Optical Imaging in the Diagnosis of OPMDs Malignant Transformation

Recent advances of photodiagnosis and treatment for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) are the most common type of head and neck tumor that severely threatens human health due to its highly aggressive nature and susceptibility to distant metastasis. The diagnosis of HNSCC currently relies on biopsy and histopathological examination of suspicious lesions. However, the early mucosal changes are subtle and difficult to detect by conventional oral examination. As for treatment, surgery is still the primary treatment modality. Due to the complex anatomy and the lack of intraoperative modalities to accurately determine the incision margins, surgeons are in a dilemma between extensive tumor removal and improving the quality of patient survival. As more knowledge is gained about HNSCC, the increasing recognition of the value of optical imaging has been emphasized. Optical technology offers distinctive possibilities for early preoperative diagnosis, intraoperative real-time visualization of tumor margins, sentinel lymph node biopsies, phototherapy. Fluorescence imaging, narrow-band imaging, Raman spectroscopy, optical coherence tomography, hyperspectral imaging, and photoacoustic imaging have been reported for imaging HNSCC. This article provides a comprehensive overview of the fundamental principles and clinical applications of optical imaging in the diagnosis and treatment of HNSCC, focusing on identifying its strengths and limitations to facilitate advancements in this field.

Application of Targeted Optical Coherence Tomography in Oral Cancer: A Cross-Sectional Preliminary Study

BACKGROUND/OBJECTIVES

The diagnosis of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) represent a significant challenge in oral medicine. Optical coherence tomography (OCT) shows promise for evaluating oral tissue microstructure but lacks standardized diagnostic protocols tailored to the structural variability and lesions of oral mucosa.

METHODS

This cross-sectional observational study aims to evaluate the diagnostic accuracy of targeted biopsy-based and site-coded OCT protocols for common OPMDs and OSCC. Adult patients clinically diagnosed with OPMDs, including oral leukoplakia (OL), oral lichen planus (OLP), and OSCC were enrolled. Clinical and OCT evaluation before and after punch scalpel-site registration preceding diagnostic biopsy on the target site was performed. Blinded observers analyzed the OCT scans for OCT-based diagnoses. Sensitivity, specificity, and diagnostic accuracy for OCT evaluations before and after punch scalpel-site registration were statistically compared with histological findings.

RESULTS

A dataset of 2520 OCT scans and 210 selected images from 21 patients was obtained. Sensitivity and specificity post-target site registration were high for OSCC (98.57%, 100.00%), OL (98.57%, 98.57%), and OLP (97.14%, 98.57%). The positive predictive values ranged from 97.14% to 100.00%, while negative predictive values ranged from 98.57% to 99.29%. Inter-observer agreements were strong for OSCC (0.84) and moderate for OL (0.54) and OLP (0.47-0.49). Targeted OCT scans significantly improved diagnostic accuracy for all conditions (p < 0.001).

CONCLUSIONS

This preliminary study supports using site-targeted OCT scans followed by a site-targeted punch biopsy, enhancing precision in oral diagnostics. This approach is foundational for developing pioneering automated algorithms guiding oral cancer and pre-cancer diagnosis via OCT imaging.

Comparative analysis of two arecoline-induced oral submucous fibrosis models

OBJECTIVE

The limited understanding of the molecular mechanism for oral submucosal fibrosis (OSF) poses challenges to the development of effective prevention and treatment strategies. The lack of suitable animal models is a major hindrance. Therefore, this study aimed to address this issue by comparing commonly used arecoline-induced water drinking and injection mouse models.

MATERIALS AND METHODS

The mice were subjected to two protocols: receiving 2 mg/mL arecoline in drinking water and 4 mg/mL arecoline saline solution injections every other day. Tissues were collected at regular 4-week intervals, with a final time point of 20 weeks. Stereo microscopy and histomorphological analysis were performed on live and harvested tissues, respectively.

RESULTS

During arecoline treatment, collagen deposition and myofibroblast proliferation progressively increased in both models. Changes in the collagen I/III ratio indicated that both models exhibited characteristics of the early and intermediate stages of OSF after 20 weeks of arecoline induction. The water-drinking model also demonstrated multi-organ fibrosis involving the tongue, lungs, and small intestine.

CONCLUSION

Both the water drinking and injection mouse models effectively induced OSF, but the water-drinking model better mirrored the observed pathogenesis in patients with OSF. These models provide valuable tools for investigating the mechanisms underlying OSF.

Malignant transformation of white sponge nevus: a case report of a novel keratin 4 mutation

BACKGROUND

White Sponge Nevus (WSN) is traditionally considered a benign genetic disorder affecting the oral mucosa, primarily caused by pathogenic mutations in keratin 4 (KRT4) or keratin 13 (KRT13). Despite its benign nature, recent evidence has begun to question the malignant potential of WSN.

CASE PRESENTATION

We report a case involving a 70-year-old man who presented with a white lesion on the right floor of his mouth. Initial diagnostic evaluations confirmed the lesion as WSN. Over a one-year follow-up, the lesion underwent malignant transformation, evolving into local epithelial moderate-to-severe dysplasia. Exome sequencing identified a novel insertion mutation in exon 1 of the KRT4 gene, resulting in a deletion-insertion amino acid mutation involving glycine. Single-cell RNA sequencing further revealed altered epithelial proliferation and differentiation dynamics within the lesion.

CONCLUSIONS

This case not only expands the known genetic spectrum of KRT4 mutations associated with WSN but also provides preliminary evidence suggesting the malignant potential of WSN. The novel pathogenic mutation in KRT4 is postulated to alter epithelial proliferation and differentiation, thereby raising concerns about the malignant transformation of WSN. Further studies are warranted to confirm these findings.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning

Background and Objective: The tongue mucosa often changes due to various local and systemic diseases or conditions. This study aimed to investigate whether deep learning can help detect abnormal regions on the dorsal tongue surface in patients and healthy adults. Materials and Methods: The study collected 175 clinical photographic images of the dorsal tongue surface, which were divided into 7782 cropped images classified into normal, abnormal, and non-tongue regions and trained using the VGG16 deep learning model. The 80 photographic images of the entire dorsal tongue surface were used for the segmentation of abnormal regions using point mapping segmentation. Results: The F1-scores of the abnormal and normal classes were 0.960 (precision: 0.935, recall: 0.986) and 0.968 (precision: 0.987, recall: 0.950), respectively, in the prediction of the VGG16 model. As a result of evaluation using point mapping segmentation, the average F1-scores were 0.727 (precision: 0.717, recall: 0.737) and 0.645 (precision: 0.650, recall: 0.641), the average intersection of union was 0.695 and 0.590, and the average precision was 0.940 and 0.890, respectively, for abnormal and normal classes. Conclusions: The deep learning algorithm used in this study can accurately determine abnormal areas on the dorsal tongue surface, which can assist in diagnosing specific diseases or conditions of the tongue mucosa.

Efficacy of optical coherence tomography in the diagnosing of oral cancerous lesion: systematic review and meta-analysis

Non-invasive diagnostic tools that facilitate visualization of potentially malignant oral lesions and cancers have been introduced. Oral lesions detected by optical coherence tomography (OCT) were compared to reference results based on histological findings. The diagnostic odds ratio (DOR), along with summary receiver operating characteristic curve (SROC), area under SROC, sensitivity, specificity, and negative predictive values, were the outcomes. The DOR of OCT was 86.9190 (95% confidence interval [CI]: 38.7435, 194.9985), and the area under SROC was 0.951. OCT showed good sensitivity (0.9138; 95% CI: 0.8758, 0.9409) and specificity (0.9110; 95% CI: 0.8568, 0.9460), and a high negative predictive value (0.9225; 95% CI: 0.8863, 0.9478). Diagnostic sensitivity was higher when using artificial intelligence and automated algorithms compared to diagnoses made by clinicians. OCT is non-invasive, provides rapid results without radiation exposure, and can aid in the diagnosis and follow-up of oral cancer and oral precancerous lesions.