Speaking legibly: Qualitative perceptions of altered voice among oral tongue cancer survivors

Optimising Patient Outcomes in Tongue Cancer: A Multidisciplinary Approach

A multidisciplinary approach to the management of tongue cancer is vital for achieving optimal patient outcomes. Nursing and allied health professionals play essential roles within the team. We developed symposia comprising a series of online lectures offering a detailed perspective on the role each discipline and consumer perspective has in the management of patients with tongue cancer. The topics, including epidemiology and prevention, diagnosis, treatment planning, surgery, adjuvant care, and the management of recurrent or metastatic disease, were thoroughly examined. The symposia highlighted the significance of fostering collaboration and continuous learning through a multidisciplinary approach. This initiative should be relevant to healthcare professionals, researchers, and policymakers striving to enhance patient outcomes in tongue cancer care through innovative collaboration.

Factors Facilitating or Creating Barriers to Returning to Work in Head and Neck Cancer Patients Within the First 6 Months After Treatment

Head and neck cancer (HNC) patients who are unable to return to work after completing treatment might face financial loss and reduced self-esteem. The aim of this study was to identify factors influencing a return to work in HNC cancer patients in the first 6 months after treatment. This cross-sectional study examined HNC patients who completed treatment from the outpatient radiation department of a single cancer center in northern Taiwan. Patients were assessed according to psychological distress, patient-perceived health status, barriers to returning to work, and facilitators of returning to work. Of the 106 HNC patients surveyed, 54.7% successfully returned to work. Barriers to returning to work included poor self-perception of health, greater psychological distress, and age ≥50 years. Patients who had higher psychological distress, returned to work after start of the pandemic, or received reconstruction surgery were less likely to experience a positive environment facilitating a return to work.

Deep learning model for tongue cancer diagnosis using endoscopic images

In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five university hospitals in South Korea, collected between 2010 and 2020 with the participation of otolaryngologists. To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed. Of the 12,400 total images, 5576 images related to the tongue were extracted. The CNN models showed a mean area under the receiver operating characteristic curve (AUROC) of 0.845 and a mean area under the precision-recall curve (AUPRC) of 0.892. The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively. Meanwhile, fair agreement between the oncologist and the developed model was shown for cancer diagnosis (kappa value = 0.685). The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.