Depth of invasion determined by magnetic resonance imaging in tongue cancer can be a predictor of cervical lymph node metastasis

Efficacy of radiological depth of invasion measurements on magnetic resonance images acquired at different magnetic field strengths and imaging sequences in predicting cervical lymph node metastasis and other outcomes in tongue cancer

OBJECTIVES

We investigated the correlation between magnetic resonance imaging (MRI) parameters and tumor pathological depth of invasion (pDOI), between pDOI and radiological DOI (rDOI), between rDOI and duration between biopsy and MRI, and between rDOI and duration between MRI and surgery to determine the efficacy of rDOI in identifying small lesions and other conditions.

STUDY DESIGN

We examined 36 adult patients who had been diagnosed histopathologically with cancer of the tongue and had undergone a glossectomy. Using 1.5 Tesla (T) and 3.0T MRI, we measured rDOI at the deepest infiltration point on 4 MRI sequences. We calculated the correlations between rDOI and the variables examined by Spearman rho analysis and evaluated the diagnostic performance of rDOI by receiver operating characteristic curve analysis.

RESULTS

Axial T2-weighted images using 1.5T MRI provided the closest approximation of pDOI. Although the correlation between rDOI and pDOI was significant, rDOI showed poor or acceptable discrimination in identifying small lesions and other conditions. There were no significant correlations between rDOI and the time between biopsy and MRI or between MRI and surgery.

CONCLUSIONS

The correlation between rDOI and pDOI is significant, but rDOI is ineffective in predicting malignancy and other conditions. Axial T2-weighted images using 1.5T MRI provide the closest approximation of pDOI.

Accuracy of magnetic resonance imaging in the assessment of depth of invasion in tongue carcinoma: A systematic review and meta-analysis

Tongue carcinoma constitutes 10.4-46.9% of all oral squamous cell carcinomas (OSCCs) and is notoriously known for invading tissues deeper than the evident gross margins. The deeper the tumor invades, the higher are its chances of future morbidity and mortality due to extensive neck dissection and risk of recurrence. Magnetic resonance imaging (MRI) is a noninvasive diagnostic aid used for measuring a preoperative tumor's depth of invasion (DOI) as it can efficiently outline soft tissue tumors from adjacent normal tissue. To assess various MRI modalities used in measuring DOI in tongue carcinoma and their reliability compared with other DOI measuring modalities. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42022330866), and the following Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) Diagnostic Test Accuracy guidelines were performed. PubMed electronic database was searched using a combination of keywords for relevant articles in the English language since 2016. Critical appraisal was carried out using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) risk-of-bias (RoB) assessment tool. A weighted mean difference (WMD) was calculated between MRI and histopathological DOI along with pooled correlation and subgroup analysis, where possible. A total of 795 records were retrieved of which 17 were included in the final review with 13 included for meta-analysis. A high RoB was found for most studies for all parameters except flow and timing. WMD showed a statistically significant MRI overestimation of 1.90 mm compared with histopathology. Subgroup analysis showed the 1.5 Tesla machine to be superior to the 3.0 Tesla machine, while imaging sequence subgroup analysis could not be performed. MRI is a viable preoperative DOI measurement modality that can help in efficient treatment planning to decrease surgical morbidity and mortality.

A nomogram for predicting survival in Patients with oral tongue keratinized squamous cell carcinoma: A SEER-based study

OBJECTIVE

Oral tongue keratinized squamous cell carcinoma (OTKSCC), a relatively rare form of tongue cancer (TC) in clinical practice, accompanied by features of cell keratosis, is an uncommon histological subtype. However, its specific clinicopathological features and prognosis have not been adequately described. In this study, we aimed to create a nomogram using R language software to predict overall survival (OS) of patients with OTKSCC to assess the prognosis of OTKSCC patients.

METHODS

We extracted clinical and related prognostic data of OTKSCC patients from 1975 to 2019 from the Surveillance, Epidemiology, and End Results database. Independent prognostic factors were selected using univariate and multivariate Cox analyses, and a nomogram was constructed using R software. The C-index, area under the curve (AUC) of receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to assess the clinical utility of the nomogram. Finally, OS was assessed using the Kaplan-Meier method.

RESULTS

A total of 2450 OTKSCC patients were included in the study. Univariate and multivariate Cox regression analyses were used to identify age, T stage, N stage, surgery, and radiation therapy as independent risk factors (p<0.05). In the training cohort, the calibration index of the nomogram was 0.725, while the AUC values for nomogram, age, T stage, N stage, surgery and radiation therapy were 0.878, 0.639, 0.781, 0.661, 0.724 and 0.354, respectively. At the same time, in the verification queue, the calibration index of the nomogram was 0.726, while the AUC values for nomogram, age, T stage, N stage, surgery and radiation therapy were 0.859,0.612,0.826,0.675,0.758 and 0.303, respectively. Ideal uniformity of the models from the training and validation cohorts was demonstrated in the calibration and DCA curves. Univariate survival analysis showed that age, T stage, N stage, surgery, and radiotherapy were statistically significant for prognosis (p<0.05).

CONCLUSION

Age, T stage, N stage, surgery, and radiation therapy are independently associated with the OS, and the established nomogram is an effective visualization tool for predicting the OS of OTKSCC patients.

Accuracy of Magnetic Resonance Imaging in Detecting Tumor Depth of Invasion in Squamous Cell Carcinoma of the Tongue: A Systematic Review

Objective

The objective of the study was to detect the accuracy of Magnetic Resonance Imaging (MRI) in assessing tumor depth of invasion (DOI) in squamous cell carcinoma (SCC) of the tongue.

Material and Methods

The electronic search of PubMed (including MEDLINE), COCHRANE CENTRAL and Google Scholar search engine for articles published from January 1, 2000, to September 31, 2021, was conducted and also searched the lists of references of relevant articles and reviews for studies involving patients with squamous cell carcinoma of the tongue.

Results

A total of 5362 articles were retrieved in the initial search. After the initial search process, 13 full-text articles were reviewed. Out of these 13 articles, seven met the inclusion criteria and were thus included in this systematic review.

Conclusion

The MRI-determined DOI based on T1-weighted sequences increases with increasing T stage. There is the highest correlation between the MRI-derived DOI and the histopathological DOI with increasing T stage. Therefore, MRI provides satisfactory diagnostic accuracy for measuring tumor DOI and, thus, may be considered a predictor of tumor stage.

Magnetic resonance imaging-based prediction models for tumor stage and cervical lymph node metastasis of tongue squamous cell carcinoma

Purpose

To evaluate the ability of preoperative MRI-based measurements to predict the pathological T (pT) stage and cervical lymph node metastasis (CLNM) via machine learning (ML)-driven models trained in oral tongue squamous cell carcinoma (OTSCC).

Materials and methods

108 patients with a new diagnosis of OTSCC were enrolled. The preoperative MRI study included post-contrast high-resolution T1-weighted images acquired in all patients. MRI-based depth of invasion (DOI) and tumor dimension-together with shape-based and intensity-based features-were extracted from the lesion volume segmentation. The entire dataset was randomly divided into a training set and a validation set, and the performances of different types of ML algorithms were evaluated and compared.

Results

MRI-based DOI and tumor dimension together with several shape-based and intensity-based signatures significantly discriminated the pT stage and LN status. The overall accuracy of the model for predicting the pT stage was 0.86 (95%CI, 0.78-0.92) and 0.81 (0.64-0.91) in the training and validation sets, respectively. There was no improvement in the model performance upon including shape-based and intensity-based features. The model for predicting CLNM based on DOI and tumor dimensions had a fair accuracy of 0.68 (0.57-0.78) and 0.69 (0.51-0.84) in the training and validation sets, respectively. The shape-based and intensity-based signatures have shown potential for improving the model sensitivity, with a comparable accuracy.

Conclusion

MRI-based models driven by ML algorithms could stratify patients with OTSCC according to the pT stages. They had a moderate ability to predict cervical lymph node metastasis.

Diagnostic and prognostic value of magnetic resonance imaging in the detection of tumor depth of invasion and bone invasion in patients with oral cavity cancer

PURPOSE

To evaluate the accuracy of preoperative contrast-enhanced magnetic resonance imaging (MRI) in the assessment of radiological depth of invasion (rDOI) and bone invasion in patients with oral cavity cancer, and the prognostic value of preoperative rDOI.

MATERIALS AND METHODS

This retrospective study included patients with surgically resected oral cavity cancer and preoperative MRI acquired within four weeks before surgery. Two readers evaluated the MRI to assess the superficial and deep bone invasion, preoperative T stage, and measured the rDOI. The rDOI was compared to the histopathological DOI (pDOI), used as reference standard. Prognostic value of preoperative features for the disease-specific survival was evaluated using the Kaplan-Meier curve and multivariable Cox proportional hazards analysis.

RESULTS

The final population included 80 patients (50 males, mean age 67.7 ± 13.6 years). There was a strong statistically significant correlation between the rDOI (median 10 mm) and the pDOI (median 9 mm) (ρ: 0.978, p < 0.001). The agreement between MRI and histopathological T stage was excellent (k = 0.93, 95% CI 0.86, 0.99). The sensitivity and specificity of preoperative MRI were 93.3% and 98.8% for deep bone invasion, while they were 75.0% and 95.8% for superficial bone invasion, respectively. The rDOI > 10 mm was associated with poorer disease-specific survival (log-rank p = 0.016). The rDOI remained the only independent preoperative predictor associated with poorer disease-specific survival at multivariable analysis (hazard ratio 5.5; 95% CI 1.14, 26.58; p = 0.033).

CONCLUSION

Preoperative MRI is accurate for the assessment of DOI and bone invasion. The rDOI is an independent preoperative predictor of disease-specific survival in patients with oral cavity cancer.

Roles of IGFBP-3 in cell migration and growth in an endophytic tongue squamous cell carcinoma cell line

Insulin-like growth factor binding protein-3 (IGFBP-3) is a member of the IGFBP family that has high affinity for IGFs and functions as either an oncogene or tumor suppressor in various types of cancer. We previously found that IGFBP3 mRNA levels are higher in endophytic-type human tongue squamous cell carcinoma (TSCC) that is more invasive and more prone to metastasis than exophytic and superficial types. This finding prompted us to investigate the roles of IGFBP-3 in TSCC using SAS cells, which were originally derived from endophytic-type TSCC. Specifically, we used SAS cells that express a fluorescent ubiquitination-based cell-cycle indicator (Fucci). RNA-sequencing analysis indicated that IGFBP-3 is associated with cell migration and cell growth. In fact, IGFBP-3 knockdown downregulates cell migration and causes cells to arrest in G₁. This migratory potential appears to be cell cycle–independent. IGFBP-3 knockdown also reduced levels of secreted IGFBP-3; however, decreased migratory potential was not rescued by exogenous recombinant human IGFBP-3. Furthermore, ERK activity was downregulated by IGFBP-3 depletion, which suggests that MEK/ERK signaling may be involved in IGFBP-3-mediated cell migration. We therefore conclude that intracellular IGFBP-3 enhances cell migration independently of the cell cycle in TSCC with a higher metastatic potential.

Development of a radiomics and machine learning model for predicting occult cervical lymph node metastasis in patients with tongue cancer

OBJECTIVE

We aimed to develop a predictive model for occult cervical lymph node metastasis in patients with tongue cancer using radiomics and machine learning from pretreatment contrast-enhanced computed tomography.

STUDY DESIGN

This study included 161 patients with tongue cancer who received local treatment. Computed tomography images were transferred to a radiomics platform. The volume of interest was the total neck node level, including levels Ia, Ib, II, III, and IVa at the ipsilateral side, and each neck node level. The dimensionality of the radiomics features was reduced using least absolute shrinkage and selection operator logistic regression analysis. We compared 5 classifiers with or without the synthetic minority oversampling technique (SMOTE).

RESULTS

For the analysis at the total neck node level, random forest with SMOTE was the best model, with an accuracy of 0.85 and an area under the curve score of 0.92. For the analysis at each neck node level, a support vector machine with SMOTE was the best model, with an accuracy of 0.96 and an area under the curve score of 0.98.

CONCLUSIONS

Predictive models using radiomics and machine learning have potential as clinical decision support tools in the management of patients with tongue cancer for prediction of occult cervical lymph node metastasis.