With the widespread application of low-dose computed tomography (LDCT) screening and increased awareness of health examinations, the detection rate of pulmonary ground-glass nodules (GGN) has been increasing, some of which are confirmed as lung cancer after surgery. Visceral pleural invasion (VPI) is an important adverse prognostic factor in non-small cell lung cancer. In recent years, whether pleura-attached GGN lung adenocarcinoma also exhibits VPI has become a focal of attention and research, but the viewpoints and conclusions remain inconsistent. Based on domestic and international literature and the authors’ long-term clinical observations, this article provides a commentary on the related issues and research progress.
Objective To investigate the correlation between quantitative parameters of dynamic contrast-enhanced MRI (DCE-MRI) and the Ki-67 labeling index (Ki-67 LI) in sinonasal malignant neoplasm (SNM), and to evaluate the value of DCE-MRI quantitative parameters in assessing the biological behavior of SNM. Methods A total of 86 patients with SNM confirmed by postoperative pathology were retrospectively collected. All patients underwent routine MRI and DCE-MRI before surgery. The Tofts two-compartment pharmacokinetic model was used to calculate DCE-MRI quantitative parameters, including volume transfer constant (Ktrans), rate constant (kep), and extracellular extravascular volume fraction (ve). Postoperative pathological specimens were analyzed using Ki-67 immunohistochemical staining. Using a cutoff value of 50% for Ki-67 LI, patients were divided into a low Ki-67 LI group (41 cases) and a high Ki-67 LI group (45 cases). According to different MRI scanners used, patients were divided into a training set (49 cases) and a validation set (37 cases). Comparisons between groups were performed using the t-test, Mann-Whitney U test, or chi-square test. Spearman correlation analysis was used to evaluate the correlation between DCE-MRI quantitative parameters and Ki-67 LI. Quantitative parameters with statistically significant differences between groups were included in multivariate logistic regression analysis to construct a predictive model for Ki-67 LI. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the model, and the area under the curve (AUC) was calculated. Results In both the training and validation sets, Ktrans and kep values in the high Ki-67 LI group were higher than those in the low Ki-67 LI group (all P<0.05), while there was no statistically significant difference in ve values between the two groups (both P>0.05). Ktrans and kep were positively correlated with Ki-67 LI (both P<0.05), whereas ve showed no correlation with Ki-67 LI (P>0.05). Multivariate logistic regression analysis demonstrated that Ktrans and kep were independent predictors of high Ki-67 LI in SNM, and a predictive model for Ki-67 LI was constructed based on these parameters. ROC curve analysis showed that the AUC of the predictive model in the training set was 0.735 (95%CI: 0.589-0.881), with a sensitivity of 75.0% and specificity of 71.4%; in the validation set, the AUC was 0.765 (95%CI: 0.607-0.922), with a sensitivity of 64.7% and specificity of 85.0%. The model demonstrated good performance (AUC>0.73 in both sets). Conclusion The predictive model based on DCE-MRI quantitative parameters Ktrans and kep can be used to evaluate the biological behavior of SNM.
Objective To evaluate the agreement and correlation between esophageal hiatus surface area (HSA) measured using computed tomography (CT)-based multiplanar reconstruction combined with double-oblique correction and intraoperative HSA measurements in patients with hiatal hernia, and to establish a predictive model for postoperative complications. Method A retrospective analysis was conducted in 198 patients who underwent hiatal hernia repair. HSA was quantified using CT post-processing techniques and compared with intraoperative measurements. According to hernia subtype, patients were categorized into a sliding hernia group (127 cases) and an other-type hernia group (71 cases), including paraesophageal hernia, mixed hernia, and giant hernia. Bland-Altman analysis and Spearman correlation analysis were performed to assess the agreement and correlation between CT-derived and intraoperatively measured HSA. Based on the time of diagnosis, patients were further divided into a training cohort (110 cases) and an internal validation cohort (88 cases). Binary logistic regression analysis was performed in the training cohort to identify independent predictors of postoperative complications. A multivariable predictive model was subsequently constructed and validated. The predictive performance of individual predictors and the combined model was assessed using the area under the receiver operating characteristic curve (AUC). Differences in predictive performance were compared using the DeLong test. Calibration curves and decision curve analysis were used to evaluate model calibration and clinical utility. Results In the sliding hernia group, CT-derived HSA demonstrated a strong correlation with intraoperative measurements (ρ=0.84, P<0.001) and good agreement, with limits of agreement ranging from -0.95 to 1.40. In the training cohort, binary logistic regression analysis identified age, smoking history, CT-measured HSA, and intraoperative HSA as independent predictors of postoperative complications (all P<0.05). A combined predictive model incorporating these variables achieved superior predictive performance compared with individual predictors alone, with an AUC of 0.88 in the training cohort and 0.87 in the validation cohort. Calibration and decision curve analyses further demonstrated good consistency between predicted and observed probabilities, as well as favorable clinical net benefit. Conclusion CT-derived HSA demonstrates strong correlation and agreement with intraoperative measurements. A predictive model integrating CT-derived HSA with clinical risk factors, including age and smoking history, may provide valuable information for risk stratification and prevention of postoperative complications in patients with hiatal hernia.
Objective To explore the value of a nomogram model based on MRI apparent diffusion coefficient (ADC) combined with clinical parameters for the noninvasive preoperative prediction of lymphovascular invasion (LVI) in gastric cancer. Methods Clinical and imaging data of 196 patients with surgically and pathologically confirmed gastric cancer were retrospectively collected, including age, sex, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), LVI status, American Joint Committee on Cancer (AJCC) stage, TNM stage, and Borrmann classification. Patients were randomly divided into a training set (136 cases) and a validation set (60 cases) at a ratio of 7∶3. According to pathological LVI status, the training set was further divided into an LVI-positive group (68 cases) and an LVI-negative group (68 cases). MRI images were analyzed in the picture archiving and communication system (PACS), and ADC parameters including minimum ADC value (ADCmin), mean ADC value (ADCmean), and maximum ADC value (ADCmax) were recorded. Relative ADC values (rADCmin, rADCmean, and rADCmax) were also calculated. Independent-samples t test, Mann-Whitney U test, Chi-square test, and Fisher’s exact test were used to compare differences between the two groups. Variables with statistically significant differences were included in multivariate logistic regression analysis using the backward stepwise method to identify independent predictors of LVI and construct a nomogram model. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC). The Hosmer-Lemeshow test and calibration curves were used to assess goodness-of-fit and calibration of the model, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Results Multivariate logistic regression analysis showed that CA19-9, AJCC stage, and ADCmin were independent predictors of LVI in gastric cancer (all P<0.05), and a nomogram prediction model was constructed based on these variables. ROC curve analysis showed that the nomogram model achieved good predictive performance for LVI in both the training and validation sets, with AUCs of 0.820 and 0.821, respectively. In the training set, the sensitivity, specificity, and accuracy of the model for predicting gastric cancer LVI were 61.8%, 94.1%, and 77.9%, respectively. The calibration curves and Hosmer-Lemeshow test demonstrated good model fit. DCA confirmed that the model provided clinical net benefit across a wide range of threshold probabilities. Conclusion The nomogram prediction model based on CA19-9, AJCC stage, and ADCmin can accurately and noninvasively predict the risk of LVI in gastric cancer preoperatively and may provide a basis for individualized treatment decision-making.
Objective To investigate the value of contrast-enhanced ultrasound (CEUS) time-intensity curve (TIC) parametric imaging in assisting physicians with different levels of experience in improving the diagnostic performance for breast focal lesions. Methods Clinical and imaging data of 98 female patients with breast tumors who underwent ultrasound examination and had pathological confirmation by biopsy or surgery were retrospectively collected. Forty-nine benign lesions and 49 malignant lesions confirmed by pathology were included as the benign group and malignant group, respectively. Two junior physicians and two senior physicians independently analyzed the CEUS and TIC parametric images of all cases using two protocols to determine whether the lesions were benign or malignant. Protocol A included conventional two-dimensional ultrasound and CEUS images, whereas Protocol B included conventional two-dimensional ultrasound, CEUS images, and TIC parametric images. Fisher’s exact test was used to compare differences in TIC parametric image classifications. Kappa analysis was performed to assess the diagnostic consistency of junior and senior physicians using different imaging methods. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of different imaging methods for breast focal lesions by physicians with different levels of experience, and the area under the curve (AUC) was calculated. Differences in AUC values were compared using the DeLong test. Results TIC parametric image classification results evaluated by physicians with different levels of experience showed that benign lesions were mainly classified as completely dispersed type and predominantly dispersed type, whereas malignant lesions were mainly classified as completely concentrated type and predominantly concentrated type. Consistency analysis showed that the diagnostic consistency of both junior and senior physicians was significantly improved after using Protocol B (all P<0.05). DeLong test results showed that Protocol B improved the diagnostic performance of both junior and senior physicians (all P<0.05). The diagnostic performance of junior physicians using Protocol B was similar to that of senior physicians using Protocol A (P>0.05). Conclusion CEUS combined with TIC parametric imaging can assist physicians with different levels of experience in improving the differential diagnostic ability for breast focal lesions and may provide a reference for clinical decision-making.
Objective To investigate the feasibility and clinical application value of further subclassifying MRI Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions into 4A, 4B, and 4C, and to compare its diagnostic performance with ultrasound (US) and mammography (MG). Methods A total of 220 patients with breast lesions were retrospectively included. All patients underwent US, MG, and breast MRI examinations and obtained pathological results by surgery or biopsy. A total of 225 lesions were classified as MRI BI-RADS category 4. According to a series of reports from the Society of Breast Imaging (SBI) and the Radiological Society of North America (RSNA), the lesions were further subclassified into MRI BI-RADS 4A, 4B, and 4C. Using pathological results as the gold standard, the malignancy rates of each subcategory were calculated and compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of US BI-RADS, MG BI-RADS, and MRI BI-RADS classifications for breast lesions, and the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Differences in AUC values were compared using the DeLong test. Results There were 42, 53, and 130 lesions classified as MRI BI-RADS 4A, 4B, and 4C, respectively, among which 5, 41, and 127 lesions were pathologically diagnosed as malignant. The corresponding malignancy rates were 11.9%, 77.4%, and 97.7%, respectively. Pairwise comparisons among the subcategories showed statistically significant differences (P<0.016). The malignancy rate was lowest in category 4A and highest in category 4C. MRI BI-RADS classification had the highest accuracy and sensitivity for differentiating benign from malignant lesions (91.11% and 97.11%, respectively), whereas MG BI-RADS classification had the highest specificity (86.54%). The AUC values of MRI BI-RADS, US BI-RADS, and MG BI-RADS were 0.910 (95%CI: 0.867-0.953), 0.871 (95%CI: 0.824-0.917), and 0.806 (95%CI: 0.748-0.863), respectively. MRI BI-RADS showed higher diagnostic performance than MG BI-RADS (Z=3.407, P=0.001), while its performance was comparable to that of US BI-RADS (Z=1.402, P=0.161). Conclusion Subcategorization of MRI BI-RADS category 4 lesions is feasible and has important clinical application value. It can provide useful diagnostic evidence for differentiating benign from malignant breast lesions classified as BI-RADS category 4.
Objective To quantitatively assess the degree of supraspinatus tendon injury and supraspinatus muscle fatty infiltration in patients with rotator cuff injury using T2 mapping and iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) sequences, and to analyze their relationship with age. Methods Sixty patients with rotator cuff injury showing supraspinatus tendon injury on conventional MRI (20 cases each with grades 1-3) and 20 healthy controls were retrospectively collected. According to the degree of injury, subjects were divided into four groups: grade 1 group (tendinitis), grade 2 group (partial tear), grade 3 group (complete tear), and healthy control group (grade 0), with 20 shoulders included in each group. All subjects underwent shoulder MRI, including conventional sequences, oblique coronal T2 mapping sequence, and oblique sagittal IDEAL-IQ sequence. T2 mapping values of the medial, middle, and lateral subregions of the supraspinatus tendon and the fat fraction (FF) of the supraspinatus muscle were measured. Kruskal-Wallis H test was used to compare differences among groups, and Spearman correlation analysis was performed to evaluate correlations of the parameters with injury grade and age. Results With increasing supraspinatus tendon injury grade, T2 mapping values in all subregions of the supraspinatus tendon and FF values of the supraspinatus muscle showed increasing trends, and the differences among the four groups were statistically significant (all P<0.05). T2 mapping values in all subregions of the supraspinatus tendon (medial: rs=0.966; middle: rs=0.963; lateral: rs=0.959) and supraspinatus muscle FF values (rs=0.968) were significantly positively correlated with supraspinatus tendon injury grade (all P<0.001). Injury grade (rs=0.800), T2 mapping values in all tendon subregions (medial: rs=0.756; middle: rs=0.800; lateral: rs=0.759), and supraspinatus muscle FF values (rs=0.772) were also significantly positively correlated with age (all P<0.001). Conclusion T2 mapping and IDEAL-IQ sequences can quantitatively assess supraspinatus tendon injury and the degree of fatty infiltration. T2 mapping values in all supraspinatus tendon subregions and supraspinatus muscle FF values are significantly positively correlated with injury grade and age. The combined application of these two MRI techniques can provide an objective and reliable imaging basis for clinical decision-making.
Objective To investigate the value of a CT radiomics model based on 18F-FDG PET/CT in detecting peritoneal malignant lesions. Methods Clinical data of 98 patients with suspected peritoneal malignant lesions who underwent whole-body 18F-FDG PET/CT examination and had pathological results were retrospectively collected. According to pathological results, the patients were divided into a peritoneal malignant lesion group (58 cases) and a non-malignant lesion group (40 cases). Pearson correlation coefficients and least absolute shrinkage and selection operator (LASSO) regression were used to select CT radiomics features, and a support vector machine (SVM) algorithm was used to establish the radiomics model. Five-fold cross-validation was performed for model training and validation. Clinical data, CT features, PET/CT parameters, and radiomics score (Radscore) with statistically significant differences between the two groups were analyzed using multivariate logistic regression to identify independent risk factors, and the clinical model, CT model, and PET model were constructed. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models, and the area under the curve (AUC) was calculated. The optimal cutoff value of the radiomics model was obtained, and the sensitivity and specificity of the radiomics model were calculated. The DeLong test was used to compare differences in AUC values among models, and decision curve analysis (DCA) was used to evaluate the clinical net benefit of the models. Results Multivariate logistic regression analysis showed that sex, abdominal distension, anorexia, ascites, peritoneal thickening, SUVmax, SUVmean, and Radscore were independent risk factors for diagnosing peritoneal malignant lesions. The clinical model was constructed using patient sex, abdominal distension, and anorexia; the CT model was constructed using ascites and peritoneal thickening; and the PET model was constructed using SUVmax and SUVmean. The diagnostic performance of the radiomics model was higher than that of the clinical model, CT model, and PET model (all P<0.05). The optimal cutoff value of the radiomics model was 0.296, with sensitivity and specificity of 0.983 and 1.000, respectively. DCA showed that the radiomics model achieved a higher clinical net benefit when the threshold probability was >0.130. Conclusion The CT radiomics model based on 18F-FDG PET/CT demonstrates excellent performance in diagnosing peritoneal malignant lesions and is significantly superior to the clinical model, the CT model based on 18F-FDG PET/CT, and the PET model.
Objective To investigate the association between imaging findings on three-dimensional fast fluid-attenuated inversion recovery (3D FLAIR) and three-dimensional real reconstruction inversion recovery (3D real IR) MRI sequences and clinical therapeutic efficacy in patients with unilateral sudden sensorineural hearing loss (SSNHL). Methods A total of 87 patients with unilateral SSNHL who underwent inner ear MRI examinations including 3D FLAIR and 3D real IR sequences were retrospectively enrolled. On 3D real IR images, the signal intensities of the cochlea in the affected and contralateral ears, as well as the signal intensity of the medulla oblongata, were measured, and the cochlea-to-medulla (CM) signal intensity ratios were calculated. Meanwhile, changes in high-signal-intensity areas in the affected ear were evaluated on the 3D FLAIR sequence. According to pure-tone audiometry results one month after treatment, patients were divided into an effective group (51 cases) and an ineffective group (36 cases). Independent-samples t tests were used to compare CM values of the affected ear and the contralateral ear, as well as the affected-to-contralateral CM ratio, between the two groups. The chi-square test was used to compare the distribution differences of clinical and imaging data (sex, affected ear, degree of hearing loss, type of hearing loss, and changes in high-signal-intensity areas) between groups. Spearman correlation analysis was performed to assess the relationships between the affected-to-contralateral CM ratio, changes in high-signal-intensity areas, and therapeutic efficacy. Multivariate logistic regression analysis was conducted to identify factors influencing ineffective treatment outcomes in unilateral SSNHL. Results The cochlear signal intensity and CM ratio of the affected ear were significantly higher than those of the contralateral ear (P<0.05). The CM values of the contralateral ear and affected ear, as well as the affected-to-contralateral CM ratio, were all significantly higher in the ineffective group than in the effective group (all P<0.05). Spearman correlation analysis showed a significant negative correlation between the affected-to-contralateral CM ratio and therapeutic efficacy (r=-0.786, P<0.001). Significant differences in changes of high-signal-intensity areas were observed between the two groups (P<0.001): the effective group was predominantly characterized by signal normalization or absorption, whereas the ineffective group mainly showed non-absorbed signals. Changes in high-signal-intensity areas were also negatively correlated with therapeutic efficacy (r=-0.430, P<0.001). Multivariate logistic regression analysis indicated that the affected-to-contralateral CM ratio and changes in high-signal-intensity areas were independent risk factors for ineffective treatment (both P<0.05). Conclusion An increased affected-to-contralateral CM ratio and persistent abnormal high-signal-intensity areas are indicative of poor therapeutic outcomes. MRI findings on 3D FLAIR and 3D real IR sequences are correlated with treatment efficacy in patients with unilateral SSNHL and constitute independent influencing factors for ineffective treatment. These imaging parameters may serve as potential reference indicators for evaluating prognosis and therapeutic efficacy in SSNHL.
Cerebral small vessel disease (cSVD) is a common cerebrovascular disorder, and MRI is the primary method for evaluating its imaging features. However, conventional MRI has limitations such as the lack of quantitative standards and strong subjectivity. Currently, deep learning (DL) technology provides a novel solution for the automated, quantitative, and high-precision interpretation of MRI features in cSVD. This article reviews the latest research progress of DL technology in the interpretation of MRI imaging features in cSVD, including its application in recent small subcortical infarcts, lacunes of presumed vascular origin, white matter hyperintensities, perivascular spaces, cerebral microbleeds, brain atrophy and total cSVD burden assessment, and further analyzes the current challenges and prospects.
Amide proton transfer (APT) is a magnetic resonance imaging technique based on chemical exchange saturation transfer (CEST). It enables noninvasive quantification of tissue protein concentration by detecting chemical exchange between amide protons in proteins and peptides and free water molecules. In breast disease research, APT technology is mainly used to differentiate benign from malignant breast lesions, evaluate responses to neoadjuvant chemotherapy, and analyze correlations with prognostic indicators such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67. This article reviews the current application and challenges of APT technology in the diagnosis and treatment of breast diseases.
Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma, and its tumor heterogeneity severely affects treatment response and prognostic assessment. This review summarizes the molecular pathological mechanisms of intratumoral and intertumoral spatial heterogeneity in ccRCC, including spatial heterogeneity of genes and related proteins, immunity and treatment response heterogeneity, and metabolic heterogeneity. In addition, the advances in CT, MRI, and molecular imaging for detecting and quantifying tumor spatial heterogeneity are reviewed. Furthermore, the impact of tumor heterogeneity on prognostic assessment and individualized treatment is discussed.
Radiomics provides a non-invasive and objective approach for the precision diagnosis and treatment of bladder cancer by enabling the high-throughput extraction of quantitative features from medical images and has been widely applied in tumor diagnosis, staging, grading, treatment response assessment, and prognosis prediction. This article reviews the research progress of radiomics in bladder cancer, including prognostic assessment (covering recurrence risk and survival analysis), prediction of treatment response, prediction of molecular subtyping (e.g., Ki-67 and HER2), determination of muscle invasion status and pathological grading, as well as prediction of lymph node metastasis. In addition, current challenges and future directions are discussed.
X-ray parameter measurement is an important basis for the diagnosis, treatment planning, and postoperative follow-up of musculoskeletal diseases. Recently, artificial intelligence has been widely used in X-ray imaging for the identification of bony anatomical structures, segmentation, and the automatic measurement of parameters such as angles, distances, and alignment. It has demonstrated good measurement consistency and efficiency in the extremities and spine. This article briefly introduces the main methods and model architectures of artificial intelligence in X-ray parameter measurement of the musculoskeletal system, and reviews its current application status and development directions.
18F-FDG PET/CT has high diagnostic value in the assessment of primary lesion, determination of regional lymph node metastasis, and detection of distant metastatic lesions in non-small cell lung cancer (NSCLC). Its various metabolic parameters can provide supplementary functional assessment for TNM staging, helping to improve staging accuracy and prognostic prediction. This article reviews the diagnostic role, incremental value, and technical limitations of 18F-FDG PET/CT in the clinical TNM staging of NSCLC.
Objective To evaluate the differential diagnostic value of the gyriform infiltration sign on T2-FLAIR sequence for adult supratentorial non-enhancing molecular glioblastomas. Methods Thirteen patients with non-enhancing molecular glioblastoma and 38 patients with diffuse lower-grade glioma were retrospectively included. Clinical and imaging data of the two groups were analyzed. All patients underwent preoperative conventional and contrast-enhanced MRI. Two radiologists, blinded to clinical information and pathological results, independently evaluated the MRI images of 51 patients, focusing on whether the gyriform infiltration sign was present on T2-FLAIR images. Independent-sample t-test and chi-square test were used to compare differences in clinical characteristics and imaging features between the two groups, and to assess the diagnostic value of the gyriform infiltration sign for molecular glioblastomas. Results The gyriform infiltration sign was observed in 8 of 13 patients (62%) with molecular glioblastoma and 1 of 38 patients (3%) with diffuse lower-grade glioma. The difference in the incidence of this sign between the two groups was statistically significant (χ2=21.13, P<0.01). The sensitivity of the gyriform infiltration sign for diagnosing molecular glioblastomas was 62% (95%CI: 35.1%-82.3%), and the specificity was 97% (95%CI: 86.2%-99.5%). Conclusion The gyriform infiltration sign is a relatively specific imaging feature of molecular glioblastoma. It can help differentiate non-enhancing molecular glioblastoma from diffuse lower-grade glioma and improve preoperative diagnostic accuracy.
Objective To explore the value of late gadolinium enhancement (LGE) cardiac MR imaging for assessing subclinical cardiac involvement in patients with idiopathic inflammatory myopathy (IIM) without a history of heart disease. Methods Twenty-eight patients without a history of heart disease and clinically diagnosed with IIM were prospectively enrolled and underwent LGE cardiac MR imaging. Left ventricular functional parameters were measured, and myocardial LGE characteristics were evaluated. According to the presence or absence of LGE, patients were divided into an LGE-positive group and an LGE-negative group. Independent-samples t-test, Mann-Whitney U test, or Fisher’s exact test was used to compare baseline characteristics and left ventricular functional parameters between the two groups. Results Among the 28 patients, 7 (25%) showed positive myocardial LGE, predominantly with a non-ischemic distribution involving the mid-wall and subepicardial regions, whereas 21 patients showed negative LGE findings. Disease duration in the LGE-positive group was longer than that in the LGE-negative group (P<0.05). There were no statistically significant differences between the two groups in left ventricular end-diastolic volume, end-systolic volume, left ventricular ejection fraction, age, cardiac troponin T (cTnT), creatine kinase (CK), creatine kinase-MB (CK-MB), CK-MB/CK >3%, or N-terminal pro-brain natriuretic peptide (all P>0.05). Conclusion Myocardial fibrosis is not uncommon in IIM patients without a history of heart disease and with preserved left ventricular ejection fraction, and is associated with longer disease duration. LGE cardiac MR imaging may help to early identify subclinical cardiac involvement in patients with IIM.
Objective To investigate the clinical and imaging characteristics of popliteal artery adventitial cystic disease (PACD), in order to improve the understanding of this disease. Methods The imaging data of one patient with lower limb ischemia caused by pathologically confirmed PACD were retrospectively analyzed, including ultrasound, computed tomography angiography (CTA), and magnetic resonance imaging (MRI), and relevant literature was reviewed. Results On ultrasound, PACD manifested as irregular hypoechoic areas within the popliteal artery. On CTA, it manifested as a non‑enhancing cystic mass causing arterial occlusion. On MR proton density weighted imaging and T2WI, the lesion showed high signal intensity, on T1WI, the lesion showed low signal intensity, with internal septations visible, demonstrating typical multilocular cystic features. Conclusion PACD has characteristic imaging findings on ultrasound, CT, and MRI. Recognition of these imaging features is helpful for making a definite diagnosis and is beneficial for differentiating it from other occlusive diseases of the popliteal artery.
