Purpose To investigate the clinical value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)-enhanced MRI for the preoperative evaluation of colorectal cancer liver metastases(CRCLM).Materials and Methods Fifty-six CRCLM patients with 156 CRCLM lesions confirmed by surgical pathology in the First Affiliated Hospital of Henan University of Chinese Medicine from September 2019 to March 2023 were collected,and all underwent dynamic Gd-EOB-DTPA-enhanced MRI.The characteristic manifestations of T2WI,diffusion-weighted imaging(DWI),arterial phase and hepatobiliary phase(HBP)were observed,and the detection rate of each sequence was calculated,and then the signal intensity ratio of lesions to liver parenchyma on HBP and the apparent diffusion coefficient(ADC)were calculated.The ADC values of lesions with reversed target and target signs and lesions with homogeneous and heterogeneous hypointensity on HBP and the detection rate of each sequence were compared.Results Among 156 CRCLM lesions,20.51%(32/156)and 38.46%(60/156)exhibited a target appearance on T2WI,51.28%(80/156)displayed a target sign on DWI,73.72%(115/156)showed rim enhancement on the arterial phase,and 34.62%(54/156)presented a target sign on HBP.The mean ADC value of lesions with reversed target and target signs on HBP did not significantly differ from that of lesions with homogeneous and heterogeneous hypointensity on HBP[(0.98±0.43)×10-3 mm2/s vs.(1.01±0.47)×10-3 mm2/s;t=-0.340,P=0.327].Based on the size of CRCLM lesions,three groups were categorized,including<1.0 cm(41 lesions),1.0-2.0 cm(55 lesions),and>2.0 cm(60 lesions).The overall detection rate of HBP(96.79%)was the highest compared with T2WI,DWI and Gd-EOB-DTPA four-phase dynamic contrast-enhanced multiphase imaging(P<0.05).Regarding<1.0 cm lesions,the detection rate of HBP(87.80%)was superior to that of T2WI,DWI and Gd-EOB-DTPA four-phase dynamic contrast-enhanced multiphase imaging(P<0.05).Conclusion Gd-EOB-DTPA-enhanced MRI has important clinical value for the preoperative evaluation of CRCLM,especially the features of target sign or reversed target sign on HBP and the excellent efficacy of detecting microscopic lesions.

Purpose To construct and validate a machine learning-based diagnostic model for distinguishing between Mycobacterium avium-intracellular complex pulmonary disease(MAC-PD)and pulmonary tuberculosis(PTB)via chest CT images.Materials and Methods Retrospective data from patients diagnosed with MAC-PD and PTB between May 2021 and August 2022 at Beijing Chest Hospital,Capital Medical University,which were collected as the training set.The prospective external validation set was obtained from patients at the First Affiliated Hospital of Henan University of Chinese Medicine between September 2022 and May 2023.Clinical and radiological data were analyzed,and multivariable logistic regression,random forest and support vector machine(SVM)models were established and externally validated using the validation set.The diagnostic performance of models were evaluated using receiver operating characteristic curve and precision-recall curve,and the differences of the areas under the curve of various models were compared via the Delong test.Results There were significant differences in age and hemoptysis rate between the two groups(t=30.414,P<0.001;χ2=6.186,P=0.013).There were statistically significant differences in cavity types and morphology between the two groups(χ2=6.546,P=0.011;χ2=24.113,P<0.001),but there was no significant difference in the distribution and characteristics of cavitary lesions(P>0.05).There were significant differences in the types and distribution of bronchiectasis between the two groups(χ2=4.634,P=0.031;χ2=23.145,P<0.001).Compared with logistic regression and random forest models,the SVM model had better differential diagnostic performance,and the area under the receiver operating characteristic curve,sensitivity,specificity,accuracy,positive predictive value and negative predictive value were 0.960(95%CI 0.935-0.985),85.7%,93.6%,90.5%,93.3%,88.0%and 0.885(95%CI 0.803-0.967),respectively,76.7%,80.0%,78.3%,79.3%,77.4%.The precision-recall curve showed that the SVM model had high precision and low recall,that was,the model performs well.Conclusion The machine learning-based models exhibits excellent diagnostic performance and can assist in differentiating MAC-PD and PTB.