ObjectiveTo explore the value of a multimodal MRI-based radiomics nomogram for differentiating human epidermal growth factor receptor-2 （HER-2） negative breast cancer molecular subtypes.MethodsA retrospective analysis was conducted on 190 patients with HER-2 negative breast cancer who underwent multimodal MRI examination， and the patients were divided into two molecular subtype groups： a HER-2 low expression group （n=108） and a HER-2 zero expression group （n=82）. The cases were randomly stratified and sampled at a ratio of 7∶3 and divided into a training set of 133 cases and a testing set of 57 cases. The clinical and radiological features of the patients were collected， the radiomics features based on T2-weighted imaging （T2WI）， diffusion-weighted imaging （DWI）， and dynamic contrast-enhanced （DCE）-MRI were extracted， and the clinical-radiological model， unimodal radiomics model， multimodal radiomics model， and combined model were constructed respectively. Then the nomogram combined multimodal radiomics signature （radsocre） with clinical-radiological features was used to construct a visualized predictive model， and the area under the curve （AUC） was used to compare the effectiveness of different models in distinguishing HER-2 low expression and zero expression subtypes.ResultsA significant difference in radscore was demonstrated between the HER-2 low and HER-2 zero expression groups in both the training （P<0.000 1） and testing sets （P<0.01）. The AUC of the multimodal radiomics model in the training set and the testing set were 0.914 and 0.836， respectively， which was superior to any unimodal radiomics model. The nomogram demonstrated great diagnostic efficacy （AUC=0.930 in training set； AUC=0.865 in testing set）.ConclusionA multimodal MRI-based nomogram incorporating radsocre and clinical-radiological features can accurately distinguish the subtypes of HER-2 negative breast cancer.

ObjectiveTo evaluate the feasibility of various strength levels of deep learning image reconstruction （DLIR） algorithms for improving non-contrast abdominal CT image quality at ultra-low radiation doses， by comparing ultra-low-dose DLIR images with low-dose filtered back projection （FBP） images. MethodsA prospective collection of 85 patients undergoing non-contrast abdominal CT scans was performed， and a self-controlled study method was employed to conduct low-dose （LD） group and ultra-low-dose （ULD） group scans. The LD group used a noise index of 10 and employed FBP for image reconstruction （LD-FBP group）. The ULD group used a noise index of 30 and employed DLIR at different levels （low， medium， high）， resulting in three subgroups of reconstructed images： ULD-DLIR-L， ULD-DLIR-M， and ULD-DLIR-H. For each group， CT values， standard devia-tion （SD）， signal-to-noise ratio （SNR）， and contrast-to-noise ratio （CNR） were measured and calculated for the liver， spleen， kidneys， aorta， psoas major， and subcutaneous fat. Effective dose （ED） was also recorded. Two radiologists independently performed subjective evaluations of image quality using a 5-point scale. ResultsCompared with the LD-FBP group， the ULD-DLIR-L group showed significantly lower SNR and CNR values in the liver， spleen， kidneys， aorta， and psoas major （P<0.001）， while the ULD-DLIR-H group exhibited significantly higher values （P<0.001）. The difference of SNR and CNR values for the ULD-DLIR-M group showed no statistically significant difference. For subjective evaluation， the scores of the ULD-DLIR-L and ULD-DLIR-M groups were lower than those of the LD-FBP group， while there was no statistically significant difference in scores between the ULD-DLIR-H group and the LD-FBP group. The ED value of the ULD group was approximately 88% lower than that of the LD group. ConclusionCompared with the LD-FBP group， the ULD-DLIR-H group significantly reduces SD values while increasing SNR and CNR values， effectively improving the image quality of non-contrast abdominal CT scans.

Objective: To explore the value of nomogram based on DCE-MRI radiomics combined with clinical-ra- diological features in predicting HR status in breast cancer with Her-2 low expression． Methods: A total of 198 pa- tients of Her-2 low expression breast cancer who underwent standardized breast MRI in our hospital from January 2019 to February 2025 were retrospectively analyzed．Patients were divided into HR ( + ) group ( n = 137) and HR ( －) group ( n = 61) ．The cases were divided into a training set ( 138 cases) and a testing set ( 60 cases) in a 7 ∶ 3 ratio．Clinical-radiological model was based on clinical and traditional radiological features，radiomics model was based on DCE-MRI，and combined model was constructed，respectively．The nomogram was drawn，and re- ceiver operating characteristic curve was used to compare the performance of different models in predicting HR sta- tus． Results: The DCE-MRI radiomics score ( Radscore) between the HR ( + ) group and the HR ( －) group showed statistical differences in both the training and testing sets ( both P＜0. 001) ．The AUC of the clinical-radio- logical model based on lesion mobility，Ki67，TIC type，enhancement pattern and maximum diameter for predicting HR status in the training set and testing set were 0. 643 and 0. 616，respectively．The AUC of the DEC-MRI ra- diomics model in the training set and testing set were 0. 897 and 0. 860，respectively．The nomogram drawn by combining clinical-radiological features and Radscore showed better predictive performance in both the training set ( AUC = 0. 913) and testing set ( AUC = 0. 898) than the clinical-radiological model ( all P＜0. 05) ． Conclusion The nomogram combined by DCE-MRI radiomics and clinical-radiological features can effectively predict HR sta-tus of breast cancer with low Her-2 expression，which is helpful to the building of individualized treatment plan for breast cancer patients．

Objective To explore the diagnostic value of amide proton transfer weighted imaging(APTWI)in conjunction with prostate-specific antigen density(PSAD)for detecting clinically significant prostate cancer(csPCa)within prostate imaging reporting and data system(PI-RADS)v2.13-5 grade lesions.Methods A retrospective analysis was conducted on the clinical and imaging data of 88 patients diagnosed with PI-RADS 3-5 grade prostate lesions.There were 59 patients with prostate cancer(PCa)and 29 with benign prostate lesion(BPL).The PCa group was divided into csPCa group(44 cases)and clinically insignificant prostate cancer(ciPCa)group(15 cases)according to Gleason score(GS).Spearman rank correlation analysis was used to analyze the correlation between APTWI-related parameters and GS in PCa.Comparative analyses were conducted to identify statistical discrepancies in APTWI and prostate-specific antigen(PSA)-related parameters across various groups.Subsequently,both solitary and combined diagnostic models were developed,and the receiver operating characteristic(ROC)curve were utilized to evaluate the diagnostic efficacy.Results APTmax and APTmean were moderately positively correlated with GS(r=0.683,r=0.705,respectively),and APTmin was weakly positively correlated with GS(r=0.547).APTWI and PSA-related parameters were significantly higher in the PCa group than in the BPL group,and APTmin had the highest efficacy in diagnosing PCa[area under the curve(AUC)=0.855].APTWI and PSA-related parameters differed among the BPL,ciPCa and csPCa groups(P＜0.05).Among the groups,statistically significant differences were observed in each parameter of APTWI and PSA-related indices between the BPL group and the csPCa group,as well as between the ciPCa group and the csPCa group(P＜0.05).In contrast,only APTmin and PSAD exhibited significant differ-ences between the BPL group and the ciPCa group(P＜0.05).The results of the combined diagnosis showed that APTmin+PSAD had the highest diagnostic efficacy for diagnosing PCa(AUC=0.899),and APTmean+PSAD had the highest diagnostic efficacy for diagnosing csPCa(AUC=0.838).Conclusion In PI-RADS 3-5 grade prostate lesions,APTWI and PSA-related parameters are statisti-cally different in the BPL,ciPCa,and csPCa groups.Notably,the combination of APTmean and PSAD exhibit the highest diagnostic efficacy for csPCa.

Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Objective To explore the diagnostic value of amide proton transfer weighted imaging(APTWI)in conjunction with prostate-specific antigen density(PSAD)for detecting clinically significant prostate cancer(csPCa)within prostate imaging reporting and data system(PI-RADS)v2.13-5 grade lesions.Methods A retrospective analysis was conducted on the clinical and imaging data of 88 patients diagnosed with PI-RADS 3-5 grade prostate lesions.There were 59 patients with prostate cancer(PCa)and 29 with benign prostate lesion(BPL).The PCa group was divided into csPCa group(44 cases)and clinically insignificant prostate cancer(ciPCa)group(15 cases)according to Gleason score(GS).Spearman rank correlation analysis was used to analyze the correlation between APTWI-related parameters and GS in PCa.Comparative analyses were conducted to identify statistical discrepancies in APTWI and prostate-specific antigen(PSA)-related parameters across various groups.Subsequently,both solitary and combined diagnostic models were developed,and the receiver operating characteristic(ROC)curve were utilized to evaluate the diagnostic efficacy.Results APTmax and APTmean were moderately positively correlated with GS(r=0.683,r=0.705,respectively),and APTmin was weakly positively correlated with GS(r=0.547).APTWI and PSA-related parameters were significantly higher in the PCa group than in the BPL group,and APTmin had the highest efficacy in diagnosing PCa[area under the curve(AUC)=0.855].APTWI and PSA-related parameters differed among the BPL,ciPCa and csPCa groups(P＜0.05).Among the groups,statistically significant differences were observed in each parameter of APTWI and PSA-related indices between the BPL group and the csPCa group,as well as between the ciPCa group and the csPCa group(P＜0.05).In contrast,only APTmin and PSAD exhibited significant differ-ences between the BPL group and the ciPCa group(P＜0.05).The results of the combined diagnosis showed that APTmin+PSAD had the highest diagnostic efficacy for diagnosing PCa(AUC=0.899),and APTmean+PSAD had the highest diagnostic efficacy for diagnosing csPCa(AUC=0.838).Conclusion In PI-RADS 3-5 grade prostate lesions,APTWI and PSA-related parameters are statisti-cally different in the BPL,ciPCa,and csPCa groups.Notably,the combination of APTmean and PSAD exhibit the highest diagnostic efficacy for csPCa.

Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

As the continuation of the left ventricle,the left atrium and left ventricle interact and play an important role in the function of the whole heart.At present,there are many techniques to evaluate the atrial structure and function,but the left atrial structure is complex and the myocardium is thin,which brings some challenges to the relevant evaluation.This paper introduces the parameters,precautions and relevant clinical applications in the process of left atrial evaluation from the aspects of myocardial strain and delayed enhancement.

Purpose To investigate the clinical value of artificial intelligence(AI)quantitative parameters in predicting the invasion degree of lung adenocarcinoma with diameter≤2 cm of ground-glass density.Materials and Methods A total of 80 patients with lung adenocarcinoma with diameter≤2 cm ground-glass density confirmed by pathology from March 2019 to April 2022 were retrospectively analyzed.A total of 90 nodules were rerolled,including 8 adenocarcinomas in situ(AIS),34 minimally invasive adenocarcinomas(MIA)and 48 invasive adenocarcinomas(IAC).They were divided into the experimental group(IAC)and the control group(AIS and MIA).The differences of the AI quantitative parameters such as volume,three-dimensional length diameter,maximum area,maximum CT value,minimum CT value and average CT value were compared between two groups,and the predictive values of AI quantitative parameters for the invasion degree of lung adenocarcinoma was evaluated.Results There were statistically significant differences with age,volume,three-dimensional length diameter,maximum area,maximum CT value and average CT value between the two groups(all P<0.05),but no statistically significant differences in gender and minimum CT value(both P>0.05).Binary Logistic regression analysis showed that the three-dimensional length diameter(odd ratio=2.020,P=0.034)and the maximum CT value(odd ratio=1.008,P=0.013)were independent predictors for lung adenocarcinoma with diameter≤2 cm of ground-glass density.The regression model based on the three-dimensional length diameter and the maximum CT value had the best predictive performance,and its AUC was 0.901.When the critical value was 2.432,its sensitivity and specificity were 93.75%and 71.43%,respectively.Conclusion AI quantitative parameters have a high value in predicting the degree of invasion of lung adenocarcinoma with diameter≤2 cm of ground-glass density,and the combined model with three dimensional long diameter and maximum CT value has the highest diagnostic efficiency.

Objective:To explore the optimal acceleration factor and feasibility of the compressed SENSE (CS) technique in non-contrast MR coronary angiography (NMRCA) for clinical practice.Methods:The image data of completed coronary CTA and 3.0 T NMRCA sequence in 31 patients with suspected coronary heart disease were prospectively recruited at Fuyang People′s Hospital from August 2021 to November 2021. NMRCA sequences included conventional SENSE2 sequence and CS sequences with acceleration factors of 4, 5, and 6, respectively. The subjective scores of image quality and the objective scores, the contrast ratios between assessed coronaries and myocardium (CMCR) were compared among the 4 groups using the Friedman and Wilcoxon rank sum test.Results:Compared with the conventional SENSE2 [(343±46)s], the scan time of CS4 (269±36), CS5 (214±29) and CS6 (178±26) s were shortened by 21.5%, 37.5% and 48.0%, respectively. There was a good consistency between the subjective scores of the four groups (Kappa=0.769, 95% Cl 0.738-0.800). There was no significant difference in subjective score and CMCR value between CS4 and SENSE2 ( P>0.05). The coronary artery segments of CS5 and CS6 were significantly different from SENSE2 group ( P<0.05). Conclusions:For 3.0 T NMRCA, CS technology shows high feasibility. The CS4 can reduce imaging time while ensuring high-quality coronary arterial images, which has a well-established clinical application value for NMRCA.