Tumor volume as an independent predictive factor of worse survival in patients with oral cavity squamous cell carcinoma

Lin, Chin Shien; Santos, André Bandiera de Oliveira; Silva, Evandro Lima e; Matos, Leandro Luongo de; Moysés, Raquel Ajub; Kulcsar, Marco Aurélio Vamondes; Pinto, Fábio Roberto; Brandão, Lenine Garcia; Cernea, Cláudio Roberto

doi:10.1002/hed.24714

Cited by 29 publications

(18 citation statements)

References 65 publications

(129 reference statements)

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“…Tumour volume significantly predicted the disease-free and overall survival of patients with already advanced (T4a) OSCC. 38 A study by Mücke et al, including 437 OSCC patients, showed similar results, that is, tumour volume was significantly associated with the overall survival of the patients. 39…”

Section: Histopathological Factorsmentioning

confidence: 69%

Clinico-pathological prognosticators in oral squamous cell carcinoma

Majumdar¹,

Patil

Sarode

et al. 2017

Translational Research in Oral Oncology

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Despite diagnostic and therapeutic advancements, mortality and morbidity rates in the patients diagnosed with oral cancer remain static. Hence, an understanding of the factors that predict the progression of oral cancer appears to be useful in deciding on the most appropriate therapy, thereby improving survival. To do so in practice, a critical knowledge of prognostic markers that have high sensitivity is essential. Though recent research has introduced newer molecular markers, challenging the conventional prognostic assessment methods their universal application is yet to be determined. In practice, clinical and histopathological parameters are widely used for the selection of treatment strategies and for the determination of prognosis in oral cancer patients. A literature search yielded a multitude of parameters to be involved in the determination of prognosis of a patient. Based on published evidence, factors of significance were pTNM, tumour volume, depth of invasion, surgical margin status, tumour budding, vascular and perineural invasion, medullary bone invasion, extracapsular spread and the presence of distant metastasis.

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Section: Histopathological Factorsmentioning

confidence: 69%

Clinico-pathological prognosticators in oral squamous cell carcinoma

Majumdar¹,

Patil

Sarode

et al. 2017

Translational Research in Oral Oncology

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“…Although there are various advanced treatment options, recurrence rates are high. Local or regional relapse and cervical lymph node metastasis are threats to patients ( 12 ). Therefore, studies have been performed to elucidate the molecular mechanism of TSCC pathogenesis and explore potential biological markers of TSCC.…”

Section: Discussionmentioning

confidence: 99%

High expression of TMEM40 contributes to progressive features of tongue squamous cell carcinoma

et al. 2018

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Transmembrane protein 40 (TMEM40) is a 23-kDa protein and its association with tongue squamous cell carcinoma (TSCC) remains unclear. This study aimed to investigate the expression and clinical significance of TMEM40 in TSCC and its roles in TSCC cells. Immunohistochemical analysis was performed to detect the expression levels of TMEM40 in 60 tongue tissue samples. Furthermore, TMEM40 was overexpressed and inhibited in two TSCC cell lines by transfection with pEZ-M98-TMEM40 plasmid or TMEM40 small interfering RNA, respectively. Cell Counting Kit-8 and colony formation assays were used to investigate the effects of TMEM40 on cell proliferation and colony formation ability, respectively. Flow cytometry was performed to determine cell apoptosis and cycle conditions of transfected cells. Wound-healing and Transwell assays were processed to explore the effects of TMEM40 on cell migration and invasion, respectively. The results indicated that TMEM40 expression levels were significantly increased in TSCC tissues compared with adjacent normal tongue tissues (P<0.01). Clinicopathological analysis revealed that TMEM40 expression was positively correlated with pathological TNM (pTNM) status (P<0.05), histological grade (P<0.001) and clinical stage (P<0.01), but not with sex or age. Results of cell proliferation, apoptosis, migration and invasion assays indicated that when TMEM40 had been successfully overexpressed or knocked down in CAL27 and SCC9 TSCC cell lines, cell growth and invasion increased in the TMEM40 overexpressing cells, while they decreased in TMEM40-knockdown cells. Furthermore, experiments revealed that TMEM40 knockdown resulted in increased levels of p53 and Bax, and decreased levels of MMP-9, which indicated that TMEM40 regulated cell apoptosis and migration via involvement of p53, Bax and MMP-9 in TSCC cells. Our findings indicated that increased expression of TMEM40 contributed to progressive features of TSCC via regulation of p53, Bax and MMP-9.

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“…Recent literature has demonstrated that many radiomic signatures show strong dependence on tumour volume 28,29 , which is a simple image-derived feature and a known prognostic factor in HNC 35 . We evaluated the correlation of all binary predictions with volume using Spearman rank correlation (fig.…”

Section: Impact Of Volume Dependence On Model Performancementioning

confidence: 99%

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

Haibe‐Kains¹,

Kazmierski²,

Welch³

et al. 2021

Preprint

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Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

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Tumor volume as an independent predictive factor of worse survival in patients with oral cavity squamous cell carcinoma

Abstract: Among patients who already have advanced cancers, tumor volume can significantly impact their prognoses. © 2017 Wiley Periodicals, Inc. Head Neck 39: 960-964, 2017.

Cited by 29 publications

References 65 publications

Clinico-pathological prognosticators in oral squamous cell carcinoma

Clinico-pathological prognosticators in oral squamous cell carcinoma

High expression of TMEM40 contributes to progressive features of tongue squamous cell carcinoma

A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

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