OBJECTIVE: To survey practices concerning the use of anticoagulants and antiplatelets in microvascular free-flap reconstruction following oncological surgery of the head and neck.
METHODS: A survey of practices was carried out between September 2022 and March 2023. An online questionnaire was sent to members of the French GETTEC Head-and-Neck Tumor Study Group in all French centers practicing head-and-neck cancer surgery with reconstruction using microvascular free-flaps. The questionnaire asked surgeons about their practices regarding the use of intra- and postoperative anticoagulants and antiplatelets, preoperative management of comorbidities, and prevention of postoperative complications.
RESULTS: Sixty-one percent of the 38 respondents (23/38) used intraoperative intravenous heparin injection, associated to flap irrigation with heparin for 76% of surgeons (29/38) and/or a heparin solution bath for 37% (14/38). Postoperative anticoagulation was used by 95% of surgeons (36/38), and antiplatelets by 40% (15/38). Postoperatively, 40% (15/38) carried out monitoring using an implantable micro-Doppler probe, associated to analysis of clinical characteristics of the flap.
CONCLUSIONS: Reconstructive surgery using microvascular free-flaps involves numerous factors that can influence success. Prospective studies, particularly concerning the management of anticoagulants, could enable a national consensus on methods for free-flap reconstruction.

OBJECTIVE: Branchial cleft anomalies (BCAs) are common pediatric head and neck lesions; however, only 1-4% involve the first branchial cleft. The rare occurrence of first BCAs, their presentation at a young age, and the possible facial nerve involvement make diagnosis and treatment challenging.
METHODS: A retrospective chart review was conducted for children diagnosed with their first BCA between 2000 and 2020. Data on demographics, presenting symptoms, physical findings, imaging features, previous surgery, and treatment outcomes were collected and analyzed.
RESULTS: The cohort included 17 patients with a median age of 5 years at presentation. Seven (41%) had undergone previous surgical intervention before definitive surgery. Eight were classified as Work Type II anomalies, and nine as Work Type I. Sixteen patients (94%) underwent definitive surgical excision at a median age of 6.9. A parotid approach was used in 10 (62%), with dissection of the mass from the facial nerve, and a retro-auricular or end-aural approach was used in 6 (38%). Complete excision was achieved in 14/16 patients (88%). Three patients had transient facial nerve paresis postoperatively. Recurrence was noted in 3/16 patients (18%). Enhancement in imaging was positively correlated with post-operative complications (R = 0.463, P = 0.018).
CONCLUSIONS: First, BCA poses a diagnostic and surgical challenge; thus, definitive surgical treatment is often delayed. The surgical approach should be tailored to the type of anomaly (Work type I or II) and possible facial nerve involvement. Risk factors for post-operative complications are a history of recurrent infections and previous surgical interventions. The presence of contrast enhancement in preoperative imaging should alert surgeons to perioperative challenges and the risk of post-operative complications.
METHODS:

OBJECTIVE: Knowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions.
METHODS: The six models differed in training size and plan composition: The KBPFull (n = 203 plans), KBP101 (n = 101), KBP50 (n = 50), and KBP25 (n = 25) were trained with plans from two head-and-neck physicians. KBPA and KBPB each contained n = 101 plans from only one physician, respectively. An independent set of 39 patients treated to 6000-7000 cGy by a third physician was re-planned with all KBP models for validation. Standard head-and-neck dosimetric parameters were used to compare resulting plans. KBPFull plans were compared to the clinical plans to evaluate overall model quality. Additionally, clinical and KBPFull plans were presented to another physician for blind review. Dosimetric comparison of KBPFull against KBP101 , KBP50 , and KBP25 investigated the effect of model size. Finally, KBPA versus KBPB tested whether training KBP models on plans from one physician only influences the resulting output. Dosimetric differences were tested for significance using a paired t-test (p < 0.05).
RESULTS: Compared to manual plans, KBPFull significantly increased PTV Low D95% and left parotid mean dose but decreased dose cochlea, constrictors, and larynx. The physician preferred the KBPFull plan over the manual plan in 20/39 cases. Dosimetric differences between KBPFull , KBP101 , KBP50 , and KBP25 plans did not exceed 187 cGy on aggregate, except for the cochlea. Further, average differences between KBPA and KBPB were below 110 cGy.
CONCLUSIONS: Overall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.

OBJECTIVE: To investigate the performance of 4 atlas-based (multi-ABAS) and 2 deep learning (DL) solutions for head-and-neck (HN) elective nodes (CTVn) automatic segmentation (AS) on CT images.
METHODS: Bilateral CTVn levels of 69 HN cancer patients were delineated on contrast-enhanced planning CT. Ten and 49 patients were used for atlas library and for training a mono-centric DL model, respectively. The remaining 20 patients were used for testing. Additionally, three commercial multi-ABAS methods and one commercial multi-centric DL solution were investigated. Quantitative evaluation was assessed using volumetric Dice Similarity Coefficient (DSC) and 95-percentile Hausdorff distance (HD95%). Blind evaluation was performed for 3 solutions by 4 physicians. One recorded the time needed for manual corrections. A dosimetric study was finally conducted using automated planning.
RESULTS: Overall DL solutions had better DSC and HD95% results than multi-ABAS methods. No statistically significant difference was found between the 2 DL solutions. However, the contours provided by multi-centric DL solution were preferred by all physicians and were also faster to correct (1.1 min vs 4.17 min, on average). Manual corrections for multi-ABAS contours took on average 6.52 min Overall, decreased contour accuracy was observed from CTVn2 to CTVn3 and to CTVn4. Using the AS contours in treatment planning resulted in underdosage of the elective target volume.
CONCLUSIONS: Among all methods, the multi-centric DL method showed the highest delineation accuracy and was better rated by experts. Manual corrections remain necessary to avoid elective target underdosage. Finally, AS contours help reducing the workload of manual delineation task.

UNASSIGNED: Interactive segmentation seeks to incorporate human knowledge into segmentation models and thereby reducing the total amount of editing of auto-segmentations. By performing only interactions which provide new information, segmentation performance may increase cost-effectively. The aim of this study was to develop, evaluate and test feasibility of a deep learning-based single-cycle interactive segmentation model with the input being computer tomography (CT) and a small amount of information rich contours.
UNASSIGNED: A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded.
UNASSIGNED: The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations.
UNASSIGNED: Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.

Penetrating vascular injury has become the topic of interest with increased gun violence in the United States. The radiologist plays a crucial role in establishing and systemizing the signs of vascular injury such as intimal flap, dissection, pseudoaneurysm, rupture, and arteriovenous fistula. Various imaging techniques such as ultrasound Doppler, computed tomographic angiography (CTA), magnetic resonance angiography, and conventional angiography are being employed based on clinical recommendations. Of all the techniques, CTA has been shown to embrace a promising role in identifying vascular injuries with superior sensitivity, specificity, and accuracy. An acquaintance of the imaging features has been shown to improve the approach to trauma patients in clinical settings. This article details the imaging modalities and the features of the head-and-neck penetrating vascular injury.

OBJECTIVE: Image-guided radiotherapy (IGRT) involves frequent in-room imaging sessions contributing to additional patient irradiation. The present work provided patient-specific dosimetric data related to different imaging protocols and anatomical sites.
METHODS: We developed a Monte Carlo based software able to calculate 3D personalized dose distributions for five imaging devices delivering kV-CBCT (Elekta and Varian linacs), MV-CT (Tomotherapy machines) and 2D-kV stereoscopic images from BrainLab and Accuray. Our study reported the dose distributions calculated for pelvis, head and neck and breast cases based on dose volume histograms for several organs at risk.
RESULTS: 2D-kV imaging provided the minimum dose with less than 1 mGy per image pair. For a single kV-CBCT and MV-CT, median dose to organs were respectively around 30 mGy and 15 mGy for the pelvis, around 7 mGy and 10 mGy for the head and neck and around 5 mGy and 15 mGy for the breast. While MV-CT dose varied sparsely with tissues, dose from kV imaging was around 1.7 times higher in bones than in soft tissue. Daily kV-CBCT along 40 sessions of prostate radiotherapy delivered up to 3.5 Gy to the femoral heads. The dose level for head and neck and breast appeared to be lower than 0.4 Gy for every organ in case of a daily imaging session.
CONCLUSIONS: This study showed the dosimetric impact of IGRT procedures. Acquisition parameters should therefore be chosen wisely depending on the clinical purposes and tailored to morphology. Indeed, imaging dose could be reduced up to a factor 10 with optimized protocols.

To address the challenges posed by fat-water chemical shift artifacts and relaxation rate discrepancies to quantitative susceptibility mapping (QSM) outside the brain, and to generate accurate susceptibility maps of the head-and-neck at 3 and 7 Tesla.
Simultaneous Multiple Resonance Frequency (SMURF) imaging was extended to 7 Tesla and used to acquire head-and-neck gradient echo images at both 3 and 7 Tesla. Separated fat and water images were corrected for Type 1 (displacement) and Type 2 (phase discrepancy) chemical shift artefacts, and for the bias resulting from differences in T1 and T2∗ relaxation rates, recombined and used as the basis for QSM. A novel phase signal-based masking approach was used to generate head-and-neck masks.
SMURF generated well-separated fat and water images of the head-and-neck. Corrections for chemical shift artefacts and relaxation rate differences removed overestimation of the susceptibility values, blurring in the susceptibility maps, and the disproportionate influence of fat in mixed voxels. The resulting susceptibility maps showed high correspondence between the paramagnetic areas and the locations of fatty tissues and the susceptibility estimates were similar to literature values. The proposed masking approach was shown to provide a simple means of generating head-and-neck masks.
Corrections for Type 1 and Type 2 chemical shift artefacts and for fat-water relaxation rate differences, mainly in T1 , were shown to be required for accurate susceptibility mapping of fatty-body regions. SMURF made it possible to apply these corrections and generate high-quality susceptibility maps of the entire head-and-neck at both 3 and 7 Tesla.

To investigate the impact of training sample size on the performance of deep learning-based organ auto-segmentation for head-and-neck cancer patients, a total of 1160 patients with head-and-neck cancer who received radiotherapy were enrolled in this study. Patient planning CT images and regions of interest (ROIs) delineation, including the brainstem, spinal cord, eyes, lenses, optic nerves, temporal lobes, parotids, larynx and body, were collected. An evaluation dataset with 200 patients were randomly selected and combined with Dice similarity index to evaluate the model performances. Eleven training datasets with different sample sizes were randomly selected from the remaining 960 patients to form auto-segmentation models. All models used the same data augmentation methods, network structures and training hyperparameters. A performance estimation model of the training sample size based on the inverse power law function was established. Different performance change patterns were found for different organs. Six organs had the best performance with 800 training samples and others achieved their best performance with 600 training samples or 400 samples. The benefit of increasing the size of the training dataset gradually decreased. Compared to the best performance, optic nerves and lenses reached 95% of their best effect at 200, and the other organs reached 95% of their best effect at 40. For the fitting effect of the inverse power law function, the fitted root mean square errors of all ROIs were less than 0.03 (left eye: 0.024, others: <0.01), and theRsquare of all ROIs except for the body was greater than 0.5. The sample size has a significant impact on the performance of deep learning-based auto-segmentation. The relationship between sample size and performance depends on the inherent characteristics of the organ. In some cases, relatively small samples can achieve satisfactory performance.

OBJECTIVE: The purpose of this study is to present a biomathematical model based on the dynamics of cell populations to predict the tolerability/intolerability of mucosal toxicity in head-and-neck radiotherapy.
METHODS: Our model is based on the dynamics of proliferative and functional cell populations in irradiated mucosa, and incorporates the three As: Accelerated proliferation, loss of Asymmetric proliferation, and Abortive divisions. The model consists of a set of delay differential equations, and tolerability is based on the depletion of functional cells during treatment. We calculate the sensitivity (sen) and specificity (spe) of the model in a dataset of 108 radiotherapy schedules, and compare the results with those obtained with three phenomenological classification models, two based on a biologically effective dose (BED) function describing the tolerability boundary (Fowler and Fenwick) and one based on an equivalent dose in 2 Gy fractions (EQD2 ) boundary (Strigari). We also perform a machine learning-like cross-validation of all the models, splitting the database in two, one for training and one for validation.
RESULTS: When fitting our model to the whole dataset, we obtain predictive values (sen + spe) up to 1.824. The predictive value of our model is very similar to that of the phenomenological models of Fowler (1.785), Fenwick (1.806), and Strigari (1.774). When performing a k = 2 cross-validation, the specificity and sensitivity in the validation dataset decrease for all models, from ˜1.82 to ˜1.55-1.63. For Fowler, the worsening is higher, down to 1.49.
CONCLUSIONS: Our model has proved useful to predict the tolerability/intolerability of a dataset of 108 schedules. As the model is more mechanistic than other available models, it could prove helpful when designing unconventional dose fractionations, schedules not covered by datasets to which phenomenological models of toxicity have been fitted.