Enhanced Prognostic Assessment of Glioblastoma Multiforme Using Machine Learning: Integrating Multimodal Imaging and Treatment Features: A review

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@article{1706189,
      author = {Rohit Choudhary, Priyabrata Thatoi, Sushree Swapnil},
      title = {Enhanced Prognostic Assessment of Glioblastoma Multiforme Using Machine Learning: Integrating Multimodal Imaging and Treatment Features: A review},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {2},
      pages = {671-679},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17061891.pdf},
      abstract = {Glioblastoma multiforme (GBM) is the primary and most malignant form of brain tumor with an unexceptionally low prognosis and a highly variable genome. The cancer's multifactorial nature and variable response to the stated therapy preclude the use of standard prognostic factors. We can improve prognosis and the individual therapeutic management plan by utilizing the domain of multimodal imagining and a new approach in machine learning. Here, the author wonders how the different modes of the image, such as MRI, PET, and CT, the therapeutic characteristics incorporated with the features linked to therapy, and the ML models can give a system check of GBM. The integrated data of anatomy, metabolism, and clinical picture enhances the accuracy of prognosis and facilitates the selection of further treatment methods for the patient. This paper examines various factors such as current techniques and approaches, the advantages of utilizing data in diverse ways, and how machine learning handles all types of data. The increased accuracy reveals facts that contribute to the model's clinical relevance, indicating a better prognosis and subsequent better treatment. The paper looks at the issue regarding handling model heterogeneity and interpretability. Future work in this field will deal with finer tuning of the learning algorithms, data communication protocols, and new real-time monitoring instruments. Therefore, integrating MMC and MTT with features and ML is valuable in improving the prognosis and treatment of GBM to optimize patients? results. },
      keywords = {Glioblastoma Multiforme (GBM), Multimodal Imaging, Machine Learning, Prognostication, Magnetic Resonance Imaging, Clinical Implications.},
      month = {August},
  }