Metabolic tumour volume as a prognostic factor for oral cavity squamous cell carcinoma treated with primary surgery

Glycolytic pathway candidate markers in the prognosis of oral squamous cell carcinoma: a systematic review with meta-analysis

Molecular changes that affect mitochondrial glycolysis have been associated with the maintenance of tumor cells. Some metabolic factors have already been described as predictors of disease severity and outcomes. This systematic review was conducted to answer the question: Is the glycolytic pathway correlated with the prognosis of oral squamous cell carcinoma (OSCC)? A search strategy was developed to retrieve studies in English from PubMed, Scopus, and ISI Web of Science using keywords related to squamous cell carcinoma, survival, and glycolytic pathway, with no restriction of publication date. The search retrieved 1273 publications. After the titles and abstracts were analyzed, 27 studies met inclusion criteria. Studies were divided into groups according to two subtopics, glycolytic pathways and diagnosis, which describe the glycolytic profile of OSCC tumors. Several components of tumor energy metabolism found in this review are important predictors of survival of patients with OSCC.

Total lesion glycolysis in oral squamous cell carcinoma as a biomarker derived from pre-operative FDG PET/CT outperforms established prognostic factors in a newly developed multivariate prediction model

PURPOSE

Retrospective study to investigate the impact of image derived biomarkers from [¹⁸F]FDG PET/CT prior to surgical resection in patients with initial diagnosis of oral squamous cell carcinoma (OSCC), namely SUV_max, SUV_mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of the primary tumor to predict overall survival (OS).

MATERIALS AND METHODS

127 subsequent patients with biopsy-proven OSCC were included who underwent [¹⁸F]FDG PET/CT before surgery. SUV_max, SUV_mean, MTV and TLG of the primary tumor were measured. OS was estimated according to Kaplan-Meier and compared between median-splitted groups by the log-rank test. Prognostic parameters were analyzed by uni-/multivariate Cox-regression.

RESULTS

During follow-up 52 (41%) of the patients died. Median OS was longer for patients with lower MTV or lower TLG. SUV_max and SUV_mean failed to be significant predictors for OS. Univariate Cox-regression identified MTV, TLG, lymph node status and UICC stage as prognostic factors. By multivariate Cox-regression MTV and TLG turned out to be independent prognostic factors for OS.

CONCLUSIONS

The pre-therapeutic [¹⁸F]FDG PET/CT parameters MTV and TLG in the primary tumor are prognostic for OS of patients with an initial diagnosis of OSCC. TLG is the strongest independent prognostic factor for OS and outperforms established prognostic parameters in OSCC.

Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma

OBJECTIVE

To assess the utility of deep learning analysis using ¹⁸F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC).

METHODS

One hundred thirteen patients with OCSCC who received pretreatment FDG-PET/CT were included. They were divided into training (83 patients) and test (30 patients) sets. The diagnosis of treatment control/failure and the DFS rate were obtained from patients' medical records. In deep learning analyses, three planes of axial, coronal, and sagittal FDG-PET images were assessed by ResNet-101 architecture. In the training set, image analysis was performed for the diagnostic model creation. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. T-stage, clinical stage, and conventional FDG-PET parameters (the maximum and mean standardized uptake value (SUVmax and SUVmean), heterogeneity index, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were also assessed with determining the optimal cutoff from training dataset and then validated their diagnostic ability from test dataset.

RESULTS

In dividing into patients with treatment control and failure, the highest diagnostic accuracy of 0.8 was obtained using deep learning classification, with a sensitivity of 0.8, specificity of 0.8, positive predictive value of 0.89, and negative predictive value of 0.67. In the Kaplan-Meier analysis, the DFS rate was significantly different only with the analysis of deep learning-based classification (p < .01).

CONCLUSIONS

Deep learning-based diagnosis with FDG-PET images may predict treatment outcome in patients with OCSCC.

KEY POINTS

• Deep learning-based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning-based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters.

Epigenetics of oropharyngeal squamous cell carcinoma: opportunities for novel chemotherapeutic targets

Epigenetic modifications are heritable changes in gene expression that do not directly alter DNA sequence. These modifications include DNA methylation, histone post-translational modifications, small and non-coding RNAs. Alterations in epigenetic profiles cause deregulation of fundamental gene expression pathways associated with carcinogenesis. The role of epigenetics in oropharyngeal squamous cell carcinoma (OPSCC) has recently been recognized, with implications for novel biomarkers, molecular diagnostics and chemotherapeutics. In this review, important epigenetic pathways in human papillomavirus (HPV) positive and negative OPSCC are summarized, as well as the potential clinical utility of this knowledge.This material has never been published and is not currently under evaluation in any other peer-reviewed publication.