OBJECTIVE:To investigate whether pre-treatment T2WI-based multiregional radiomics can predict the probability of post-treatment tumor deposit (TD) and prognostic outcomes in patients with resectable rectal cancer after neoadjuvant therapy. MATERIALS AND METHODS:This retrospective study included 159 patients with pathologically confirmed rectal cancer who received neoadjuvant therapy and then underwent surgery from March 2013 to March 2024. Radiomics features were extracted from the intratumoral region, a 3-mm-region straddling the tumor margin, and peritumoral 3 mm region on pre-treatment T2WI images. Clinical-radiomics nomogram was developed based on the most predictive radiomics signatures and clinical risk factors. Prognostic model for 5-year recurrence-free survival (RFS) was constructed by Cox regression analysis. RESULTS:The nomogram integrating clinical risk factors (Tumor distance to anal margin and MRI-reported extramural vascular invasion (EMVI)) with an intra-straddle 3 mm radiomics signature score (radscore) demonstrated optimal predictive performance with area under the receiver operating characteristic curve (AUC) of 0.953 (95% CI: 0.877-0.988), 0.810 (95% CI: 0.629-0.928) and 0.952 (95% CI: 0.857-0.992) in the training cohort, validation cohort and test cohort, respectively. The prognostic model constructed by intra-straddle 3 mm radscore (hazard ratio [HR] = 3.60, 95% CI: 1.59-8.16) and MRI-reported EMVI (HR = 6.07, 95% CI: 2.51-14.63) showed good performance for predicting 5‑year RFS with AUC of 0.827 (95% CI: 0.772-0.890) in the entire cohort. CONCLUSION:The nomogram, incorporating pre-treatment MRI-based intra-straddle 3 mm radscore along with clinical risk factors, facilitates noninvasive assessment of the likelihood of TD positivity following neoadjuvant therapy, and has the power to predict 5-year RFS in patients with resectable rectal cancer.

RATIONALE AND OBJECTIVES:To develop and compare general and treatment-specific radiomics models based on pretreatment computed tomography (CT) for predicting pathological response to neoadjuvant therapy (NAT) in gastric cancer (GC), and to explore a dual-score framework for individualized treatment selection. MATERIALS AND METHODS:This retrospective study included 405 patients with GC who underwent neoadjuvant chemotherapy (NAC) or neoadjuvant immunochemotherapy (NAIC) followed by radical gastrectomy, comprising 337 in the development cohort and 68 in a temporal test cohort. The development cohort was randomly divided into training (n = 235) and validation (n = 102) sets. Radiomics features were extracted from portal venous-phase CT images. Four machine learning classifiers were used to construct general and treatment-specific models. Treatment-specific models were cross-applied to generate paired NAC and NAIC response probabilities. RESULTS:In validation, the general model achieved an AUC of 0.679 (NAC, 0.732; NAIC, 0.659), whereas the NAC-specific and NAIC-specific models achieved AUCs of 0.770 and 0.753. Repeated random-split analyses more frequently favored treatment-specific models. In the temporal test cohort, the NAIC-specific model outperformed the general model (AUC, 0.707 vs 0.626), whereas the NAC-specific model showed no advantage (AUC, 0.563 vs 0.625). In the dual-score framework, patients who received model-recommended treatment showed higher pathological response rates (NAC-recommended: 46.4% vs 23.3%, p = 0.043; NAIC-recommended: 40.5% vs 19.4%, p < 0.0001). CONCLUSION:Treatment-specific radiomics models showed better discrimination than the general model for predicting pathological response to NAT in gastric cancer. The dual-score framework may provide an exploratory approach for individualized treatment selection.

Cardiovascular diseases with severe vascular stenosis or occlusion can lead to tissue hypoxia, multi-organ dysfunction, and high mortality, emphasizing the need for accurate and safe vascular imaging. While digital subtraction angiography and computed tomography angiography are widely used, they involve ionizing radiation and iodinated contrast agents, posing risks of nephrotoxicity and allergic reactions. Contrast-enhanced magnetic resonance angiography (CE-MRA) offers a non-ionizing alternative, but gadolinium-based agents are limited by rapid clearance, risks of nephrogenic systemic fibrosis, and CNS accumulation. Metal-doped superparamagnetic iron oxide nanoparticles (SPIONs) represent promising T-1 probes; however, conventional surface coatings often increase hydrodynamic size, limiting renal clearance. Here, we develop ultrasmall MnFe2O4 nanoparticles functionalized with zwitterionic dopamine sulfonate (ZDS), exhibiting excellent water solubility, high longitudinal relaxivity (R-1 = 5.47 mM(-1).s(-1)), and a compact hydrodynamic diameter (similar to 6 nm) suitable for renal elimination. Under clinical 3.0 T MRI, ZDS@MnFe2O4 enables high-resolution imaging of cervical and abdominal vasculature, resolving vessels as small as 0.38 mm up to 1-h post-injection. The outstanding imaging capability allows real-time monitoring of carotid recanalization and detection of acute superior mesenteric veins and deep vein thromboses. This platform offers a promising approach for advanced vascular diagnostics by striking an optimal balance among imaging performance, biosafety, and clinical practicality.

Early and accurate detection of multiple cancers through a single test remains an unmet clinical need, hindered by current limitations in accuracy, throughput, automation, and multiplexing. Here, we present an AI-powered SERS chip that combines automated exosome capture with AI-enabled molecular fingerprinting to accurately distinguish ten common cancer types from a single serum test. The system employs a peptide-functionalized SERS chip enabling the selective enrichment of exosomes directly from patient serum, enhancing label-free Raman fingerprint signals. AI-driven spectral analysis achieved 97.4% accuracy in distinguishing cancer from healthy samples, 97.08% accuracy for early-stage cancer detection, and 93.89% accuracy in classifying ten common cancer types, including breast, thyroid, esophageal, kidney, pancreatic, duodenal, lung, colorectal, ovarian, and gastric cancers. Crucially, based on molecular profiling, we identified exosomal deoxyadenosine triphosphate as a promising pan-cancer biomarker consistently upregulated across diverse tumor types. This discovery establishes a potential pan-cancer diagnostic marker, while the fully automated, scalable platform offers significant promise for clinical translation in early and differential cancer diagnosis.

PurposeTo evaluate the potential of radiomics approach for predicting No. 14v station lymph node metastasis (14vM) in gastric cancer (GC).MethodsThe contrast enhanced CT (CECT) images with corresponding clinical information of 288 GC patients were retrospectively collected. Patients were separated into training set (n = 202) and testing set (n = 86). A total of 1,316 radiomics feature were extracted from portal venous phase images of CECT. Seven machine learning (ML) algorithms including naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was established by combining radiomics with important clinicopathological factors. The diagnostic ability of the signature and model were evaluated.ResultsLR algorithm was chosen for signature construction. The radiomics signature exhibited good discrimination accuracy of 14vM with AUCs of 0.83 in the training and 0.77 in the testing set. The risk of 14vM showed significant association with higher radiomics score. A combined model exhibited increased predictive ability and good agreement in the training (AUC = 0.87) and testing (AUC = 0.85) sets.ConclusionThe ML-based radiomics model provided a promising image biomarker for preoperative detection of 14vM and may help the surgeon to decide whether to add 14v dissection to lymphadenectomy.

Purpose:To establish and validate a machine learning based radiomics model for detection of perineural invasion (PNI) in gastric cancer (GC).Methods:This retrospective study included a total of 955 patients with GC selected from two centers; they were separated into training (n=603), internal testing (n=259), and external testing (n=93) sets. Radiomic features were derived from three phases of contrast-enhanced computed tomography (CECT) scan images. Seven machine learning (ML) algorithms including least absolute shrinkage and selection operator (LASSO), naïve Bayes (NB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), eXtreme gradient boosting (XGBoost) and support vector machine (SVM) were trained for development of optimal radiomics signature. A combined model was constructed by aggregating the radiomic signatures and important clinicopathological characteristics. The predictive ability of the radiomic model was then assessed with receiver operating characteristic (ROC) and calibration curve analyses in all three sets.Results:The PNI rates for the training, internal testing, and external testing sets were 22.1, 22.8, and 36.6%, respectively. LASSO algorithm was selected for signature establishment. The radiomics signature, consisting of 8 robust features, revealed good discrimination accuracy for the PNI in all three sets (training set: AUC = 0.86; internal testing set: AUC = 0.82; external testing set: AUC = 0.78). The risk of PNI was significantly associated with higher radiomics scores. A combined model that integrated radiomics and T stage demonstrated enhanced accuracy and excellent calibration in all three sets (training set: AUC = 0.89; internal testing set: AUC = 0.84; external testing set: AUC = 0.82).Conclusion:The suggested radiomics model exhibited satisfactory prediction performance for the PNI in GC.

Purpose: To evaluate the potential of machine learning (ML)-based radiomics approach for predicting tumor mutation burden (TMB) in gastric cancer (GC).Methods: The contrast enhanced CT (CECT) images with corresponding clinical information of 256 GC patients were retrospectively collected. Patients were separated into training set (n = 180) and validation set (n = 76). A total of 3,390 radiomics features were extracted from three phases images of CECT. The least absolute shrinkage and selection operator (LASSO) model was used for feature screening. Seven machine learning (ML) algorithms were employed to find the optimal classifier. The predictive ability of radiomics model (RM) was evaluated with receiver operating characteristic. The correlation between RM and TMB values was evaluated using Spearman’s correlation coefficient. The explainability of RM was assessed by the Shapley Additive explanations (SHAP) method.Results: Logistic regression algorithm was chosen for model construction. The RM showed good predictive ability of TMB status with AUCs of 0.89 [95% confidence interval (CI): 0.85–0.94] and 0.86 (95% CI: 0.74–0.98) in the training and validation sets. The correlation analysis revealed a good correlation between RM and TMB levels (correlation coefficient: 0.62, p < 0.001). The RM also showed favorable and stable predictive accuracy within the cutoff value range 6–16 mut/Mb in both sets.Conclusion: The ML-based RM offered a promising image biomarker for predicting TMB status in GC patients.

The presence of hypoxia in tumors is characteristic of most solid tumors and it promotes not only tumor angiogenesis but also tumor cell invasion and metastasis. It also results in resistance of tumor tissue to radiation, leading to poor outcomes of tumor radiotherapy. Therefore, to address this conundrum, highly selective gold nanoclusters were prepared as fluorescent imaging agents and radiosensitizers and then loaded with tumor hypoxia-activated prodrugs to prepare nanoprobes which synergistically improved the anti-tumor efficacy by combining radiotherapy and hypoxia-activated therapy. The designed nanoprobes have ultra-small size, high selectivity for integrin αvβ3 receptor-positive tumor cells and tumor neovascular endothelial cells, and excellent fluorescence imaging performance. The experimental procedures were carried out in vitro and in vivo to demonstrate that the developed nanoprobes have a high level of biocompatibility, efficient radiosensitization effect, and anti-tumor efficacy at cell and tissue levels. The combined application of radiotherapy and hypoxia-activated therapy can overcome the radiation resistance caused by tumor hypoxia, compensate for the limitations of single radiotherapy, inhibit tumor growth, improve the efficacy of tumor radiotherapy, and provide new possibilities for the development of more precise and effective treatment strategies.

Background: Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is of great importance for appropriate management of advanced gastric cancer (AGC) patients. This study aims to develop and validate a CT-based radiomics model for prediction of HER2 overexpression in AGC.Materials and Methods: Seven hundred and forty-five consecutive AGC patients (median age, 59 years; interquartile range, 52–66 years; 515 male and 230 female) were enrolled and separated into training set (n = 521) and testing set (n = 224) in this retrospective study. Radiomics features were extracted from three phases images of contrast-enhanced CT scans. A radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator method. Univariable and multivariable logistical regression analysis were used to establish predictive model with independent risk factors of HER2 overexpression. The predictive performance of radiomics model was assessed in the training and testing sets.Results: The positive rate of HER2 was 15.9% and 13.8% in the training set and testing set, respectively. The positive rate of HER2 in intestinal-type GC was significantly higher than that in diffuse-type GC. The radiomics signature comprised eight robust features demonstrated good discrimination ability for HER2 overexpression in the training set (AUC = 0.84) and the testing set (AUC = 0.78). A radiomics-based model that incorporated radiomics signature and pathological type showed good discrimination and calibration in the training (AUC = 0.85) and testing (AUC = 0.84) sets.Conclusion: The proposed radiomics model showed favorable accuracy for prediction of HER2 overexpression in AGC.

Background:Accurate evaluation of human epidermal growth factor receptor 2 (HER2) status is very important for appropriate management of advanced gastric cancer (AGC) patients. In this study, we aimed to develop and validate a computed tomography (CT)-based radiomics signature for preoperative prediction of HER2 overexpression and treatment efficacy of trastuzumab in AGC.Methods:We retrospectively enrolled 536 consecutive AGC patients (median age, 59 years; interquartile range, 52-65 years; 377 male, 159 female) and separated them into a training set (n=357) and a testing set (n=179). Radiomic features were extracted from 3 different phase images of contrast-enhanced CT scans, and a radiomics signature was built based on highly reproducible features using the least absolute shrinkage and selection operator (LASSO) method. The predictive performance of the radiomics signature was assessed in the training and testing sets. Univariable and multivariable logistical regression analyses were used to identify independent risk factors of HER2 overexpression. Univariable and multivariable Cox regression analyses were used to identify the risk factors of overall survival (OS) and progression-free survival (PFS). The predictive value of the radiomics signature for treatment efficacy of trastuzumab was also evaluated.Results:The radiomics signature comprised eight robust features that demonstrated good discrimination ability for HER2 overexpression in the training set [area under the curve (AUC) =0.85] and the testing set (AUC =0.81). Multivariable Cox regression analysis revealed that the radiomics signature was an independent risk factor for OS [hazard ratio (HR) =2.01, P=0.001] and PFS (HR =1.32, P=0.01). The radiomics score of patients who achieved disease control was significantly lower than that of patients with progressive disease (P=0.023).Conclusions:The proposed radiomics signature showed favorable accuracy for prediction of HER2 overexpression and prognosis in AGC. It has promising potential as a noninvasive approach for selecting patients for target therapy.

In order to improve the effectiveness of database repetitive recording detection, we propose a database iterative record detection algorithm based on deep learning. As a result, it was proven that the database repetition record detection rate and efficiency of this algorithm were high, and the result of the database repetition record was obviously superior to other algorithms. The results show that the database duplicate record detection results are significantly better than other current algorithms.

Objective To prepare pH-responsive osmotic nanocarriers (pMPPs),observe their distribution in the genital tract mucosa in mice,and evaluate their radiosensitizing effects in tumor cells.Methods Amphiphilic polymers containing pH-sensitive hydrazone bonds were synthesized and pMPPs were prepared by ultrasonic emulsification.At the same time,the hydrophobic polymer polylactic acid-glycolic acid copolymer (PLGA) and the amphiphilic polymer PLGA-polyethylene glycol without hydrazine bond were selected,and the mucoadhesive nanoparticles(MPs) and mucus-penetrating particles (MPPs) were prepared in the same way.Fluorescence microscopy was used to observe the distribution of three kinds of nanocarriers labeled with fluorescent dye Cy5.5 in the genital tract mucosa.The toxicity of nanocarriers to human cervical cancer cell line HeLa was tested by thiazolyl blue assay.The amphiphilic polymer containing pH-sensitive hydrazone bond was combined with oil-soluble gold nanoparticles to form a multi-encapsulated nanocarrier,and its radiotherapy sensitization effect in HeLa cells was evaluated by thiazole blue assay.Results The pMPPs were successfully prepared with relatively uniform particle size and good dispersion.Fluorescence microscopy showed that pMPPs not only had good mucus permeability,but also could improve the endocytosis efficiency of the nanocarriers in reproductive tract mucosa.The results of thiazolyl blue test showed that when the concentration of the carrier reached to 0.80 mg/ml,the survival rate of HeLa cells in the pMPPs group was higher than 90％ which was higher than that in the MPs and the MPPs groups,indicating that pMPPs had good biosafety.The HeLa cell survival rate of the CMNa group (0.80 mg/ml) was higher than that of the multi-package nanocarrier group under different doses of X-ray irradiation (4 Gy:82.90％ vs.61.79％;8 Gy:64.75 ％ vs.42.36％).This result indicated that compared with the CMNa,a commonly used clinical radiotherapy sensitizer,the multi-encapsulated nanocarriers can more effectively enhance the sensitivity of tumor cells to radiation therapy,thereby improving the lethality of radiation therapy on tumor cells.Conclusion This study solved the conflict between mucus permeation and endocytosis design of nanocarriers in mucosal tissue application,and provided new insight for the treatment of mucosal tissue diseases.

Objective To prepare polyethylene glycol/cyclic asparagines-glycine-arginine functionalized gold nanoparticles (GNPs-PEG@cNGR) and evaluate their effectiveness in CT imaging of breast cancer angiogenesis.Methods The GNPs were synthesized by one-step reduction of chloroauric acid by sodium citrate.The thiolated PEG and cysteine-modified cNGR were coupled to the surface of GNPs through Au-S bonds,respectively.The GNPs-PEG@cNGR was characterized by transmission electron microscopy,Zeta potential/hydration particle size analyzer,and Fourier transform infrared spectrometer.The uptake and CT imaging effect of GNPs-PEG@cNGR were assessed by human umbilical vein endothelial cells (HUVEC) and human hepatoma cells (HepG2) positively expressed for aminopeptidase N (APN/CD 13).The in vivo CT imaging effects on tumor angiogenesis and biocompatibility in mice of GNPs-PEG@cNGR were studied by BALB/c mouse model of 4T1 breast cancer.Results A specific CT molecular probe,i.e.GNPs-PEG@cNGR,was successfully constructed,which can target angiogenesis.The probe was spherical,with a hydration particle size of (35.7± 1.0) nm and a Zeta potential of (-13.54± 1.12) mV,and had good stability and biocompatibility.The GNPs-PEG@cNGR has good CT imaging results and can specifically target CD13-positive HUVEC and HepG2 cells.The CT imaging results in 4T1 breast cancer mice indicated that GNPs-PEG@cNGR could be specifically enriched in the tumor tissue after injection.The CT value of tumors in GNPs-PEG@cNGRz group was higher than that of GNPs-PEG group,and the difference was statistically significant (P＜0.05).Conclusions GNPs-PEG@cNGR can specifically target CD13 positive cells and can be used as a CT contrast agent for imaging tumor angiogenesis.

Local administration has many advantages for treating diseases. However, the surface mucus layer becomes a major obstacle that easily traps and fast removes local administrated drugs and genes in mucosal tissues. Fortunately, the rapidly developing nanocarriers with special physical and chemical properties may help to refine the treatment of mucosal tissues via delivering drugs and genes to the target tissue, and prolong the drug action time. Therefore, this review focuses on the strategies to apply different nanocarriers for drug-delivery in mucosal tissues, including mucoadhesive and mucus-penetrating types. Delivering drugs and genes to anatomical sites with high mucus turnover becomes more feasible and effective, and maintains sufficient local drug concentration to improve treatment efficacy.

Mucus is a viscoelastic and adhesive obstacle which protects vaginas, eyes and other mucosal surfaces against foreign pathogens. Numerous diseases that affect the mucosa could be afforded prophylactic and therapeutic treatments with fewer systemic side effects if drugs and genes could be sufficiently delivered to the target mucosal tissues. But drugs and genes are trapped effectively like other pathogens and rapidly removed by mucus clearance mechanism. The emergence of micro- and nano-delivery technologies combined with the realization of non-invasive and painless administration routes brings new hope for the treatment of disease. For retained drugs and genes to mucosal tissues, carriers must increase retention time in the mucus to make full contact with epithelial cells and be transported to target tissues. This review focuses on the current development of micro- and nano-carriers to improve the localized therapeutic efficiency of targeted and sustained drug and gene delivery in mucosal tissues.

We developed activatable ultrasmall gold nanorods (AUGNRs) to realize “off–on” switched fluorescence imaging-guided efficient PTT.

Objective To develop a fluorescence signal activatable multifunctional molecular probe with theranostics function through combining ultrasmall gold nanorods(UGNRs) with fluorescein,and to evaluate its therapeutic effect on photothermal therapy (PTT) in breast cancer cells.Methods The UGNRs were synthesized by the one-pot seedless method,then the functionalized modification of UGNRs were conducted using cysteamine.Finally,the activatable ultrasmall gold nanorods (AUGNRs) were synthesized by amide condensation of —NH2 of cysteamine and —COOH of carboxylated fluorescein CyS.The cell uptake ability and GSH-mediated imaging ability of AUGNRs were studied using breast cancer 4T1 cells.4T1 cells co-cultured with AUGNRs were irradiated with 808 nm excitation light,and the PTT effects were assessed by MTT colorimetric staining and calcein-AM/PI staining.Results The AUGNRs were synthesized successfully,which could be uptaken by 4T1 cells quickly and efficiently,and could achieve intracellular glutathione (GSH) triggered fluorescence recovery.No obvious cytotoxicity of AUGNRs to 4T1 cells was observed in the co-cultivation.Moreover,obvious PTT effects could be induced by 808 nm laser,which could effectively kill 4T1 cancer cells.Conclusion The fluorescence signals of AUGNRs can be induced by intracellular GSH,and tumor cell destruction can be achieved by 808 laser-excitated PTT effects.