Immune-inflammatory markers and clinical characteristics as predictors of the depth of response and prognosis of patients with PD-L1 ≥50% metastatic non-small cell lung cancer receiving first-line immunotherapy.

Background: Patients with programmed cell death-ligand 1 (PD-L1) ≥50% metastatic non-small cell lung cancer (NSCLC) treated with first-line immunotherapy showed heterogeneous tumor responses. In this study, we investigated the clinical and immune-inflammatory markers distinguishing patients with metastatic NSCLC achieving high depth of tumor response (HDPR) from those with non-high depth of response (NHDPR). The impact of clinical features on the prognosis of patients with PD-L1 ≥50% were further clarified.

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.

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.

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.

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.

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.

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).

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.