Academic Radiology最新文献

Rationale and objectives: Accurately predicting the pathological response to chemotherapy before treatment is important for selecting the appropriate treatment groups, formulating individualized treatment plans, and improving the survival rates of patients with gastric cancer (GC).

Methods: We retrospectively enrolled 151 patients diagnosed with GC who underwent preoperative chemotherapy and surgical resection at the Affiliated Hospital of Qingdao University between January 2015 and June 2023. Both pretreatment-enhanced computer technology images and whole slide images of pathological hematoxylin and eosin-stained sections were available for each patient. The image features were extracted and used to construct an ensemble radiopathomics machine learning model. In addition, a nomogram was developed by combining the imaging features and clinical characteristics.

Results: In total, 962 radiomics and 999 pathomics signatures were extracted from 106 patients in the training cohort. A fusion radiopathomics model was constructed using 13 radiomics and 5 pathomics signatures. The fusion model showed favorable performance compared to single-omics models, with an area under the curve (AUC) of 0.789 in the validation cohort. Moreover, a combined radiopathomics nomogram (RPN) was developed based on radiopathomics features and the Borrmann type, which is a classification method for advanced GC according to tumor growth pattern and gross morphology. The RPN showed superior predictive performance in the training (AUC 0.880) and validation cohorts (AUC 0.797). The decision curve analysis showed that RPN could provide favorable clinical benefits to patients with GC.

Conclusions: RPN was able to predict the pathological response to preoperative chemotherapy with high accuracy, and therefore provides a novel tool for personalized treatment of GC.

Rationale and objectives: This study aimed to evaluate whether Doppler ultrasonography (DUS) and contrast-enhanced ultrasonography (CEUS) within 3 days postoperative can identify 1-month graft failure after split liver transplantation (SLT).

Materials and methods: A total of 58 consecutive patients who underwent SLT between February 2022 and September 2023 were included. The DUS and CEUS images and parameters within 3 days postoperatively were analyzed and recorded. The DUS parameters included peak systolic velocity (PSV), resistive index, and systolic acceleration time for the hepatic artery and PSV for the portal vein and hepatic vein. The CEUS qualitative analysis variables included the liver parenchyma enhancement pattern and the posterior enhancement attenuation. Logistic regression and Cox proportional hazard regression were used to evaluate the relationship between DUS/CEUS findings and 1-month graft failure.

Results: Seven of the 58 liver grafts failed within 1 month. Poor CEUS enhancement (pattern Ⅱ/Ⅲ) was observed in five of seven patients (71.4%) of graft failure, whereas good contrast enhancement (pattern Ⅰ) was found in 47 of the 51 patients (92.1%) in the successful group on postoperative day 3. Multivariate logistic regression analysis revealed that 1-month graft failure was independently predicted by operative time (odds ratio [OR] = 3.79, 95% confidence interval [CI]: 1.27-11.29, p = .017) and CEUS enhancement pattern on postoperative day 3 (OR = 90.88, 95% CI: 2.77-2979.56, p = .011). Cox proportional hazard regression showed that operative time (hazard ratio [HR] = 1.6, 95% CI: 1.15-2.22, p = .005) and CEUS enhancement pattern on postoperative day 3 (HR = 11.947, 95% CI: 2.04-69.98, p = .006) were independent predictors for graft failure.

Conclusion: Poor CEUS enhancement (pattern Ⅱ/Ⅲ) was associated with 1-month graft failure in SLT recipients. CEUS may serve as a noninvasive, valuable prognostic tool to predict clinical outcomes early after SLT.

Rationale and objectives: This study aimed to evaluate the safety and effectiveness of transbrachial access (TBA) and transradial access (TRA) compared to transfemoral access (TFA) for large-bore neuro stenting (≥7 F).

Methods: From January 2019 to January 2024, 4752 patients received large-bore neuro stenting in our center. The primary outcomes were procedural metrics. Safety outcomes were significant access site complications, including substantial hematoma, pseudoaneurysm, artery occlusion, and complications requiring treatment (medicine, intervention, or surgery). After propensity score matching with a ratio of 1:1:2 (TBA: TRA: TFA), adjusting for age, gender, aortic arch type, and neuro stenting as covariates, outcomes were compared between groups.

Results: 46 TBA, 46 TRA and 92 TFA patients were enrolled. The mean age was 67.8 ± 11.2 years, comprising 127 (69.0%) carotid artery stenting and 57 (31.0%) vertebral artery stenting. The rates of technical success (TBA: 100%, TRA: 95.7%, TFA: 100%) and significant access site complications (TBA: 4.3%, TRA: 6.5%, TFA: 1.1%) were comparable between the groups (P > 0.05). Compared to TFA, the TRA cohort exhibited significant delays in angiosuite arrival to puncture time (14 vs. 8 min, P = 0.039), puncture to angiography completion time (19 vs. 11 min, P = 0.027), and procedural duration (42 vs. 29 min, P = 0.031). There were no substantial differences in procedural time metrics between TBA (10, 14, and 31 min, respectively) and TFA.

Conclusion: TBA and TRA as the primary access for large-bore neuro stenting are safe and effective. Procedural delays in TRA may favor TBA as the first-line alternative access to TFA.

Rationale and objectives: To develop and validate a clinical-radiomics model of dynamic contrast-enhanced MRI (DCE-MRI) for the preoperative discrimination of Vessels encapsulating tumor clusters (VETC)- microvascular invasion (MVI) and prognosis of hepatocellular carcinoma (HCC).

Materials and methods: 219 HCC patients from Institution 1 were split into internal training and validation groups, with 101 patients from Institution 2 assigned to external validation. Histologically confirmed VETC-MVI pattern categorizing HCC into VM-HCC+ (VETC+/MVI+, VETC-/MVI+, VETC+/MVI-) and VM-HCC- (VETC-/MVI-). The regions of intratumor and peritumor were segmented manually in the arterial, portal-venous and delayed phase (AP, PP, and DP, respectively) of DCE-MRI. Six radiomics models (intratumor and peritumor in AP, PP, and DP of DCE-MRI) and one clinical model were developed for assessing VM-HCC. Establishing intra-tumoral and peri-tumoral models through combining intratumor and peritumor features. The best-performing radiomics model and the clinical model were then integrated to create a Combined model.

Results: In institution 1, pathological VM-HCC+ were confirmed in 88 patients (training set: 61, validation set: 27). In internal testing, the Combined model had an AUC of 0.85 (95% CI: 0.76-0.93), which reached an AUC of 0.75 (95% CI: 0.66-0.85) in external validation. The model's predictions were associated with early recurrence and progression-free survival in HCC patients.

Conclusions: The clinical-radiomics model offers a non-invasive approach to discern VM-HCC and predict HCC patients' prognosis preoperatively, which could offer clinicians valuable insights during the decision-making phase.

Rationale and objectives: Deep learning can enhance the performance of multimodal image analysis, which is known for its noninvasive attributes and complementary efficacy, in predicting axillary lymph node (ALN) metastasis. Therefore, we established a multimodal deep learning model incorporating ultrasound (US) and magnetic resonance imaging (MRI) images to predict ALN metastasis in patients with breast cancer.

Materials and methods: A retrospective cohort of patients with histologically confirmed breast cancer from two hospitals composed of the primary cohort (n = 465) and the external validation cohort (n = 123). All patients had undergone both preoperative US and MRI scans. After data preprocessing, three convolutional neural network models were used to analyze the US and MRI images, respectively. After integrating the US and MRI deep learning prediction results (DL_US and DL_MRI, respectively), a multimodal deep learning (DL_MRI+US+Clinical parameter) model was constructed. The predictive ability of the proposed model was compared to that of the DL_US, DL_MRI, combined bimodal (DL_MRI+US), and clinical parameter models. Evaluation was performed using the area under the receiver operating characteristic curves (AUCs) and decision curves.

Results: A total of 588 patients with breast cancer participated in this study. The DL_MRI+US+Clinical parameter model outperformed the alternative models, achieving the highest AUCs of 0.819 (95% confidence interval [CI] 0.734-0.903) and 0.809 (95% CI 0.723-0.895) on the internal and external validation sets, respectively. The decision curve analysis confirmed its clinical usefulness.

Conclusion: The DL_MRI+US＋Clinical parameter model demonstrates the feasibility and reliability of its performance for ALN metastasis prediction in patients with breast cancer.

Rationale and objectives: The tumor-tropic properties of mesenchymal stem cells (MSCs) enable them to serve as appealing cellular vehicles for delivering therapeutic agents to treat malignant glioma. However, the exact engraftment status of MSCs in glioma via different administration routes remains unclear due to the lack of quantitative analysis. This study aimed to quantify the engraftment of MSCs in glioma after administration via different routes using non-invasive dual-modality magnetic resonance imaging (MRI) and bioluminescence imaging (BLI).

Materials and methods: MSCs were transduced with a lentivirus overexpressing ferritin heavy chain (FTH) and firefly luciferase (FLUC) reporter genes to yield FTH- and FLUC-overexpressed MSCs (FTH-FLUC-MSCs). Wistar rats bearing intracranial C6 glioma received peritumoral, intratumoral, intra-arterial, and intravenous injection of FTH-FLUC-MSCs, respectively. MRI and BLI were performed to monitor FTH-FLUC-MSCs in vivo.

Results: FTH-FLUC-MSCs administered via peritumoral, intratumoral and intra-arterial routes migrated specially toward the intracranial glioma in vivo, as detected by MRI and BLI. As quantified by the BLI signal intensity, the percentages of FTH-FLUC-MSCs in the glioma were significantly higher with peritumoral injection (61%) and intratumoral injection (71%) compared to intra-arterial injection (30%) and intravenous injection (0%). Peritumorally injected FTH-FLUC-MSCs showed a gradual decline, with approximately 6% of FTH-FLUC-MSCs still retained within the tumor up to 11 days after injection. Meanwhile, the number of FTH-FLUC-MSCs injected via other routes dropped quickly, and none were detectable by day 11 post-injection.

Conclusion: Peritumoral delivery of FTH-FLUC-MSCs offers robust engraftment and could be used as the optimal delivery route for treating malignant glioma.

Rationale and objectives: To compare perioperative and oncology outcomes of ablation and partial nephrectomy in small renal masses (SRMs).

Methods: We conduct this meta-analysis strictly according to the PRISMA standard, and the quality evaluation follows the AMSTAR standard. Four databases, Embase, PubMed, Cochrane Library, and Web of Science, were systematically searched. The search time range is from database creation to November 2023. Stata16 statistical software was used for statistical analysis. Weighted mean difference (WMD) represented continuity variables, odds ratio or relative risk (OR/RR) represented dichotomies variables, and 95% confidence intervals (95%CI) were calculated.

Results: A total of 27 studies, including 6030 patients. Results showed that patients undergoing partial nephrectomy were younger (WMD = -5.45 years, 95%CI [-7.44, -3.46], P < 0.05), had longer operation time (WMD = 64.91 min, 95%CI [44.47, 85.34], P < 0.05), had longer length of stay (WMD = 2.91 days, 95%CI [2.04, 3.78], P < 0.05), and had more estimated blood loss (WMD = 97.76 ml, 95%CI [69.48, 126.04]. P < 0.05), the overall complication rate was higher (OR = 1.84, 95%CI [1.48, 2.29], P < 0.05), the major complication rate was higher (OR = 1.98, 95%CI [1.36, 2.88], P < 0.05), and the recurrence rate was lower (OR = 0.32, 95%Cl [0.20, 0.50], P < 0.05). However, there were no differences between ablation and partial nephrectomy in cancer-specific survival (CSS) (HR = 2.07, 95%CI [0.61, 7.04], P > 0.05), overall survival (OS) (HR = 1.24, 95%CI [0.58, 2.65], P > 0.05), and recurrence-free survival (RFS) (HR = 2.68, 95%CI [0.91, 7.88], P > 0.05).

Conclusion: Patients undergoing partial nephrectomy are younger, have longer operation time and length of stay, and have higher complication rate. However, there was no significant difference in CSS, OS, and RFS between partial nephrectomy and ablation, but more well-designed, high-quality studies are needed to confirm this.