Unilateral biportal endoscopic lumbar interbody fusion (ULIF) versus endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) in the treatment of lumbar spinal stenosis along with intervertebral disc herniation: a retrospective analysis

OBJECTIVE

This study aims to compare the clinical effects and imaging data of patients who underwent endoscopic transforaminal lumbar interbody fusion (Endo-TLIF) with those who received unilateral biportal endoscopic lumbar interbody fusion (ULIF).

METHODS

A retrospective analysis was conducted on the clinical data of 69 patients presenting with typical intermittent claudication and signs and symptoms indicative of unilateral lower extremity nerve root compression, meeting inclusion criteria between April 2022 and June 2022. Among the cohort, 35 patients underwent ULIF group, while 34 patients underwent Endo-TLIF group. We compared perioperative parameters, including intraoperative blood loss, duration of hospital stay, and operation time between the two groups. Pre-operative and post-operative changes in the height and cross-sectional area of the target intervertebral space were also compared between the groups. Finally, we evaluated bone graft size and interbody fusion rates at 6 and 12 months post-surgery using the Brantigan scoring system.

RESULTS

The ULIF group had significantly shorter operative times compared to the Endo-TLIF group (P < 0.05). Conversely, the Endo-TLIF group exhibited significantly shorter hospital stays compared to the ULIF group (P < 0.05). However, there were no significant differences in intraoperative bleeding between the two groups (P > 0.05). Furthermore, both groups exhibited postoperative increases in vertebral canal volume compared to baseline (P < 0.05), with no significant difference in the change in the cross-sectional area of the target intervertebral space between the two surgical methods (P > 0.05). Interbody fusion rates were comparable between the two groups at both 6 and 12 months after surgery (P > 0.05). Lastly, the ULIF group had a significantly larger area of bone graft than the Endo-TLIF group (P < 0.05).

CONCLUSION

In summary, the ULIF technique, as a novel spinal endoscopy approach, is a safer and more effective minimally invasive surgical method for addressing lumbar spinal stenosis and intervertebral disc herniation in patients. Both surgical methods have their own advantages and drawbacks. With the development of technology and related instruments, the limitations of both techniques can be mitigated for to a certain extent, and they can be applied by more doctors in diverse medical fields in the future.

Disulfidptosis-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in colon adenocarcinoma

BACKGROUND

Disulfidptosis is independent of apoptosis, ferroptosis, and cuproptosis and is associated with cancer progression, treatment response, and prognosis. However, the predictive potential of disulfidptosis-associated lncRNAs in colon adenocarcinoma (COAD) and their features in the tumor immune microenvironment (TIME) require further elucidation.

METHODS

RNA transcriptome, clinical information, and mutation data of COAD samples were obtained from the TCGA database. The risk model was first constructed by co-expression analysis of disulfidptosis genes and lncRNAs, and prognostic lncRNAs were screened using Cox regression, followed by least absolute shrinkage and selection operator analysis. Enrichment analyses were performed to explore the underlying biological functions and signaling of model-associated differentially expressed genes (MADEGs). Moreover, TIME of MADEGs was analyzed to assess the immunotherapy. Finally, the expression levels of the lncRNAs were verified by taking specimens of patients with COAD from the Affiliated Hospital of Qingdao University.

RESULTS

We constructed a prognosis-related risk model based on four disulfidptosis-associated lncRNAs (ZEB1-AS1, SNHG16, SATB2-AS1, and ALMS1-IT1). By analyzing the survival of patients in the whole, training, and test groups, we found that patients with COAD in the low-risk group had better overall survival than those in the high-risk group. Validation of the model via Cox analysis and clinical indicators demonstrated that the model had a decent potential for predicting the prognosis of patients with COAD. Enrichment analyses revealed that the MADEGs were related to disulfidptosis-associated biological functions and cancer pathways. Furthermore, patients with COAD in the high-risk group had more positive responses to immune checkpoint inhibitors (ICIs) than those in the low-risk group, as confirmed by TIME analysis. ZEB1-AS1, SNHG16, and ALMS1-IT1 were expressed at higher levels in tumor samples than those in the corresponding paracancerous samples (p < 0.05), whereas SATB2-AS1 was upregulated in the paracancerous samples (p < 0.05).

CONCLUSIONS

This signature may guide prognosis, molecular mechanisms, and treatment strategies, including ICIs and chemotherapy, in patients with COAD.

Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image

OBJECTIVE

The indocyanine green retention rate at 15 min (ICG-R15) is a useful tool to evaluate the functional liver reserve before hepatectomy for liver cancer. Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients.

METHODS

A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n = 133, MR: n = 78) and a test dataset (CT: n = 57, MR: n = 34). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation.

RESULTS

A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC = 0.917 and ACC = 0.882 when the threshold was set as ICG-R15 = 10%. When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.961 and ACC = 0.941. For CT groups, the classifier XGBoost performed best when ICG-R15 = 10% with AUC = 0.822 and ACC = 0.842. When ICG-R15 = 20%, classifier SVM performed best with AUC = 0.860 and ACC = 0.842. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.938 and ACC = 0.965.

CONCLUSIONS

Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.