Subjective and Objective Measures in Assessing Neck Disability and Pain in Head and Neck Cancer

Relationship between neck kinematics and neck dissability index. An approach based on functional regression

Numerous studies use numerical variables of neck movement to predict the level of severity of a pathology. However, the correlation between these numerical variables and disability levels is low, less than 0.4 in the best cases, even less in subjects with nonspecific neck pain. This work aims to use Functional Data Analysis (FDA), in particular scalar-on-function regression, to predict the Neck Disability Index (NDI) of subjects with nonspecific neck pain using the complete movement as predictors. Several functional regression models have been implemented, doubling the multiple correlation coefficient obtained when only scalar predictors are used. The best predictive model considers the angular velocity curves as a predictor, obtaining a multiple correlation coefficient of 0.64. In addition, functional models facilitate the interpretation of the relationship between the kinematic curves and the NDI since they allow identifying which parts of the curves most influence the differences in the predicted variable. In this case, the movement's braking phases contribute to a greater or lesser NDI. So, it is concluded that functional regression models have greater predictive capacity than usual ones by considering practically all the information in the curve while allowing a physical interpretation of the results.

Return to work in head and neck cancer survivors: its relationship with functional, psychological, and disease-treatment factors

PURPOSE

Return to work (RTW) is important for survivors of head and neck cancer (HNC). The purposes of the study were to investigate the RTW ratio among HNC survivors and identify factors significantly affecting RTW in this population.

METHODS

A cross-sectional study with consecutive sampling was conducted in a medical center in Taiwan, with 111 patients with HNC who had completed major treatments within 5 years and were employed before their cancer diagnosis enrolled as participants. Cervical range of motion (CROM) functionality, handgrip and hip flexor strength, maximal mouth opening (MMO), selected symptoms, depression, and disease/treatment-related factors were assessed. All of the factors were analyzed using t-test, chi-square test, and multiple logistic regression.

RESULTS

Less than half (44.1%, n = 49) of the participants had returned to work. The t-test/chi-square test results showed the RTW group to be younger in age and better educated; have better handgrip/hip flexor strength, MMO, and CROM; have less speech difficulty and pain; and have less-advanced cancer than the non-RTW group. Further analysis of the above significant variables by logistic regression revealed early cancer stage, dominant handgrip strength, and less speech difficulty were the robust factors related to RTW.

CONCLUSIONS

The RTW ratio is low in HNC survivors. RTW in HNC survivors is a multifactorial and complicated issue and needs to be further examined.

IMPLICATIONS FOR CANCER SURVIVORS

Assessing the factors related to RTW systematically and developing comprehensive interventions and rehabilitation programs to reduce related dysfunctions are necessary to enhance RTW ability in HNC survivors.

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning

BACKGROUND

Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations.

METHODS

In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer.

RESULTS

After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878.

CONCLUSIONS

We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Evaluation of Neck Disability Using Computed-Tomography in Head and Neck Cancer Survivors

Objective

We have previously reported that 55% of head and neck cancer survivors have neck disability. However, it is unclear what factors contribute to their neck disability. Our study aim is to determine if survivors with neck disability have evidence of cervical spine degenerative disease assessed by computed tomography (CT).

Materials/Methods

Cross-sectional analysis of patient-reported neck disability, prospectively collected on survivors of squamous cell carcinomas without recurrence or metastasis over one-year post-treatment. Neck disability and its impact on daily life was measured using the Neck Disability Index (NDI) and compared with cervical CT scans within 6 months. Scans were evaluated for degeneration of the disc and facet of the cervical vertebrae rated on a 5-point scale where 5 indicates more severe disease. Multivariable linear regression was used to analyze the association between NDI and radiographic findings.

Results

116 survivors of oropharyngeal carcinomas were identified, predominantly male (81.9%) with an average age of 62.8 ± 8.2 (range 43.8–81.4). Most survivors had advanced stage III-IVa cancer (94.0%) with treatment modalities including surgery (n=26, 52.0%), chemotherapy (n = 45, 90.0%), and radiation therapy (n = 49, 98.0%). Absence of neck disability was observed in 44.0% of survivors, 39.7% had mild disability, and 16.4% moderate disability. The time from treatment to clinic visit was an average of 3.1 ± 2.7 years (range 1.1–13.4). Multivariable analysis of NDI controlling for age, time since treatment, and treatment modality identified an inverse association between NDI and spinal degenerative disease examining cervical discs (−1.46 95% confidence interval (CI) [−2.86, −0.06], p = 0.041) and age (−0.24 95% CI[−0.40, −0.08], p = 0.004).

Conclusions

Our study shows that neck impairment and pain in head and neck cancer survivors is not sufficiently explained by cervical degeneration related to age or trauma, supporting the theory that post-treatment neck disability occurs as a side effect of treatment. These results support the further assessment of structure and function of cervical musculature and degeneration following HNC treatment.