-Infrared Reversible Ratiometric Fluorescent and Photoacoustic Probe for In Vivo Tumor Imaging.

Tumor-specific imaging is a major challenge in clinical tumor resection. To overcome this problem, several activatable probes have been developed for use in tumor imaging. However, most of these probes are activated based on a single-factor stimulation and are irreversible. Therefore, false signals that make tumor-specific imaging difficult are easily generated. We have developed a new dual-stimulus responsive near-infrared (NIR) reversible adenosine 5'-triphosphate (ATP)-pH probe for fluorescence and photoacoustic ratiometric imaging of tumors. Since the H+ and ATP content is significantly higher in the tumor microenvironment than that in normal tissues, the Förster resonance energy transfer-based probe ATP-pH was constructed with silicon rhodamine as the donor, CS dye as the acceptor, and ATP/H+ recognition units that could only be activated when both H+ and ATP were connected to the acceptor. The ATP-pH probe is reversibly activated by both the H+ and ATP, which effectively reduces the cumulative response of the probe in circulation after intravenous injection. Further, the NIR ratiometric property of the probe makes it suitable for in vivo imaging. Finally, our probe was successfully utilized in ratiometric photoacoustic and fluorescence tumor imaging and ratiometric fluorescence imaging-guided tumor resection.

Precipitated Fluorophore-Based Probe for Accurate Detection of Mitochondrial Analytes.

Mitochondria-targeted fluorescent probes are highly important to obtain mitochondrial function information. However, the accuracy of the current mitochondria-targeted fluorescent probes is unsatisfactory owing to the following two reasons. In the first case, some probes that always have a mitochondria-targeting group, thus, would react with the analytes outside of mitochondria and enter mitochondria with the generated fluorophore signal, which leads to a false-positive result. In the other case, after response to the analytes in mitochondria, some probes could diffuse from mitochondria to other organelles, thus triggering a false-negative result. To avoid the two problems, herein, we develop a precipitated fluorophore-based probe, which precipitates in situ after reacting with analytes, for the accurate detection of mitochondrial analytes. The probe was modified with HQPQ, a novel solid-state fluorophore that is insoluble in water. As a proof of concept, we designed and synthesized a probe (HQPQ-B) for H2O2 detection. Based on the different mitochondria-targeting capacities of quinoline salts and quinolone, HQPQ loses the mitochondria-targeting ability after reacting with analytes outside of mitochondria, thus avoiding a false-positive result. On the contrary, when the probe first localized in mitochondria and then reacted with analytes, HQPQ would precipitate and remain in mitochondria without diffusing to other sites, thus avoiding a false-negative result. Therefore, HQPQ enables the accurate detection of mitochondrial analytes. We believe that the novel strategy based on HQPQ will be a general strategy for accurate detection of mitochondrial analytes without interference from other sites, which enables an accurate study on mitochondrial function.

The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.

BACKGROUND: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique. A pragmatic and objective method in the clinic to assess OS is strongly in need. PURPOSE: To construct a radiomics nomogram to stratify GBM patients into long- vs. short-term survival. STUDY TYPE: Retrospective. POPULATION: One-hundred and fifty-eight GBM patients from Brain Tumor Segmentation Challenge 2018 (BRATS2018) were for model construction and 32 GBM patients from the local hospital for external validation. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T MRI Scanners, T1 WI, T2 WI, T2 FLAIR, and contrast-enhanced T1 WI sequences ASSESSMENT: All patients were divided into long-term or short-term based on a survival of greater or fewer than 12 months. All BRATS2018 subjects were divided into training and test sets, and images were assessed for ependymal and pia mater involvement (EPI) and multifocality by three experienced neuroradiologists. All tumor tissues from multiparametric MRI were fully automatically segmented into three subregions to calculate the radiomic features. Based on the training set, the most powerful radiomic features were selected to constitute radiomic signature. STATISTICAL TESTS: Receiver operating characteristic (ROC) curve, sensitivity, specificity, and the Hosmer-Lemeshow test. RESULTS: The nomogram had a survival prediction accuracy of 0.878 and 0.875, a specificity of 0.875 and 0.583, and a sensitivity of 0.704 and 0.833, respectively, in the training and test set. The ROC curve showed the accuracy of the nomogram, radiomic signature, age, and EPI for external validation set were 0.858, 0.826, 0.664, and 0.66 in the validate set, respectively. DATA CONCLUSION: Radiomics nomogram integrated with radiomic signature, EPI, and age was found to be robust for the stratification of GBM patients into long- vs. short-term survival. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion.

BACKGROUND: Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). METHODS: A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. RESULTS: The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665-0.875) and 0.792 (95% CI: 0.633-0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701-0.916) and 0.833 (95% CI: 0.702-0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614-0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712-0.928). CONCLUSIONS: The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

Cyclic Amplification of the Afterglow Luminescent Nanoreporter Enables the Prediction of Anti-cancer Efficiency.

We developed a cyclic amplification method for an organic afterglow nanoreporter for the real-time visualization of self-generated reactive oxygen species (ROS). We promoted semiconducting polymer nanoparticles (PFODBT) as a candidate for emitting near-infrared afterglow luminescence. Introduction of a chemiluminescent substrate (CPPO) into PFODBT (PFODBT@CPPO) resulted in a significant enhancement of afterglow intensity through the dual cyclic amplification pathway involving singlet oxygen (1 O2 ). 1 O2 produced by PFODBT@CPPO induced cancer cell necrosis and promoted the release of damage-related molecular patterns, thereby evoking immunogenic cell death (ICD)-associated immune responses through ROS-based oxidative stress. The afterglow luminescent signals of the nanoreporter were well correlated with light-driven 1 O2 generation and anti-cancer efficiency. This imaging strategy provides a non-invasive tool for predicting the therapeutic outcome that occurs during ROS-mediated cancer therapy.

An Acidity-Unlocked Magnetic Nanoplatform Enables Self-Boosting ROS Generation through Upregulation of Lactate for Imaging-Guided Highly Specific Chemodynamic Therapy.

Chemodynamic therapy is an emerging tumor therapeutic strategy. However, the anticancer effects are greatly limited by the strong acidity requirements for effective Fenton-like reaction, and the inevitably "off-target" toxicity. Herein, we develop an acidity-unlocked nanoplatform (FePt@FeOx @TAM-PEG) that can accurately perform the high-efficient and tumor-specific catalysis for anticancer treatment, through dual pathway of cyclic amplification strategy. Notably, the pH-responsive peculiarity of tamoxifen (TAM) drug allows for the catalytic activity of FePt@FeOx to be "turn-on" in acidic tumor microenvironments, while keeping silence in neutral condition. Importantly, the released TAM within cancer cells is able to inhibit mitochondrial complex I, leading to the upregulated lactate content and thereby the accumulated intracellular H+ , which can overcome the intrinsically insufficient acidity of tumor. Through the positive feedback loop, large amount of active FePt@FeOx nanocatalyzers are released and able to access to the endogenous H2 O2 , exerting the improved Fenton-like reaction within the more acidic condition. Finally, such smart nanoplatform enables self-boosting generation of reactive oxygen species (ROS) and induces strong intracellular oxidative stress, leading to the substantial anticancer outcomes in vivo, which may provide a new insight for tumor-specific cascade catalytic therapy and reducing the "off-target" toxicity to surrounding normal tissues.

High-Selectivity Fluorescent Reporter toward Peroxynitrite in a Coexisting Nonalcoholic Fatty Liver and Drug-Induced Liver Diseases Model.

Peroxynitrite (ONOO-), a highly reactive species, is profoundly involved in many physiological and pathological processes. Change of the ONOO- level usually indicates an abnormal body function. Thus, it is desired to develop a highly reliable ONOO- assay to elucidate its roles in a related disease environment. In this work, we have constructed a ratiometric molecule fluorescent probe RTFP toward ONOO- with high specificity by the combination strategy of probe screening and a rational design method. RTFP displayed excellent detection sensitivity (detection limit: 4.1 nM) and produced a highly ratiometric emission signal (130-fold). Leveraging this probe, we showed the change of ONOO- content in the free-fatty-acid-induced nonalcoholic fatty liver disease (NAFLD) and acetaminophen-induced drug-induced liver injury (DILI) cellular model and for the first time disclosed the involved mechanism of cytochrome P450 2E1 (CYP2E1) enzyme in NAFLD with a DILI pathological environment. Furthermore, RTFP also was utilized to visualize ONOO- fluctuation of living liver tissues in a high-fat-diet-caused NAFLD model. We expected that this probe may help the study of liver injury in the exploration of mechanism and signal path.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults.

BACKGROUND: White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need. PURPOSE: To develop and validate a radiomics nomogram for the prediction of WMH progression. STUDY TYPE: Retrospective. POPULATION: Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old. FIELD STRENGTH/SEQUENCE: T1 -fluid attenuated inversion recovery images were acquired using a 3T scanner. ASSESSMENT: WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction. STATISTICAL TESTS: A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed. RESULTS: The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively. DATA CONCLUSION: This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Radiomics from magnetic resonance imaging may be used to predict the progression of white matter hyperintensities and identify associated risk factors.

OBJECTIVE: The progression of white matter hyperintensities (WMH) varies considerably in adults. In this study, we aimed to predict the progression and related risk factors of WMH based on the radiomics of whole-brain white matter (WBWM). METHODS: A retrospective analysis was conducted on 141 patients with WMH who underwent two consecutive brain magnetic resonance (MR) imaging sessions from March 2014 to May 2018. The WBWM was segmented to extract and score the radiomics features at baseline. Follow-up images were evaluated using the modified Fazekas scale, with progression indicated by scores ≥ 1. Patients were divided into progressive (n = 65) and non-progressive (n = 76) groups. The progressive group was subdivided into any WMH (AWMH), periventricular WMH (PWMH), and deep WMH (DWMH). Independent risk factors were identified using logistic regression. RESULTS: The area under the curve (AUC) values for the radiomics signatures of the training sets were 0.758, 0.749, and 0.775 for AWMH, PWMH, and DWMH, respectively. The AUC values of the validation set were 0.714, 0.697, and 0.717, respectively. Age and hyperlipidemia were independent predictors of progression for AWMH. Age and body mass index (BMI) were independent predictors of progression for DWMH, while hyperlipidemia was an independent predictor of progression for PWMH. After combining clinical factors and radiomics signatures, the AUC values were 0.848, 0.863, and 0.861, respectively, for the training set, and 0.824, 0.818, and 0.833, respectively, for the validation set. CONCLUSIONS: MRI-based radiomics of WBWM, along with specific risk factors, may allow physicians to predict the progression of WMH. KEY POINTS: • Radiomics features detected by magnetic resonance imaging may be used to predict the progression of white matter hyperintensities. • Radiomics may be used to identify risk factors associated with the progression of white matter hyperintensities. • Radiomics may serve as non-invasive biomarkers to monitor white matter status.