1 Introduction
Vocal cord leukoplakia is a clinical descriptor for the identification of a white plaque or patch on the vocal cords upon macroscopic examination without consideration of its histological features or prognosis. Pathologically, vocal cord leukoplakia may be accompanied by squamous hyperplasia, epithelial dysplasia, or carcinoma; and thus, it is considered a precancerous lesion within the spectrum of transformation of the laryngeal epithelium toward malignancy[1].
Laryngeal cancer is typically preceded by dysplasia, and the degree of dysplasia is directly proportional to the rate of malignant transformation of vocal cord leukoplakia[2]. While the rate of malignant transformation varies widely with estimates as low as 1.7% and as high as 46.3%[3], early diagnosis and treatment of vocal cord leukoplakia may prevent progression and malignancy[4]. The 2017 World Health Organization Classification of Head and Neck Tumors proposed a two-tier classification system for dysplasia, with reasonably clear histopathological criteria for the two groups: 1) low-grade (LG) dysplasia including squamous hyperplasia and mild dysplasia, and 2) high-grade (HG) dysplasia including moderate and severe dysplasia and carcinoma in situ[5,6]. In response to this classification, some otolaryngologists proposed that patients in the LG group of vocal cord leukoplakia with a low malignancy risk would generally require a conservative treatment or watch-and-wait policy, whereas patients in the HG group of vocal cord leukoplakia would demand both surgical treatment and close follow-up to monitor possible progression to a more aggressive pathology[7]. However, a clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion, and to accordingly establish the optimal therapeutic schedule[8].
Laryngoscopy is the most important examination method for detecting vocal cord leukoplakia, but to date laryngoscopy alone cannot determine the degree or scope of vocal cord leukoplakia without biopsy. Some otolaryngologists and pathologists therefore recommend a combination of laryngoscope and random 3-spot biopsy specimens to enable early detection and follow-up. However, this procedure is invasive, time-consuming, and difficult to comply with[9]. Moreover, preoperative biopsy under laryngoscopy is unlikely to fully agree with postoperative pathology results. This discrepancy often leads to overtreatment or undertreatment even for experienced endoscopists. Another challenge in clinical practice is that not all cases of vocal cord leukoplakia need laryngoscopy with histological examination, and there is difficulty in deciding which cases indicate biopsy.
Considering the above controversies and uncertainties, further improvements in the detection of vocal cord leukoplakia, possibly using new techniques, is highly urgent for its clinical management. Currently, image-enhanced endoscopy (IEE), such as contact endoscopy (CE)[10] and narrow band imaging (NBI)[9], in addition to white light imaging (WLI) has been used for accurate diagnosis of laryngeal lesions. However, the observation procedure is time-consuming and may be biased based on the observers’ experience.
Artificial intelligence (AI) using deep learning (DL) with convolutional neural networks (CNNs) has recently emerged and showed inspiring results as a method for the detection of gastrointestinal cancers[11-13]. Moreover, one single-institution study showed that an AI system for detecting pharyngeal cancers had promising performance with high sensitivity and acceptable specificity[14]. However, no study to date has applied AI for simultaneous segmentation and classification of vocal cord leukoplakia. We therefore developed an AI system that applies DL with CNNs to assist in real-time automated diagnosis of vocal cord leukoplakia and uses pathological diagnosis as the gold standard.