Objective To systematically review the correlation of activated partial thromboplastin time (APTT) and prognosis after ECMO treatment. Methods The PubMed, EMbase, MEDLINE, CNKI, WanFang Data and VIP databases were electronically searched to collect studies on the correlation of APTT and prognosis after ECMO treatment from database inception to April 11th, 2022. Two researchers independently screened the literature, extracted data, and evaluated the risk of bias of the included studies. Meta-analysis was then performed using RevMan 5.4 software. Results A total of 22 studies, involving 2 913 patients were included. The level of APTT in the bleeding group was higher than that in the non-bleeding group during ECMO support treatment (MD=10.34, 95%CI 1.32 to 19.37, P=0.02). The APTT level in the thrombus group was lower than that in the non-thrombus group (MD=−3.58, 95%CI −5.89 to −1.27, P=0.002). The level of APTT in the death group was significantly higher than that in the survival group (MD=8.97, 95%CI 5.89 to 12.06, P<0.00001). Conclusion The APTT level of ECMO patients is closely related to the prognosis of bleeding, thrombosis and death. Due to the limited quantity and quality of the included studies, the above conclusion needs to be verified by more high-quality studies.
ObjectiveTo investigate the value of a predictive model for sentinel lymph node (SLN) metastasis after neoadjuvant therapy (NAT) based on the radiomic features from multi-modality magnetic resonance imaging (MRI) in combination with clinicopathologic data. MethodsThe clinical data and MRI images of breast cancer patients (initially diagnosed with cN0, all underwent NAT and surgical treatment) from two hospitals (Affiliated Hospital of Southwest Medical University and Suining Central Hospital) from January 2018 to September 2024, were retrospectively collected. The radiomic features from the multi-modality images, including T2-weighted short tau inversion recovery (T2STIR), diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE), were extracted and selected. The predictive models for SLN metastasis after NAT were constructed using four algorithms: LightGBM, XGBoost, support vector machine (SVM), and logistic regression (LR), in combination with clinicopathologic data. The models were evaluated for performance and interpretability using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis, and Shapley additive explanation (SHAP) analysis. ResultsA total of 236 breast cancer patients were enrolled in this study. Among them, 216 patients from the Southwest Medical University were subdivided in an 8∶2 ratio into a training set (n=173) and internal validation set (n=43), while 20 patients from the Suining Central Hospital served as the external validation set. The multivariate logistic regression analysis showed that the lymphovascular invasion [OR (95%CI)=21.215 (4.404, 102.202), P ＜0.001] and perineural invasion [OR (95%CI)=25.867 (1.870, 357.790), P=0.002] were the risk factors, while high Ki-67 expression [OR (95%CI)=0.119 (0.035, 0.404), P＜0.001] was the protective factor of SLN metastasis after NAT. The predictive models utilizing multi-modality MRI and clinicopathologic data yielded area under the ROC curve values of the internal and external validation sets of 0.750 [95%CI=(0.395, 1.000)] / 0.625 [95%CI=(0.321, 0.926)] for LightGBM, 0.878 [95%CI=(0.707, 1.000)] / 0.778 [95%CI=(0.525, 0.986)] for XGBoost, 0.641 [95%CI=(0.488, 0.795)] / 0.681 [95%CI=(0.345, 1.000)] for SVM, and 0.667 [95%CI=(0.357, 0.945)] / 0.583 [95%CI=(0.196, 0.969)] for LR. The XGBoost demonstrated the best predictive performance. Further SHAP analysis revealed that the lymphovascular invasion, T2STAR-MRI_FIRSTORDER_Minimum, and platelet were the key features influencing the predictions of the models. ConclusionThe findings of this study suggest that XGBoost prediction model based on radiomic features derived from multi-modality MRI (T2STIR, DWI, and DCE) in combination with clinicopathologic data is able to predict SLN metastasis after NAT in patients with breast cancer.