Presentation, Radiologic Features, and Treatment Options of Congenital Tongue Tumors: A Comprehensive Review.

AIM: Congenital tumors of the tongue are rare in pediatric patients but encompass a diverse range of entities. Each tumor type exhibits distinct clinical behaviors, necessitating a precise approach to differentiating the tumor types and a tailored, tumor-specific treatment regimen. Advanced imaging techniques, such as diffusion-weighted imaging and perfusion studies, play a vital role in differentiating benign and malignant tongue tumors. This review summarizes current knowledge regarding the presentation, imaging features, and treatment of congenital tongue tumors. METHODS: A literature review was conducted by searching studies on congenital tongue tumors in databases such as PubMed, Embase, Web of Science, and Scopus. Relevant data, such as clinical features, radiologic characteristics, treatment modalities, and outcomes for different tumor types, were extracted from the selected articles. RESULTS: Our literature review reveals the various entities of congenital tongue tumors, which can be categorized in terms of hereditary pattern, phenotype, and rarity. Congenital tongue tumors include a range of vascular malformations, such as hemangiomas, lymphatic malformations, arteriovenous malformations, and venous malformations. Another entity is represented by cystic lesions, including dermoid cysts, epidermoid cysts, ranulas, and mucous retention cysts. Rare malignant neoplasms include teratomas and rhabdomyosarcomas. These tumor types vary in terms of swelling, respiratory distress, or impaired oral function, depending on size and location. The detection of these tumors can be carried out using imaging modalities, such as ultrasound, magnetic resonance imaging, and computed tomography, which are utilized to facilitate diagnosis and differentiation. At present, surgical excision remains the cornerstone of treatment, while other modalities may be adopted, depending on tumor type and extent. The prognosis of congenital tongue tumors can be affected by tumor's site, size, involvement of vital structures, and malignancy. CONCLUSIONS: Given their diversity and complexity, congenital tongue tumors, albeit uncommon, require specialized clinical treatments tailored to each tumor type's characteristics. Understanding the variable presentations and imaging features enables accurate diagnosis, while customized treatment strategies are key to optimizing outcomes and minimizing morbidity in pediatric tongue tumors. This review summarizes current knowledge aimed at enhancing differential diagnosis and management of these diverse entities.

Feasibility of a Novel 3D Ultrasound Imaging Technique for Intraoperative Margin Assessment during Tongue Cancer Surgery.

Squamous cell carcinoma (SCC) of the tongue is the most prevalent form of oral cavity cancer, with surgical intervention as the preferred method of treatment. Achieving negative or free resection margins of at least 5 mm is associated with improved local control and prolonged survival. Nonetheless, margins that are close (1-5 mm) or positive (less than 1 mm) are often observed in practice, especially for the deep margins. Ultrasound is a promising tool for assessing the depth of invasion, providing non-invasive, real-time imaging for accurate evaluation. We conducted a clinical trial using a novel portable 3D ultrasound imaging technique to assess ex vivo surgical margin assessment in the operating room. During the operation, resected surgical specimens underwent 3D ultrasound scanning. Four head and neck surgeons measured the surgical margins (deep, medial, and lateral) and tumor area on the 3D ultrasound volume. These results were then compared with the histopathology findings evaluated by two head and neck pathologists. Six patients diagnosed with tongue SCC (three T1 stage and three T2 stage) were enrolled for a consecutive cohort. The margin status was correctly categorized as free by 3D ultrasound in five cases, and one case with a "free" margin status was incorrectly categorized by 3D ultrasound as a "close" margin. The Pearson correlation between ultrasound and histopathology was 0.7 (p < 0.001), 0.6 (p < 0.001), and 0.3 (p < 0.05) for deep, medial, and lateral margin measurements, respectively. Bland-Altman analysis compared the mean difference and 95% limits of agreement (LOA) for deep margin measurement by 3D ultrasound and histopathology, with a mean difference of 0.7 mm (SD 1.15 mm). This clinical trial found that 3D ultrasound is accurate in deep margin measurements. The implementation of intraoperative 3D ultrasound imaging of surgical specimens may improve the number of free margins after tongue cancer treatment.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Background: Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods: The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results: Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions: The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

Prognostic value of radiological T category using conventional MRI in patients with oral tongue cancer: comparison with pathological T category.

PURPOSE: This study aimed to compare the radiological tumor (T)-category using multiparametric MRI with the pathological T category in patients with oral tongue squamous cell carcinoma (OTSCC) and to examine which is a better predictor of prognosis. METHODS: This retrospective study included 110 consecutive patients with surgically resected primary OTSCC who underwent preoperative contrast-enhanced MRI. T categories determined by maximum diameter and depth of invasion were retrospectively assessed based on the pathological specimen and multiparametric MRI. The MRI assessment included the axial and coronal T1-weighted image (T1WI), axial T2-weighted image (T2WI), coronal fat-suppressed T2WI, and axial and coronal fat-suppressed contrast-enhanced T1WI (CET1WI). Axial and coronal CET1WI measurements were divided into two groups: measurements excluding peritumoral enhancement (MEP) and measurements including peritumoral enhancement. The prognostic values for recurrence and disease-specific survival after radiological and pathological T categorization of cases into T1/T2 and T3/T4 groups were compared. RESULTS: The T category of MEP on coronal CET1WI was the most relevant prognostic factor for recurrence [hazard ratio (HR) = 3.30, p = 0.001] and the HR was higher than the HR for pathological assessment (HR = 2.26, p = 0.026). The T category determined by MEP on coronal CET1WI was also the most relevant prognostic factor for disease-specific survival (HR = 3.12, p = 0.03), and the HR was higher than the HR for pathological assessment (HR = 2.02, p = 0.20). CONCLUSION: The T category determined by MEP on the coronal CET1WI was the best prognostic factor among all radiological and pathological T category measurements.

Magnetic resonance imaging accuracy in assessing depth of invasion in tongue squamous cell carcinomas and predicting cervical nodal metastasis.

OBJECTIVE: The aim of this study was to evaluate magnetic resonance imaging (MRI) accuracy in assessing the depth of invasion (DOI) compared to pathological DOI in oral tongue squamous cell carcinoma (SCC) and to determine whether MRI-measured DOI can predict lymph node metastasis in the cervical region. PATIENTS AND METHODS: This retrospective study comprised 36 patients diagnosed with oral tongue SCC who underwent head and neck MRI 1-30 days before surgery and were surgically treated at King Fahad Medical City between January 2017 and November 2022. Relevant information was collected from the patients' records, and the data were analyzed to determine the radiological-histopathological correlations for the DOI and ascertain the cutoff point for nodal metastasis. RESULTS: A value for Pearson's correlation coefficient between MRI-measured and pathological DOI was 0.86, indicating that these measures were highly associated and consistent with each other. The MRI-measured DOI coronal view (CV) was slightly overestimated than the pathological DOI by 1.72 mm. The cutoff values for the MRI-measured DOI CV and pathological DOI that indicated nodal metastasis were 7.08 mm and 9.04 mm, respectively. CONCLUSIONS: Preoperative MRI is a valuable tool to accurately stage oral tongue SCC by measuring the depth of tumor invasion.

Ultrasonography use for tongue cancer management: A scoping review.

BACKGROUND: Tongue cancer is associated with debilitating diseases and poor prognostic outcomes. The use of imaging techniques like ultrasonography to assist in the clinical management of affected patients is desirable, but its reliability remains debatable. Therefore, the aim of this study is to investigate the importance of ultrasound use for the clinicopathological management of tongue cancer. METHODS: A scoping review was carried out using specific search strategies in the following electronic databases: PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Collected data included bibliographical information, study design, ultrasound equipment, the aim of the ultrasonography use, the timing of ultrasound use during oncological treatment (pre-, trans-, and/or post-operatively), and the advantages and disadvantages of the use of the ultrasound. RESULTS: A total of 47 studies were included in this review after following the selection process. The majority of the studies investigated the use of ultrasound pre-operatively for the investigation of lymph node metastases or to determine the tumor thickness and depth of invasion. The sensitivity, specificity, and accuracy of ultrasound to determine clinical lymph node metastases ranged from 47% to 87.2%, from 84.3% to 95.8%, and from 70% to 86.2%, respectively. The sensitivity and specificity to determine the microscopic depth of invasion were 92.3% and from 70.6% to 82.1%, respectively. CONCLUSION: Ultrasonography seems to be a reliable imaging technique for the investigation of important prognostic parameters for tongue cancer, including depth of invasion and lymph node metastases.

Machine learning-based MRI radiomics for assessing the level of tumor infiltrating lymphocytes in oral tongue squamous cell carcinoma: a pilot study.

BACKGROUND: To investigate the value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in assessing tumor-infiltrating lymphocyte (TIL) levels in patients with oral tongue squamous cell carcinoma (OTSCC). METHODS: The study included 68 patients with pathologically diagnosed OTSCC (30 with high TILs and 38 with low TILs) who underwent pretreatment MRI. Based on the regions of interest encompassing the entire tumor, a total of 750 radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted (ceT1WI) imaging. To reduce dimensionality, reproducibility analysis by two radiologists and collinearity analysis were performed. The top six features were selected from each sequence alone, as well as their combination, using the minimum-redundancy maximum-relevance algorithm. Random forest, logistic regression, and support vector machine models were used to predict TIL levels in OTSCC, and 10-fold cross-validation was employed to assess the performance of the classifiers. RESULTS: Based on the features selected from each sequence alone, the ceT1WI models outperformed the T2WI models, with a maximum area under the curve (AUC) of 0.820 versus 0.754. When combining the two sequences, the optimal features consisted of one T2WI and five ceT1WI features, all of which exhibited significant differences between patients with low and high TILs (all P < 0.05). The logistic regression model constructed using these features demonstrated the best predictive performance, with an AUC of 0.846 and an accuracy of 80.9%. CONCLUSIONS: ML-based T2WI and ceT1WI radiomics can serve as valuable tools for determining the level of TILs in patients with OTSCC.

Magnetic resonance imaging-based radiomics and deep learning models for predicting lymph node metastasis of squamous cell carcinoma of the tongue.

OBJECTIVE: This study aimed to establish a combined method of radiomics and deep learning (DL) in magnetic resonance imaging (MRI) to predict lymph node metastasis (LNM) preoperatively in patients with squamous cell carcinoma of the tongue. STUDY DESIGN: In total, MR images of 196 patients with lingual squamous cell carcinoma were divided into training (n = 156) and test (n = 40) cohorts. Radiomics and DL features were extracted from MR images and selected to construct machine learning models. A DL radiomics nomogram was established via multivariate logistic regression by incorporating the radiomics signature, the DL signature, and MRI-reported LN status. RESULTS: Nine radiomics and 3 DL features were selected. In the radiomics test cohort, the multilayer perceptron model performed best with an area under the receiver operating characteristic curve (AUC) of 0.747, but in the DL cohort, the best model (logistic regression) performed less well (AUC = 0.655). The DL radiomics nomogram showed good calibration and performance with an AUC of 0.934 (outstanding discrimination ability) in the training cohort and 0.757 (acceptable discrimination ability) in the test cohort. The decision curve analysis demonstrated that the nomogram could offer more net benefit than a single radiomics or DL signature. CONCLUSION: The DL radiomics nomogram exhibited promising performance in predicting LNM, which facilitates personalized treatment of tongue cancer.

Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-dimensional ultrasound volumes.

Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes. Additionally, it investigates the clinical effect of automatic segmentation. A 3D No New U-Net (nnUNet) was trained on 113 manually annotated ultrasound volumes of resected tongue carcinoma. The model was implemented on a mobile workstation and clinically validated on 16 prospectively included tongue carcinoma patients. Different prediction settings were investigated. Automatic segmentations with multiple islands were adjusted by selecting the best-representing island. The final margin status (FMS) based on automatic, semi-automatic, and manual segmentation was computed and compared with the histopathological margin. The standard 3D nnUNet resulted in the best-performing automatic segmentation with a mean (SD) Dice volumetric score of 0.65 (0.30), Dice surface score of 0.73 (0.26), average surface distance of 0.44 (0.61) mm, Hausdorff distance of 6.65 (8.84) mm, and prediction time of 8 seconds. FMS based on automatic segmentation had a low correlation with histopathology (r = 0.12, p = 0.67); MS resulted in a moderate but insignificant correlation with histopathology (r = 0.4, p = 0.12, n = 16). Implementing the 3D nnUNet yielded fast, automatic segmentation of tongue carcinoma in 3D ultrasound volumes. Correlation between FMS and histopathology obtained from these segmentations was lower than the moderate correlation between MS and histopathology.

Machine learning based on magnetic resonance imaging and clinical parameters helps predict mesenchymal-epithelial transition factor expression in oral tongue squamous cell carcinoma: a pilot study.

OBJECTIVES: This study aimed to develop machine learning models to predict phosphorylated mesenchymal-epithelial transition factor (p-MET) expression in oral tongue squamous cell carcinoma (OTSCC) using magnetic resonance imaging (MRI)-derived texture features and clinical features. METHODS: Thirty-four patients with OTSCC were retrospectively collected. Texture features were derived from preoperative MR images, including T2WI, apparent diffusion coefficient mapping, and contrast-enhanced (ce)-T1WI. Dimension reduction was performed consecutively with reproducibility analysis and an information gain algorithm. Five machine learning methods-AdaBoost, logistic regression (LR), naïve Bayes (NB), random forest (RF), and support vector machine (SVM)-were adopted to create models predicting p-MET expression. Their performance was assessed with fivefold cross-validation. RESULTS: In total, 22 and 12 cases showed low and high p-MET expression, respectively. After dimension reduction, 3 texture features (ADC-Minimum, ce-T1WI-Imc2, and ce-T1WI-DependenceVariance) and 2 clinical features (depth of invasion [DOI] and T-stage) were selected with good reproducibility and best correlation with p-MET expression levels. The RF model yielded the best overall performance, correctly classifying p-MET expression status in 87.5% of OTSCCs with an area under the receiver operating characteristic curve of 0.875. CONCLUSION: Differences in p-MET expression in OTSCCs can be noninvasively reflected in MRI-based texture features and clinical parameters. Machine learning can potentially predict biomarker expression levels, such as MET, in patients with OTSCC.

Incidental Tongue Cancer Detected With Total-Body 13 N-NH 3 PET/CT.

ABSTRACT: A 68-year-old man with chest tightness underwent cardiac blood perfusion imaging on total-body 13 N-NH 3 PET/CT. Incidentally, mildly increased 13 N-NH 3 activity was observed in the left side of the body of the tongue. Pathological diagnosis proved to be mucosal squamous cell carcinoma.

Depth of Invasion in Carcinoma Tongue: Evaluation of Clinical and Imaging Techniques.

BACKGROUND: "Depth of invasion" is an additional index incorporated in 8th AJCC staging system for oral cavity squamous cell carcinoma based on its prognostic significance. Pre-operative assessment by clinical palpation and imaging modalities has been used with limitations. The aim of the study is to compare different techniques including clinical palpation, ultrasound, and magnetic resonance imaging with histopathology for assessment of depth of tumor invasion. MATERIALS: Fifty patients of carcinoma tongue (T1-T3) were enrolled. Clinical palpation, Ultrasound tongue, and Magnetic resonance imaging were used to assess depth of tumor invasion. Microscopic depth of invasion was considered as reference. Statistical analysis was done to assess the level of agreement, reliability, and internal consistency. ROC analysis was done to find the "Area Under Curve" for microscopic depth versus ultrasound, MRI, and gross histopathological "depth of invasion". RESULTS: Ultrasound tongue showed highest "area under curve", Intra class correlation (ICC:0.786) with a good consistency (Cronbach's Alpha:0.880) with histological reference compared to MRI(ICC:0.689;CA:0.816). Clinical palpation showed weak agreement (Kappa:0.43) for assessing depth. To observe the concordance between ultrasound and microscopic depth, Lin's Concordance Correlation Coefficient (CCC = 0.782) was calculated with 95% limits of agreement. Lin's concordance correlation between ultrasound and microscopic depth showed a good agreement. CONCLUSIONS: Ultrasound tongue is a reliable imaging modality for pre-operative T staging by assessing tumor "depth of invasion" in carcinoma tongue patients with good internal consistency as per 8th AJCC staging system. LEVEL OF EVIDENCE: 2 (CEBM-Level of Evidence-2.1) Laryngoscope, 134:215-221, 2024.

The application of radiomics machine learning models based on multimodal MRI with different sequence combinations in predicting cervical lymph node metastasis in oral tongue squamous cell carcinoma patients.

BACKGROUND: The purpose of this study was to explore preliminary the performance of radiomics machine learning models based on multimodal MRI to predict the risk of cervical lymph node metastasis (CLNM) for oral tongue squamous cell carcinoma (OTSCC) patients. METHODS: A total of 400 patients were enrolled in this study and divided into six groups according to the different combinations of MRI sequences. Group I consisted of patients with T1-weighted images (T1WI) and FS-T2WI (fat-suppressed T2-weighted images), group II consisted of patients with T1WI, FS-T2WI, and contrast enhanced MRI (CE-MRI), group III consisted of patients with T1WI, FS-T2WI, and T2-weighted images (T2WI), group IV consisted of patients with T1WI, FS-T2WI, CE-MRI, and T2WI, group V consisted of patients with T1WI, FS-T2WI, T2WI, and apparent diffusion coefficient map (ADC), and group VI consisted of patients with T1WI, FS-T2WI, CE-MRI, T2WI, and ADC. Machine learning models were constructed. The performance of the models was compared in each group. RESULTS: The machine learning model in group IV including T1WI, FS-T2WI, T2WI, and CE-MRI presented best prediction performance, with AUCs of 0.881 and 0.868 in the two sets. The models with CE-MRI performed better than the models without CE-MRI(I vs. II, III vs. IV, V vs. VI). CONCLUSIONS: The radiomics machine learning models based on CE-MRI showed great accuracy and stability in predicting the risk of CLNM for OTSCC patients.

Prediction of Prognosis of Tongue Squamous Cell Carcinoma Based on Clinical MR Imaging Data Modeling.

Objective: Tongue squamous cell carcinoma (TSCC) is one of the most common and poor prognosis head and neck tumors. The purpose of this study is to establish a model for predicting TSCC prognosis based on clinical and MR radiomics data and to develop a nomogram. Methods: A retrospective analysis was performed on the clinical and imaging data of 211 patients with pathologically confirmed TSCC who underwent radical surgery at xx hospital from February 2011 to January 2020. Patients were divided into a study group (recurrence, metastasis, and death, n = 76) and a control group (normal survival, n = 135) according to 1 to 6 years of follow-up. A training set and a test set were established based on a ratio of 7:3 and a time point. In the training set, 3 prediction models (clinical data model, imaging model, and combined model) were established based on the MR radiomics score (Radscore) combined with clinical features. The predictive performance of these models was compared using the Delong curve, and the clinical net benefit of the model was tested using the decision curve. Then, the external validation of the model was performed in the test set, and a nomogram for predicting TSCC prognosis was developed. Results: Univariate analysis confirmed that betel nut consumption, spicy hot pot or pickled food, unclean oral sex, drug use, platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), depth of invasion (DOI), low differentiation, clinical stage, and Radscore were factors that affected TSCC prognosis (P < .05). In the test set, the combined model based on these factors had the highest predictive performance for TSCC prognosis (area under curve (AUC) AUC: 0.870, 95% CI [0.761-0.942]), which was significantly higher than the clinical model (AUC: 0.730, 95% CI [0.602-0.835], P = .033) and imaging model (AUC: 0.765, 95% CI [0.640-0.863], P = .074). The decision curve also confirmed the higher clinical net benefit of the combined model, and these results were validated in the test set. The nomogram developed based on the combined model received good evaluation in clinical application. Conclusion: MR-LASSO extracted texture parameters can help improve the performance of TSCC prognosis models. The combined model and nomogram provide support for postoperative clinical treatment management of TSCC.

Radiomics analysis of intraoral ultrasound images for prediction of late cervical lymph node metastasis in patients with tongue cancer.

BACKGROUND: We investigated the predictability of late cervical lymph node metastasis using radiomics analysis of ultrasonographic images of tongue cancer. METHODS: We selected 120 patients with tongue cancer who underwent intraoral ultrasonography, 30 of which had late cervical lymph node metastasis. Radiomics analysis was used to extract and quantify the image features. Bootstrap forest (BF), support vector machine (SVM), and neural tanh boost (NTB) were used as the machine learning models, and receiver operating characteristic curve analysis was conducted to determine diagnostic performance. RESULTS: The sensitivity, specificity, accuracy, and AUC in the validation group were, respectively, 0.600, 0.967, 0.875, and 0.923 for the BF model; 0.700, 0.967, 0.900, and 0.950 for the SVM model; and 0.900, 0.967, 0.950, and 0.967 for NTB model. CONCLUSIONS: Radiomics analysis and machine learning models using ultrasonographic images of pretreated tongue cancer could predict late cervical lymph node metastasis with high accuracy.

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.

Long-term control of lung metastasis of tongue cancer treated effectively with cetuximab combination chemotherapy.

Generally, systemic chemotherapy is indicated for oral squamous cell carcinoma with distant metastasis and has a poor prognosis. Recently, the advent of molecular targeted drugs, such as cetuximab and immune checkpoint inhibitors, has dramatically improved prognosis, though controlling distant metastasis remains challenging. We report a case of tongue cancer in which lung metastases disappeared in the long term. A 60-year-old Japanese male with squamous cell carcinoma of the tongue underwent preoperative chemoradiotherapy and surgery including subtotal glossectomy, bilateral modified radical neck dissection, and immediate reconstruction with an anterolateral thigh flap. One month after surgery, multiple nodules less than 10 mm in diameter appeared in both lungs on CT imaging. Multiple lung metastases were diagnosed with no local recurrence or regional lymph node metastasis. The patient continues to receive a 4-week treatment course of chemotherapy that included cetuximab every 3 months and the lung metastases were markedly reduced in size or had disappeared. No local recurrence or newly emerged metastases were observed. The patient has been doing well for nine years since the appearance of the lung metastases.

Prognostic significance of the harmonized maximum standardized uptake value of 18F-FDG-PET/CT in patients with resectable oral tongue squamous cell carcinoma: a multicenter study.

OBJECTIVES: To investigate the usefulness of harmonized 18F-FDG-PET/CT parameters for predicting the postoperative recurrence and prognosis of oral tongue squamous cell carcinoma (OTSCC). METHODS: We retrospectively analyzed the cases of 107 OTSCC patients who underwent surgical resection at four institutions in Japan in 2010-2016 and evaluated the harmonized PET parameters of the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for the primary tumor as the pSUVmax, pMTV, and pTLG. For lymph node metastasis, we used harmonized PET parameters of nodal-SUVmax, nodal-total MTV (tMTV), and nodal-total TLG (tTLG). The associations between the harmonized PET parameters and the patients' relapse-free survival (RFS) and overall survival (OS) were evaluated by the Kaplan-Meier method and Cox proportional hazard regression analysis for model 1 (preoperative stage) and model 2 (preoperative + postoperative stages). RESULTS: The harmonized SUVmax values were significantly lower than those before harmonization (p=0.012). The pSUVmax was revealed as a significant preoperative risk factor for RFS and OS. Nodal-SUVmax, nodal-tMTV, and nodal-tTLG were significant preoperative risk factors for OS. The combination of pSUVmax + nodal-SUVmax significantly stratified the patients into a low-risk group (pSUVmax <3.97 + nodal-SUVmax <2.85 or ≥2.85) and a high-risk group (pSUVmax ≥3.97 + nodal-SUVmax <2.85 or pSUVmax ≥3.97 + nodal-SUVmax ≥2.85) for recurrence and prognosis (RFS: p=0.001; OS: p<0.001). CONCLUSIONS: The harmonized pSUVmax is a significant prognostic factor for the survival of OTSCC patients. The combination of pSUVmax and nodal-SUVmax identified OTSCC patients at high risk for recurrence and poor prognosis at the preoperative stage.

Imaging nodal volume and survival in oral tongue cancer with cervical lymph node metastasis.

OBJECTIVES: Oral tongue cancer is the most prevalent type of oral cavity cancer and presents the worst prognosis. With the use of TNM staging system, only the size of primary tumor and lymph node are considered. However, several studies have considered the primary tumor volume as a possible significant prognostic factor. Our study, therefore, aimed to explore the role of nodal volume from imaging as a prognostic implication. METHODS: Medical records and imaging (either from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan) of 70 patients diagnosed with oral tongue cancer with cervical lymph node metastasis between January 2011 and December 2016 were retrospectively reviewed. The pathological lymph node was identified, and nodal volume was measured using the Eclipse radiotherapy planning system and was further analysed for its prognostic implications, particularly on overall survival, disease-free survival, and distant metastasis-free survival. RESULTS: From A Receiver Operating Characteristic (ROC) curve analysis, the optimal cut-off value of the nodal volume was 3.95 cm3, to predict the disease prognosis, in terms of overall survival and metastatic-free survival (p ≤ 0.001 and p = 0.005, respectively), but not the disease-free survival (p = 0.241). For the multivariable analysis, the nodal volume, but not TNM staging, was a significant prognostic factor for distant metastasis. CONCLUSIONS: In patients with oral tongue cancer and cervical lymph node metastasis, the presence of an imaging nodal volume of ≥3.95 cm3 was a poor prognostic factor for distant metastasis. Therefore, the lymph node volume may have a potential role to adjunct with the current staging system to predict the disease prognosis. LEVEL OF EVIDENCE: 2b.