Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00701-008-0176-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Technological advances in clinical individualized medication for cancer therapy: from genes to whole organism.
Per Med
January 2025
Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet.
View Article and Find Full Text PDFApparent diffusion coefficient and magnetic resonance imaging characteristics in predicting response to radiosurgery in patients with vestibular schwannomas.
Neuroradiol J
January 2025
Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Chulalongkorn University, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Bangkok, Thailand.
Objective: Predicting treatment response in patients with vestibular schwannomas (VSs) remains challenging. This study aimed to evaluate the use of pre-treatment normalized apparent diffusion coefficient (nADC) values and magnetic resonance (MR) imaging characteristics in predicting treatment outcomes in patients with VSs undergoing radiosurgery.
Methods: The MR images of 44 patients with VSs who underwent radiosurgery at our institution were retrospectively reviewed, and the patients were categorized into tumor control ( = 28) and progression ( = 16) groups based on treatment response after treatment initiation, with a median follow-up duration of 29.
Diagnostic utility of magnetic resonance imaging in autoimmune encephalitis: prognostic implications.
Acta Radiol
January 2025
R Madhavan Nayar Center for Comprehensive Epilepsy Care, Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala, India.
Background: The role of imaging in autoimmune encephalitis (AIE) remains unclear, and there are limited data on the utility of magnetic resonance imaging (MRI) to diagnose, treat, or prognosticate AIE.
Purpose: To evaluate whether MRI is a diagnostic and prognostic marker for AIE and assess its efficacy in distinguishing between various AIE subtypes.
Material And Methods: We analyzed data from 96 AIE patients from our prospective autoimmune registry.
Frequently asked questions on surrogate endpoints in oncology-opportunities, pitfalls, and the way forward.
EClinicalMedicine
October 2024
Department of Oncology, Queen's University, Kingston, Canada.
Patients with cancer expect prolonged life (overall survival, OS) or better life (quality of life, QOL) from cancer treatments. However, majority of new cancer drugs are now being approved not based on improved OS or QOL, but based on surrogate endpoints such as tumor shrinkage or delayed tumor progression. These surrogate endpoints, including their validity as a proxy for overall survival, differ based on disease settings and lines of treatment but in general, most surrogate measures have weak correlation with outcomes that matter to patients.
View Article and Find Full Text PDFAI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.
Hum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!