A radiomics model to predict γδ T-cell abundance and overall survival in head and neck squamous cell carcinoma.

γδ T cells are becoming increasingly popular because of their attractive potential for antitumor immunotherapy. However, the role and assessment of γδ T cells in head and neck squamous cell carcinoma (HNSCC) are not well understood. We aimed to explore the prognostic value of γδ T cell and predict its abundance using a radiomics model. Computer tomography images with corresponding gene expression data and clinicopathological data were obtained from online databases. After outlining the volumes of interest manually, the radiomic features were screened using maximum melevance minimum redundancy and recursive feature elimination algorithms. A radiomics model was developed to predict γδ T-cell abundance using gradient boosting machine. Kaplan-Meier survival curves and univariate and multivariate Cox regression analyses were used for the survival analysis. In this study, we confirmed that γδ T-cell abundance was an independent predictor of favorable overall survival (OS) in patients with HNSCC. Moreover, a radiomics model was built to predict the γδ T-cell abundance level (the areas under the operating characteristic curves of 0.847 and 0.798 in the training and validation sets, respectively). The calibration and decision curves analysis demonstrated the fitness of the model. The high radiomic score was an independent protective factor for OS. Our results indicated that γδ T-cell abundance was a promising prognostic predictor in HNSCC, and the radiomics model could discriminate its abundance levels and predict OS. The noninvasive radiomics model provided a potentially powerful prediction tool to aid clinical judgment and antitumor immunotherapy.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors. METHODS: This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve. RESULTS: After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model's performance, increasing the validation cohort AUC to 0.87. CONCLUSION: In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach. CLINICAL RELEVANCE STATEMENT: This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.

Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis.

PURPOSE: This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. METHODS: Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS: Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. CONCLUSION: Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

Impact of Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography on Therapeutic Decisions and Radiotherapy Planning in Head and Neck Squamous Carcinoma: A Retrospective Study of 46 Patients.

BACKGROUND Positron emission tomography/computed tomography (PET/CT) using fluorodeoxyglucose (FDG) is essential in oncology for precise tumor delineation. This study evaluated FDG PET/CT's impact on therapeutic decisions in head and neck cancer, comparing metabolic tumor volumes (MTV) measured by different methods with radiotherapy targets, crucial for treatment planning and patient outcomes. MATERIAL AND METHODS We retrospectively analyzed 46 patients with histologically confirmed head and neck cancer who underwent FDG PET/CT examination before radiotherapy. The mean age was 62 years (46-78 years). Then, we calculated MTV of the primary tumor or local recurrence using a local threshold of 41% of the standard uptake volume (SUV) corrected for lean body mass (SULmax) of the lesion and absolute threshold of SUV 2.5. Descriptive analysis of the recruited patients was assessed based on the clinical database (Medsol). RESULTS The study included 45 patients with squamous carcinoma and 1 with sarcoid cell carcinoma. PET/CT examination led to therapeutic decision changes in 11 cases. No significant difference was found in median values of Gross Tumor Volume (GTV) and MTV absolute (p=0.130). However, significant differences were observed in MTV local, MTV absolute, and GTV median values (p<0.001), with both MTVs showing significant correlation with GTV (p<0.01), especially MTV absolute (r=0.886). CONCLUSIONS FDG PET/CT examination prior to radiotherapy significantly influences therapeutic decisions in head and neck cancer patients. Based on our findings, the absolute threshold method (SUV: 2.5) appears to be an effective approach for calculating MTV for radiotherapy planning purposes.

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.

Apparent diffusion coefficient for differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck: a systematic review and meta-analysis.

BACKGROUND: Radiological differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck is often difficult due to their similarities. PURPOSE: To evaluate the diagnostic benefit of apparent diffusion coefficient (ADC) calculated from diffusion-weighted imaging (DWI) in differentiating the two. MATERIAL AND METHODS: A systematic review was performed by searching the MEDLINE, Scopus, and Embase databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Forest plots and the pooled mean difference of ADC values were calculated to describe the relationship between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Heterogeneity among studies was evaluated using the Cochrane Q test and I2 statistic. RESULTS: The review identified eight studies with 440 patients (441 lesions) eligible for meta-analysis. Among all studies, the mean ADC values of squamous cell carcinoma was 0.88 × 10-3mm2/s and that of lymphoma was 0.64 × 10-3mm2/s. In the meta-analysis, the ADC value of lymphoma was significantly lower than that of squamous cell carcinoma (pooled mean difference = 0.235, 95% confidence interval [CI] = 0.168-0.302, P <0.0001). The Cochrane Q test (chi-square = 55.7, P <0.0001) and I2 statistic (I2 = 87.4%, 95% CI = 77.4-93.0%) revealed significant heterogeneity. CONCLUSION: This study highlights the value of quantitative assessment of ADC for objective and reliable differentiation between extra-nodal lymphoma and squamous cell carcinoma in the head and neck. Conclusions should be interpreted with caution due to heterogeneity in the study data.

Potential imaging targets in primary head and neck squamous cell carcinoma and lymph node metastases.

PURPOSE: To investigate glycoprotein nonmetastatic melanoma protein B (GPNMB) and vascular endothelial growth factor (VEGF) as potential fluorescent imaging markers by comparing their protein expression to epidermal growth factor receptor (EGFR). MATERIALS AND METHODS: Thirty-eight paired samples of untreated head and neck squamous cell carcinoma (HNSCC) primary tumours (PT) and corresponding synchronous lymph node metastases (LNM) were selected. After immunohistochemical staining, expression was assessed and compared by the percentage of positive tumour cells. Data were analysed using the Mann-Whitney test, effect sizes (ESr) and Spearman's correlation coefficient (r). RESULTS: GPNMB expression was observed in 100 % of PT, and median 80 % (range 5-100 %) of tumour cells, VEGF in 92 % and 60 % (0-100 %), EGFR in 87 % and 60 % (0-100 %) respectively. In corresponding LNM, GPNMB expression was observed in 100 % of LNM and median 90 % (20-100 %) of tumour cells, VEGF in 87 % and 65 % (0-100 %), and EGFR in 84 % and 35 % (0-100 %). A positive correlation was found between expression in PT and LNM for GPNMB (r = 0.548) and EGFR (r = 0.618) (p < 0.001), but not for VEGF (r = -0.020; p = 0.905). GPNMB expression was present in a higher percentage of tumour cells compared to EGFR in PT (p = 0.015, ESr = -0.320) and in LNM (p < 0.001, ESr = -0.478), while VEGF was not (p = 1.00, ESr = -0.109 and - 0.152, respectively). CONCLUSION: GPNMB expression is higher than EGFR in untreated HNSCC PT and corresponding LNM, while VEGF expression is comparable to EGFR. GPNMB is a promising target for fluorescent imaging in HNSCC.

The prognostic effect of radiological extranodal extension in HPV-positive oropharyngeal squamous cell carcinomas: a retrospective cohort analysis.

PURPOSE: Radiological extranodal extension (rENE) is a well-known negative prognosticator in head and neck squamous cell carcinoma (HNSCC). However, controversy remains regarding the prognostic effect of rENE in HPV-positive oropharyngeal SCCs (OPSCC). This single-center retrospective cohort analysis assessed the prognostic role of rENE in an HPV + OPSCC population and tried to validate a recently proposed modification of the TNM8 N-classification. METHODS: 129 patients with HPV + OPSCC, of whom 106 cN + patients, were included. Radiological imaging (CT, MRI or both) was reanalyzed by a senior head and neck radiologist. Overall survival (OS), disease-free survival (DFS), locoregional recurrence-free survival (LRFS), and disease-specific survival (DSS) were evaluated. Cox proportional hazard models were used for estimating hazard ratios (HR). RESULTS: A non-significant trend towards better outcomes in the rENE- group, as compared to the rENE + population, was observed for 5 year OS [80.99% vs 68.70%, HR: 2.05, p = 0.160], 5 year RFS [78.81% vs 67.87%, HR: 1.91, p = 0.165], 5 year DFS [77.06% vs 60.16%, HR: 2.12, p = 0.0824] and 5 year DSS [88.83% vs 81.93%, HR: 2.09, p = 0.195]. OS declined with ascending levels of rENE (p = 0.020). Multivariate analysis identified cT-classification and smoking as independent negative predictors for OS/DFS. The proposed modification of the TNM8 N-classification could not be validated. CONCLUSIONS: Although rENE could not be identified as an independent negative prognosticator for outcome in our HPV + OPSCC population, outcomes tend to deteriorate with increasing rENE.

The Prognostic Significance of Tumor SUVmax Value in Pre- and Post-Chemoradiotherapy 18F-FDG PET/CT Imaging in Patients with Localized and Advanced Head and Neck Squamous Cell Carcinoma.

BACKGROUND: Some parameters of 18F-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) can predict tumor chemosensitivity and survival in patients with head and neck squamous cell carcinoma (HNSCC). AIM: The aim of the study was to investigate the prognostic value of pre- and post-treatment maximum standardized uptake values (SUVmax) in 18F-FDG PET/CT imaging for predicting mortality in patients with HNSCC, as well as its prognostic value in terms of disease progression, overall survival (OS), and progression-free survival (PFS). METHODS: This retrospective study included 37 patients with a histopathological diagnosis of HNSCCs between 2015 and 2018. In patients with HNSCC, the first 18F-FDG PET/CT imaging was performed for pre-treatment staging, and the second imaging was performed to evaluate post-treatment response. In these imaging studies, SUVmax values of the primary tumor before and after treatment were determined. After the second imaging, patients were re-evaluated and followed up. ROC analysis was used to determine the predictive value of 18F-FDG PET/CT SUVmax parameters in terms of death and progression, and Cox regression analysis was used to investigate the prognostic value in terms of OS and PFS. RESULTS: Cut-off value 15 for SUVmax1 (pre-treatment) had a significant predictive value for mortality (P = 0.02). Cut-off value 3.1 for SUVmax2 (post-treatment) had a significant predictive value for progression (P = 0.024). In univariate analysis, both SUVmax1 and SUVmax2 values were significant prognostic factors for OS (P = 0.047, P = 0.004). However, for PFS, only the SUVmax2 value was a significant prognostic factor (P = 0.001). CONCLUSION: SUVmax1 value of the primary tumor at diagnosis in HNSCC patients has a predictive value for mortality and a prognostic value for OS. However, the SUVmax2 value in the primary tumor after treatment is a predictive factor for progression and a prognostic factor for both OS and PFS.

The Importance of the Time Interval Between Preoperative 18F-FDG PET/CT Imaging and Neck Dissection for the Detection of Nodal Metastases in Patients with Head and Neck Squamous Cell Carcinoma.

BACKGROUND: Detection of nodal metastasis is critical for the treatment and prognosis of head and neck cancer (HNC). Positron emission tomography/computed tomography (PET/CT) is increasingly being used to detect cervical lymph node involvement. AIM: The purposes of this study were to (1) investigate the diagnostic accuracy of PET/CT for the detection of neck metastasis in patients with HNC and (2) determine the effect of the time interval between surgery and PET/CT. METHODS: Fifty patients with head and neck squamous cell carcinoma who underwent PET/CT before surgery were included in this study. Preoperative PET/CT images that determined lymph node metastasis were compared with the histopathological analysis of neck dissection samples. Neck dissections were divided into three groups according to the time interval between surgery and PET/CT (0-2 weeks, >2-4 weeks, and >4 weeks). The concordance between PET/CT and histopathology was measured using the neck sides at different time intervals. The specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV) of PET/CT in detecting metastatic lymph nodes in the neck were calculated. RESULTS: A total of 79 neck dissections were included in the study as 29 (58%) of the patients underwent bilateral neck dissection. The overall accuracy of PET/CT in detecting nodal metastasis was highest for the 0-2 weeks interval (95.6%). During this time interval, the sensitivity, specificity, NPV, and PPV of PET/CT were 100%, 90.9%, 100%, and 92.3%, respectively. CONCLUSIONS: Although PET/CT is an important and reliable diagnostic method for detecting nodal metastases in patients with HNC, its reliability decreases as the time between surgeries increases. The optimal interval was 2 weeks; however, up to 4 weeks was acceptable.

Oxygen-enhanced MRI assessment of tumour hypoxia in head and neck cancer is feasible and well tolerated in the clinical setting.

BACKGROUND: Tumour hypoxia is a recognised cause of radiotherapy treatment resistance in head and neck squamous cell carcinoma (HNSCC). Current positron emission tomography-based hypoxia imaging techniques are not routinely available in many centres. We investigated if an alternative technique called oxygen-enhanced magnetic resonance imaging (OE-MRI) could be performed in HNSCC. METHODS: A volumetric OE-MRI protocol for dynamic T1 relaxation time mapping was implemented on 1.5-T clinical scanners. Participants were scanned breathing room air and during high-flow oxygen administration. Oxygen-induced changes in T1 times (ΔT1) and R2* rates (ΔR2*) were measured in malignant tissue and healthy organs. Unequal variance t-test was used. Patients were surveyed on their experience of the OE-MRI protocol. RESULTS: Fifteen patients with HNSCC (median age 59 years, range 38 to 76) and 10 non-HNSCC subjects (median age 46.5 years, range 32 to 62) were scanned; the OE-MRI acquisition took less than 10 min and was well tolerated. Fifteen histologically confirmed primary tumours and 41 malignant nodal masses were identified. Median (range) of ΔT1 times and hypoxic fraction estimates for primary tumours were -3.5% (-7.0 to -0.3%) and 30.7% (6.5 to 78.6%) respectively. Radiotherapy-responsive and radiotherapy-resistant primary tumours had mean estimated hypoxic fractions of 36.8% (95% confidence interval [CI] 17.4 to 56.2%) and 59.0% (95% CI 44.6 to 73.3%), respectively (p = 0.111). CONCLUSIONS: We present a well-tolerated implementation of dynamic, volumetric OE-MRI of the head and neck region allowing discernment of differing oxygen responses within biopsy-confirmed HNSCC. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04724096 . Registered on 26 January 2021. RELEVANCE STATEMENT: MRI of tumour hypoxia in head and neck cancer using routine clinical equipment is feasible and well tolerated and allows estimates of tumour hypoxic fractions in less than ten minutes. KEY POINTS: • Oxygen-enhanced MRI (OE-MRI) can estimate tumour hypoxic fractions in ten-minute scanning. • OE-MRI may be incorporable into routine clinical tumour imaging. • OE-MRI has the potential to predict outcomes after radiotherapy treatment.

Combined average standard uptake value and rate constant can predict expression of EGFR in hypopharyngeal squamous cell carcinoma: A pilot study with integrated PET/MRI.

PURPOSE: To investigate whether the quantitative multiparameters of 18F-FDG PET/MRI can predict expression of epidermal growth factor receptor (EGFR) of hypopharyngeal squamous cell carcinoma (HSCC). METHODS: Twenty-one patients with HSCC confirmed by biopsy underwent neck integrated 18F-FDG PET/MRI and EGFR expression detection. Quantitative parameters derived from 18F-FDG PET, difusion-weighted imaging (DWI), and dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) were measured. The efficacies of quantitative multiparameters derived from 18F-FDG PET/MRI for predicting the expression of EGFR of HSCC were evaluated. RESULTS: The patients were divided into positive expression group (PEG, n = 14) and negative expression group (NEG, n = 7). Mann-Whitney U nonparametric test showed that SUVmean and Kep had statistical difference between PEG and NPG, while other parameters had no statistical difference. Using 14.50 and 2.10 min-1 as the threshold values, areas under the curve (AUCs) for SUVmean and Kep were 0.786 with specificity of 92.9 % and sensitivity of 57.1 %. The combined use of SUVmean and Kep had better efficacy to evaluate the expression of EGFR with AUC of 0.980, sensitivity of 92.9 %, and specificity of 100.0 %. CONCLUSION: Combined use of SUVmean and Kep showed good performance in predicting the expression of EGFR in HSCC. Integrated 18F-FDG PET/MRI enables simultaneous acquisition of SUVmean and Kep, so it represents as a powerful tool to noninvasively and repeatably evaluate the expression of EGFR during the management of HSCC.

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.

Quantitative US Delta Radiomics to Predict Radiation Response in Individuals with Head and Neck Squamous Cell Carcinoma.

Purpose To investigate the role of quantitative US (QUS) radiomics data obtained after the 1st week of radiation therapy (RT) in predicting treatment response in individuals with head and neck squamous cell carcinoma (HNSCC). Materials and Methods This prospective study included 55 participants (21 with complete response [median age, 65 years {IQR: 47-80 years}, 20 male, one female; and 34 with incomplete response [median age, 59 years {IQR: 39-79 years}, 33 male, one female) with bulky node-positive HNSCC treated with curative-intent RT from January 2015 to October 2019. All participants received 70 Gy of radiation in 33-35 fractions over 6-7 weeks. US radiofrequency data from metastatic lymph nodes were acquired prior to and after 1 week of RT. QUS analysis resulted in five spectral maps from which mean values were extracted. We applied a gray-level co-occurrence matrix technique for textural analysis, leading to 20 QUS texture and 80 texture-derivative parameters. The response 3 months after RT was used as the end point. Model building and evaluation utilized nested leave-one-out cross-validation. Results Five delta (Δ) parameters had statistically significant differences (P < .05). The support vector machines classifier achieved a sensitivity of 71% (15 of 21), a specificity of 76% (26 of 34), a balanced accuracy of 74%, and an area under the receiver operating characteristic curve of 0.77 on the test set. For all the classifiers, the performance improved after the 1st week of treatment. Conclusion A QUS Δ-radiomics model using data obtained after the 1st week of RT from individuals with HNSCC predicted response 3 months after treatment completion with reasonable accuracy. Keywords: Computer-Aided Diagnosis (CAD), Ultrasound, Radiation Therapy/Oncology, Head/Neck, Radiomics, Quantitative US, Radiotherapy, Head and Neck Squamous Cell Carcinoma, Machine Learning Clinicaltrials.gov registration no. NCT03908684 Supplemental material is available for this article. © RSNA, 2024.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images.

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images.Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics-the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)-to evaluate the agreement between the manual and semi-automatic methods.Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC.

Radiomic Application for Head and Neck Squamocellular Tumor: Systematic Review.

Abstract: Radiomics represents the convergence of artificial intelligence and radiological data analysis, primarily applied in the diagnosis and treatment of cancer. In the head and neck region, squamous cell carcinoma is the most prevalent type of tumor. Recent radiomics research has revealed that specific bio-imaging characteristics correlate with various molecular features of Head and Neck Squamous Cell Carcinoma (HNSCC), particularly Human Papillomavirus (HPV). These tumors typically present a unique phenotype, often affecting younger patients, and show a favorable response to radiation therapy. This study provides a systematic review of the literature, summarizing the application of radiomics in the head and neck region. It offers a comprehensive analysis of radiomics-based studies on HNSCC, evaluating its potential for tumor evaluation, risk stratification, and outcome prediction in head and neck cancer treatment.

Association of circulating tumor HPV16DNA levels and quantitative PET parameters in patients with HPV-positive head and neck squamous cell carcinoma.

Circulating tumor DNA (ctDNA), which circulates in the blood after being shed from cancer cells in the body, has recently gained attention as an excellent tumor marker. The purpose of this study was to evaluate whether ct human papillomavirus (HPV) 16 DNA (ctHPV16DNA) levels were associated with quantitative PET parameters in patients with HPV-positive head and neck (HN) squamous cell carcinoma (SCC). Fifty patients with oropharyngeal SCC (OPSCC) and 5 with SCC of unknown primary (SCCUP) before treatment were included. They all underwent blood sampling to test ctHPV16DNA levels and FDG PET-CT examinations. Quantitative PET parameters included SUVmax, metabolic tumor volume (MTV), MTV of whole-body lesions (wbMTV), and 56 texture features. ctHPV16DNA levels were compared to texture features of primary tumors in OPSCC patients (Group A) or the largest primary or metastatic lymph node lesions in OPSCC and SCCUP patients (Group B) and to other PET parameters. Spearman rank correlation test and multiple regression analysis were used to confirm the associations between ctHPV16DNA levels and PET parameters. ctHPV16DNA levels moderately correlated with wbMTV, but not with SUVmax or MTV in Groups A and B. ctHPV16DNA levels exhibited a weak negative correlation with low gray-level zone emphasis in Groups A and B. Multiple regression analysis revealed that wbMTV and high gray-level zone emphasis were the significant factors for ctHPV16DNA levels in Group B. These results were not observed in Group A. This study demonstrated that ctHPV16DNA levels correlated with the whole-body tumor burden and tumor heterogeneity visualized on FDG PET-CT in patients with HPV-positive HNSCC.

Evaluation of bone marrow invasion on the machine learning of 18 F-FDG PET texture analysis in lower gingival squamous cell carcinoma.

OBJECTIVES: Lower gingival squamous cell carcinoma (LGSCC) has the potential to invade the alveolar bone. Traditionally, the diagnosis of LGSCC relied on morphological imaging, but inconsistencies between these assessments and surgical findings have been observed. This study aimed to assess the correlation between LGSCC bone marrow invasion and PET texture features and to enhance diagnostic accuracy by using machine learning. METHODS: A retrospective analysis of 159 LGSCC patients with pretreatment 18 F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) examination from 2009 to 2017 was performed. We extracted radiomic features from the PET images, focusing on pathologic bone marrow invasion detection. Extracted features underwent the least absolute shrinkage and selection operator algorithm-based selection and were then used for machine learning via the XGBoost package to distinguish bone marrow invasion presence. Receiver operating characteristic curve analysis was performed. RESULTS: From the 159 patients, 88 qualified for further analysis (59 men; average age, 69.2 years), and pathologic bone marrow invasion was identified in 69 (78%) of these patients. Three significant radiological features were identified: Gray level co-occurrence matrix_Correlation, INTENSITY-BASED_IntensityInterquartileRange, and MORPHOLOGICAL_SurfaceToVolumeRatio. An XGBoost machine-learning model, using PET radiomic features to detect bone marrow invasion, yielded an area under the curve value of 0.83. CONCLUSION: Our findings highlighted the potential of 18 F-FDG PET radiomic features, combined with machine learning, as a promising avenue for improving LGSCC diagnosis and treatment. Using 18 F-FDG PET texture features may provide a robust and accurate method for determining the presence or absence of bone marrow invasion in LGSCC patients.