Objective:To evaluate the diagnostic value of multimodal Nomogram model combining 18F-FDG PET/CT and ultrasound for triple negative breast cancer (TNBC) . Methods:A total of 61 breast cancer patients admitted at Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from November 2016 to May 2024 were selected as the study subjects, including 12 cases of TNBC and 49 cases of non-TNBC. 18F-FDG PET/CT metabolic parameters maximum standardized uptake value (SUV max), mean standardized uptake value (SUV mean), minimum standardized uptake value (SUV min), tumor metabolic volume (MTV), and total lesion glycolysis (TLG), as well as the ultrasound parameters long diameter, short diameter, echogenicity, morphology, boundaries, posterior echogenicity, aspect ratio, microcalcifications, blood flow grading and Breast Imaging Reporting and Data System (BI-RADS) grading were compared between patients with and without TNBC. Least absolute shrinkage and selection operator (LASSO) regression was used for feature screening, and binary multivariate logistic regression analysis was conducted on the screened variables to obtain the independent influencing factors for diagnosing TNBC. The independent factors influencing the diagnosis of TNBC were established as Nomogram model and visualized. Receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to evaluate the diagnostic efficacy, accuracy and clinical practicability of the model, respectively. Results:There were statistically significant differences in SUV max ( Z=-2.43, P=0.015), SUV mean ( Z=-2.54, P=0.011), morphology ( P=0.004), boundaries ( χ2=4.86, P=0.028), posterior echogenicity ( P=0.027), and blood flow grading ( χ2=4.52, P=0.034) between TNBC and non-TNBC patients. LASSO regression screened out three variables: SUV max, morphology and blood flow grading. Multivariate analysis showed that, SUV max ( OR=1.20, 95% CI: 1.04-1.38, P=0.012), morphology ( OR=0.02, 95% CI: 0.01-0.49, P=0.016), and blood flow grading ( OR=0.06, 95% CI: 0.01-0.74, P=0.028) were the independent influencing factors for diagnosing TNBC. A Nomogram model was established based on the above independent influencing factors. ROC curve showed that, area under the curve (AUC) of SUV max, morphology, blood flow grading, and the Nomogram model were 0.73 (95% CI: 0.60-0.83), 0.66 (95% CI: 0.52-0.77), 0.67 (95% CI: 0.54-0.79), 0.90 (95% CI: 0.79-0.96), respectively, and the diagnostic value of the Nomogram model was higher than that of SUV max ( Z=2.71, P=0.007), morphology ( Z=3.61, P<0.001), and blood flow grading ( Z=2.51, P=0.012) alone. Calibration curve and DCA showed better accuracy and clinical practicability of the Nomogram model. Conclusions:Nomogram model constructed by combining the SUV max of 18F-FDG PET/CT with the morphology and blood flow grading of ultrasound has a promising potential for diagnosing TNBC.

Objective:To investigate the value of 18F-fluorodeoxyglucose ( 18F-FDG) PET/CT in the differential diagnosis of primary intestinal diffuse large B-cell lymphoma (PIDLBCL) and colon cancer in the ileocecal region. Methods:A total of 42 patients with ileocecal tumors admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from June 2013 to December 2023 were selected as the study objects, including 17 cases of PIDLBCL and 25 cases of colon cancer. General data and 18F-FDG PET/CT parameters were compared between patients with PIDLBCL and colon cancer in the ileocecal region. Binary logistic regression was used to analyze the independent influencing factors for the differential diagnosis of PIDLBCL and colon cancer in the ileocecal region. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic efficacy of independent influencing factors. Results:There were statistically significant differences in tumor length diameter ( Z=-2.63, P=0.009), maximum thickness ( Z=-3.26, P=0.001), ileal involvement ( χ2=6.04, P=0.014), intestinal dilation ( χ2=10.38, P=0.001), maximum standardized uptake value (SUV max) ( Z=-3.73, P<0.001), SUV mean ( Z=-3.40, P<0.001), metabolic tumor volume ( Z=-2.37, P=0.018) and total lesion glycolysis ( Z=-2.93, P=0.003) between patients with PIDLBCL and colon cancer in the ileocecal region. Multivariate analysis showed that SUV max ( OR=1.16, 95% CI: 1.04-1.31, P=0.011) and intestinal dilation ( OR=6.64, 95% CI: 1.13-39.10, P=0.036) were both independent influencing factors for the differential diagnosis of PIDLBCL and colon cancer in the ileocecal region. ROC curve analysis showed that, the areas under the curve of SUV max and intestinal dilation for the differential diagnosis of PIDLBCL and colon cancer in the ileocecal region were 0.84 (95% CI: 0.70-0.94) and 0.73 (95% CI: 0.58-0.86), respectively. The optimal cut-off value for SUV max was determined to be 19.14, with a sensitivity of 70.6% and a specificity of 88.0%, while intestinal dilation exhibited a sensitivity of 58.8% and a specificity of 88.0%. Conclusions:18F-FDG PET/CT can be used for the differential diagnosis of PIDLBCL and colon cancer in the ileocecal region, and SUV max and intestinal dilation have high diagnostic efficacy.

Objective:To investigate the value of conventional ultrasound combined with shear wave elastography (SWE) in the differential diagnosis of non-mass ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC) . Methods:A total of 102 patients with non-mass breast cancer admitted to Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School from March 2019 to April 2022 were selected as the study objects, including 32 cases of DCIS and 70 cases of IBC. Conventional ultrasound parameters echo, microcalcification, location, posterior echo, blood flow, axillary lymph node, breast imaging reporting and data system (BI-RADS) score and SWE-related parameters maximum shear wave velocity (SWV max), minimum shear wave velocity (SWV min), mean shear wave velocity (SWV mean) and median shear wave velocity (SWV median) were compared between patients with non-mass DCIS and IBC. Binary logistic regression was used to analyze the independent factors for the differential diagnosis of non-mass DCIS and IBC. Based on the results of multivariate analysis, a nomogram prediction model was constructed and the predictive efficacy of the prediction model was evaluated by receiver operator characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and practicability of the model. Results:There were statistically significant differences in blood flow ( χ2=8.47, P=0.004), axillary lymph nodes ( χ2=9.11, P=0.003), SWV max ( Z=-3.32, P<0.001), SWV mean ( t=3.00, P=0.003), SWV median ( Z=-2.69, P=0.007) between patients with non-mass DCIS and IBC. Multivariate analysis showed that, blood flow ( OR=3.56, 95% CI: 1.28-9.89, P=0.015), axillary lymph nodes ( OR=3.04, 95% CI: 1.10-8.42, P=0.032) and SWV max ( OR=1.40, 95% CI: 1.13-1.73, P=0.002) were independent factors for distinguishing non-mass DCIS from IBC. A nomogram prediction model was constructed based on blood flow, axillary lymph nodes and SWV max. ROC curve analysis showed that, the area under the curve of blood flow, axillary lymph nodes, SWV max, and prediction model for differential diagnosis of non-mass DCIS and IBC were 0.64 (95% CI: 0.52-0.76), 0.66 (95% CI: 0.55-0.77), 0.71 (95% CI: 0.60-0.81), and 0.79 (95% CI: 0.70-0.88), respectively, and the differential diagnostic value of prediction model was higher than that of blood flow ( Z=2.92, P=0.004), axillary lymph nodes ( Z=2.94, P=0.003), and SWV max ( Z=1.88, P=0.060) alone. The C-index of the prediction model for the differential diagnosis of non-mass DCIS and IBC was 0.77, and the calibration curve showed that the prediction probability of the prediction model was close to the actual probability. DCA showed that this prediction model could provide higher clinical net benefit and had certain clinical practicability. Conclusion:Blood flow and axillary lymph nodes in conventional ultrasound parameters and SWV max of SWE-related parameters are independent factors in the differential diagnosis of non-mass DCIS and IBC. The nomogram prediction model constructed by this method has a high value in the differential diagnosis of non-mass DCIS and IBC.

Objective:To establish and validate a nomogram model for early prediction of the risk of acute pancreatitis (AP) progressing to severe acute pancreatitis (SAP).Methods:CT signs and clinical laboratory parameters of 361 AP patients admitted to our Hospital from January 2016 to July 2022 were retrospectively collected. There were 221 males (61.2%) and 140 females (38.8%). According to the Atlantic score, all patients were divided into the SAP group (64 cases) and the non-SAP (NSAP) group (297 cases). Univariate analysis was used to screen out variables with statistically significant differences. Multivariate Logistic regression analysis was used to screen out the independent risk factors of SAP, and finally a nomogram prediction model was established. Receiver operating characteristic (ROC) curve, calibration curve and decision curve (DCA) were used to evaluate the predictive efficacy, accuracy and clinical practicability of the model, and Bootstrap method was used to verify the model internally.Results:Univariate analysis and multivariate Logistic regression analysis showed that pleural effusion ( OR=7.353, 95% CI: 3.344-16.170), posterior pararenal space (PPS) involvement ( OR=3.149, 95% CI: 1.314-7.527), serum creatinine concentration (Cr) ( OR=1.027, 95% CI: 1.017-1.038) and serum calcium concentration (Ca 2+) ( OR=0.038, 95% CI: 0.009-0.166) were independent risk factors for SAP ( P<0.05). A Nomogram model was established based on these four factors. The area under the ROC curve (AUC) of this model was 0.905 (95% CI: 0.869-0.933), indicating high predictive efficiency. Internal verification showed that the model had good accuracy in predicting SAP, and C-index was 0.90. DCA analysis showed that the model had high clinical practicability. Conclusions:The Nomogram model combining pleural effusion, PPS involvement, Cr and Ca 2+ had a good effect on early prediction of SAP, which could provide a new reference tool for clinical diagnosis and treatment.

From 1987 to 2017, cardiovascular disease (CVD) had been ranking the first cause of death in Suzhou, and the mortality rate showed an upward trend annual percentage changes (APC=0.62%, P=0.001), while the standardized mortality rate showed a downward trend (APC=-2.65%, P<0.001). The probability of premature death of CVD declined consistently from 7.06% in 1987 to 2.00% in 2017 (APC=-4.45%, P<0.001). When the life expectancy was set at 70, the potential years of life lost rate (PYLLR) decreased from 6.35‰ in 1987 to 3.30‰ in 2017, and the standardized PYLLR decreased from 7.30‰ to 2.68‰. When the life expectancy was set at 75, the PYLLR decreased from 10.12‰ to 5.19‰, and the standardized PYLLR decreased from 11.44‰ to 3.88‰. With the increase of years, all PYLLR and standardized PYLLR showed a significantly downward trend (APC=-2.51%--3.89%, P<0.001).

Developing an acoustic radiation force excitation module including 64 channels based in FPGA for ultrasound elastography. The circuit of the module was derived in bipolar, and the parameters such as excitation frequency, pulse repetition frequency, pulse number, element number and focus depth were adjustable. The acoustic field for special parameter was experimented with OptiSon laser acoustic field system with a result which reflects the width of focal spot is about 3 mm. The acoustic power was experimented with RFB2000 radiation force balance with a result which reflects acoustic power is increasing linearly with the number of pulses and the number of elements, and is increasing squarely with the peak-to-peak value of excitation voltage. The module is promising in factual application which can be triggered externally in synchronously, and can be combined with B-mode ultrasound system for ultrasound elastography.
Acoustics ; Elasticity Imaging Techniques ; Ultrasonics

Developing an acoustic radiation force excitation module including 64 channels based in FPGA for ultrasound elastography. The circuit of the module was drived in bipolar, and the parameters such as excitation frequency, pulse repetition frequency, pulse number, element number and focus depth were ajustable. The acoustic field for special parameter was experimented with OptiSon laser acoustic field system with a result which reflects the width of focal spot is about 3 mm. The acoustic power was experimented with RFB2000 radiation force balance with a result which reflects acoustic power is increasing linearly with the number of pulses and the number of elements, and is increasing squarely with the peak-to-peak value of excitation voltage. The module is promising in factual application which can be triggered external y in synchronously, and can be combined with B-mode ultrasound system for ultrasound elastograpy.

Based on LCD Module and Visual C++ development environment, this paper proposes a new method which can quickly develop the human-machine interface .We define a LCD module programming interface by designing Serial Communication Class(SCS). On this basis,we achieve the transplantation on an Embedded ARM Platform to fulfil the requirements of Medical Diagnostic Instruments (MDI). Experimental results show that this method has advantages of short development cycle and high level transplantation which has broad application prospects in the field of Medical Diagnosis Instrument.
Diagnosis, Computer-Assisted ; instrumentation ; Equipment Design ; Liquid Crystals ; Robotics ; instrumentation ; methods ; Software ; User-Computer Interface