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.

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.

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.

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.

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.

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.

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.

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.

Au/Mn nanodot platform for in vivo CT/MRI/FI multimodal bioimaging and photothermal therapy against tongue cancer.

Surgical resection is the main method for oral tongue squamous cell carcinoma (OTSCC) treatment. However, the oral physiological function and aesthetics may be seriously damaged during the operation with a high risk of recurrence. Therefore, it is important to develop an alternative strategy with precise guidance for OTSCC treatment. Herein, multifunctional Au/Mn nanodots (NDs) are designed and synthesized. They can perform multimodal bioimaging, including computed tomography (CT) and magnetic resonance imaging (MRI) simultaneously, and exhibit bright near-infrared fluorescence imaging (FI) for navigation, and even integrate photothermal therapy (PTT) property. The localization of OTSCC relies on visual and tactile cues of surgeons while lacking noninvasive pretreament labeling and guidance. Au/Mn NDs provide CT/MRI imaging, giving two means of accurate positioning pretherapy. Meanwhile, the fluorescence of the Au/Mn NDs in the near-infrared region (NIR) is beneficial for noninvasive labeling and intuitive observation with the naked eye to determine the tumor boundary during PTT. Further, Au/Mn NDs showed excellent results in ablating tumors in vivo. Above all, the Au/Mn NDs provide a key platform for multimodal bioimaging and PTT in a single nanoagent, which demonstrated attractive performance for OTSCC treatment.

The role of radiomics in tongue cancer: A new tool for prognosis prediction.

BACKGROUND: Radiomics represents an emerging field of precision-medicine. Its application in head and neck is still at the beginning. METHODS: Retrospective study about magnetic resonance imaging (MRI) based radiomics in oral tongue squamous cell carcinoma (OTSCC) surgically treated (2010-2019; 79 patients). All preoperative MRIs include different sequences (T1, T2, DWI, ADC). Tumor volume was manually segmented and exported to radiomic-software, to perform feature extraction. Statistically significant variables were included in multivariable analysis and related to survival endpoints. Predictive models were elaborated (clinical, radiomic, clinical-radiomic models) and compared using C-index. RESULTS: In almost all clinical-radiomic models radiomic-score maintained statistical significance. In all cases C-index was higher in clinical-radiomic models than in clinical ones. ADC provided the best fit to the models (C-index 0.98, 0.86, 0.84 in loco-regional recurrence, cause-specific mortality, overall survival, respectively). CONCLUSION: MRI-based radiomics in OTSCC represents a promising noninvasive method of precision medicine, improving prognosis prediction before surgery.

A 3D map of the lingual artery-The perfect tool for transoral robotic surgeries on the base of tongue.

BACKGROUND: Knowledge about the anatomy of the lingual artery (LA) is of immense importance when performing procedures on the base of tongue (BOT). METHODS: A retrospective analysis was performed to establish morphometric data of the LA. The measurements were performed on 55 consecutive patients who underwent head and neck computed tomography angiographies (CTA). RESULTS: A total of 96 LAs were analyzed. Additionally, a three-dimensional heat map (showing the oropharyngeal region from the lateral, anterior, and superior point of view) of the occurrence of the LA and its branches was created. CONCLUSION: The length of the main trunk of the LA was measured to be 31.94 ± 11.44 mm. This reported distance is thought to be a surgical safe zone when performing transoral robotic surgery (TORS) on the BOT because it represents the area where the LA does not give off any major branches.

Stiffness as measured with strain elastography is a prognostic factor for pT1/T2 tongue squamous cell carcinoma with muscle-layer invasion.

OBJECTIVE: The objective was to evaluate stiffness as a prognostic factor for tongue squamous cell carcinoma (TSCC). STUDY DESIGN: This retrospective study included 55 patients with pathologic stage pT1 or T2 TSCC with muscle-layer invasion who underwent preoperative strain elastography of the tongue, followed by surgery, as the primary treatment modality at our cancer center. The stiffness of TSCC was semi-quantified as the ratio of the strain value of a non-tumor site to the strain value of the tumor site (strain ratio [SR]) using ultrasound strain elastography findings. RESULTS: SR cutoff values that maximized the significance of the difference for prognosis of delayed cervical lymph node metastasis (DCLNM) and overall survival (OS) were 7.10 and 7.49, respectively. In univariate analysis, SR, age, depth of invasion, pT stage, and perineural invasion were significant risk factors for DCLNM, whereas SR, sex, and DCLNM were identified as having an association with OS. In multivariate analysis, SR was a significant risk factor for DCLNM (hazard ratio [HR] = 3.102; P = .021) and a non-significant but relevant risk factor for OS (HR = 8.774; P = .073). Age also had an association with OS (HR = 0.382; 95% CI 0.127-1.152; P = .088). CONCLUSION: Tongue stiffness is a prognostic factor in patients with pT1/T2 TSCC with muscle-layer invasion. SR values >7.10 indicate a poor prognosis, thereby warranting a strict follow-up regimen in these cases.