Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT

Measurement plane of the cross-sectional area of the masseter muscle in patients with skeletal Class III malocclusion: An artificial intelligence model

INTRODUCTION

This study aimed to determine a measurement plane that could represent the maximum cross-sectional area (MCSA) of masseter muscle using an artificial intelligence model for patients with skeletal Class III malocclusion.

METHODS

The study included 197 patients, divided into subgroups according to sex, mandibular symmetry, and mandibular plane angle. The volume, MCSA, and the cross-sectional area (CSA) at different levels were calculated automatically. The vertical distance between MCSA and mandibular foramen, along with the ratio of the masseter CSA at different levels to the MCSA (R), were also calculated.

RESULTS

The MCSA and volume showed a strong correlation in the total sample and each subgroup (P <0.001). The correlation between the CSA at each level and MCSA was statistically significant (P <0.001). The peak of the r and the correlation coefficient between the CSA at different levels and MCSA were mostly present 5-10 mm above the mandibular foramen for the total sample and the subgroups. The mean of RA5 to RA10 was >0.93, whereas the corresponding correlation coefficient was >0.96, both for the entire sample and for the subgroups.

CONCLUSIONS

MCSA could be used as an indicator for masseter muscle size. For patients with skeletal Class III malocclusion, the CSA 5-10 mm above the mandibular foramen, parallel to the Frankfort plane, could be used to estimate the masseter muscle MCSA.

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

PURPOSE OF REVIEW

The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer.

RECENT FINDINGS

Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer.

SUMMARY

Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

Bi-Graph Reasoning for Masticatory Muscle Segmentation From Cone-Beam Computed Tomography

Automated segmentation of masticatory muscles is a challenging task considering ambiguous soft tissue attachments and image artifacts of low-radiation cone-beam computed tomography (CBCT) images. In this paper, we propose a bi-graph reasoning model (BGR) for the simultaneous detection and segmentation of multi-category masticatory muscles from CBCTs. The BGR exploits the local and long-range interdependencies of regions of interest and category-specific prior knowledge of masticatory muscles by reasoning on the category graph and the region graph. The category graph of the learnable muscle prior knowledge handles high-level dependencies of muscle categories, enhancing the feature representation with noise-agnostic category knowledge. The region graph models both local and global dependencies of the candidate muscle regions of interest. The proposed BGR accommodates the high-level dependencies and enhances the region features in the presence of entangled soft tissue and image artifacts. We evaluated the proposed approach by segmenting masticatory muscles on clinically acquired CBCTs. Extensive experimental results show that the BGR effectively segments masticatory muscles with state-of-the-art accuracy.

Advances in and applications of imaging and radiomics in head and neck cancer survivorship

PURPOSE OF REVIEW

Radiological imaging is an essential component of head/neck cancer (HNC) care. Advances in imaging modalities (including CT, PET, MRI and ultrasound) and analysis have enhanced our understanding of tumour characteristics and prognosis. However, the application of these methods to evaluate treatment-related toxicities and functional burden is still emerging. This review showcases recent literature applying advanced imaging and radiomics to the assessment and management of sequelae following chemoradiotherapy for HNC.

RECENT FINDINGS

Whilst primarily early-stage/exploratory studies, recent investigations have showcased the feasibility of using radiological imaging, particularly advanced/functional MRI (including diffusion-weighted and dynamic contrast-enhanced MRI), to quantify treatment-induced tissue change in the head/neck musculature, and the clinical manifestation of lymphoedema/fibrosis and dysphagia. Advanced feature analysis and radiomic studies have also begun to give specific focus to the prediction of functional endpoints, including dysphagia, trismus and fibrosis.

SUMMARY

There is demonstrated potential in the use of novel imaging techniques, to help better understand pathophysiology, and improve assessment and treatment of functional deficits following HNC treatment. As larger studies emerge, technologies continue to progress, and pathways to clinical translation are honed, the application of these methods offers an exciting opportunity to transform clinical practices and improve outcomes for HNC survivors.

An investigation into the risk of population bias in deep learning autocontouring

BACKGROUND AND PURPOSE

To date, data used in the development of Deep Learning-based automatic contouring (DLC) algorithms have been largely sourced from single geographic populations. This study aimed to evaluate the risk of population-based bias by determining whether the performance of an autocontouring system is impacted by geographic population.

MATERIALS AND METHODS

80 Head Neck CT deidentified scans were collected from four clinics in Europe (n = 2) and Asia (n = 2). A single observer manually delineated 16 organs-at-risk in each. Subsequently, the data was contoured using a DLC solution, and trained using single institution (European) data. Autocontours were compared to manual delineations using quantitative measures. A Kruskal-Wallis test was used to test for any difference between populations. Clinical acceptability of automatic and manual contours to observers from each participating institution was assessed using a blinded subjective evaluation.

RESULTS

Seven organs showed a significant difference in volume between groups. Four organs showed statistical differences in quantitative similarity measures. The qualitative test showed greater variation in acceptance of contouring between observers than between data from different origins, with greater acceptance by the South Korean observers.

CONCLUSION

Much of the statistical difference in quantitative performance could be explained by the difference in organ volume impacting the contour similarity measures and the small sample size. However, the qualitative assessment suggests that observer perception bias has a greater impact on the apparent clinical acceptability than quantitatively observed differences. This investigation of potential geographic bias should extend to more patients, populations, and anatomical regions in the future.

Young oncologists benefit more than experts from deep learning-based organs-at-risk contouring modeling in nasopharyngeal carcinoma radiotherapy: A multi-institution clinical study exploring working experience and institute group style factor

Background

To comprehensively investigate the behaviors of oncologists with different working experiences and institute group styles in deep learning-based organs-at-risk (OAR) contouring.

Methods

A deep learning-based contouring system (DLCS) was modeled from 188 CT datasets of patients with nasopharyngeal carcinoma (NPC) in institute A. Three institute oncology groups, A, B, and C, were included; each contained a beginner and an expert. For each of the 28 OARs, two trials were performed with manual contouring first and post-DLCS edition later, for ten test cases. Contouring performance and group consistency were quantified by volumetric and surface Dice coefficients. A volume-based and a surface-based oncologist satisfaction rate (VOSR and SOSR) were defined to evaluate the oncologists' acceptance of DLCS.

Results

Based on DLCS, experience inconsistency was eliminated. Intra-institute consistency was eliminated for group C but still existed for group A and group B. Group C benefits most from DLCS with the highest number of improved OARs (8 for volumetric Dice and 10 for surface Dice), followed by group B. Beginners obtained more numbers of improved OARs than experts (7 v.s. 4 in volumetric Dice and 5 v.s. 4 in surface Dice). VOSR and SOSR varied for institute groups, but the rates of beginners were all significantly higher than those of experts for OARs with experience group significance. A remarkable positive linear relationship was found between VOSR and post-DLCS edition volumetric Dice with a coefficient of 0.78.

Conclusions

The DLCS was effective for various institutes and the beginners benefited more than the experts.

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review

BACKGROUND

Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process.

METHODS

The PubMed database was queried, and a total of 168 articles (2016-2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery.

RESULTS

The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models.

CONCLUSIONS

AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.

Dosimetric impact of adaptive proton therapy in head and neck cancer - A review

Background

Intensity Modulated Proton Therapy (IMPT) in head and neck cancer (HNC) is susceptible to anatomical changes and patient set-up inaccuracies during the radiotherapy course, which can cause discrepancies between planned and delivered dose. The discrepancies can be counteracted by adaptive replanning strategies. This article reviews the observed dosimetric impact of adaptive proton therapy (APT) and the timing to perform a plan adaptation in IMPT in HNC.

Methods

A literature search of articles published in PubMed/MEDLINE, EMBASE and Web of Science from January 2010 to March 2022 was performed. Among a total of 59 records assessed for possible eligibility, ten articles were included in this review.

Results

Included studies reported on target coverage deterioration in IMPT plans during the RT course, which was recovered with the application of an APT approach. All APT plans showed an average improved target coverage for the high- and low-dose targets as compared to the accumulated dose on the planned plans. Dose improvements up to 2.5 Gy (3.5 %) and up to 4.0 Gy (7.1 %) in the D98 of the high- and low dose targets were observed with APT. Doses to the organs at risk (OARs) remained equal or decreased slightly after APT was applied. In the included studies, APT was largely performed once, which resulted in the largest target coverage improvement, but eventual additional APT improved the target coverage further. There is no data showing what is the most appropriate timing for APT.

Conclusion

APT during IMPT for HNC patients improves target coverage. The largest improvement in target coverage was found with a single adaptive intervention, and an eventual second or more frequent APT application improved the target coverage further. Doses to the OARs remained equal or decreased slightly after applying APT. The most optimal timing for APT is yet to be determined.

Artificial intelligence can use physiological parameters to optimize treatment strategies and predict clinical deterioration of sepsis in ICU

Objective.Sepsis seriously threatens human life. Early identification of a patient's risk status and appropriate treatment can reduce septic shock risk and mortality. Our purpose is to design and validate an adjunctive therapy system based on deep reinforcement learning (DRL), which can provide treatment recommendations with providence and assess the patient's risk status and treatment options in the early stages.Approach.Data is from the Beth Israel Deaconess Medical Center. The raw data included 53 423 patients from MIMIC-III. Of these, 19 620 eligible samples were screened to form the final cohort. First, the patient's physiological parameters were fed into the DRL therapy strategy recommendation module (TSRM), which provides a forward-looking recommendation for treatment strategy. The recommended strategies were then fed into the reinforcement learning risk assessment module (RAM), which predicts the patient's risk status and treatment strategy from a long-term perspective. The DRL model designed in this paper assists in formulating treatment plans and evaluating treatment risks and patient status through continuous interaction with patient trajectory; this model therefore has the foresight that a supervising deep learning model does not.Main results.The experiment shows that, in the test set for the TSRM, mortality is the lowest when the treatment strategy that is actually implemented is the same as the AI-recommended strategy. Regarding the RAM, it can accurately grasp a patient's deterioration trend, and can reasonably assess a patient's risk status and treatment plans at an early stage. The assessment results of the model were matched with the actual clinical records.Significance.A DRL-based sepsis adjunctive therapy model is proposed. It can prospectively assist physicians in proposing treatment strategies, assess the patient's risk status and treatment methods early on, and detect deterioration trends in advance.

Automatic Masseter Muscle Accurate Segmentation from CBCT Using Deep Learning-Based Model

Segmentation of the masseter muscle (MM) on cone-beam computed tomography (CBCT) is challenging due to the lack of sufficient soft-tissue contrast. Moreover, manual segmentation is laborious and time-consuming. The purpose of this study was to propose a deep learning-based automatic approach to accurately segment the MM from CBCT under the refinement of high-quality paired computed tomography (CT). Fifty independent CBCT and 42 clinically hard-to-obtain paired CBCT and CT were manually annotated by two observers. A 3D U-shape network was carefully designed to segment the MM effectively. Manual annotations on CT were set as the ground truth. Additionally, an extra five CT and five CBCT auto-segmentation results were revised by one oral and maxillofacial anatomy expert to evaluate their clinical suitability. CBCT auto-segmentation results were comparable to the CT counterparts and significantly improved the similarity with the ground truth compared with manual annotations on CBCT. The automatic approach was more than 332 times shorter than that of a human operation. Only 0.52% of the manual revision fraction was required. This automatic model could simultaneously and accurately segment the MM structures on CBCT and CT, which can improve clinical efficiency and efficacy, and provide critical information for personalized treatment and long-term follow-up.

Can Botulinum Toxin-A Contribute to Reconstructing the Physiological Homeostasis of the Masticatory Complex in Short-Faced Patients during Occlusal Therapy? A Prospective Pilot Study

The physiological homeostasis of the masticatory complex in short-faced patients is too robust to be disintegrated and reconstructed due to the powerful masseter muscle. This study innovatively introduced the botulinum toxin-A (BTX-A) into the field of dental occlusal treatment, providing a novel and minimally invasive therapy perspective for the two major clinical problems in these patients (low treatment efficiency and high rates of complications). In total, 10 adult patients with skeletal low angle seeking occlusal treatment (age: 27.0 ± 6.1 years; 4 males and 6 females) were administered 30−50 U of BTX-A in each masseter muscle and evaluated before and 3 months after injection based on cone-beam computed tomography (CBCT). We found a significant reduction in the thickness of the masseter muscle (MMT) (p < 0.0001). With regards to occlusion, we found a significant increase in the height of the maxillary second molar (U7-PP) (p < 0.05) with significantly flattened occlusal curves (the curve of Spee [COS] (p < 0.01), and the curve of Wilson [COW] (p < 0.05)). Furthermore, the variations in the temporomandibular joint exhibited a significant reduction in the anterior joint space (AJS) (p < 0.05) and superior joint space (SJS) (p < 0.05). In addition, the correlation analysis of the masticatory complex provided the basis for the following multiple regression equation: MMT = 10.08 − 0.11 COW + 2.73 AJS. The findings from our pilot study indicate that BTX-A, as a new adjuvant treatment attempt of occlusal therapy for short-faced patients, can provide a more favorable muscular environment for subsequent occlusal therapy through the adjustment of the biting force and may contribute to the reconstruction of healthier homeostasis of the masticatory complex. However, further research is required to establish the reliability and validity of these findings.