Cervical cystic hygroma in adults: a case report.

BACKGROUNDS: Manifestation of cystic hygroma in adulthood is very rare. The rarity of cystic hygroma in adults has caused problems in its diagnosis and management and few studies have reported cystic hygroma in adults. CASE PRESENTATION: In this study, we reported a rare case with cervical cystic hygroma in adults. We report a 20-year-old Iranian male (Iranian ethnicity) with a diagnosis of right-side neck cystic hygroma and discuss the presentation, diagnosis, and clinical, radiological, and operative aspects of it. CONCLUSION: Cystic hygromas are a rare occurrence in adults. They are typically asymptomatic, rarely complicated, and can be mistaken for a cystic neck mass. This study showed that in our case, surgical resection may be a safe and effective treatment for cystic hygroma, with minimal risk of complications during the procedure.

[Application of new imaging modalities for brachytherapy of cancer patients]. / Új képalkotó eljárások alkalmazása daganatos betegek brahiterápiájában.

In the Radiotherapy Centre of the National Institute of Oncology, Budapest, a 0.55 T MR scanner (MAGNETOM Free. Max) and a ring-like X-ray machine (ImagingRing) have been in operation since 2022. The MR scanner has a tunnel diameter of 80 cm, the X-ray machine has a ring diameter of 121 cm. The latter can also be used for cone-beam CT (CBCT) imaging. The MR scanner is mainly used for planning gynaecological brachytherapy (BT) treatments. Image distortions in MR imaging were investigated with a special grid phantom. After head and neck and breast implant, image quality of ImagingRing CBCT and planning CT was compared. The position of the radiation source was verified by radiographs taken during treatment. Despite the lower field strength, the image quality of the MR scanner was found to be adequate for treatment planning of gynaecological BT. Image distortions were found to be clinically negligible. On CBCT images obtained with ImagingRing, catheters could always be well identified, and anatomical organs were adequately visualized for head and neck treatments, but not for breast implants. The MR scanner is suitable for treatment planning for gynaecological BT due to its good image quality and low image distortion. The image quality of the ImagingRing is suitable for treatment planning for small body sizes, but not for larger sizes. The device can be used to in vivo check of the radiation source position during treatment.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis.

Objective.Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons.Approach.We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network.Results.The proposed model achieves a mean absolute error (MAE) of18.76±5.167in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of0.95±0.09and a Frechet inception distance (FID) of145.60±8.38. The model yields a MAE of26.83±8.27to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of0.73±0.06and a FID distance equal to122.58±7.55. The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of27.89±2.22and26.08±2.95to synthesize MRI from CT input.Significance.The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS: Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS: DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT: Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS: Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

Feasibility of dose calculation for treatment plans using electron density maps from a novel dual-layer detector spectral CT simulator.

BACKGROUND: Conventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans. METHODS: 30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software. RESULTS: In terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%. CONCLUSION: Compared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

Head and neck tumor segmentation from [18F]F-FDG PET/CT images based on 3D diffusion model.

Objective.Head and neck (H&N) cancers are among the most prevalent types of cancer worldwide, and [18F]F-FDG PET/CT is widely used for H&N cancer management. Recently, the diffusion model has demonstrated remarkable performance in various image-generation tasks. In this work, we proposed a 3D diffusion model to accurately perform H&N tumor segmentation from 3D PET and CT volumes.Approach.The 3D diffusion model was developed considering the 3D nature of PET and CT images acquired. During the reverse process, the model utilized a 3D UNet structure and took the concatenation of 3D PET, CT, and Gaussian noise volumes as the network input to generate the tumor mask. Experiments based on the HECKTOR challenge dataset were conducted to evaluate the effectiveness of the proposed diffusion model. Several state-of-the-art techniques based on U-Net and Transformer structures were adopted as the reference methods. Benefits of employing both PET and CT as the network input, as well as further extending the diffusion model from 2D to 3D, were investigated based on various quantitative metrics and qualitative results.Main results.Results showed that the proposed 3D diffusion model could generate more accurate segmentation results compared with other methods (mean Dice of 0.739 compared to less than 0.726 for other methods). Compared to the diffusion model in 2D form, the proposed 3D model yielded superior results (mean Dice of 0.739 compared to 0.669). Our experiments also highlighted the advantage of utilizing dual-modality PET and CT data over only single-modality data for H&N tumor segmentation (with mean Dice less than 0.570).Significance.This work demonstrated the effectiveness of the proposed 3D diffusion model in generating more accurate H&N tumor segmentation masks compared to the other reference methods.

PET-CT-guided, symptom-based, patient-initiated surveillance versus clinical follow-up in head neck cancer patients (PETNECK2): study protocol for a multicentre feasibility study and non-inferiority, randomised, phase III trial.

BACKGROUND: Approximately 40% of treated head and neck cancer (HNC) patients develop recurrence. The risk of recurrence declines with time from treatment. Current guidelines recommend clinical follow-up every two months for the first two years after treatment, with reducing intensity over the next three years. However, evidence for the effectiveness of these regimes in detecting recurrence is lacking, with calls for more flexible, patient-centred follow-up strategies. METHODS: PETNECK2 is a UK-based multi-centre programme examining a new paradigm of follow-up, using positron emission tomography-computed tomography (PET-CT)-guided, symptom-based, patient-initiated surveillance. This paradigm is being tested in a unblinded, non-inferiority, phase III, randomised controlled trial (RCT). Patients with HNC, one year after completing curative intent treatment, with no clinical symptoms or signs of loco-regional or distant metastasis will be randomised using a 1:1 allocation ratio to either regular scheduled follow-up, or to PET-CT guided, patient-initiated follow-up. Patients at a low risk of recurrence (negative PET-CT) will receive a face-to-face education session along with an Information and Support (I&S) resource package to monitor symptoms and be in control of initiating an urgent appointment when required. The primary outcome of the RCT is overall survival. The RCT also has an in-built pilot, a nested QuinteT Recruitment Intervention (QRI), and a nested mixed-methods study on patient experience and fear of cancer recurrence (FCR). An initial, single-arm feasibility study has been completed which determined the acceptability of the patient-initiated surveillance intervention, the completion rates of baseline questionnaires, and optimised the I&S resource prior to implementation in the RCT. DISCUSSION: We hypothesise that combining an additional 12-month post-treatment PET-CT scan and I&S resource will both identify patients with asymptomatic recurrence and identify those at low risk of future recurrence who will be empowered to monitor their symptoms and seek early clinical follow-up when recurrence is suspected. This change to a patient-centred model of care may have effects on both quality of life and fear of cancer recurrence. TRIAL REGISTRATION: ISRCTN: 13,709,798; 15-Oct-2021.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment.

Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

Diagnostic efficacy of dynamic contrast-enhanced magnetic resonance perfusion imaging in detecting local tumor recurrence in patients with head and neck malignancies after definitive treatment.

BACKGROUND AND AIM: Accurate interpretation of post-treatment imaging in head and neck malignancies poses a challenge due to treatment sequelae. Magnetic resonance (MR) perfusion helps in this scenario by evaluating the hemodynamic characteristics of lesions. This study aimed to elucidate the diagnostic efficacy of dynamic contrast-enhanced (DCE)-MR perfusion imaging in detecting recurrence in patients after they underwent definitive treatment for head and neck tumors. MATERIALS AND METHODS: Thirty patients who had received definitive curative-intent treatment for histopathology-proven malignant head and neck tumors and in whom recurrent tumor was detected on precontrast MR imaging (MRI) were accrued in the study. Patients underwent DCE-MR perfusion imaging. Time to peak (TTP), relative maximum enhancement (RME), and relative washout (RWO) ratio were calculated by using time-intensity curve (TIC). The diagnostic accuracy was compared with histopathology. RESULTS: A cut-off value of ≥125.3 for RME showed a sensitivity of 76.2% and specificity of 66.7% for differentiating post-radiation changes and recurrence. The optimal cut-off for RWO ratio was ≥-6.24 with a sensitivity of 76.2% and specificity of 55.6%. The optimal cut-off of TTP was ≤45.8 s with a sensitivity of 61.9% and specificity of 77.8%. Diagnostic accuracies of RME, RWO, and TTP were 73.3%, 70%, and 66.7%, respectively. CONCLUSIONS: DCE-MRI had significant diagnostic accuracy in detecting and differentiating recurrences. TIC analysis of high-temporal resolution DCE-MRI can provide information regarding microcirculation of tumors, and hence can be considered as an imaging modality of choice for assessment of early local tumor recurrence in head and neck tumors.

DiffuseRT: predicting likely anatomical deformations of patients undergoing radiotherapy.

Objective. Predicting potential deformations of patients can improve radiotherapy treatment planning. Here, we introduce new deep-learning models that predict likely anatomical changes during radiotherapy for head and neck cancer patients.Approach. Denoising diffusion probabilistic models (DDPMs) were developed to generate fraction-specific anatomical changes based on a reference cone-beam CT (CBCT), the fraction number and the dose distribution delivered. Three distinct DDPMs were developed: (1) theimage modelwas trained to directly generate likely future CBCTs, (2) the deformable vector field (DVF) model was trained to generate DVFs that deform a reference CBCT and (3) thehybrid modelwas trained similarly to the DVF model, but without relying on an external deformable registration algorithm. The models were trained on 9 patients with longitudinal CBCT images (224 CBCTs) and evaluated on 5 patients (152 CBCTs).Results. The generated images mainly exhibited random positioning shifts and small anatomical changes for early fractions. For later fractions, all models predicted weight losses in accordance with the training data. The distributions of volume and position changes of the body, esophagus, and parotids generated with the image and hybrid models were more similar to the ground truth distribution than the DVF model, evident from the lower Wasserstein distance achieved with the image (0.33) and hybrid model (0.30) compared to the DVF model (0.36). Generating several images for the same fraction did not yield the expected variability since the ground truth anatomical changes were only in 76% of the fractions within the 95% bounds predicted with the best model. Using the generated images for robust optimization of simplified proton therapy plans improved the worst-case clinical target volume V95 with 7% compared to optimizing with 3 mm set-up robustness while maintaining a similar integral dose.Significance. The newly developed DDPMs generate distributions similar to the real anatomical changes and have the potential to be used for robust anatomical optimization.

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.

Basaloid squamous cell carcinoma clinically and radiologically masquerading as a head and neck paraganglioma: a case report and review of the literature.

BACKGROUND: This paper reports the first case of basaloid squamous cell carcinoma clinically and radiologically masquerading as a head and neck paraganglioma. CASE PRESENTATION: A 66-year-old Sinhalese male with unilateral hearing impairment and 7th-12th (excluding 11th) cranial nerve palsies was diagnosed radiologically with a head and neck paraganglioma by magnetic resonance imaging of the brain, which revealed a hypointense and hyperintense punctate mass centered at the jugular fossa with intracranial extension. The ascending pharyngeal artery, recognized as the major feeder, was embolized by percutaneous embolization following digital subtraction angiography. Gross total resection of the tumor was followed by an uneventful postoperative recovery. Combined immunohistochemistry and histopathological morphology revealed a basaloid squamous cell carcinoma, following which the patient completed radiotherapy and is at 3-month follow-up currently. CONCLUSION: This case report discusses the diagnostic pitfalls and management challenges of this rare entity on the basis of prior evidence, as well as a literature review and clinical and surgical analysis.

Ectopic cervical thymoma: a rare entity in the anterior neck.

Thymoma is a rare malignancy with usual location in the antero-superior mediastinum. Ectopic cervical thymoma (ECT) is an extremely rare tumor that originates from ectopic tissue, and is caused by the aberrant migration of the embryonic thymus. Our patient was a 56-year-old man who had a nodular lesion in the neck for several years. Computed tomography and Enhanced magnetic resonance imaging were performed. He underwent surgery, and a histological examination resulted in a diagnosis of type AB thymoma.

Criteria for the diagnosis of extranodal extension detected on radiological imaging in head and neck cancer: Head and Neck Cancer International Group consensus recommendations.

Extranodal extension of tumour on histopathology is known to be a negative prognostic factor in head and neck cancer. Compelling evidence suggests that extranodal extension detected on radiological imaging is also a negative prognostic factor. Furthermore, if imaging detected extranodal extension could be identified reliably before the start of treatment, it could be used to guide treatment selection, as patients might be better managed with non-surgical approaches to avoid the toxicity and cost of trimodality therapy (surgery, chemotherapy, and radiotherapy together). There are many aspects of imaging detected extranodal extension that remain unresolved or are without consensus, such as the criteria to best diagnose them and the associated terminology. The Head and Neck Cancer International Group conducted a five-round modified Delphi process with a group of 18 international radiology experts, representing 14 national clinical research groups. We generated consensus recommendations on the terminology and diagnostic criteria for imaging detected extranodal extension to harmonise clinical practice and research. These recommendations have been endorsed by 19 national and international organisations, representing 34 countries. We propose a new classification system to aid diagnosis, which was supported by most of the participating experts over existing systems, and which will require validation in the future. Additionally, we have created an online educational resource for grading imaging detected extranodal extensions.

Interim 18F-FDG-PET based response-adaptive dose escalation of proton therapy for head and neck cancer: a treatment planning feasibility study.

BACKGROUND: Image-driven dose escalation to tumor subvolumes has been proposed to improve treatment outcome in head and neck cancer (HNC). We used 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) acquired at baseline and into treatment (interim) to identify biologic target volumes (BTVs). We assessed the feasibility of interim dose escalation to the BTV with proton therapy by simulating the effects to organs at risk (OARs). METHODS: We used the semiautomated just-enough-interaction (JEI) method to identify BTVs in 18F-FDG-PET images from nine HNC patients. Between baseline and interim FDG-PET, patients received photon radiotherapy. BTV was identified assuming that high standardized uptake value (SUV) at interim reflected tumor radioresistance. Using Eclipse (Varian Medical Systems), we simulated a 10% (6.8 Gy(RBE1.1)) and 20% (13.6 Gy(RBE1.1)) dose escalation to the BTV with protons and compared results with proton plans without dose escalation. RESULTS: At interim 18F-FDG-PET, radiotherapy resulted in reduced SUV compared to baseline. However, spatial overlap between high-SUV regions at baseline and interim allowed for BTV identification. Proton therapy planning demonstrated that dose escalation to the BTV was feasible, and except for some 20% dose escalation plans, OAR doses did not significantly increase. CONCLUSION: Our in silico analysis demonstrated the potential for interim 18F-FDG-PET response-adaptive dose escalation to the BTV with proton therapy. This approach may give more efficient treatment to HNC with radioresistant tumor subvolumes without increasing normal tissue toxicity. Studies in larger cohorts are required to determine the full potential for interim 18F-FDG-PET-guided dose escalation of proton therapy in HNC.

Deep learning-based voxel sampling for particle therapy treatment planning.

Objective.Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency.Approach.Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based onP-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels.Main results.On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection.Significance.The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

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.

Infrared Fluorescence-guided Surgery for Tumor and Metastatic Lymph Node Detection in Head and Neck Cancer.

In patients with head and neck cancer (HNC), surgical removal of cancerous tissue presents the best overall survival rate. However, failure to obtain negative margins during resection has remained a steady concern over the past 3 decades. The need for improved tumor removal and margin assessment presents an ongoing concern for the field. While near-infrared agents have long been used in imaging, investigation of these agents for use in HNC imaging has dramatically expanded in the past decade. Targeted tracers for use in primary and metastatic lymph node detection are of particular interest, with panitumumab-IRDye800 as a major candidate in current studies. This review aims to provide an overview of intraoperative near-infrared fluorescence-guided surgery techniques used in the clinical detection of malignant tissue and sentinel lymph nodes in HNC, highlighting current applications, limitations, and future directions for use of this technology within the field. Keywords: Molecular Imaging-Cancer, Fluorescence © RSNA, 2024.

Pushing boundaries: Anterolateral thigh free flaps for extensive scalp defects beyond previous limits, leveraging imaging modalities with ultrasound and indocyanine green.

BACKGROUND: Scalp defect reconstruction poses considerable challenges, with ongoing debates regarding the most effective strategies. While the latissimus dorsi (LD) flap has traditionally been favored, the anterolateral thigh (ALT) flap has been well described as a versatile alternative for addressing extensive scalp defects. This study underscores the success of scalp reconstruction using ALT flaps, notably pushing the boundaries of previously reported flap sizes. Our approach leverages the use of indocyanine green (ICG) perfusion to guide precise preoperative planning and vascular modification, contributing to improved outcomes in challenging cases. METHODS: We performed 43 ALT flap reconstructions for scalp defects between 2016 and 2023. We collected patients' demographic and clinical data and evaluated flap size and recipient vessels and additional surgical techniques. Detailed preoperative plans with ultrasound and ICG use for intraoperative plans were performed to find perforators location. The cohort was divided into two, with or without complications on flaps, and analyzed depending on its surgical details. RESULTS: This study involved 38 patients with extensive scalp defects (mean age: 69.4 ± 11 years) who underwent ALT perforator flap transfers (mean flap size: 230.88 ± 145.6 cm2). There was only one case of unsuccessful flap transfer, and four cases had a few complications. The characteristics of the complication group included a large flap size (303.1 ± 170.9 vs. 214.9 ± 136.6 cm2, P = .211), few perforator numbers without pedicle manipulation, lack of intraoperative indocyanine green administration (75% vs. 25%, P = .607), and the use of superficial temporal vessels as recipient vessels. CONCLUSIONS: Scalp reconstruction using large ALT free flaps with the aid of imaging modalities facilitates the optimization of surgical techniques, such as pedicle manipulation, perforator numbers, and vein considerations, thereby contributing to successful reconstruction.

Ultra-fast confocal fluorescence microscopy for neck lymph node imaging in head and neck cancer.

OBJECTIVES: We evaluated ultra-fast confocal fluorescence microscopy (UFCM) as a new modality for pathology practice in head and neck cancer (HNC). This was assessed through an ex vivo study to estimate the accuracy, specificity, and sensitivity of interpretation of UFCM images by pathologists for the detection of metastatic lymph nodes in HNC patients undergoing sentinel lymph node biopsy or selective or complete neck dissection. MATERIALS AND METHODS: 44 patients with 32 cN0 and 12 cN+ HNC were included. The macroscopically non-invaded fresh bisected lymph nodes were stained with acridine orange and imaged with Histolog® Scanner (UFCM). Two pathologists interpreted independently the UFCM images postoperatively and gave a consensus diagnosis in case of disagreement. The gold standard was the diagnosis based on hematoxylin-eosin-saffron (HES) sections. RESULTS: 201 lymph nodes were imaged by UFCM. Thirty nodes (15 %) were invaded on final histology: 3 with micrometastases and 27 with macrometastases. The concordance rate between the pathologists on the UFCM images was 192/201 = 95.5 % and the Cohen kappa coefficient was 0.80. The accuracy of UFCM was 95.5 % (95 %CI: 91.7 %-97.9 %) with a high specificity at 98.8 % (95 %CI: 95.8 %-99.9 %) but an insufficient sensitivity at 76.7 % (95 %CI: 57.7 %-90.1 %). The three micrometastases and four of the 27 macrometastases were missed on UFCM images. CONCLUSION: The UFCM is providing promising detection values with a very good specificity and moderate sensitivity carrying room for improvement.