Value of radiological depth of invasion in non-pT4 Oral tongue squamous cell carcinoma: implication for preoperative MR T-staging.

OBJECTIVE: The prognostic stratification for oral tongue squamous cell carcinoma (OTSCC) is heavily based on postoperative pathological depth of invasion (pDOI). This study aims to propose a preoperative MR T-staging system based on tumor size for non-pT4 OTSCC. METHODS: Retrospectively, 280 patients with biopsy-confirmed, non-metastatic, pT1-3 OTSCC, treated between January 2010 and December 2017, were evaluated. Multiple MR sequences, including axial T2-weighted imaging (WI), unenhanced T1WI, and axial, fat-suppressed coronal, and sagittal contrast-enhanced (CE) T1WI, were utilized to measure radiological depth of invasion (rDOI), tumor thickness, and largest diameter. Intra-class correlation (ICC) and univariate and multivariate analyses were used to evaluate measurement reproducibility, and factors' significance, respectively. Cutoff values were established using an exhaustive method. RESULTS: Intra-observer (ICC = 0.81-0.94) and inter-observer (ICC = 0.79-0.90) reliability were excellent for rDOI measurements, and all measurements were significantly associated with overall survival (OS) (all p < .001). Measuring the rDOI on axial CE-T1WI with cutoffs of 8 mm and 12 mm yielded an optimal MR T-staging system for rT1-3 disease (5-year OS of rT1 vs rT2 vs rT3: 94.0% vs 72.8% vs 57.5%). Using multivariate analyses, the proposed T-staging exhibited increasingly worse OS (hazard ratio of rT2 and rT3 versus rT1, 3.56 [1.35-9.6], p = .011; 4.33 [1.59-11.74], p = .004; respectively), which outperformed pathological T-staging based on nonoverlapping Kaplan-Meier curves and improved C-index (0.682 vs. 0.639, p < .001). CONCLUSIONS: rDOI is a critical predictor of OTSCC mortality and facilitates preoperative prognostic stratification, which should be considered in future oral subsite MR T-staging. CLINICAL RELEVANCE STATEMENT: Utilizing axial CE-T1WI, an MR T-staging system for non-pT4 OTSCC was developed by employing rDOI measurement with optimal thresholds of 8 mm and 12 mm, which is comparable with pathological staging and merits consideration in future preoperative oral subsite planning. KEY POINTS: • Tumor morphology, measuring sequences, and observers could impact MR-derived measurements and compromise the consistency with histology. • MR-derived measurements, including radiological depth of invasion (rDOI), tumor thickness, and largest diameter, have a prognostic impact on OS (all p < .001). • rDOI with cutoffs of 8 mm and 12 mm on axial CE-T1WI is an optimal predictor of OS and could facilitate risk stratification in non-pT4 OTSCC disease.

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.

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.

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.

A novel nomogram for predicting overall survival in patients with tongue squamous cell carcinoma using clinical features and MRI radiomics data: a pilot study.

OBJECTIVE: Tongue squamous cell carcinoma (TSCC) accounts for 43.4% of oral cancers in China and has a poor prognosis. This study aimed to explore whether radiomics features extracted from preoperative magnetic resonance imaging (MRI) could predict overall survival (OS) in patients with TSCC. METHODS: The clinical imaging data of 232 patients with pathologically confirmed TSCC at Xiangyang No. 1 People's Hospital were retrospectively analyzed from February 2010 to October 2022. Based on 2-10 years of follow-up, patients were categorized into two groups: control (healthy survival, n = 148) and research (adverse events: recurrence or metastasis-related death, n = 84). A training and a test set were established using a 7:3 ratio and a time node. Radiomics features were extracted from axial T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging (DWI) sequences. The corresponding radiomics scores were generated using the least absolute shrinkage and selection operator algorithm. Kaplan-Meier and multivariate Cox regression analyses were used to screen for independent factors affecting adverse events in patients with TSCC using clinical and pathological results. A novel nomogram was established to predict the probability of adverse events and OS in patients with TSCC. RESULTS: The incidence of adverse events within 2-10 years after surgery was 36.21%. Kaplan-Meier analysis revealed that hot pot consumption, betel nut chewing, platelet-lymphocyte ratio, drug use, neutrophil-lymphocyte ratio, Radscore, and other factors impacted TSCC survival. Multivariate Cox regression analysis revealed that the clinical stage (P < 0.001), hot pot consumption (P < 0.001), Radscore 1 (P = 0.01), and Radscore 2 (P < 0.001) were independent factors affecting TSCC-OS. The same result was validated by the XGBoost algorithm. The nomogram based on the aforementioned factors exhibited good discrimination (C-index 0.86/0.81) and calibration (P > 0.05) in the training and test sets, accurately predicting the risk of adverse events and survival. CONCLUSION: The nomogram constructed using clinical data and MRI radiomics parameters may accurately predict TSCC-OS noninvasively, thereby assisting clinicians in promptly modifying treatment strategies to improve patient prognosis.

The prognostic role of MRI-based radiomics in tongue carcinoma: a multicentric validation study.

PURPOSE: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC). MATERIALS AND METHODS: Retrospective multicentric study on OTSCC surgically treated from 2010 to 2019. All patients performed preoperative MRI, including contrast-enhanced T1-weighted (CE-T1), diffusion-weighted sequences and apparent diffusion coefficient map. We evaluated overall survival (OS), locoregional recurrence-free survival (LRRFS), cause-specific mortality (CSM). We elaborated different models based on clinical and radiomic data. C-indexes assessed the prediction accuracy of the models. RESULTS: We collected 112 consecutive independent patients from three Italian Institutions to validate the previously published MRI radiomic models based on 79 different patients. The C-indexes for the hybrid clinical-radiomic models in the validation cohort were lower than those in the training cohort but remained > 0.5 in most cases. CE-T1 sequence provided the best fit to the models: the C-indexes obtained were 0.61, 0.59, 0.64 (pretreatment model) and 0.65, 0.69, 0.70 (posttreatment model) for OS, LRRFS and CSM, respectively. CONCLUSION: Our clinical-radiomic models retain a potential to predict OS, LRRFS and CSM in heterogeneous cohorts across different centers. These findings encourage further research, aimed at overcoming current limitations, due to the variability of imaging acquisition, processing and tumor volume delineation.

Three-dimensional assessment of tongue cancer prognosis by preoperative MRI.

PURPOSE: To obtain the relative volume by measuring the tongue volume and the lesion volume, and further explore its relationship with the prognosis of patients, hoping to supplement the TNM staging with a new index. METHODS: ITK-SANP software was used to outline the patients' MRI. After MRI reconstruction and measurement, slicer software was used to estimate tumor volume. RESULTS: A total of 64 patients with tongue cancer who met the inclusion criteria were included in the study. The estimated tumor volume after MRI reconstruction revealed a significant and robust correlation with tumor stage (p < 0.05, Rs = 0.6207) and a substantial and medium correlation with early lymph node metastasis (p < 0.05, Rs = 0.4873). CONCLUSIONS: We classified tongue cancer into three grades based on tumor volume (Stage I, tumors smaller than 1500 mm³; Stage II, tumors 1500-9000 mm³; and Stage III, tumors larger than 9000 mm³), and such grading could be used as a reference for tumor staging, lymph node metastasis, and patient prognosis to a certain extent.

Intraoral Ultrasound in Early Stage Tongue Carcinoma.

Preoperative assessment of depth of invasion (DOI) is critical in the surgical management of early stage carcinoma tongue. Intraoral ultrasound (IOUS) has been recently described as a cost-effective alternative to MRI in the accurate measurement of DOI. We describe the technique of IOUS in different types of lesions (surface, ulcerative, and exophytic); and provide key imaging pearls for the routine use of this novel ultrasound application.

Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer.

BACKGROUND: Adequate safe margin in tongue cancer radical surgery is one of the most important prognostic factors. However, the role of peritumoral tissues in predicting lymph node metastasis (LNM) and prognosis using radiomics analysis remains unclear. PURPOSE: To investigate whether magnetic resonance imaging (MRI)-based radiomics analysis with peritumoral extensions contributes toward the prediction of LNM and prognosis in tongue cancer. STUDY TYPE: Retrospective. POPULATION: Two hundred and thirty-six patients (38.56% female) with tongue cancer (training set, N = 157; testing set, N = 79; 37.58% and 40.51% female for each). FIELD STRENGTH/SEQUENCE: 1.5 T; T2-weighted turbo spin-echo images. ASSESSMENT: Radiomics models (Rprim , Rprim+3 , Rprim+5 , Rprim+10 , Rprim+15 ) were developed with features extracted from the primary tumor without or with peritumoral extensions (3, 5, 10, and 15 mm, respectively). Clinicopathological characteristics selected from univariate analysis, including MRI-reported LN status, radiological extrinsic lingual muscle invasion, and pathological depth of invasion (DOI) were further incorporated into radiomics models to develop combined radiomics models (CRprim , CRprim+3 , CRprim+5 , CRprim+10 , CRprim+15 ). Finally, the model performance was validated in the testing set. DOI was measured from the adjacent normal mucosa to the deepest point of tumor invasion. STATISTICAL TESTS: Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, decision analysis, spearman correlation analysis. The Delong test was used to compare area under the ROC (AUC). P < 0.05 was considered statistically significant. RESULTS: Of all the models, the CRprim+10 reached the highest AUC of 0.995 in the training set and 0.872 in the testing set. Radiomics features were significantly correlated with pathological DOI (correlation coefficients, -0.157 to -0.336). The CRprim+10 was an independent indicator for poor disease-free survival (hazard ratio, 5.250) and overall survival (hazard ratio, 17.464) in the testing set. DATA CONCLUSION: Radiomics analysis with a 10-mm peritumoral extension had excellent power to predict LNM and prognosis in tongue cancer.

Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.

OBJECTIVES: To investigate the feasibility of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI for predicting occult lymph node metastasis (LNM) in early-stage oral tongue squamous cell cancer (OTSCC). MATERIALS AND METHODS: This retrospective study included 55 early-stage OTSCC (cT1-2N0M0) patients; 34 with pathological LNM and 21 without. Eight whole-tumor histogram features were extracted from quantitative apparent diffusion coefficient (ADC) maps and two semi-quantitative DCE parametric maps (wash-in and wash-out). The clinicopathological factors and histogram features were compared between the two groups. Stepwise logistic regression was used to identify independent predictors. Receiver operating characteristic curves were generated to assess the performances of significant variables and a combined model for predicting occult LNM. RESULTS: MRI-determined depth of invasion and ADCentropy was significantly higher in the LNM group, with respective areas under the curve (AUCs) of 0.67 and 0.69, and accuracies of 0.73 and 0.73. ADC10th. ADCuniformity and wash-inskewness were significantly lower in the LNM group, with respective AUCs of 0.68, 0.71, and 0.69, and accuracies of 0.65, 0.71, and 0.64. Histogram features from wash-out maps were not significantly associated with cervical node status. In the logistic regression analysis, ADC10th, ADCuniformity, and wash-inskewness were independent predictors. The combined model yielded the best predictive performance, with an AUC of 0.87 and an accuracy of 0.82. CONCLUSIONS: Whole-tumor histogram analysis of ADC and wash-in maps is a feasible tool for preoperative evaluation of cervical node status in early-stage OTSCC. KEY POINTS: • Histogram analysis of parametric maps from DWI and DCE-MRI may assist the prediction of occult LNM in early-stage OTSCC. • ADC10th, ADCuniformity, and wash-inskewness were independent predictors. • The combined model exhibited good predictive performance, with an accuracy of 0.82.

Assessment of tumor depth in oral tongue squamous cell carcinoma with multiparametric MRI: correlation with pathology.

OBJECTIVES: To compare the correlation of depth of invasion (DOI) measured on multiple magnetic resonance imaging (MRI) sequences and pathological DOI, in order to determine the optimal MRI sequence for measurement. METHODS: A total of 122 oral tongue squamous cell carcinoma (OTSCC) patients were retrospectively analyzed, who had received preoperative MRI and surgical resection. DOIs measured on fat-suppressed T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic enhanced-T1 high-resolution insotropic volume examination (e-THRIVE), and contrast-enhanced fat-suppressed T1WI (CE-T1WI) were respectively compared to those measured in pathologic specimens. The cutoff value of the best correlated MRI sequence was determined, and the T staging accuracy of MRI-derived DOI was evaluated. RESULTS: DOI derived from e-THRIVE showed the best correlation (r = 0.936, p < 0.001) with pathological DOI. The area under the curve values of MRI-derived DOI distinguishing T1 stage from T2 stage and distinguishing T2 stage from T3 stage were 0.969 and 0.974, respectively. The T staging criteria of MRI-derived DOI were 6.2 mm and 11.4 mm, with a staging accuracy of 86.9% compared to pathological DOI criteria of 5 mm and 10 mm. CONCLUSION: E-THRIVE was the optimal MR sequence to measure the MR-derived DOI, and DOI derived from e-THRIVE could serve as a potential cut-off value as a clinical T staging indicator of OTSCC. KEY POINTS: • Multiparametric MRI helps radiologists to assess the neoplasm invasion in patients with oral tongue squamous cell carcinoma. • Retrospective study indicated that measurement was most accurate on enhanced-T1 high-resolution insotropic volume examination dynamic contrast enhancement images. • T staging of oral tongue squamous cell carcinoma was accurate according to the dynamic contrast enhancement MRI-derived depth of invasion.

A 3-Dimensional Analysis of Flap Volume Change in Hemi-Tongue Reconstruction.

BACKGROUND: An adequate volume of the tongue flap is essential to preserve speech and swallowing functions. However, it is generally known that the volume of the free flap tends to decrease over time because of various reasons. Especially in hemi-tongue reconstruction, as half of the normal tongue is retained, some functions are maintained; consequently, there are few studies related to the volume of the flap and function. This study investigated the relationship between flap volume change and function after hemi-tongue reconstruction. METHODS: A retrospective chart review of 26 patients who underwent hemi-tongue reconstruction between 2003 and 2020 was performed. Patient demographic data, postoperative radiotherapy (RT), and data on flap types were collected. The volume of the flap was measured by converting the computed tomography and magnetic resonance images into 3 dimensions using the Mimics software. In addition, speech scores and feeding scores were collected. RESULTS: The first follow-up computed tomography or magnetic resonance imaging (T1) was performed after an average of 2.09 months, and the second follow-up imaging study (T2) was performed at 16.58 months on average. On average, the T2 volume was 64% of the T1 volume (range, 45.75%-90.54%). Factors including speech and swallowing functions were compared by dividing the group into a group with a more than average decrease in volume (group 1) and a group with a less than average decrease in volume (group 2). In group 1, there were significantly more cases of postoperative RT than in group 2 (85.7%, 50.0%, in group 1 and group 2, respectively; P = 0.049). However, there was no difference in the functional aspects between the 2 groups. CONCLUSIONS: The present study revealed that the flap volume decreased more when RT was performed. However, there was no association between the degree of volume loss and speech and swallowing functions. Regardless of the group, the loss of function was not severe, probably because the remaining half of the tongue was functioning.

Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer.

BACKGROUND AND PURPOSE: To determine the neck management of tongue cancer, this study attempted to construct an artificial neural network (ANN)-assisted model based on computed tomography (CT) radiomics of primary tumors to predict neck lymph node (LN) status in patients with tongue squamous cell carcinoma (SCC). MATERIALS AND METHODS: Three hundred thirteen patients with tongue SCC were retrospectively included and randomly divided into training (60%), validation (20%) and internally independent test (20%) sets. In total, 1673 feature values were extracted after the semiautomatic segmentation of primary tumors and set as input layers of a classical 3-layer ANN incorporated with or without clinical LN (cN) status after dimension reduction. The receiver operating characteristic (ROC) curve, accuracy (ACC), sensitivity (SEN), specificity (SPE), area under curve (AUC) and Net Reclassification Index (NRI), were used to evaluate and compare the models. RESULTS: Four models with different settings were constructed. The ACC, SEN, SPE and AUC reached 84.1%, 93.1%, 76.5% and 0.943 (95% confidence interval: 0.891-0.996, p<.001), respectively, in the test set. The NRI of models compared with radiologists reached 40% (p<.001). The occult nodal metastasis rate was reduced from 30.9% to a minimum of 12.7% in the T1-2 group. CONCLUSION: ANN-based models that incorporated CT radiomics of primary tumors with traditional LN evaluation were constructed and validated to more precisely predict neck LN metastasis in patients with tongue SCC than with naked eyes, especially in early-stage cancer.

Application value of indocyanine green fluorescence in sentinel lymph node biopsy for early-stage tongue cancer and oropharyngeal cancer. / å²å“šèç»¿è§å ‰æ˜¾å½±åœ¨æ—©æœŸèˆŒç™ŒåŠå£å’½ç™Œå‰å“¨æ·‹å·´ç»“æ´»æ£€ä¸­çš„åº”ç”¨ä»·å€¼.

OBJECTIVES: Occult cervical lymph node metastasis is the most important reason for recurrence of early-stage tongue cancer and oropharyngeal cancer. Cervical sentinel lymph node (SLN) biopsy may help to identify them. Pigment dyes and radionuclide were used to label SLN. Both of them had shortage. This study aims to investigate the application and clinical value of indocyanine green fluorescence imaging in cervical SLN biopsy for patients with early-stage tongue cancer and oropharyngeal cancer. METHODS: Retrospective analysis was conducted on 23 patients with early tongue cancer and oropharyngeal cancer, who received surgical treatment and used indocyanine green as a tracer to find SLN in Hunan Cancer Hospital from April to October 2021. The detection rate of SLN was calculated and the distribution of SLN in different regions of the neck was analyzed. RESULTS: SLN was successfully identified in 22 of 23 patients, with a detection rate of 95.65%. Among these 22 patients, 3 patients were found to have cancer metastasis, and the rate of occult lymph node metastasis was 13.63%. No pathologically positive lymph nodes were detected in SLN-negative patients, and thus the positive predictive rate was 100%. For patients with primary lesions located in the anterior 2/3 of the tongue, the constituent ratios of SLN in neck area I, II, III, and IV were 15.15%, 71.72%, 13.13%, and 0, respectively. For patients with primary lesions located in base of the tongue, the constituent ratios of SLN in neck area I, II, III, and IV were 0, 44.44%, 44.44%, and 11.12%, respectively. CONCLUSIONS: Indocyanine green fluorescence imaging has a high detection rate with accurate positive prediction in the anterior cervical SLN biopsy in patients with early-stage tongue cancer and oropharyngeal cancer. Meanwhile, it can also reflect the lymphatic drainage of tumors located at different primary sites, which has high clinical value.

Machine learning-based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma.

OBJECTIVES: To develop and compare several machine learning models to predict occult cervical lymph node (LN) metastasis in early-stage oral tongue squamous cell cancer (OTSCC) from preoperative MRI texture features. MATERIALS AND METHODS: We retrospectively enrolled 116 patients with early-stage OTSCC (cT1-2N0) who had been surgically treated by tumor excision and elective neck dissection (END). For each patient, we extracted 86 texture features from T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (ceT1WI), respectively. Dimension reduction was performed in three consecutive steps: reproducibility analysis, collinearity analysis, and information gain algorithm. Models were created using six machine learning methods, including logistic regression (LR), random forest (RF), naïve Bayes (NB), support vector machine (SVM), AdaBoost, and neural network (NN). Their performance was assessed using tenfold cross-validation. RESULTS: Occult LN metastasis was pathologically detected in 42.2% (49/116) of the patients. No significant association was identified between node status and patients' gender, age, or clinical T stage. Dimension reduction steps selected 6 texture features. The NB model gave the best overall performance, which correctly classified the nodal status in 74.1% (86/116) of the carcinomas, with an AUC of 0.802. CONCLUSION: Machine learning-based MRI texture analysis offers a feasible tool for preoperative prediction of occult cervical node metastasis in early-stage OTSCC. KEY POINTS: • A machine learning-based MRI texture analysis approach was adopted to predict occult cervical node metastasis in early-stage OTSCC with no evidence of node involvement on conventional images. • Six texture features from T2WI and ceT1WI of preoperative MRI were selected to construct the predictive model. • After comparing six machine learning methods, naïve Bayes (NB) achieved the best performance by correctly identifying the node status in 74.1% of the patients, using tenfold cross-validation.

Comparison of Whiskbroom and Pushbroom darkfield elastic light scattering spectroscopic imaging for head and neck cancer identification in a mouse model.

The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model's capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.

Determination of Cervical Lymph Nodes Metastasis and Extra Nodal Extension Status by Quantitative Assessment of Border Irregularity and Apparent Diffusion Coefficient in Patients With Tongue Squamous Cell Carcinoma.

OBJECTIVE: The objective of this study was to determine the diagnostic value of quantitative border irregularity assessment and apparent diffusion coefficient (ADC) in patients with squamous cell carcinoma of the tongue (SCCT). METHODS: Cervical lymph nodes (n = 192) from 63 patients with SCCT were examined preoperatively by magnetic resonance imaging, including routine head and neck sequences, dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, ADC, surface regularity (SR), and visually assessed variables, and evaluated pathologically after surgery. RESULTS: Necrosis, lymphatic hilum, unclear margin, higher SR, long to short axis ratio, and ADC were associated with metastasis in cervical lymph nodes (M-cLNs) and extranodal extension (ENE), and thickened nodal rim with ENE alone. Apparent diffusion coefficient, SR, unclear margin, and visible necrosis were strongly associated with M-cLN, whereas SR, unclear margin, and visible necrosis were associated with ENE status on logistic regression analysis. CONCLUSIONS: Quantitative SR and ADC data greatly improved diagnosis of M-cLNs and ENE, relative to visible variables alone in patients with SCCT.

Dynamic contrast-enhanced magnetic resonance imaging in correlation with tongue cancer stages.

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a significant role in tumor stage as it can be used to measure tissue perfusion and permeability of tumors. PURPOSE: To investigate the relationships between both quantitative and semi-quantitative variables obtained from DCE-MRI and tongue cancer stages. MATERIAL AND METHODS: Mean values of Ktrans, enhancement ratio (ER), wash-in slope (slope), and the 95th percentile (95%) values of the distribution for Ktrans, ER, and slope values (Ktrans (95%), ER (95%), and slope (95%), respectively) were calculated for 53 patients with tongue cancers (American Joint Committee on Cancer 8th Edition stage group: 10 in stages I and II, 14 in stage III, 21 in stage IVa, and eight in stage IVb as determined by histopathologic assessment). The relationship between tumor staging and each of the six DCE-MRI parameters was assessed separately using ordinal logistic regression. RESULTS: The logistic regression analysis revealed that both mean and 95th percentile values of Ktrans were significantly and positively correlated with tongue cancer stage (P < 0.01). More aggressive tumor stages had larger kinetic parameter. Moreover, the semi-quantitative parameters, such as ER (95%) and slope (95%), may be more significant predictors for evaluating tongue cancer stages than the mean ER and mean slope. CONCLUSION: Both quantitative and semi-quantitative imaging biomarkers are useful for evaluating the stages of tongue cancer, and the indices obtained from DCE-MRI were positively correlated with the tumor stages. These parameters have the potential to non-invasively evaluate the stages of tongue cancer in the clinical setting.

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study.

BACKGROUND: Histologic grade assessment plays an important part in the clinical decision making and prognostic evaluation of squamous cell carcinoma (SCC) of the oral tongue and floor of mouth (FOM). PURPOSE: To assess the value of apparent diffusion coefficient (ADC)-based radiomics in discriminating between low- and high-grade SCC of the oral tongue and FOM. MATERIAL AND METHODS: We included data from 88 patients (training cohort: n = 59; testing cohort: n = 29) who underwent diffusion-weighted imaging with a 3.0-T magnetic resonance imaging scanner before treatment. A total of 526 radiomics features were extracted from ADC maps to construct a radiomics signature with least absolute shrinkage and selection operator logistic regression. Receiver operating characteristic curves and areas under the curve (AUCs) were used to evaluate the performance of radiomic signature. RESULTS: Five features were selected to construct the radiomics signature for predicting histologic grade. The ADC-based radiomics signature performed well for discriminating between low- and high-grade tumors, with AUCs of 0.83 in both cohorts. Based on the cut-off value of the training cohort, the radiomics signature achieved accuracies of 0.78 and 0.79, sensitivities of 0.65 and 0.71, and specificities of 0.85 and 0.82 in the training and testing cohorts, respectively. CONCLUSION: ADC-based radiomics can be a useful and promising non-invasive method for predicting histologic grade of SCC of the oral tongue and FOM.

Intraoral Ultrasonographic Features of Tongue Cancer and the Incidence of Cervical Lymph Node Metastasis.

PURPOSE: The purpose of this study was to investigate the relationship between the visual characteristics of tongue lesion images obtained through intraoral ultrasonographic examination and the occurrence of late cervical lymph node metastasis in patients with tongue cancer. PATIENTS AND METHODS: This study investigated patients with primary tongue cancer who were examined using intraoral ultrasonography at Hiroshima University Hospital between January 2014 and December 2017. The inclusion criteria were squamous cell carcinoma, curative treatment administration, lateral side of tongue, surgery or brachytherapy alone, no cervical lymph node or distant metastasis as primary treatment, and treatment in our hospital. The exclusion criteria were carcinoma in situ, palliative treatment, dorsum of tongue, and multiple primary cancers. The follow-up period was more than 1 year. The primary endpoint was the occurrence of late cervical lymph node metastasis, and the primary predictor variables were age, gender, longest diameter, thickness, margin or border shapes of the lesion, and treatment methods. The relationship between the occurrence of late cervical lymph node metastasis and the longest diameter, thickness, margin types, and border types as evaluated through intraoral ultrasonography were assessed. The data were collected through a retrospective chart review. RESULTS: Fifty-four patients were included in this study. The analysis indicated that irregular lesion margins were significantly associated with the occurrence of late cervical lymph node metastasis (P < .0001). The cutoff value for late cervical lymph node metastasis was 21.2 mm for the longest diameter and 3.9 mm for the thickness. CONCLUSIONS: The results of this study indicates that the irregular lesion margin assessed using intraoral ultrasonography may serve as an effective predictor of late cervical lymph node metastasis in N0 cases.