OBJECTIVE:To investigate the predictive value of MOLLI T1 mapping imaging for the longitudinal changes in Minimal Clinical Important Difference (MCID) after surgery for patients with Cervical Spondylotic Myelopathy (CSM). METHODS:A prospective study enrolled 163 surgically treated CSM patients (October 2022-October 2023). All underwent preoperative conventional MRI and MOLLI T1 mapping. The MCID was determined based on the difference in Modified Japanese Orthopedic Association (m JOA) scores before and at 3, 6, and 12 months after surgery. Patients with an MCID less than 2 were defined as the poor prognosis group, while those with an MCID of 2 or higher were defined as the good prognosis group. Patients were classified into the good prognosis and poor prognosis groups at 3, 6,12 months postoperatively. The differences in conventional MRI features and T1 values between the two groups at these time points were compared. Univariate and multivariate logistic regression identified recovery predictors, with forest plots generated. Receiver operating characteristic (ROC) curve analysis assessed diagnostic performance. RESULTS:At 3 months, (27 good vs 136 poor). Only T2WI hyperintensity differed between groups [Area under the receiver operating characteristic curve (AUC)=0.597, cut off = 0.1942; sensitivity = 40.74 %, specificity = 78.68 %]. At 6 months (60 good vs 103 poor), T1 values showed significance (AUC = 0.690, cut off = 0.3091; sensitivity = 83.33 %, specificity = 47.57 %). At 12 months (100 good vs 63 poor), T1 values demonstrated superior prediction (AUC = 0.875,cut off = 0.6389; sensitivity = 75.00 %, specificity = 88.89 %). CONCLUSION:MOLLI T1 mapping effectively predicts CSM recovery at 6,12 months postoperatively, with optimal diagnostic performance at 12 months. CRITICAL RELEVANCE STATEMENT:This study first employs MRI T1 mapping to predict MCID outcomes in CSM patients, demonstrating its high predictive value for favorable postoperative prognosis and providing a reliable imaging parameter for outcome assessment.
ObjectiveTo retrospectively analyze cases of whole-body 18F-FDG PET/CT imaging, identify abnormal images of subcutaneous nodules, and explore the clinical application value of PET/CT in detecting subcutaneous soft tissue metastases and its impact on survival.MethodAn analysis was conducted on cases from August 2019 to August 2024, and 57 cases of subcutaneous nodules were found to have positive 18F-FDG imaging, all of which underwent pathological puncture or biopsy. Collect patient histological subtypes, metastasis patterns, treatment, and survival rates. Kaplan Meier curves were used to estimate survival time, and Mantel-Cox univariate analysis was used to determine the correlation between extensive metastasis, degree of soft tissue involvement, systemic and local treatment, and survival time.ResultAmong the 57 patients confirmed by pathology, 10 were benign and 47 were malignant. Among 47 cases of malignant tumors, 39 cases were postoperative cases, 8 cases were preoperative cases, and 4 cases had unknown primary lesions. A total of 88 subcutaneous lesions were found, including 28 patients with a single lesion, 12 patients with two lesions, and 8 patients with three or more lesions. One patient with unknown primary lesion reported abdominal wall nodules as the main symptom. PET/CT assisted in qualitative localization in 11 cases. Univariate analysis showed that the median survival time (22 months) of patients who did not receive local radiotherapy was significantly shorter than that of patients who did receive local radiotherapy (60 months) (P<0.05). There is no correlation between gender, metastasis pattern, metastasis at initial diagnosis, use of chemotherapy, and survival time.ConclusionWhole body PET/CT imaging has important application value in the diagnosis of subcutaneous soft tissue metastasis. By comprehensively evaluating the patient’s overall condition and accurately determining whether there is a simple soft tissue metastasis, local intervention measures can bring more favorable prognosis for patients with good general condition and only soft tissue metastasis.
To develop and validate a machine learning (ML) model which combined computed tomography (CT) semantic and radiomics features to preoperatively predict Ki-67 expression in gastrointestinal stromal tumors (GISTs) patients. We retrospectively collected the clinical, imaging and pathological data of 149 GISTs patients. We randomly assigned the patients in a ratio of 7:3 to a training set (104 cases) and a validation (45 cases) set. We divided the patients into low and high Ki-67 expression group according to postoperative pathology. CT semantic features were analyzed from preoperative enhancement CT images and radiomics features were extracted from venous phase-enhanced images. We used intraclass correlation coefficient, maximal relevance and minimal redundancy and least absolute shrinkage and selection operator method to screen radiomics features and build radiomics label. 6 ML models were used for model construction. Receiver operating characteristic curves were used to evaluate the predictive efficiency of ML models. SHAP analysis was used to explain the contribution of different variables and their risk threshold. AUC of radscores in predicting Ki-67 expression of GIST patients were 0.749 and 0.729 in training and validation set. Among the 6 ML models, SVM exhibited best prediction accuracy. AUC of SVM model in predicting Ki-67 expression of GIST patients were 0.840, 0.767 and 0.832 in training, validation and test set. SHAP analysis showed that radscores and tumor diameter had highly positive contribution to the model. Therefore, the interpretable SVM model can predict Ki-67 expression of GISTs patients individually before surgery, which can provide reliable imaging biomarkers for clinical treatment decisions.
Rationale and Objective To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years. Materials and Methods In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model. Conclusion PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.
Objective To investigate the diagnostic value of combined 99Tcm-DX lymphoscintigraphy and CT lymphangiography (CTL) in primary chylopericardium. Methods ༚Fifty-five patients diagnosed with primary chylopericardium clinically were retrospectively analyzed. 99Tcm-DX lymphoscintigraphy and CTL were performed in all patients. Primary chylopericardium was classified into three types, according to the 99Tcm-DX lymphoscintigraphy results. The evaluation indexes of CTL include: (1) abnormal contrast distribution in the neck, (2) abnormal contrast distribution in the chest, (3) dilated thoracic duct was defined as when the widest diameter of thoracic duct was > 3 mm, (4) abnormal contrast distribution in abdominal. CTL characteristics were analyzed between different groups, and P < 0.05 was considered a statistically significant difference. Results Primary chylopericardium showed 12 patients of type Ⅰ, 14 patients with type II, and 22 patients with type III. The incidence of abnormal contrast distribution in the posterior mediastinum was greater in type I than type III (P = 0.003). The incidence of abnormal contrast distribution in the pericardial and aortopulmonary windows, type I was greater than type III (P = 0.008). And the incidence of abnormal distribution of contrast agent in the bilateral cervical or subclavian region was greater in type II than type III (P = 0.002). Conclusion The combined application of the 99Tcm-DX lymphoscintigraphy and CTL is of great value for the localized and qualitative diagnosis of primary chylopericardium and explore the pathogenesis of lesions.
