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How to protect the proximal bronchial tree during stereotactic radiotherapy of ultracentral lung tumors: Lessons from MR-guided treatment.
Clin Transl Radiat Oncol
March 2025
Department of Radiation Oncology, University Hospital Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
Purpose: To use imaging data from stereotactic MR-guided online adaptive radiotherapy (SMART) of ultracentral lung tumors (ULT) for development of a safe non-adaptive approach towards stereotactic body radiotherapy (SBRT) of ULT.
Patients And Methods: Analysis is based on 19 patients with ULT who received SMART (10 × 5.0-5.
A Hitchhiker's Guide Toward CSF Biomarkers for Neuro-Oncology.
Neuro Oncol
December 2024
Department of Neurological Surgery, Mayo Clinic, Rochester, MN, USA.
Cerebrospinal fluid (CSF) has emerged as a valuable liquid biopsy source for glioma biomarker discovery and validation. CSF produced within the ventricles circulates through the subarachnoid space, where the composition of glioma-derived analytes is influenced by the proximity and anatomical location of sampling relative to tumor, in addition to underlying tumor biology. The substantial gradients observed between lumbar and intracranial CSF compartments for tumor-derived analytes underscore the importance of sampling site selection.
View Article and Find Full Text PDFSmad2 Cooperating with TGIF2 Contributes to EMT and Cancer Stem Cells Properties in Pancreatic Cancer via Co-Targeting SOX2.
Int J Biol Sci
January 2025
Department of General Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
The underlying mechanisms between cancer stem cells (CSC) and epithelial-mesenchymal transition (EMT) in pancreatic cancer (PC) remain unclear. In this study, we identified TGIF2 as a target gene of CSC using sncRNA and machine learning. TGIF2 is closely related to the expression of SOX2, EGFR, and E-cadherin, indicating poor prognosis.
View Article and Find Full Text PDFThe potential of GPT-4 advanced data analysis for radiomics-based machine learning models.
Neurooncol Adv
December 2024
Division for Medical Image Computing (MIC), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Background: This study aimed to explore the potential of the Advanced Data Analytics (ADA) package of GPT-4 to autonomously develop machine learning models (MLMs) for predicting glioma molecular types using radiomics from MRI.
Methods: Radiomic features were extracted from preoperative MRI of = 615 newly diagnosed glioma patients to predict glioma molecular types (IDH-wildtype vs IDH-mutant 1p19q-codeleted vs IDH-mutant 1p19q-non-codeleted) with a multiclass ML approach. Specifically, ADA was used to autonomously develop an ML pipeline and benchmark performance against an established handcrafted model using various MRI normalization methods (N4, Zscore, and WhiteStripe).
Lymph Node Dissection for Melanoma: Contemporary Trends in Postoperative Outcomes and Patient Selection With Reduced Case Volumes in the Post-MSLT2 Era.
J Surg Oncol
January 2025
Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Background And Objectives: Since the publication of the German Cooperative Oncology Group Selective Lymphadenectomy Trial and Multicenter Selective Lymphadenectomy Trial II (MSLT2) trials, the treatment paradigm for node-positive melanoma has shifted from completion lymph node dissection (LND) to nodal ultrasound surveillance. We sought to identify the impact of this practice change on postoperative outcomes in a national cohort.
Methods: The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients diagnosed with truncal/extremity malignant melanoma who underwent axillary/inguinal LND.
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