Jie Tian’s research while affiliated with China University of Mining and Technology-Beijing and other places

Patients with diabetes mellitus (DM) have an increased risk of in stent restenosis (ISR). Neovascularization (NV) is considered as a unique pathophysiology factor of ISR in diabetic patients. However, the restructuring patterns of in vivo human coronary NV and their relationship with ISR, especially in diabetic patients remain unclear. In this study, we aimed to investigate the NV structure differentiations between patients with and without DM after coronary stent implantation using optical coherence tomography (OCT). We included 136 patients with ISR (70 patients in DM group and 66 patients in non-DM group) who underwent OCT during coronary angiography follow-up. NVs were manually segmented, after which three-dimensional (3D) rendering of OCT images was conducted. NVs greater than 1 mm in length were classified as longitudinal running or coral tree types based on their 3D structures. NV structures were compared between DM and non-DM patients. The prevalence of the coral tree pattern NV in the DM group was 2.14-fold higher than in the non-DM group(p = 0.012). 47.14% of patients in the DM group and 51.51% of patients in the non-DM group presented longitudinal running NV (p = 0.610). The number of coral tree pattern NV was relatively higher in DM patients than in the non-DM patients (p = 0.019). However, the number of longitudinal running NV showed no difference between the two groups (p = 0.872). The normalized NV volume was significantly larger in the DM group (p = 0.008). Patients with coral tree pattern NV have thinner minimum fibrous cap thickness (p = 0.030). DM was the risk factor for coral tree pattern NV formation in ISR lesions after adjustment for other factors. NV with specific restructuring patterns, such as longitudinal running and coral tree patterns, can be identified in ISR lesions. NV with a coral tree pattern, characterized by higher leakiness and immaturity, is more commonly found in patients with DM and is associated with tissue instability in ISR. Accurate and feasible imaging modalities for NV might offer promising opportunities to evaluate NV and prevent progression of ISR in diabetic patients.

The diagnosis of lymph node metastasis (LNM) is essential for colorectal cancer (CRC) treatment. The primary method of identifying LNM is to perform frozen sections and pathologic analysis, but this method is labor-intensive and time-consuming. Therefore, combining intraoperative fluorescence imaging with deep learning (DL) methods can improve efficiency. The majority of recent studies only analyze uni-modal fluorescence imaging, which provides less semantic information. In this work, we mainly established a multi-modal fluorescence imaging feature fusion prediction (MFI-FFP) model combining white light, fluorescence, and pseudo-color imaging of lymph nodes for LNM prediction. Firstly, based on the properties of various modal imaging, distinct feature extraction networks are chosen for feature extraction, which could significantly enhance the complementarity of various modal information. Secondly, the multi-modal feature fusion (MFF) module, which combines global and local information, is designed to fuse the extracted features. Furthermore, a novel loss function is formulated to tackle the issue of imbalanced samples, challenges in differentiating samples, and enhancing sample variety. Lastly, the experiments show that the model has a higher area under the receiver operating characteristic (ROC) curve (AUC), accuracy (ACC), and F1 score than the uni-modal and bi-modal models and has a better performance compared to other efficient image classification networks. Our study demonstrates that the MFI-FFP model has the potential to help doctors predict LNM and shows its promise in medical image analysis.

Magnetic particle imaging (MPI) has demonstrated versatile applications in biomedicine, including tumor imaging, cell tracking, and image‐guided hyperthermia. Despite these advancements, the prevalent use of clinically approved tracers has posed limitations on MPI′s resolution and sensitivity. In this study, we engineered a bimagnetic core/shell nanocrystals (BMCS) tailored for MPI by optimizing the heterostructure and modulating the exchange coupling effect between the two magnetic components. The resulting BMCS exhibited remarkably heightened susceptibility and magnetization while maintaining low coercivity, thereby substantially improved both MPI resolution and sensitivity compared to conventional tracers such as VivoTrax. At an equivalent mass concentration, BMCS demonstrated a notable 5.08‐fold increase in signal intensity and achieved an unprecedentedly high resolution down to 1 mm. The excellent MPI performance contributes to high resolution MPI and the sensitive detection of orthotopic colorectal cancer in mice. The design strategy employed in BMCS, centered on the exchange coupling effect, introduces an efficacious approach for the development of high performance MPI tracers.

Background This study aims to develop a hierarchical classification method to automatically assess the severity of knee osteoarthritis (KOA). Methods This retrospective study recruited 4074 patients. Clinical diagnostic indicators and clinical diagnostic processes were applied to develop a hierarchical classification method that involved four sub-task classifications. These four sub-task classifications were the classification of Kellgren-Lawrence (KL) grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. To extract the features of clinical diagnostic indicators, four U-Net models were first used to segment the total joint space (TJS), the lateral joint space (LJS), the medial joint space (MJS), and osteophytes, respectively. Based on the segmentation result of TJS, the region of knee subchondral bone was generated. Then, geometric features were extracted based on segmentation results of the LJS, MJS, TJS, and osteophytes, while radiomic features were extracted from the knee subchondral bone. Finally, the geometric features, radiomic features, and combination of geometric features and radiomic features were used to construct the geometric model, radiomic model, and combined model in KL grading, respectively. A strict decision strategy was used to evaluate the performance of the hierarchical classification method in all X-ray images of testing cohort. Results The U-Net models achieved relatively satisfying performances in the segmentation of the TJS, the LJS, the MJS, and the osteophytes with the dice similarity coefficient of 0.88, 0.86, 0.88, and 0.64 respectively. The combined models achieved the best performance in KL grading. The accuracy of combined models was 98.50%, 81.65%, 82.07%, and 74.10% in the classification of KL grade 0–2 and KL grade 3–4, KL grade 3 and KL grade 4, KL grade 0 and KL grade 1–2, and KL grade 1 and KL grade 2, respectively. For all X-ray images of the testing cohort, the accuracy of the hierarchical classification method was 65.98%. Conclusion The hierarchical classification method developed in the current study is a feasible approach to assess the severity of KOA.

Objectives This study aimed to develop a quantitative intratumoral heterogeneity (ITH) model for assessing the risk of early recurrence (ER) in pre‐treatment multimodal imaging for hepatocellular carcinoma (HCC) patients undergoing ablation treatments. Methods This multi‐centre study enrolled 633 HCC patients who underwent ultrasound‐guided local ablation between January 2015 and September 2022. Among them, 422, 85, 57 and 69 patients underwent radiofrequency ablation (RFA), microwave ablation (MWA), laser ablation (LA) and irreversible electroporation (IRE) ablation, respectively. Vision‐Transformer‐based quantitative ITH (ViT‐Q‐ITH) features were extracted from the US and MRI sequences. Multivariable logistic regression analysis was used to identify variables associated with ER. A combined model integrated clinic‐radiologic and ViT‐Q‐ITH scores. The prediction performance was evaluated concerning calibration, clinical usefulness and discrimination. Results The final training cohort and internal validation cohort included 318 patients and 83 patients, respectively, who underwent RFA and MWA. The three external testing cohorts comprised of 106 patients treated with RFA, 57 patients treated with LA and 69 patients who underwent IRE ablation. The combined model showed excellent predictive performance for ER in the training (AUC: .99, 95% CI: .99–1.00), internal validation (AUC: .86, 95% CI: .78–.94), external testing (AUC: .83, 95% CI: .73–.92), LA (AUC: .84, 95% CI: .73–.95) and IRE (AUC: .82, 95% CI: .72–.93) cohorts, respectively. Decision curve analysis further affirmed the clinical utility of the combined model. Conclusions The multimodal‐based model, incorporating clinic‐radiologic factors and ITH features, demonstrated superior performance in predicting ER among early‐stage HCC patients undergoing different ablation modalities.