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.

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.

Image denoising and model-independent parameterization for IVIM MRI.

Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method's ability to recover artificial 'pseudokernels'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis.

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

The Diagnostic Performance of Magnetic Resonance Imaging in the Categorization of Pediatric Neck Lymph Nodes: Radiologic and Pathologic Correlations.

BACKGROUND/AIM: To present MRI features of neck lymph nodes in benign and malignant conditions in the pediatric population. MATERIALS AND METHODS: MRIs of the neck of 51 patients 1 to 18 years old (40 boys, 11 girls [10.08±4.73]) with lymph node biopsy were retrospectively analyzed. Those were grouped as benign including reactive (27 [52.9%]) and lymphadenitis (11 [21.6%]), and malignant (13 [25.5%]). The groups were evaluated multiparametrically in terms of quantitative and qualitative variables. RESULTS: The long axis, short axis, area, and apparent diffusion coefficient (ADC) values of the largest lymph node were 21 (17 to 24) mm, 14 (12 to 18) mm, 228.60 (144.79 to 351.82) mm 2 , 2531 (2457 to 2714) mm 2 /s for reactive, 24 (19 to 27) mm, 15 (11 to 20) mm, 271.80 (231.43 to 412.20) mm 2 , 2534 (2425 to 2594) mm 2 /s for lymphadenitis, 27 (23.50 to 31.50) mm, 20 (15 to 22) mm, 377.08 (260.47 to 530.94) mm 2 , 2337 (2254 to 2466) mm 2 /s for malignant, respectively. Statistical analysis of our data suggests that the following parameters are associated with a higher likelihood of malignancy: long axis >22 mm, short axis >16 mm, area >319 cm 2 , ADC value <2367 mm 2 /s, and supraclavicular location. Perinodal and nodal heterogeneity, posterior cervical triangle location are common in lymphadenitis ( P <0.001). Reactive lymph nodes are distributed symmetrically in both neck halves ( P <0.001). CONCLUSION: In the MRI-based approach to lymph nodes, not only long axis, short axis, surface area, and ADC, but also location, distribution, perinodal, and nodal heterogeneity should be used.

Impact Of PET/CT On Treatment In Patients With Head And Neck Squamous Cell Carcinoma.

<b><br>Introduction:</b> Although PET/CT is effective for staging HNSCC, its impact on patient management is somewhat controversial. For this reason, we considered it necessary to carry out a study in order to verify whether PET/CT helps to improve the prognosis and treatment in patients. This study was designed to address the impact of PET-FDG imaging when used alongside CT in the staging and therapeutic management of patients with HNSCC.</br> <b><br>Material and methods:</b> Data was collected from 169 patients diagnosed with HNSCC with both CT and PET/CT (performed within a maximum of 30 days of each other). It was evaluated whether discrepancies in the diagnosis of the two imaging tests had impacted the treatment.</br> <b><br>Results:</b> The combined use of CT and PET/CT led to a change in the treatment of 67 patients, who represented 39.7% of the sample. In 27.2% of cases, it entailed a change in the type of treatment which the patient received. In 3.0% of the cases, using both diagnostic tests led to modifications of the therapeutic intention of our patients.</br> <b><br>Conclusions:</b> Using PET/CT in addition to the conventional imaging method in staging resulted in more successful staging and more appropriate therapeutic decision-making.</br>.

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.

MRI radiomics in head and neck cancer from reproducibility to combined approaches.

The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.

Performance evaluation of commercial and non-commercial shear wave elastography implementations for vascular applications.

BACKGROUND: Shear wave elastography (SWE) is mainly used for stiffness estimation of large, homogeneous tissues, such as the liver and breasts. However, little is known about its accuracy and applicability in thin (∼0.5-2 mm) vessel walls. To identify possible performance differences among vendors, we quantified differences in measured wave velocities obtained by commercial SWE implementations of various vendors over different imaging depths in a vessel-mimicking phantom. For reference, we measured SWE values in the cylindrical inclusions and homogeneous background of a commercial SWE phantom. Additionally, we compared the accuracy between a research implementation and the commercially available clinical SWE on an Aixplorer ultrasound system in phantoms and in vivo in patients. METHODS: SWE measurements were performed over varying depths (0-35 mm) using three ultrasound machines with four ultrasound probes in the homogeneous 20 kPa background and cylindrical targets of 10, 40, and 60 kPa of a multi-purpose phantom (CIRS-040GSE) and in the anterior and posterior wall of a homogeneous polyvinyl alcohol vessel-mimicking phantom. These phantom data, along with in vivo SWE data of carotid arteries in 23 patients with a (prior) head and neck neoplasm, were also acquired in the research and clinical mode of the Aixplorer ultrasound machine. Machine-specific estimated phantom stiffness values (CIRS phantom) or wave velocities (vessel phantom) over all depths were visualized, and the relative error to the reference values and inter-frame variability (interquartile range/median) were calculated. Correlations between SWE values and target/vessel wall depth were explored in phantoms and in vivo using Spearman's correlations. Differences in wave velocities between the anterior and posterior arterial wall were assessed with Wilcoxon signed-rank tests. Intra-class correlation coefficients were calculated for a sample of ten patients as a measure of intra- and interobserver reproducibility of SWE analyses in research and clinical mode. RESULTS: There was a high variability in obtained SWE values among ultrasound machines, probes, and, in some cases, with depth. Compared to the homogeneous CIRS-background, this variation was more pronounced for the inclusions and the vessel-mimicking phantom. Furthermore, higher stiffnesses were generally underestimated. In the vessel-mimicking phantom, anterior wave velocities were (incorrectly) higher than posterior wave velocities (3.4-5.6 m/s versus 2.9-5.9 m/s, p ≤ 0.005 for 3/4 probes) and remarkably correlated with measurement depth for most machines (Spearman's ρ = -0.873-0.969, p < 0.001 for 3/4 probes). In the Aixplorer's research mode, this difference was smaller (3.3-3.9 m/s versus 3.2-3.6 m/s, p = 0.005) and values did not correlate with measurement depth (Spearman's ρ = 0.039-0.659, p ≥ 0.002). In vivo, wave velocities were higher in the posterior than the anterior vessel wall in research (left p = 0.001, right p < 0.001) but not in clinical mode (left: p = 0.114, right: p = 0.483). Yet, wave velocities correlated with vessel wall depth in clinical (Spearman's ρ = 0.574-0.698, p < 0.001) but not in research mode (Spearman's ρ = -0.080-0.466, p ≥ 0.003). CONCLUSIONS: We observed more variation in SWE values among ultrasound machines and probes in tissue with high stiffness and thin-walled geometry than in low stiffness, homogeneous tissue. Together with a depth-correlation in some machines, where carotid arteries have a fixed location, this calls for caution in interpreting SWE results in clinical practice for vascular applications.

Prognostic Value of a Combined Nomogram Model Integrating 3-Dimensional Deep Learning and Radiomics for Head and Neck Cancer.

OBJECTIVE: The preoperative prediction of the overall survival (OS) status of patients with head and neck cancer (HNC) is significant value for their individualized treatment and prognosis. This study aims to evaluate the impact of adding 3D deep learning features to radiomics models for predicting 5-year OS status. METHODS: Two hundred twenty cases from The Cancer Imaging Archive public dataset were included in this study; 2212 radiomics features and 304 deep features were extracted from each case. The features were selected by univariate analysis and the least absolute shrinkage and selection operator, and then grouped into a radiomics model containing Positron Emission Tomography /Computed Tomography (PET/CT) radiomics features score, a deep model containing deep features score, and a combined model containing PET/CT radiomics features score +3D deep features score. TumorStage model was also constructed using initial patient tumor node metastasis stage to compare the performance of the combined model. A nomogram was constructed to analyze the influence of deep features on the performance of the model. The 10-fold cross-validation of the average area under the receiver operating characteristic curve and calibration curve were used to evaluate performance, and Shapley Additive exPlanations (SHAP) was developed for interpretation. RESULTS: The TumorStage model, radiomics model, deep model, and the combined model achieved areas under the receiver operating characteristic curve of 0.604, 0.851, 0.840, and 0.895 on the train set and 0.571, 0.849, 0.832, and 0.900 on the test set. The combined model showed better performance of predicting the 5-year OS status of HNC patients than the radiomics model and deep model. The combined model was shown to provide a favorable fit in calibration curves and be clinically useful in decision curve analysis. SHAP summary plot and SHAP The SHAP summary plot and SHAP force plot visually interpreted the influence of deep features and radiomics features on the model results. CONCLUSIONS: In predicting 5-year OS status in patients with HNC, 3D deep features could provide richer features for combined model, which showed outperformance compared with the radiomics model and deep model.

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.

3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction.

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64.7%). FDG-PET/CT imaging is often used for early diagnosis and staging of H&N tumors, thus improving these patients' survival rates. This work presents a novel 3D-Inception-Residual aided with 3D depth-wise convolution and squeeze and excitation block. We introduce a 3D depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder (3D-IncNet), which not only helps to recalibrate the channel-wise features but adaptively through explicit inter-dependencies modeling but also integrate the coarse and fine features resulting in accurate tumor segmentation. We further demonstrate the effectiveness of inception-residual encoder-decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. We applied random forest for survival prediction on deep, clinical, and radiomics features. Experiments are conducted on the benchmark HECKTOR21 challenge, which showed significantly better performance by surpassing the state-of-the-artwork and achieved 0.836 and 0.811 concordance index and dice scores, respectively. We made the model and code publicly available.

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.

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.

The utility of ultrasonographic surveillance in management of a presumed branchial cleft cyst later confirmed HPV-associated oropharyngeal cancer.

While branchial cleft cysts are often considered benign pathologies, the literature discusses cases of squamous cell carcinoma (SCC) arising from these cystic lesions as either a primary or metastatic tumor. We illustrate our institutional experience and review the current literature to identify recommendations for best diagnostic, surveillance, and treatment guidelines for SCC identified in a branchial cleft cyst. A 61-year-old male presented with a right sided neck mass, with suspicion of a branchial cleft cyst due to benign findings on fine needle aspiration. Following surgical excision, a focus of SCC was found on surgical pathology. Despite PET/CT and flexible laryngoscopy, no primary tumor was identified prompting routine surveillance every 3 months with cervical ultrasonography and flexible nasolaryngoscopy. Two and a half years following his initial presentation, pathologic right level II lymphadenopathy was detected on ultrasound without evidence of primary tumor. Subsequent transoral robotic surgery with right tonsillectomy and partial pharyngectomy, with right lateral neck dissection revealed a diagnosis of pT1N1 HPV-HNSCC and he was referred for adjuvant chemotherapy and radiation. To our knowledge there are less than 10 cases of confirmed HPV-associated oropharyngeal SCC arising from a branchial cleft cyst. Here we demonstrate the utility of ultrasound as a surveillance tool and emphasize a higher index of suspicion for carcinoma in adult patients with cystic neck masses.

Anatomical variations in the superficial venous system of the neck: an image-based study using contrast-enhanced computed tomography.

PURPOSE: The superficial venous system (SVS) of the neck receives blood from the face and oral cavity. The SVS comprises the anterior jugular vein (AJV), external jugular vein (EJV), and facial vein (FV). Comprehensive knowledge of the normal anatomy and potential variations in the venous system is valuable in surgical and radiological procedures. This study aimed to update the anatomic knowledge of the SVS using a radiographic approach, which is a beneficial data source in clinical practice. METHODS: Contrast-enhanced computed tomography images of the neck of patients with head and neck cancer treated between 2017 and 2020 were retrospectively evaluated. Each side of the neck was counted separately. A total of 302 necks of 151 patients were enrolled in this study. RESULTS: The medial AJV was absent in 49.7% (75/151) of the patients on the left side, which was significantly greater than the 19.2% (29/151) on the right (p < 0.001). The left AJV drained into the right venous system in 6.6% (10/151) of the necks. In 48.3% (146/302) of the necks, the FV did not flow into the internal jugular vein but rather into the EJV or AJV; these findings were significantly more frequent than those reported in previous studies. The diameters of the veins were significantly larger when they received blood from the FV than when they were not connected to the FV. CONCLUSION: These findings indicate that the AJV has a rightward preference during its course. The course of the FV is diverse and affects the diameter of connected veins.

Multi-organ segmentation of organ-at-risk (OAR's) of head and neck site using ensemble learning technique.

INTRODUCTION: This paper presents a novel approach to automate the segmentation of Organ-at-Risk (OAR) in Head and Neck cancer patients using Deep Learning models combined with Ensemble Learning techniques. The study aims to improve the accuracy and efficiency of OAR segmentation, essential for radiotherapy treatment planning. METHODS: The dataset comprised computed tomography (CT) scans of 182 patients in DICOM format, obtained from an institutional image bank. Experienced Radiation Oncologists manually segmented seven OARs for each scan. Two models, 3D U-Net and 3D DenseNet-FCN, were trained on reduced CT scans (192 × 192 x 128) due to memory limitations. Ensemble Learning techniques were employed to enhance accuracy and segmentation metrics. Testing was conducted on 78 patients from the institutional dataset and an open-source dataset (TCGA-HNSC and Head-Neck Cetuximab) consisting of 31 patient scans. RESULTS: Using the Ensemble Learning technique, the average dice similarity coefficient for OARs ranged from 0.990 to 0.994, indicating high segmentation accuracy. The 95% Hausdorff distance (mm) ranged from 1.3 to 2.1, demonstrating precise segmentation boundaries. CONCLUSION: The proposed automated segmentation method achieved efficient and accurate OAR segmentation, surpassing human expert performance in terms of time and accuracy. IMPLICATIONS FOR PRACTICE: This approach has implications for improving treatment planning and patient care in radiotherapy. By reducing manual segmentation reliance, the proposed method offers significant time savings and potential improvements in treatment planning efficiency and precision for head and neck cancer patients.

Biomechanical imaging biomarker during chemoradiotherapy predicts treatment response in head and neck squamous cell carcinoma.

Objective. For response-adapted adaptive radiotherapy (R-ART), promising biomarkers are needed to predict post-radiotherapy (post-RT) responses using routine clinical information obtained during RT. In this study, a patient-specific biomechanical model (BM) of the head and neck squamous cell carcinoma (HNSCC) was proposed using the pre-RT maximum standardized uptake value (SUVmax) of18F-fluorodeoxyglucose (FDG) and tumor structural changes during RT as evaluated using computed tomography (CT). In addition, we evaluated the predictive performance of BM-driven imaging biomarkers for the treatment response of patients with HNSCC who underwent concurrent chemoradiotherapy (CCRT).Approach. Patients with histologically confirmed HNSCC treated with definitive CCRT were enrolled in this study. All patients underwent CT two times as follows: before the start of RT (pre-RT) and 3 weeks after the start of RT (mid-RT). Among these patients, 67 patients who underwent positron emission tomography/CT during the pre-RT period were included in the final analysis. The locoregional control (LC), progression-free survival (PFS), and overall survival (OS) prediction performances of whole tumor stress change (TS) between pre- and mid-RT computed using BM were assessed using univariate, multivariate, and Kaplan-Meier survival curve analyses, respectively. Furthermore, performance was compared with the pre and post-RT SUVmax, tumor volume reduction rate (TVRR) during RT, and other clinical prognostic factors.Main results. For both univariate, multivariate, and survival curve analyses, the significant prognostic factors were as follows (p< 0.05): TS and TVRR for LC; TS and pre-RT FDG-SUVmaxfor PFS; and TS only for OS. In addition, for 2 year LC, PFS, and OS prediction, TS showed a comparable predictive performance to post-RT FDG-SUVmax.Significance. BM-driven TS is an effective prognostic factor for tumor treatment response after CCRT. The proposed method can be a feasible functional imaging biomarker that can be acquired during RT using only routine clinical data and may provide useful information for decision-making during R-ART.

Posttreatment Head and Neck Cancer Imaging: Anatomic Considerations Based on Cancer Subsites.

Posttreatment imaging surveillance of head and neck cancer is challenging owing to complex anatomic subsites and diverse treatment modalities. Early detection of residual disease or recurrence through surveillance imaging is crucial for devising optimal treatment strategies. Posttreatment imaging surveillance is performed using CT, fluorine 18-fluorodeoxyglucose PET/CT, and MRI. Radiologists should be familiar with postoperative imaging findings that can vary depending on surgical procedures and reconstruction methods that are used, which is dictated by the primary subsite and extent of the tumor. Morphologic changes in normal structures or denervation of muscles within the musculocutaneous flap may mimic recurrent tumors. Recurrence is more likely to occur at the resection margin, margin of the reconstructed flap, and deep sites that are difficult to access surgically. Radiation therapy also has a varying dose distribution depending on the primary site, resulting in various posttreatment changes. Normal tissues are affected by radiation, with edema and inflammation occurring in the early stages and fibrosis in the late stages. Distinguishing scar tissue from residual tumor becomes necessary, as radiation therapy may leave behind residual scar tissue. Local recurrence should be carefully evaluated within areas where these postradiation changes occur. Head and Neck Imaging Reporting and Data System (NI-RADS) is a standardized reporting and risk classification system with guidance for subsequent management. Familiarity with NI-RADS has implications for establishing surveillance protocols, interpreting posttreatment images, and management decisions. Knowledge of posttreatment imaging characteristics of each subsite of head and neck cancers and the areas prone to recurrence empowers radiologists to detect recurrences at early stages. ©RSNA, 2024 Test Your Knowledge questions in the supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.