Literature DB >> 24430914

Histologic and systemic prognosticators for local control and survival in margin-negative transoral laser microsurgery treated oral cavity squamous cell carcinoma.

Parul Sinha1, Mitra Mehrad, Rebecca D Chernock, James S Lewis, Samir K El-Mofty, Ningying Wu, Brian Nussenbaum, Bruce H Haughey.   

Abstract

BACKGROUND: Appreciable local recurrence rates observed in patients with margin-negative, transoral laser microsurgery (TLM)-treated oral cavity squamous cell carcinoma (SCC) necessitate identification of new prognosticators for local control and survival. A histopathologic index (Brandwein-Gensler score [BGS]) and intrinsic/iatrogenic/chronic conditions causing immune compromise are investigated.
METHODS: From a prospectively assembled database of TLM-treated oral cavity SCC, specimens for 60 patients with a minimum of 2-years follow-up could undergo BGS assignment. Local control, disease-specific survival (DSS), and overall survival (OS) were study endpoints.
RESULTS: "Low-BGS" was recorded in 28 patients (47%) and "high-BGS" in 32 patients (53%), whereas immune compromise was observed in 18%. In multivariate analyses, immune compromise was the only predictor for local control. T classification and immune compromise were prognostic for DSS and OS. "High-BGS" was prognostic only for OS.
CONCLUSION: "High-BGS" was associated with recurrences but immune compromise was the most significant predictor of local control and survival in margin-negative, TLM-treated oral cavity SCC. Strategies that maintain/restore tumor-specific immune responses in immune compromised oral cavity SCC hosts need to be developed.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  Brandwein-Gensler score risk model; immune compromise; local control; oral carcinoma; transoral laser microsurgery (TLM)

Mesh:

Year:  2014        PMID: 24430914      PMCID: PMC4053486          DOI: 10.1002/hed.23553

Source DB:  PubMed          Journal:  Head Neck        ISSN: 1043-3074            Impact factor:   3.147


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7.  Histology segmentation using active learning on regions of interest in oral cavity squamous cell carcinoma.

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