Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To compare 99Tc m-hydrazinonicotinamide-(polyethylene glycol) 4-E((polyethylene glycol) 4-c((Arg-Gly-Asp)fK)) 2(3PRGD 2) SPECT/CT with 18F-FDG PET/CT in the evaluation of patients with oral malignancy and cervical lymph node metastases. Methods:From January 2019 to October 2019, 22 patients (16 males, 6 females; age (56.9±9.8) years) with oral malignancy who underwent both 99Tc m-3PRGD 2 SPECT/CT and 18F-FDG PET/CT at the First Affiliated Hospital of Fujian Medical University were retrospectively analyzed. The pathology was used as the gold standard, and McNemar test was used to compare the diagnostic efficacy of the two imaging methods for cervical lymph node metastases. ROC curve analysis was performed to determine the diagnostic performance for lymph node metastases. The correlation between 99Tc m-3PRGD 2 uptake and integrin α vβ 3 expression was analyzed by Spearman rank correlation analysis. Results:Both imaging modalities identified all primary tumors. Diagnostic efficiency analyses based on patient, neck side, nodal region and lymph node all showed that there were no significant differences in the sensitivity, specificity and accuracy between the two imaging modalities in diagnosing cervical metastatic lymph nodes ( χ2 values: 0-3.36, all P>0.05). The AUC of SUV max in metastatic lymph nodes detected by 99Tc m-3PRGD 2 and 18F-FDG imaging were 0.825 and 0.855, with the sensitivity, specificity and accuracy of 71.9%(23/32), 93.9%(92/98), 88.5%(115/130) and 78.1%(25/32), 93.9%(92/98), 90.0%(117/130), respectively ( χ2 values: 0.05-0.10, all P>0.05). SUV max of 99Tc m-3PRGD 2 in primary tumors and cervical metastatic lymph nodes were positively correlated with the expression of integrin α vβ 3 ( rs values: 0.58, 0.51, P values: 0.019, 0.013). Conclusion:99Tc m-3PRGD 2 SPECT/CT is a valuable diagnostic tool for oral malignancy and cervical lymph node metastases, which is comparable to 18F-FDG PET/CT.

Objective:To investigate the value of habitat radiomic features based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI in establishing a predictive model for cytokeratin 19 (CK19) expression in hepatocellular carcinoma (HCC) and to evaluate its role in prognostic risk stratification.Methods:This multicenter case-control study retrospectively enrolled 489 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA-enhanced MRI between June 2016 and June 2024. Among them, 346 patients from the First Affiliated Hospital of Soochow University were divided into a training cohort ( n=245) and an internal test cohort ( n=101) via stratified sampling at a 7∶3 ratio. And 143 patients from Nantong Third Hospital Affiliated to Nantong University served as an external validation cohort. The training cohort included 53 CK19-positive and 192 CK19-negative patients. The internal test cohort included 21 CK19-positive and 80 CK19-negative patients. The external validation cohort included 30 CK19-positive and 113 CK19-negative patients. Univariate logistic regression analysis was performed to identify potential factors associated with CK19 expression, and a clinical-radiologic model was constructed. The k-means clustering algorithm was applied to segment target HCC lesions into 3 subregions. Radiomic features were extracted and selected from these habitat subregions. Habitat radiomics models were constructed for the arterial phase (AP), portal venous phase, hepatobiliary phase (HBP), and combined phases (CP). Multivariate logistic regression analysis identified independent clinical and radiologic predictors of CK19 expression, and the optimal habitat model score was integrated to build a clinical-radiologic-habitat combined model. The area under the receiver operating characteristic curve (AUC) was used to evaluate model predictive performance. Recurrence-free survival (RFS) was analyzed using the Kaplan-Meier method and the differences in survival curves were compared with the log-rank test. Results:Univariate logistic regression analysis revealed that alpha-fetoprotein (AFP) ( OR=2.629, 95% CI 1.412-4.896, P=0.002), AP enhancement ( OR=3.636, 95% CI 1.642-8.052, P=0.001), AP peritumoral enhancement ( OR=2.219, 95% CI 1.084-4.542, P=0.029), and HBP peritumoral hypointensity ( OR=2.010, 95% CI 1.004-4.021, P=0.049) were potential factors associated with CK19 expression, which were incorporated into the clinical-radiologic model. In the internal and external validation cohorts, the AUC of the clinical-radiologic model was 0.690 (95% CI 0.590-0.778) and 0.650 (95% CI 0.565-0.727), respectively. The habitat radiomics model based on CP images demonstrated the highest performance. It achieved AUC of 0.729 (95% CI 0.622-0.836) and 0.725 (95% CI 0.607-0.842) in the internal and external validation cohorts, respectively. Multivariate analysis identified AFP ( OR=2.494, 95% CI 1.163-5.348, P=0.019), AP enhancement ( OR=5.230, 95% CI 1.868-14.643, P=0.002) and habitat radiomics model score ( OR=4.105, 95% CI 2.643-6.368, P<0.001) as independent predictors of CK19 positivity. Based on these factors, a combined clinical-radiologic-habitat combined model was established. The clinical-radiologic-habitat combined model achieved AUCs of 0.767 (95% CI 0.671-0.846) and 0.730 (95% CI 0.649-0.801) in the internal and external validation cohorts, respectively. Significant differences in RFS were observed between the CK19-positive group (25.1 month) and CK19-negative group (51.0 month) as predicted by the clinical-radiologic-habitat model ( χ2=4.17, P=0.041). Conclusion:The clinical-radiologic-habitat combined model based on Gd-EOB-DTPA-enhanced MRI habitat radiomics demonstrates good predictive performance for CK19 expression in HCC and offers valuable prognostic stratification for clinical practice.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objective:To compare 99Tc m-hydrazinonicotinamide-(polyethylene glycol) 4-E((polyethylene glycol) 4-c((Arg-Gly-Asp)fK)) 2(3PRGD 2) SPECT/CT with 18F-FDG PET/CT in the evaluation of patients with oral malignancy and cervical lymph node metastases. Methods:From January 2019 to October 2019, 22 patients (16 males, 6 females; age (56.9±9.8) years) with oral malignancy who underwent both 99Tc m-3PRGD 2 SPECT/CT and 18F-FDG PET/CT at the First Affiliated Hospital of Fujian Medical University were retrospectively analyzed. The pathology was used as the gold standard, and McNemar test was used to compare the diagnostic efficacy of the two imaging methods for cervical lymph node metastases. ROC curve analysis was performed to determine the diagnostic performance for lymph node metastases. The correlation between 99Tc m-3PRGD 2 uptake and integrin α vβ 3 expression was analyzed by Spearman rank correlation analysis. Results:Both imaging modalities identified all primary tumors. Diagnostic efficiency analyses based on patient, neck side, nodal region and lymph node all showed that there were no significant differences in the sensitivity, specificity and accuracy between the two imaging modalities in diagnosing cervical metastatic lymph nodes ( χ2 values: 0-3.36, all P>0.05). The AUC of SUV max in metastatic lymph nodes detected by 99Tc m-3PRGD 2 and 18F-FDG imaging were 0.825 and 0.855, with the sensitivity, specificity and accuracy of 71.9%(23/32), 93.9%(92/98), 88.5%(115/130) and 78.1%(25/32), 93.9%(92/98), 90.0%(117/130), respectively ( χ2 values: 0.05-0.10, all P>0.05). SUV max of 99Tc m-3PRGD 2 in primary tumors and cervical metastatic lymph nodes were positively correlated with the expression of integrin α vβ 3 ( rs values: 0.58, 0.51, P values: 0.019, 0.013). Conclusion:99Tc m-3PRGD 2 SPECT/CT is a valuable diagnostic tool for oral malignancy and cervical lymph node metastases, which is comparable to 18F-FDG PET/CT.

Objective:To establish a long-term mortality rate prediction model for patients aged 60 years and over with atrial fibrillation and coronary heart disease using the machine learning method, and identify the corresponding risk factors of mortality.Methods:In this retrospective cohort study, a total of 329(11 cases lost of follow-up)patients with 183 males(55.6%)and 146 females(44.4%), aged(77.8±7.3)years, and 142 patients aged 80 years or older(43.2%)were selected in our hospitals from January 2013 to March 2015.And their clinical data on atrial fibrillation and coronary heart disease were analyzed.They were divided into the death group(151 cases)and the survival group(167 cases)according to the survival outcome.In addition, 60 patients aged 60 years and over admitted to our hospitals from April to July 2015 with atrial fibrillation and coronary heart disease were selected as external data validation set.The clinical data included age, gender, body mass index, diagnosis, co-morbidity, laboratory indicators, electrocardiogram, echocardiogram, treatment data.These patients were followed up for at least 6 years, and the main adverse cardiovascular and cerebrovascular events(MACCE), including death, were recorded.Finally, the data of the enrolled patients were randomly divided into the training set and the test set according to the ratio of 9∶1, Different models were established to predict the long-term mortality of patients with atrial fibrillation and coronary heart disease by machine learning algorithm.The optimal model was established by substituting external data(60 cases)into the model for verification and comparison.The top 20 risk factors for mortality were determined by Shapley additive explanation(SHAP)algorithm.Results:A total of 329 hospitalized patients were included in this study, the overall median follow-up time was 77.0 months(95% CI: 54.0~84.0), 11 cases lost during follow-up(3.3%), and 151 cases died(45.9%). The analysis found that the areas under the ROC curve for a support vector machine(SVM)model, k-Nearest Neighbor(KNN)model, decision tree model, random forest model, ADABoost model, XGBoost model and logistic regression model were 0.76, 0.75, 0.75, 0.91, 0.86, 0.85 and 0.81, respectively.The random forest model had the highest prediction efficiency, with the accuracy of 0.789 and F1 value of 0.806, which was better than the logistic regression model[the Area Under Receiver Operating Characteristic Curve(AUC): 0.91 vs.0.81, P<0.05]. D-dimer, age, number of MACCE, left ventricular ejection fraction, serum albumin level, anemia, New York Heart Association(NYHA)grade, history of old myocardial infarction, estimated glomerular filtration rate(eGFR)and resting heart rate were important risk factors for predicting long-term mortality. Conclusions:The random forest model based on machine learning method can predict the long-term mortality of patients with atrial fibrillation and coronary heart disease aged 60 years and over, have a good identification ability.Its accuracy is higher than that of the traditional Logistic regression model.Reducing the long-term mortality and improving the long-term outcomes can be achieved by intervening on D-dimer levels, correcting hypoproteinemia and anemia, improving cardiac function and controlling resting ventricular rates.

OBJECTIVETo explore the genetic etiology for 11 sporadic patients with neurofibromatosis type 1.

METHODSChip targeting capture and high-throughput sequencing were employed to detect potential mutations of NF1 and NF2 genes among the 11 patients. The data was filtered through multiple mutational databases and in-house whole exome sequence database. Sanger sequencing was used for analysis of family members of the patients.

RESULTSEleven pathogenic variants were found among the 11 patients, which included two splicing mutations, one missense mutation, two nonsense mutations, and six frame-shifting mutations. None of the mutations was recorded by the public database or the in-house database generated from 1775 samples through whole exome sequencing. None of the unaffected parents carried the same mutation. Seven mutations were associated with neurofibromatosis type 1 previously, while the remaining four were discovered for the first time. Prenatal diagnosis of two high-risk pregnancies suggested that neither fetus has inherited the NF1 mutation from their affected parents.

CONCLUSIONIdentification of causative mutations in patients with sporadic-type neurofibromatosis type 1 has provided a basis for genetic counseling. The four novel mutations have enriched the spectrum of NF1 gene mutations.

Objective To prepare 99 Tcm-HYNIC-c( isoDGRKy) as a SPECT/CT imaging molecu-lar probe targeting integrin αvβ3 , and evaluate its biodistribution and feasibility on SPECT/CT imaging for integrinαvβ3-positive tumor in U87MG human glioma xenograft mouse models. Methods The bifunctional chelator HYNIC was conjugated to c( isoDGRKy) , and tricine and TPPTS were used as coligands for 99 Tcm labeling to prepare 99 Tcm-HYNIC-c( isoDGRKy) . The radiochemical purity and stability of the product were measured. The expression of integrin αvβ3 and binding affinity ( half maximal inhibitory concentration, IC50 ) of c ( isoDGRKy ) was detected in U87MG cells by cell experiments in vitro. Biodistribution and SPECT/CT imaging of 99 Tcm-HYNIC-c( isoDGRKy) including blocking experiments were performed respec-tively in nude mice bearing U87MG human glioma xenografts. Results The radiochemical purity of 99 Tcm-HYNIC-c( isoDGRKy) was over 99%, and was still over 99% after 4 h incubation in saline at room temper-ature. Flow cytometry assay showed that U87MG cells were integrinαvβ3-positive ( expressive rate:70%) . The IC50 of c(isoDGRKy) was 6.67×10-8 mol/L. Biodistribution results showed 99Tcm-HYNIC-c(isoDGRKy) with a rapid clearance from blood was excreted mainly via the kidneys. The 99 Tcm-HYNIC-c( isoDGRKy) uptake values in U87MG tumors were (7.31±1.42) and (1.09±0.11) %ID/g at 15 and 45 min post-injection re-spectively, and tumor-to-muscle ratio reached 5.01±1.47 at 15 min post-injection. The tumors were clearlyvisualized with low background from 0.5 to 1 h post-injection in tumor bearing mice. In the blocking experi-ment, the tumor was barely visualized after co-injection of excess cold c(RGDfK) peptide with 99Tcm-HYNIC-c(isoDGRKy). Conclusions 99Tcm-HYNIC-c(isoDGRKy) may be easily and steadily prepared. It may be a RGD-like promising SPECT/CT imaging probe for integrinαvβ3-positive tumor.

Objective To prepare a novel dual-modality imaging probe based on Cerasome nano-materials, and evaluate its in vivo biodistribution and pharmacokinetic properties. Methods ICG encapsu-lated Cerasome was modified with chelating agent DOTA for 111 In-labeling. Normal mice firstly were used for in vivo studies. Animals were sacrificed at different time points after tail vein administration, blood samples were taken and the organs of interest were captured to evaluate the pharmacokinetic properties and in vivo biodistribution of 111 In-ICG-DPDCs. The subcutaneous Lewis lung carcinoma ( LLC ) tumor model in C57BL/6 mouse was established. The tumor-bearing mice were subjected to optical imaging in small animal IVIS and SPECT imaging in small animal nanoScanSPECT/CT system for tumor uptake of 111 In-ICG-DPDCs. Results The size of the nanoparticle probe was about 90 nm, and the 111 In-labeling was successfully per-formed with 99.93% radiochemical purity after purification. 111 In-ICG-DPDCs showed excellent in vitro sta-bility with 97.10% radiochemical purity at 48 h post-purification. In vivo blood clearance experiments showed that 111 In-ICG-DPDCs had a relative long blood circulation time with the fast and slow phase half-lives of 40 and 132.7 min. 111In-ICG-DPDCs accumulated mainly in the liver and spleen, with long retention time. NanoScanSPECT/CT imaging showed that LLC tumors were significantly visualized at 4 h post-injection, and the other major accumulated organs were the liver and spleen, which were consistent with the results of biodistribution. Optical imaging showed significant uptake of the nanoparticle probe in the tumor, confirming the SPECT imaging results. Conclusion The Cerasome based probe designed could be used for tumor SPECT and optical dual-modality imaging, and has potential for therapeutic use.