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Concluding Remarks and Perspectives for Future Research

  • Antonio Giordano
  • Giovanni Luca Gravina
  • Luigi PirtoliEmail author
Chapter
  • 610 Downloads
Part of the Current Clinical Pathology book series (CCPATH)

Abstract

Early reports have demonstrated the usefulness of postoperative radiotherapy in improving survival outcomes of Glioblastoma (GB) after surgery, but presently a unanimous opinion in the oncology community is that the most significant, recent improvement in the prognosis of GB patients is due to Temozolomide chemotherapy, after well-known random trials. However, large database collections have shown also a highly significant role and a deep impact of the modern conformal radiation therapy techniques on prognosis, a result not shown by random studies for obvious ethical reasons. The possibility of further improving the results of radiotherapy in disease control by safely increasing radiation effectiveness is relevant. However, previous studies on radiation dose escalation have not achieved valuable results, due both to the radiation vulnerability of the brain and to the resistance of GB to high radiation doses, that is, an inherent feature of the tumor. This “active” adaptation to the radiation threat is probably more efficient than in other neoplasms. This intriguing aspect is presently the subject of intensive radiobiology preclinical research, aimed at circumventing the obstacle instead of escalating radiation dose. However, some limitations of prospective random trials addressing agents enhancing radiation effectiveness on biomolecular bases are the relatively small series, selective patients, long time of accrual, and reliability of results only within a restricted domain, which make it difficult to translate the results into generally improved clinical outcomes. Data mining techniques and ontology platforms, based on suitably constructed large databases including both pathobiology and clinical parameters, may produce very reliable working hypotheses for novel, radiation-based therapeutic strategies for GB.

Keywords

Data Mining Technique Deep Impact Restricted Domain Intrigue Aspect Unanimous Opinion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Antonio Giordano
    • 1
    • 2
  • Giovanni Luca Gravina
    • 3
  • Luigi Pirtoli
    • 4
    • 5
    Email author
  1. 1.Sbarro Institute for Cancer Research and Molecular Medicine and Center for BiotechnologyTemple UniversityPhiladelphiaUSA
  2. 2.Department of Medicine, Surgery & NeurosciencesUniversity of SienaSienaItaly
  3. 3.Department of Radiological, Oncological, and Anatomo-Pathological SciencesUniversity of Rome “La Sapienza”RomeItaly
  4. 4.Tuscany Tumor InstituteFlorenceItaly
  5. 5.Unit of Radiation Oncology, Department of Medicine, Surgery and NeurosciencesUniversity of SienaSienaItaly

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