Fenhua Zhao

Graft impingement is a critical cause of anterior cruciate ligament reconstruction (ACLR) failure. Identifying its contributing factors is essential for improving surgical outcomes. This retrospective study aimed to evaluate the incidence of graft impingement following ACLR using magnetic resonance imaging (MRI) and to investigate potential anatomical and surgical risk factors. The findings are intended to provide theoretical support for reducing impingement rates and enhancing functional recovery. We retrospectively reviewed clinical and MRI data of 122 patients (68 males and 54 females) who underwent ACLR at our institution from January 2015 to December 2023. MRI was used to identify graft impingement and to measure potential anatomical and surgical factors, including graft angle, posterior tibial slope, tibial intercondylar eminence angle, intercondylar notch width, notch height, and roof inclination, tibial tunnel position, preoperative and postoperative tibial displacement (measured as anterior tibial translation), and concomitant injuries. Patients were categorized based on the presence or absence of impingement. Univariate analysis was followed by multivariable logistic regression to identify independent risk factors. Graft impingement occurred in 65 patients (53.3% of cases). Multivariable logistic regression revealed that smaller graft angles (odds ratio [OR] = 0.930, 95% confidence interval [CI]: 0.873–0.991, p = 0.026), anterior–inferior osteophytes of the intercondylar notch roof (OR = 3.620, 95% CI: 1.408–9.311, p = 0.008), bony abnormalities at the tibial tunnel inlet (OR = 3.814, 95% CI: 1.509–9.632, p = 0.005) and postoperative tibial displacement >5 mm (OR = 6.573, 95% CI: 1.120–38.582, p = 0.037) were independent risk factors for graft impingement. Graft impingement after ACLR is independently associated with reduced graft angle, anterior–inferior osteophytes of the intercondylar notch, excessive postoperative tibial displacement, and bony protrusions at the tibial tunnel inlet. These findings emphasize the importance of accurate tunnel positioning and anatomical assessment during surgery to improve patient outcomes.

The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semiautomatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This article systematically reviews methods for the automatic and semiautomatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.

To construct and validate a multi-phase contrast-enhanced computed tomography delta-radiomics signature for preoperatively predicting lymphovascular invasion (LVI) and perineural invasion (PNI) in patients with rectal cancer (RC). This study retrospectively enrolled 519 patients with RC between January 2017 and December 2022, with patients assigned to the training (n = 363) or validation (n = 156) sets. Radiomic features were extracted from routine scanning (A0), the arterial phase (A1), and the venous phase (A2). Delta-1 and Delta-2 radiomic signatures were derived by subtracting radiomic features acquired from A0 images from those of A2 and A1, respectively. Subsequently, Delta-3 and Delta-4 radiomic features were obtained by performing image subtraction between the A0 images and A2 and A1 images, then extracting the radiomic features from the resulting residual images. A delta-radiomics model was constructed using the Least Absolute Shrinkage and Selection Operator method. Model performance was evaluated using receiver operating characteristic, calibration, and decision curves. Delta-1–Delta-4 models exhibited moderate predictive performance for LVI and PNI in patients with RC, with area under the curve (AUC) values of 0.73, 0.73, 0.67, and 0.68, respectively. The combined model (C-Delta-12) showed the best predictive performance (AUC, 0.81; accuracy, 0.76; sensitivity, 0.86; specificity, 0.65). Calibration curves confirmed high goodness of fit, and decision curve analysis confirmed the clinical value. Integrating delta-radiomics signature and clinical predictors into a radiomics prediction model enables accurate and non-invasive risk assessments of PNI and LVI in RC. Stratifying patients based on their PNI and LVI status may facilitate more individualised treatment.

Purpose: To develop and validate an accurate computed tomography–based radiomics model for predicting high-grade (micropapillary/solid) patterns in T1-stage lung invasive adenocarcinoma (IAC) after propensity score matching (PSM). Materials and Methods: We enrolled 546 participants from 2 cohorts with histologically diagnosed lung IAC after complete surgical resection between January 2020 and August 2021. The patients were divided into high-grade and non–high-grade groups and matched using PSM. Matched patient HRCT images were used to delineate regions of interest from tumors and extract radiomics features, and the random forest method was used to construct a radiomics model. The area under the receiver operating characteristic curve (area under the curve) was used to evaluate the model’s performance, and external validation was performed to assess the model’s generalizability. Results: Before PSM, there was no statistically significant difference in age between the two groups, though nodule type and sex exhibited significant differences (P < 0.05) in both cohorts. After PSM, we matched 176 and 97 pairs of patients in the 2 cohorts. In both cohorts, sex and nodule type were equal between the two groups, with a higher percentage of males and solid nodules in both groups. Our model exhibited moderate predictive performance after PSM, with area under the curve values of 0.75 (95% CI: 0.70-0.80) and 0.71 (95% CI: 0.63-0.80) for the development and external validation cohorts, respectively. Conclusion: Although the nodule type compromised the validity of the model’s performance, our results suggest that our acute computed tomography–based radiomics model could preoperatively predict micropapillary/solid patterns in patients with stage I lung IAC after PSM.

Background: The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models’ generalization ability.Methods: We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance.Results: The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05).Conclusion: The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.

Background: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground‐glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them.Methods: We developed and validated a three‐dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre‐training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end‐to‐end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers.Results: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group).Conclusions: Our 3D deep transfer learning model provides a noninvasive, low‐cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation.Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04).Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.