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.