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Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated
1Mutagenesis & Cancer Prevention Unit, IRCCS Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genova, Italy.
Abstract:
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma.
Insights
Advances in AI and imaging for high-grade glioma treatment show promise, but clinical adoption faces hurdles. Improving patient end-of-life care and addressing legislative gaps are crucial for progress.
Area of Science:
- Neuro-oncology
- Medical Imaging
- Artificial Intelligence
Background:
- High-grade gliomas are aggressive brain tumors with poor prognoses.
- Recent advances in AI and imaging offer potential for optimized treatment strategies.
- Current clinical integration of these advancements is limited.
Purpose of the Study:
- To review recent research in AI and image-based models for high-grade glioma treatment.
- To identify barriers hindering the clinical application of these advanced techniques.
- To discuss end-of-life care and legislative aspects for high-grade glioma patients, particularly in Italy.
Main Methods:
- Literature review of major research published in the first half of 2022.
- Analysis of study designs, methodologies, and sample sizes.
- Examination of palliative care and legislative frameworks.
Main Results:
- Significant progress in AI and imaging for glioma treatment optimization.
- Key barriers include small sample sizes and methodological heterogeneity.
- End-of-life care for high-grade glioma patients is often inadequate and delayed.
Conclusions:
- Accelerating the clinical translation of AI and imaging requires addressing research and implementation challenges.
- Enhancing palliative care and adapting legislative instruments are essential for improving patient outcomes and end-of-life support.
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