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REVIEW ARTICLE
Year : 2015  |  Volume : 1  |  Issue : 1  |  Page : 21-32

Utilization and incorporation of tumor volume data in staging and prognostication of head and neck squamous cell carcinoma treated with definitive radiotherapy: A systematic review


Department of Radiation Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Rohini, New Delhi, India

Correspondence Address:
Dr. Parveen Ahlawat
Department of Radiation Oncology, Rajiv Gandhi Cancer Institute and Research Centre, Sector 5, Rohini - 110 085, New Delhi
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2454-6798.165089

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Head and neck squamous cell cancers (HNSCC) are a group of heterogeneous tumors, evident by their diverse behavior and natural history. The largest diameter of tumor measured for T classification may not necessarily reflect the true tumor dimension. There is a need to take into account certain other feature(s) of these tumors other than the maximum single dimension which can reflect the true tumor burden more accurately. Tumor volume has been shown to be a useful and accurate tool burden because it is a measurement of tumor burden in all three dimensions. This review article has compiled and reviewed the literature published in past on impact of tumor volumes (TVs) on the prognosis of head and neck cancers. A comprehensive literature search was performed in PubMed for terms "clonogens," "TV" or "primary TV (PTV)" or "nodal volume" or "total TV (TTV)" or "volumetric analysis of TV in head and neck" or "predicting response in head and neck cancer" "prognostic factors head and neck cancers" and "outcome in head and neck cancer." We identified 33 studies which have commented on the impact of TV in HNSCC on treatment outcome, 9 of these had analyzed PTV, 11 studies had analyzed total nodal volume, and 14 studies have analyzed TTV. Besides these, we have dealt with laryngeal cancers separately with 9 studies. This review article is also aimed to enhance our knowledge further regarding how best a physician can incorporate TV data in staging and predicting response to radiotherapy.


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