Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

BACKGROUND: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. METHODS: Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. RESULTS: We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. CONCLUSIONS: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Radiotherapy is used as a treatment for people with oropharyngeal cancer. It is important to distinguish the areas where cancer is present so the radiotherapy treatment can be targeted at the cancer. Computational methods based on artificial intelligence can automate this task but need to be able to distinguish areas where it is unclear whether cancer is present. In this study we compare these computational methods that are able to highlight areas where it is unclear whether or not cancer is present. Our approach accurately predicts how well these areas are distinguished by the models. Our results could be applied to improve the computational methods used during radiotherapy treatment. This could enable more targeted treatment to be used in the future, which could result in better outcomes for people with oropharyngeal cancer.

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography (PET), are used in the routine clinic to assist radiation oncologists for accurate GTV delineation. However, the availability of PET imaging may not always be guaranteed. PURPOSE: To develop a deep learning segmentation framework for automated GTV delineation of HN cancers using a combination of PET/CT images, while addressing the challenge of missing PET data. METHODS: Two datasets were included for this study: Dataset I: 524 (training) and 359 (testing) oropharyngeal cancer patients from different institutions with their PET/CT pairs provided by the HECKTOR Challenge; Dataset II: 90 HN patients(testing) from a local institution with their planning CT, PET/CT pairs. To handle potentially missing PET images, a model training strategy named the "Blank Channel" method was implemented. To simulate the absence of a PET image, a blank array with the same dimensions as the CT image was generated to meet the dual-channel input requirement of the deep learning model. During the model training process, the model was randomly presented with either a real PET/CT pair or a blank/CT pair. This allowed the model to learn the relationship between the CT image and the corresponding GTV delineation based on available modalities. As a result, our model had the ability to handle flexible inputs during prediction, making it suitable for cases where PET images are missing. To evaluate the performance of our proposed model, we trained it using training patients from Dataset I and tested it with Dataset II. We compared our model (Model 1) with two other models which were trained for specific modality segmentations: Model 2 trained with only CT images, and Model 3 trained with real PET/CT pairs. The performance of the models was evaluated using quantitative metrics, including Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff Distance (HD95). In addition, we evaluated our Model 1 and Model 3 using the 359 test cases in Dataset I. RESULTS: Our proposed model(Model 1) achieved promising results for GTV auto-segmentation using PET/CT images, with the flexibility of missing PET images. Specifically, when assessed with only CT images in Dataset II, Model 1 achieved DSC of 0.56 ± 0.16, MSD of 3.4 ± 2.1 mm, and HD95 of 13.9 ± 7.6 mm. When the PET images were included, the performance of our model was improved to DSC of 0.62 ± 0.14, MSD of 2.8 ± 1.7 mm, and HD95 of 10.5 ± 6.5 mm. These results are comparable to those achieved by Model 2 and Model 3, illustrating Model 1's effectiveness in utilizing flexible input modalities. Further analysis using the test dataset from Dataset I showed that Model 1 achieved an average DSC of 0.77, surpassing the overall average DSC of 0.72 among all participants in the HECKTOR Challenge. CONCLUSIONS: We successfully refined a multi-modal segmentation tool for accurate GTV delineation for HN cancer. Our method addressed the issue of missing PET images by allowing flexible data input, thereby providing a practical solution for clinical settings where access to PET imaging may be limited.

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

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.

Does Speckle Tracking Transthoracic Echocardiography Indicate Subtle Changes in Left Ventricular Function in Heart Failure Patients with Reduced Ejection Fraction Treated by Sacubitril-valsartan?

Background: In heart failure patients and reduced ejection fraction (HFrEF), assessing subtle changes in left ventricle (LV) function is crucial for monitoring treatment efficacy. This study aims to determine the effect of valsartan/sacubitril on LV ejection fraction (EF) assessed by two-dimensional (2D) transthoracic echocardiography (TTE) in comparison to that assessed by 2D TTE speckle tracking in patients with HFrEF ≤35% after 6 months of treatment. Patients and Methods: A prospective study will be conducted on 200 heart failure patients with reduced EF (HFrEF) undergoing sacubitril-valsartan treatment. Each participant underwent a comprehensive evaluation, including physical examination, history taking, serial 12-lead electrocardiogram, and 2D echo to evaluate cardiac parameters. In addition, 2D speckle tracking echocardiography (STE) assessments were conducted before and after 6 months of valsartan/sacubitril treatment. Results: The enrolled patients had an average age of 48 years with 63% females. At the beginning of the study, 9 (4.5%) patients were classified as New York Heart Association (NYHA) FC I, 120 (60%) as NYHA FC II, 64 (32%) as NYHA FC III, and 7 (3.5%) as FC IV. Following treatment, 82 (41%) patients improved to NYHA FC I, and 118 (59%) were in NYHA FC II. Notably, 82 (41%) patients showed improved left ventricular EF (LVEF), detected either by traditional TTE or STE, whereas 118 (59%) showed no improvement in EF through traditional TTE. In addition, 74 (37%) patients demonstrated improvement detected by STE. In contrast, 44 (22%) patients demonstrated no improvement in EF detected by either TTE or STE. Conclusion: STE was a more reliable diagnostic method for seeing early LVEF improvement in patients with HFrEF receiving valsartan/sacubitril treatment not seen by conventional TTE.

Detection of Alteration in Carotid Artery Volumetry Using Standard-of-care Computed Tomography Surveillance Scans Following Unilateral Radiation Therapy for Early-stage Tonsillar Squamous Cell Carcinoma Survivors: A Cross-Sectional Internally-Matched Carotid Isodose Analysis.

Purpose: Radiation induced carotid artery disease (RICAD) is a major cause of morbidity and mortality among survivors of oropharyngeal cancer. This study leveraged standard-of-care CT scans to detect volumetric changes in the carotid arteries of patients receiving unilateral radiotherapy (RT) for early tonsillar cancer, and to determine dose-response relationship between RT and carotid volume changes, which could serve as an early imaging marker of RICAD. Methods and Materials: Disease-free cancer survivors (>3 months since therapy and age >18 years) treated with intensity modulated RT for early (T1-2, N0-2b) tonsillar cancer with pre- and post-therapy contrast-enhanced CT scans available were included. Patients treated with definitive surgery, bilateral RT, or additional RT before the post-RT CT scan were excluded. Pre- and post-treatment CTs were registered to the planning CT and dose grid. Isodose lines from treatment plans were projected onto both scans, facilitating the delineation of carotid artery subvolumes in 5 Gy increments (i.e. received 50-55 Gy, 55-60 Gy, etc.). The percent-change in sub-volumes across each dose range was statistically examined using the Wilcoxon rank-sum test. Results: Among 46 patients analyzed, 72% received RT alone, 24% induction chemotherapy followed by RT, and 4% concurrent chemoradiation. The median interval from RT completion to the latest, post-RT CT scan was 43 months (IQR 32-57). A decrease in the volume of the irradiated carotid artery was observed in 78% of patients, while there was a statistically significant difference in mean %-change (±SD) between the total irradiated and spared carotid volumes (7.0±9.0 vs. +3.5±7.2, respectively, p<.0001). However, no significant dose-response trend was observed in the carotid artery volume change withing 5 Gy ranges (mean %-changes (±SD) for the 50-55, 55-60, 60-65, and 65-70+ Gy ranges [irradiated minus spared]: -13.1±14.7, -9.8±14.9, -6.9±16.2, -11.7±11.1, respectively). Notably, two patients (4%) had a cerebrovascular accident (CVA), both occurring in patients with a greater decrease in carotid artery volume in the irradiated vs the spared side. Conclusions: Our data show that standard-of-care oncologic surveillance CT scans can effectively detect reductions in carotid volume following RT for oropharyngeal cancer. Changes were equivalent between studied dose ranges, denoting no further dose-response effect beyond 50 Gy. The clinical utility of carotid volume changes for risk stratification and CVA prediction warrants further evaluation.

Biodegradable suture development-based albumin composites for tissue engineering applications.

Recent advancements in the field of biomedical engineering have underscored the pivotal role of biodegradable materials in addressing the challenges associated with tissue regeneration therapies. The spectrum of biodegradable materials presently encompasses ceramics, polymers, metals, and composites, each offering distinct advantages for the replacement or repair of compromised human tissues. Despite their utility, these biomaterials are not devoid of limitations, with issues such as suboptimal tissue integration, potential cytotoxicity, and mechanical mismatch (stress shielding) emerging as significant concerns. To mitigate these drawbacks, our research collective has embarked on the development of protein-based composite materials, showcasing enhanced biodegradability and biocompatibility. This study is dedicated to the elaboration and characterization of an innovative suture fabricated from human serum albumin through an extrusion methodology. Employing a suite of analytical techniques-namely tensile testing, scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA)-we endeavored to elucidate the physicochemical attributes of the engineered suture. Additionally, the investigation extends to assessing the influence of integrating biodegradable organic modifiers on the suture's mechanical performance. Preliminary tensile testing has delineated the mechanical profile of the Filament Suture (FS), delineating tensile strengths spanning 1.3 to 9.616 MPa and elongation at break percentages ranging from 11.5 to 146.64%. These findings illuminate the mechanical versatility of the suture, hinting at its applicability across a broad spectrum of medical interventions. Subsequent analyses via SEM and TGA are anticipated to further delineate the suture's morphological features and thermal resilience, thereby enriching our comprehension of its overall performance characteristics. Moreover, the investigation delves into the ramifications of incorporating biodegradable organic constituents on the suture's mechanical integrity. Collectively, the study not only sheds light on the mechanical and thermal dynamics of a novel suture material derived from human serum albumin but also explores the prospective enhancements afforded by the amalgamation of biodegradable organic compounds, thereby broadening the horizon for future biomedical applications.

Longitudinal changes in the carotid arteries of head and neck cancer patients following radiation therapy: Results from a prospective serial imaging biomarker characterization study.

INTRODUCTION: We prospectively evaluated morphologic and functional changes in the carotid arteries of patients treated with unilateral neck radiation therapy (RT) for head and neck cancer. METHODS: Bilateral carotid artery duplex studies were performed at 0, 3, 6, 12, 18 months and 2, 3, 4, and 5 years following RT. Intima media thickness (IMT); global and regional circumferential, as well as radial strain, arterial elasticity, stiffness, and distensibility were calculated. RESULTS: Thirty-eight patients were included. A significant difference in the IMT from baseline between irradiated and unirradiated carotid arteries was detected at 18 months (median, 0.073 mm vs -0.003 mm; P = 0.014), which increased at 3 and 4 years (0.128 mm vs 0.013 mm, P = 0.016, and 0.177 mm vs 0.023 mm, P = 0.0002, respectively). A significant transient change was noted in global circumferential strain between the irradiated and unirradiated arteries at 6 months (median difference, -0.89, P = 0.023), which did not persist. No significant differences were detected in the other measures of elasticity, stiffness, and distensibility. CONCLUSIONS: Functional and morphologic changes of the carotid arteries detected by carotid ultrasound, such as changes in global circumferential strain at 6 months and carotid IMT at 18 months, may be useful for the early detection of radiation-induced carotid artery injury, can guide future research aiming to mitigate carotid artery stenosis, and should be considered for clinical surveillance survivorship recommendations after head and neck RT.

A Novel PTRH2 Gene Mutation Causing Infantile-onset Multisystem Neurologic, Endocrine, and Pancreatic Disease in a Bahraini Patient.

Infantile-onset multisystem neurologic, endocrine, and pancreatic disease (IMNEPD) is a rare autosomal recessive multisystemic disease with a prevalence of < 1/1 000 000. The wide spectrum of symptoms and associated diseases makes the diagnosis of this disease particularly challenging. Here, we report a 12-year-old Bahraini male who presented with the core clinical features of IMNEPD including intellectual disability, global developmental delay, sensorineural hearing loss, endocrine dysfunction, and exocrine pancreatic insufficiency. The diagnosis was confirmed by genetic testing using whole exome sequencing. This is the first reported case of IMNEPD from Bahrain and was found to have a novel homozygous peptidyl-tRNA hydrolase 2 (PTRH2) gene mutation (NM_001015509.2: c.370del p.(Glu124Lysfs*4)). Moreover, we conducted an extensive literature review with an emphasis on the variable clinical spectrum and genotypes of previously reported patients in comparison to our case.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

BACKGROUND: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review.

Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Development and implementation of optimized endogenous contrast sequences for delineation in adaptive radiotherapy on a 1.5T MR-linear-accelerator: a prospective R-IDEAL stage 0-2a quantitative/qualitative evaluation of in vivo site-specific quality-assurance using a 3D T2 fat-suppressed platform for head and neck cancer.

Purpose: To improve segmentation accuracy in head and neck cancer (HNC) radiotherapy treatment planning for the 1.5T hybrid magnetic resonance imaging/linear accelerator (MR-Linac), three-dimensional (3D), T2-weighted, fat-suppressed magnetic resonance imaging sequences were developed and optimized. Approach: After initial testing, spectral attenuated inversion recovery (SPAIR) was chosen as the fat suppression technique. Five candidate SPAIR sequences and a nonsuppressed, T2-weighted sequence were acquired for five HNC patients using a 1.5T MR-Linac. MR physicists identified persistent artifacts in two of the SPAIR sequences, so the remaining three SPAIR sequences were further analyzed. The gross primary tumor volume, metastatic lymph nodes, parotid glands, and pterygoid muscles were delineated using five segmentors. A robust image quality analysis platform was developed to objectively score the SPAIR sequences on the basis of qualitative and quantitative metrics. Results: Sequences were analyzed for the signal-to-noise ratio and the contrast-to-noise ratio and compared with fat and muscle, conspicuity, pairwise distance metrics, and segmentor assessments. In this analysis, the nonsuppressed sequence was inferior to each of the SPAIR sequences for the primary tumor, lymph nodes, and parotid glands, but it was superior for the pterygoid muscles. The SPAIR sequence that received the highest combined score among the analysis categories was recommended to Unity MR-Linac users for HNC radiotherapy treatment planning. Conclusions: Our study led to two developments: an optimized, 3D, T2-weighted, fat-suppressed sequence that can be disseminated to Unity MR-Linac users and a robust image quality analysis pathway that can be used to objectively score SPAIR sequences and can be customized and generalized to any image quality optimization protocol. Improved segmentation accuracy with the proposed SPAIR sequence will potentially lead to improved treatment outcomes and reduced toxicity for patients by maximizing the target coverage and minimizing the radiation exposure of organs at risk.

Weekly Intra-Treatment Diffusion Weighted Imaging Dataset for Head and Neck Cancer Patients Undergoing MR-linac Treatment.

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. [Table: see text].

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool.

Purpose: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering. Material and methods: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans. Symptom ratings were collected via the MD Anderson Symptom Inventory (MDASI) given weekly during, and 6 months post-treatment. Drymouth, trouble swallowing, mucus, and vocal dysfunction were selected for analysis in this study. Patient stratifications were obtained by applying Bayesian Mixture Models with three components to patient's dose histograms for relevant organs. The clusters with the highest total mean doses were translated into dose thresholds using rule mining. Patient stratifications were compared against Tumor staging information using multivariate likelihood ratio tests. Model performance for prediction of moderate/severe symptoms at 6 months was compared against normal tissue complication probability (NTCP) models using cross-validation. Results: A total of 349 patients were included for long-term symptom prediction. High-risk clusters were significantly correlated with outcomes for severe late drymouth (p <.0001, OR = 2.94), swallow (p = .002, OR = 5.13), mucus (p = .001, OR = 3.18), and voice (p = .009, OR = 8.99). Simplified clusters were also correlated with late severe symptoms for drymouth (p <.001, OR = 2.77), swallow (p = .01, OR = 3.63), mucus (p = .01, OR = 2.37), and voice (p <.001, OR = 19.75). Proposed cluster stratifications show better performance than NTCP models for severe drymouth (AUC.598 vs.559, MCC.143 vs.062), swallow (AUC.631 vs.561, MCC.20 vs -.030), mucus (AUC.596 vs.492, MCC.164 vs -.041), and voice (AUC.681 vs.555, MCC.181 vs -.019). Simplified dose thresholds also show better performance than baseline models for predicting late severe ratings for all symptoms. Conclusion: Our results show that leveraging the 3-D dose histograms from radiation therapy plan improves stratification of patients according to their risk of experiencing long-term severe radiation associated symptoms, beyond existing NTPC models. Our rule-based method can approximate our stratifications with minimal loss of accuracy and can proactively identify risk factors for radiation-associated toxicity.

Development and Validation of an Automated Image-Based Deep Learning Platform for Sarcopenia Assessment in Head and Neck Cancer.

Importance: Sarcopenia is an established prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC); the quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical skeletal muscle segmentation and cross-sectional area. However, manual muscle segmentation is labor intensive, prone to interobserver variability, and impractical for large-scale clinical use. Objective: To develop and externally validate a fully automated image-based deep learning platform for cervical vertebral muscle segmentation and SMI calculation and evaluate associations with survival and treatment toxicity outcomes. Design, Setting, and Participants: For this prognostic study, a model development data set was curated from publicly available and deidentified data from patients with HNSCC treated at MD Anderson Cancer Center between January 1, 2003, and December 31, 2013. A total of 899 patients undergoing primary radiation for HNSCC with abdominal computed tomography scans and complete clinical information were selected. An external validation data set was retrospectively collected from patients undergoing primary radiation therapy between January 1, 1996, and December 31, 2013, at Brigham and Women's Hospital. The data analysis was performed between May 1, 2022, and March 31, 2023. Exposure: C3 vertebral skeletal muscle segmentation during radiation therapy for HNSCC. Main Outcomes and Measures: Overall survival and treatment toxicity outcomes of HNSCC. Results: The total patient cohort comprised 899 patients with HNSCC (median [range] age, 58 [24-90] years; 140 female [15.6%] and 755 male [84.0%]). Dice similarity coefficients for the validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI, 0.90-0.91) and 0.90 (95% CI, 0.89-0.91), respectively, with a mean 96.2% acceptable rate between 2 reviewers on external clinical testing (n = 377). Estimated cross-sectional area and SMI values were associated with manually annotated values (Pearson r = 0.99; P < .001) across data sets. On multivariable Cox proportional hazards regression, SMI-derived sarcopenia was associated with worse overall survival (hazard ratio, 2.05; 95% CI, 1.04-4.04; P = .04) and longer feeding tube duration (median [range], 162 [6-1477] vs 134 [15-1255] days; hazard ratio, 0.66; 95% CI, 0.48-0.89; P = .006) than no sarcopenia. Conclusions and Relevance: This prognostic study's findings show external validation of a fully automated deep learning pipeline to accurately measure sarcopenia in HNSCC and an association with important disease outcomes. The pipeline could enable the integration of sarcopenia assessment into clinical decision making for individuals with HNSCC.

Preservation of epidermal melanocyte integrity in an ex vivo co-culture skin model with sebocytes.

A direct contact co-culture of skin explants to SZ95 sebocytes (3D-SeboSkin) has been shown to preserve the integrity of epidermal keratinocytes and dermis. In this study, the properties of epidermal melanocytes were evaluated in the same 3D SeboSkin ex vivo model. Skin explants (n = 6) were maintained in the 3D-SeboSkin model, in direct contact to fibroblasts and alone in serum-free medium (SFM). Histopathological, immunohistochemical, apoptosis and oil red staining evaluations were performed at Days 0 and 6 of incubation. Results revealed preservation and prominent proliferation of basal keratinocytes of the skin explants in addition to preservation of dermal collagen and vasculature at Day 6 in the 3D-SeboSkin culture model and to a lesser extent in co-culture with fibroblasts but not in SFM alone. Melan-A+/Ki67- epidermal melanocytes remained attached to the dermis even at sites of epidermal detachment in the three skin explant models tested. However, the number of epidermal melanocytes was significantly conserved in 3D-SeboSkin cultures in comparison with skin explants in SFM (p < 0.05), whereas no difference was found in comparison with the co-culture with fibroblasts. Few DAPI/TUNEL+ apoptotic melanocytes could mostly be observed in SFM-incubated skin explants. Furthermore, only SZ95 sebocytes in contact to skin explants in 3D-SeboSkin exhibited increased lipogenesis with accumulation of abundant lipid droplets. These results denote that the 3D-SeboSkin model yielded significant preservation of epidermal melanocytes and hence it is optimal for ex vivo studies of abnormalities of skin pigmentation, melanocyte neoplasms and effects of different hormones, cytokines, carcinogens and various therapeutics in a pattern that recapitulates the in vivo environment.