The 3-Dimensional Intelligent Structured Light Technique: A New Registration Method in Stereotactic Neurosurgery.

BACKGROUND AND OBJECTIVES: Surface-based facial scanning registration emerged as an essential registration method in the robot-assisted neuronavigation surgery, providing a marker-free way to align a patient's facial surface with the imaging data. The 3-dimensional (3D) structured light was developed as an advanced registration method based on surface-based facial scanning registration. We aspire to introduce the 3D structured light as a new registration method in the procedure of the robot-assisted neurosurgery and assess the accuracy, efficiency, and safety of this method by analyzing the relative operative results. METHODS: We analyzed the results of 47 patients who underwent Ommaya reservoir implantation (n = 17) and stereotactic biopsy (n = 30) assisted by 3D structured light at our hospital from January 2022 to May 2023. The accuracy and additional operative results were analyzed. RESULTS: For the Ommaya reservoir implantation, the target point error was 3.2 ± 2.2 mm and the entry point error was 3.3 ± 2.4 mm, while the operation duration was 35.8 ± 8.3 minutes. For the stereotactic biopsy, the target point error was 2.3 ± 1.3 mm and the entry point error was 2.7 ± 1.2 mm, while the operation duration was 24.5 ± 6.3 minutes. CONCLUSION: The 3D structured light technique reduces the patients' discomfort and offers the advantage of a simpler procedure, which can improve the clinical efficiency with the sufficient accuracy and safety to meet the clinical requirements of the puncture and navigation.

The prognostic value of cancer stage-associated genes in clear cell renal cell carcinoma.

OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is a highly prevalent subtype of malignant renal tumor, but unfortunately, the survival rate remains unsatisfactory. The aim of the present study is to explore genomic features that are correlated with cancer stage, allowing for the identification of subgroups of ccRCC patients with high risk of unfavorable outcomes and enabling prompt intervention and treatment. METHODS: We compared the gene expression levels across ccRCC patients with diverse cancer stages from The Cancer Genome Atlas (TCGA) database, which revealed characteristic genes associated with tumor stage. We then extracted prognostic genes and used least absolute shrinkage selection operator (LASSO) regression to select four genes for feature extraction and the construction of a prognostic risk model. RESULTS: We have identified a total of 171 differentially expressed genes (DEGs) that are closely linked to the tumor stage of ccRCC through difference analysis. A prognostic risk model constructed based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 holds significant prognostic value in ccRCC. The results of the functional enrichment analysis imply that the DEGs are mainly involved in the regulation of immune-related signaling pathways, and therefore may have a significant function in immune system regulation of ccRCC. CONCLUSIONS: Our study has successfully identified significant DEGs between high- and low-staging groups of ccRCC using bioinformatics methods. The construction of a prognostic risk model based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 has displayed promising prognostic significance, indicating its valuable potential for clinical application.

Bevacizumab reduces peritumoral brain edema in lung cancer brain metastases after radiotherapy.

BACKGROUND: The aim of this study was to investigate the efficacy of bevacizumab (Bev) in reducing peritumoral brain edema (PTBE) after stereotactic radiotherapy (SRT) for lung cancer brain metastases. METHODS: A retrospective analysis was conducted on 44 patients with lung cancer brain metastases (70 lesions) who were admitted to our oncology and Gamma Knife center from January 2020 to May 2022. All patients received intracranial SRT and had PTBE. Based on treatment with Bev, patients were categorized as SRT + Bev and SRT groups. Follow-up head magnetic resonance imaging was performed to calculate PTBE and tumor volume changes. The edema index (EI) was used to assess the severity of PTBE. Additionally, the extent of tumor reduction and intracranial progression-free survival (PFS) were compared between the two groups. RESULTS: The SRT + Bev group showed a statistically significant difference in EI values before and after radiotherapy (p = 0.0115), with lower values observed after treatment, but there was no difference in the SRT group (p = 0.4008). There was a difference in the distribution of EI grades in the SRT + Bev group (p = 0.0186), with an increased proportion of patients at grades 1-2 after radiotherapy, while there was no difference in the SRT group (p > 0.9999). Both groups demonstrated a significant reduction in tumor volume after radiotherapy (p < 0.05), but there was no difference in tumor volume changes between the two groups (p = 0.4089). There was no difference in intracranial PFS between the two groups (p = 0.1541). CONCLUSION: Bevacizumab significantly reduces the severity of PTBE after radiotherapy for lung cancer. However, its impact on tumor volume reduction and intracranial PFS does not reach statistical significance.

Thyroid transcription factor 1 (TTF-1) negativity as a predictor of unfavorable response to EGFR-TKI therapy in advanced lung adenocarcinoma patients with EGFR mutations.

BACKGROUND: The absence of thyroid transcription factor 1 (TTF-1) is associated with a lower frequency of epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma (LUAD). The aim of this study was to assess the impact of TTF-1 expression on the clinical response to EGFR-tyrosine kinase inhibitor (TKI) treatment in patients with advanced LUAD. METHODS: The data of patients with advanced LUAD who were admitted to the Beijing Tiantan Hospital and Peking University Cancer Hospital (China) between April 2009 and May 2023 was retrospectively analyzed. RESULTS: A total of 227 patients diagnosed with advanced LUAD were included, of which 28.2% (64/227) had TTF-1-negative adenocarcinoma, while 54.6% (124/227) harbored EGFR mutations. Negative TTF-1 expression significantly correlated with male sex (68.8% vs. 42.3%, p < 0.001), history of heavy smoking (57.8% vs. 36.2%, p = 0.003), poorly differentiated tumors (86.5% vs. 43.2%, p < 0.001), and lower frequency of EGFR mutations (26.6% vs. 65.6%, p < 0.001) compared with TTF-1 positivity. Multivariable logistic regression showed that low prevalence of EGFR mutations (p < 0.001) and male sex (p = 0.006) were independent predictive factors for the negative expression of TTF-1. Patients lacking TTF-1 also exhibited worse overall response rate (ORR; 23.5% vs. 54.2%, p = 0.019), disease control rate (DCR; 58.8% vs. 89.7%, p = 0.003), and median progression-free survival (PFS; 2.9 vs. 11.6 months, p < 0.001) following treatment with EGFR-TKIs compared to the TTF-1-positive patients with EGFR mutations. CONCLUSIONS: Patients with TTF-1-negative and EGFR-mutant LUAD show a diminished response to EGFR-TKIs.

Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms.

Purpose: To explore the application of deep learning (DL) methods based on T2 sagittal MR images for discriminating between spinal tuberculosis (STB) and spinal metastases (SM). Patients and Methods: A total of 121 patients with histologically confirmed STB and SM across four institutions were retrospectively analyzed. Data from two institutions were used for developing deep learning models and internal validation, while the remaining institutions' data were used for external testing. Utilizing MVITV2, EfficientNet-B3, ResNet101, and ResNet34 as backbone networks, we developed four distinct DL models and evaluated their diagnostic performance based on metrics such as accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score, and confusion matrix. Furthermore, the external test images were blindly evaluated by two spine surgeons with different levels of experience. We also used Gradient-Class Activation Maps to visualize the high-dimensional features of different DL models. Results: For the internal validation set, MVITV2 outperformed other models with an accuracy of 98.7%, F1 score of 98.6%, and AUC of 0.98. Other models followed in this order: EfficientNet-B3 (ACC: 96.1%, F1 score: 95.9%, AUC: 0.99), ResNet101 (ACC: 85.5%, F1 score: 84.8%, AUC: 0.90), and ResNet34 (ACC: 81.6%, F1 score: 80.7%, AUC: 0.85). For the external test set, MVITV2 again performed excellently with an accuracy of 91.9%, F1 score of 91.5%, and an AUC of 0.95. EfficientNet-B3 came second (ACC: 85.9, F1 score: 91.5%, AUC: 0.91), followed by ResNet101 (ACC:80.8, F1 score: 80.0%, AUC: 0.87) and ResNet34 (ACC: 78.8, F1 score: 77.9%, AUC: 0.86). Additionally, the diagnostic accuracy of the less experienced spine surgeon was 73.7%, while that of the more experienced surgeon was 88.9%. Conclusion: Deep learning based on T2WI sagittal images can help discriminate between STB and SM, and can achieve a level of diagnostic performance comparable with that produced by experienced spine surgeons.

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). METHODS: We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. RESULTS: For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. CONCLUSIONS: The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.

Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.

OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS: We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS: The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION: The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.

Comparison between Thermal Desorption Tubes and Stainless Steel Canisters Used for Measuring Volatile Organic Compounds in Petrochemical Factories.

OBJECTIVE: The purpose of this study was to compare thermal desorption tubes and stainless steel canisters for measuring volatile organic compounds (VOCs) emitted from petrochemical factories. METHODS: Twelve petrochemical factories in the Mailiao Industrial Complex were recruited for conducting the measurements of VOCs. Thermal desorption tubes and 6-l specially prepared stainless steel canisters were used to simultaneously perform active sampling of environmental air samples. The sampling time of the environmental air samples was set up on 6 h close to a full work shift of the workers. A total of 94 pairwise air samples were collected by using the thermal adsorption tubes and stainless steel canisters in these 12 factories in the petrochemical industrial complex. To maximize the number of comparative data points, all the measurements from all the factories in different sampling times were lumped together to perform a linear regression analysis for each selected VOC. Pearson product-moment correlation coefficient was used to examine the correlation between the pairwise measurements of these two sampling methods. A paired t-test was also performed to examine whether the difference in the concentrations of each selected VOC measured by the two methods was statistically significant. RESULTS: The correlation coefficients of seven compounds, including acetone, n-hexane, benzene, toluene, 1,2-dichloroethane, 1,3-butadiene, and styrene were >0.80 indicating the two sampling methods for these VOCs' measurements had high consistency. The paired t-tests for the measurements of n-hexane, benzene, m/p-xylene, o-xylene, 1,2-dichloroethane, and 1,3-butadiene showed statistically significant difference (P-value < 0.05). This indicated that the two sampling methods had various degrees of systematic errors. Looking at the results of six chemicals and these systematic errors probably resulted from the differences of the detection limits in the two sampling methods for these VOCs. CONCLUSIONS: The comparison between the concentrations of each of the 10 selected VOCs measured by the two sampling methods indicted that the thermal desorption tubes provided high accuracy and precision measurements for acetone, benzene, and 1,3-butadiene. The accuracy and precision of using the thermal desorption tubes for measuring the VOCs can be improved due to new developments in sorbent materials, multi-sorbent designs, and thermal desorption instrumentation. More applications of thermal desorption tubes for measuring occupational and environmental hazardous agents can be anticipated.