This feature article focuses on introducing three kinds of non-invasive imaging technology,i.e., fluorescence, ultrasonic and photoacoustic imaging, in which optical and/or acoustic signals are employed for diagnosing various diseases.

Background: Ex vivo and in vivo detection and imaging of adenosine triphosphate (ATP) is critically important for the diagnosis and treatment of diseases, which still remains challenges up to present. Results: We herein demonstrate that ATP could be fluorescently detected and imaged ex vivo and in vivo. In particular, we fabricate a kind of fluorescent ATP probes, which are made of titanium carbide (TC) nanosheets modified with the ROX-tagged ATP-aptamer (TC/Apt). In the constructed TC/Apt, TC shows superior quenching efficiency against ROX (e.g., ~ 97%). While in the presence of ATP, ROX-tagged aptamer is released from TC surface, leading to the recovery of fluorescence of ROX under the 545-nm excitation. Consequently, a wide dynamic range from 1 μM to 1.5 mM ATP and a high sensitivity with a limit of detection (LOD) down to 0.2 μM ATP can be readily achieved by the prepared TC/Apt. We further demonstrate that the as-prepared TC/Apt probe is feasible for accurate discrimination of ATP in different samples including living cells, body fluids (e.g., mouse serum, mouse urine and human serum) and mouse tumor models. Conclusions: Fluorescence detection and imaging of ATP could be readily achieved in living cells, body fluids (e.g., urine and serum), as well as mouse tumor model through a new kind of fluorescent ATP nanoprobes, offering new powerful tools for the treatment of diseases related to abnormal fluctuation of ATP concentration.

Objective: To develop and validate a radiomics nomogram that could incorporate clinicopathological characteristics and ultrasound (US)-based radiomics signature to non-invasively predict Ki-67 expression level in patients with breast cancer (BC) preoperatively.Methods: A total of 328 breast lesions from 324 patients with BC who were pathologically confirmed in our hospital from June 2019 to October 2020 were included, and they were divided into high Ki-67 expression level group and low Ki-67 expression level group. Routine US and shear wave elastography (SWE) were performed for each lesion, and the ipsilateral axillary lymph nodes (ALNs) were scanned for abnormal changes. The datasets were randomly divided into training and validation cohorts with a ratio of 7:3. Correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomics features obtained from gray-scale US images of BC patients, and each radiomics score (Rad-score) was calculated. Afterwards, multivariate logistic regression analysis was used to establish a radiomics nomogram based on the radiomics signature and clinicopathological characteristics. The prediction performance of the nomogram was assessed by the area under the receiver operating characteristic curve (AUC), the calibration curve, and decision curve analysis (DCA) using the results of immunohistochemistry as the gold standard.Results: The radiomics signature, consisted of eight selected radiomics features, achieved a nearly moderate prediction efficacy with AUC of 0.821 (95% CI:0.764-0.880) and 0.713 (95% CI:0.612-0.814) in the training and validation cohorts, respectively. The radiomics nomogram, incorporating maximum diameter of lesions, stiff rim sign, US-reported ALN status, and radiomics signature showed a promising performance for prediction of Ki-67 expression level, with AUC of 0.904 (95% CI:0.860-0.948) and 0.890 (95% CI:0.817-0.964) in the training and validation cohorts, respectively. The calibration curve and DCA indicated promising consistency and clinical applicability.Conclusion: The proposed US-based radiomics nomogram could be used to non-invasively predict Ki-67 expression level in BC patients preoperatively, and to assist clinicians in making reliable clinical decisions.

Current diagnostics for sepsis‐associated acute kidney injury (SA‐AKI) detect kidney damage only at advanced stages, limiting opportunities for timely intervention. A DNA origami‐based nanoplatform is developed for the early diagnosis and treatment of SA‐AKI. Modified with a fluorophore (Cy5) and quencher (BHQ3), the DNA origami remains nonfluorescent under normal conditions. During SA‐AKI, elevated microRNA‐21 triggers a strand displacement reaction that restores the fluorescence signal, enabling real‐time detection. Additionally, the photoacoustic changes of BHQ3, driven by different excretion rates of the nanostructure and released DNA strands, enable dual‐mode imaging, enhancing diagnostic accuracy. Therapeutically, DNA origami scavenges reactive oxygen species and, when conjugated with the antimicrobial peptide Leucine‐Leucine‐37 (LL‐37), exhibits bactericidal effects. This combination boosts survival rates by 80% in SA‐AKI models. This dual‐response nanoplatform integrates precise imaging and targeted therapy, offering a powerful strategy for SA‐AKI management and advancing applications of DNA origami in precision nanomedicine.

In the past few years, the axillary lymph node dissection technique has been steadily replaced by sentinel lymph node biopsy for treating and diagnosing breast cancer, thereby minimizing the complications and sequelae of the patients. Nevertheless, sentinel lymph node biopsy still presents limitations, such as high operation requirements, prolonged surgical duration, and adverse reactions to tracer agents. This study developed a novel non-invasive method to predict sentinel lymph node metastasis in breast cancer by analyzing the ultrasound imaging characteristics of the primary tumor, combined with the analysis of peripheral blood T-cell subsets that reflect the immune status of the body. The radiomic features analyzed in this study were extracted from preoperative ultrasound images of 199 solitary breast cancer patients, who were undergoing surgery and were pathologically diagnosed at the Yancheng First People's Hospital. All cases were randomly categorized in a 4:1 ratio to the training (n = 159) and validation (n = 40) cohorts. The extracted radiomics features were subjected to dimensional reduction with the help of the least absolute shrinkage and selection operator technique, resulting in the inclusion of 19 radiomics features. Four classifiers, including naïve Bayesian, logistic regression, classification decision tree, and support vector machine, were utilized to model the radiomics features, conventional ultrasound features, and peripheral blood T cell subsets in the training dataset, and validated using the validation dataset. The best-performing model was chosen for constructing the combined model. The radiomics model constructed using the logistic regression showed the best performance, with the training and validation cohorts showing areas under the curve (AUCs) of 0.77 and 0.68, respectively. The conventional ultrasound and peripheral blood T cell models constructed using the classification decision tree showed the best performance, wherein the training cohort presented AUCs of 0.71 and 0.81, respectively, while the validation cohort presented AUCs of 0.68 and 0.69, respectively. The combined model constructed by logistic regression showed AUCs of 0.91 and 0.79 in the training and validation datasets, respectively. The resulting combined model can be considered a simple, non-invasive method with strong reproducibility and clinical significance. Thus, it can be utilized to predict sentinel lymph node metastasis in breast cancer. Furthermore, the combined model can be effectively used to guide clinical decisions related to the selection of surgical procedures in breast surgery.

Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7–15 days) and large-field view (e.g., > 1000 × 1000 μm2). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion. Herein, we develop a molecular imaging strategy for minimally invasive lymphoma diagnosis. By spreading lymphoma tissue sections tightly on a surface-enhanced Raman scattering (SERS) chip, label-free images of DNA double strand breaks (DSBs) in 30 × 30 μm2 tissue sections could be achieved in ~ 15 min. To establish a proof of concept, the Raman image datasets collected from clinical samples of normal lymphatic tissues and non-Hodgkin's lymphoma (NHL) tissues were well organized and trained in a deep convolutional neural network model, finally achieving a recognition rate of ~ 91.7 ± 2.1%. The molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma.