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\begin{document}

\title{Automated Detection of Laryngeal Carcinoma in Laryngoscopic Images from
a Multicenter Database using a Convolutional Neural Network}



\author[1]{Peikai Yan}%
\author[2]{Shaohua Li}%
\author[3]{Zhou Zhou}%
\author[4]{Qian Liu}%
\author[1]{Jiahui Wu}%
\author[1]{Qingyi Ren}%
\author[5]{Qiuhuan Chen}%
\author[6]{Zhipeng Chen}%
\author[7]{Ze Chen}%
\author[8]{Shao-hua Chen}%
\author[9]{Austin Scholp}%
\author[9]{Jack J Jiang}%
\author[1]{Jing Kang}%
\author[10]{Pingjiang Ge}%
\affil[1]{Guangdong Provincial People's Hospital}%
\affil[2]{Zhongshan Hospital of Traditional Chinese Medicine}%
\affil[3]{Shenzhen People’s Hospital}%
\affil[4]{ Shenzhen People’s Hospital}%
\affil[5]{Zhaoqing Gaoyao People’s Hospital}%
\affil[6]{ The Second People’s Hospital of Longgang District}%
\affil[7]{Gaozhou People’s Hospital}%
\affil[8]{Guangdong general Hospital \&Guangdong Academy of Medical Science}%
\affil[9]{University of Wisconsin-Madison School of Medicine and Public Health}%
\affil[10]{Guangdong General Hospital}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\selectlanguage{english}
\begin{abstract}
OBJECTIVE Little is known about the efficacy of using artificial
intelligence to identify laryngeal carcinoma from images of vocal
lesions taken in different hospitals with multiple laryngoscope systems.
This multicenter study was aimed to establish an artificial intelligence
system and provide a reliable auxiliary tool to screen for laryngeal
carcinoma. Study Design: Multicentre case-control study Setting: Six
tertiary care centers Participants: The laryngoscopy images were
collected from 2179 patients with vocal lesions. Outcome Measures: An
automatic detection system of laryngeal carcinoma was established based
on Faster R-CNN, which was used to distinguish vocal malignant and
benign lesions in 2179 laryngoscopy images acquired from 6 hospitals
with 5 types of laryngoscopy systems. Pathology was the gold standard to
identify malignant and benign vocal lesions. Results: Among 89 cases of
the malignant group, the classifier was able to evaluate the laryngeal
carcinoma in 66 patients (74.16\%, sensitivity), while the classifier
was able to assess the benign laryngeal lesion in 503 cases among 640
cases of the benign group (78.59\%, specificity). Furthermore, the
CNN-based classifier achieved an overall accuracy of 78.05\% with a
95.63\% negative prediction for the testing dataset. Conclusion: This
automatic diagnostic system has the potential to assist clinical
laryngeal carcinoma diagnosis, which may improve and standardize the
diagnostic capacity of endoscopists using different laryngoscopes.%
\end{abstract}%



\sloppy


\textbf{Automated Detection of Laryngeal Carcinoma in Laryngoscopic
Images from a Multicenter Database using a Convolutional Neural Network}

\textbf{ABSTRACT}

\textbf{OBJECTIVE} Little is known about the efficacy of using
artificial intelligence to identify laryngeal carcinoma from images of
vocal lesions taken in different hospitals with multiple laryngoscope
systems. This multicenter study was aimed to establish an artificial
intelligence system and provide a reliable auxiliary tool to screen for
laryngeal carcinoma.

\textbf{Study Design:} Multicentre case-control study

\textbf{Setting:} Six tertiary care centers

\textbf{Participants:} The laryngoscopy images were collected from 2179
patients with vocal lesions.

\textbf{Outcome Measures:} An automatic detection system of laryngeal
carcinoma was established based on Faster R-CNN, which was used to
distinguish vocal malignant and benign lesions in 2179 laryngoscopy
images acquired from 6 hospitals with 5 types of laryngoscopy systems.
Pathology was the gold standard to identify malignant and benign vocal
lesions.

\textbf{Results:} Among 89 cases of the malignant group, the classifier
was able to evaluate the laryngeal carcinoma in 66 patients (74.16\%,
sensitivity), while the classifier was able to assess the benign
laryngeal lesion in 503 cases among 640 cases of the benign group
(78.59\%, specificity). Furthermore, the R-CNN-based classifier achieved
an overall accuracy of 78.05\% with a 95.63\% negative prediction for
the testing dataset.

\textbf{Conclusion:} This automatic diagnostic system has the potential
to assist clinical laryngeal carcinoma diagnosis, which may improve and
standardize the diagnostic capacity of endoscopists using different
laryngoscopes.

\textbf{Keywords:} Artificial intelligence, Vocal fold lesions,
Laryngoscope, Laryngeal Carcinoma, multicenter study

\textbf{Key points:}

\begin{itemize}
\tightlist
\item
  Artificial intelligence (AI) has achieved outstanding performance
  within medical imaging interpretation and triage tasks and has been
  successfully used to diagnose carcinoma.
\item
  Higher sensitivity was achieved for the malignancy by autonomous
  classification of endoscopic images with artificial intelligence
  technology in a single center.
\item
  It is still unknown whether different laryngoscopic systems would
  impact automatic recognition accuracy from multicenter clinics.
\item
  An automatic classifier for laryngeal carcinoma based on Faster
  R-CNNs(region-based convolutional neural networks) was established.
\item
  High accuracy was also obtained for multiple clinics and multiple
  laryngoscopic systems.
\end{itemize}

\textbf{Introduction}

Laryngeal carcinoma is one of the most common malignant tumors of the
head and neck with incidence estimated to be more than 24,500 cases per
year by 2030.\textsuperscript{1} Survival outcomes in laryngeal cancer
are affected by several aspects, including tumor
stage,\textsuperscript{2}
subsites,\textsuperscript{3}age,\textsuperscript{2,4} treatment
modality,\textsuperscript{2,5}comorbidities\textsuperscript{3}, etc. An
early-stage diagnosis is one of the most crucial factors to decrease the
mortality rate and preserve both laryngeal anatomy and vocal function.
The 5-year survival rates are 100\% and 80\% for patients with stage 0
and stage I laryngeal carcinoma, respectively, while it decreases to
only 70\% for patients with advanced-stage cancer.\textsuperscript{6}

Currently, the optic laryngoscope is a routine method to diagnose
laryngeal cancer, as well as identify the extent of invasion and provide
accurate clinical staging. However, physicians still have difficulty
distinguishing early-stage cancer from mucosal
abnormalities.\textsuperscript{7} Thus, misdiagnoses and missed
diagnoses are not rare while only using a laryngoscope. High levels of
diagnostic inconsistency have been observed, even among
experts.\textsuperscript{8}

Today, artificial intelligence (AI) has achieved outstanding performance
within medical imaging interpretation and triage tasks and has been
successfully used to diagnose skin cancer,\textsuperscript{9} lung
cancer,\textsuperscript{10} glioma,\textsuperscript{11} and breast
histopathology.\textsuperscript{12} As a popular technique of
deep-learning algorithms, region-based convolutional neural networks
(R-CNNs) proposed an efficient method in object detection that utilizes
a feature map from a convolutional neural
network.\textsuperscript{13}Several recent approaches, including Fast
R-CNN, Faster R-CNN, and Mask R-CNN, were developed based on
R-CNNs.\textsuperscript{14-16} In particular, Faster R-CNN is one of the
first end-to-end two-stage detectors that has displayed remarkable
efficiency and accuracy.\textsuperscript{17}

Recently, two studies regarding the automatic recognition of
laryngoscopy images based on the convolutional neural network have
achieved promising results. In one previous study, the binary classifier
distinguished benign and malignant-premalignant lesions with an overall
accuracy of 93.0\%.\textsuperscript{18} A sensitivity of 89\% and a
specificity of 99.33\% were achieved for the malignancy by autonomous
classification of endoscopic images with artificial intelligence
technology.\textsuperscript{19} However, the studies mentioned above
were both carried out in a single center. It is still unknown whether
the source of laryngoscopic images, the resolution of the laryngoscopy
images, and the different endoscopic systems would impact automatic
recognition accuracy. Therefore, a multicenter clinical trial is
essential to determine whether the AI technique could cope with complex
situations in the real world.

In this study, a multicenter experiment of laryngeal carcinoma detection
was carried out based on an autonomous endoscopic image classifier using
the Faster R-CNN system. Our research established an artificial
intelligence system for the detection of laryngeal carcinoma and
evaluated the performance of this system, aiming to provide a reliable
auxiliary tool to diagnose early-stage laryngeal carcinoma efficiently
and help untrained technicians accomplish objective and accurate
screening.

\textbf{Methods}

\textbf{2.1 Data Collection and Study Population}

All subjects were patients that underwent laryngoscopy prior to surgery
and were diagnosed with malignant or benign laryngeal lesions via
pathological diagnosis. All duplicate images, unclear images with low
resolution, and images of unexposed vocal folds were excluded from this
study. Twenty-six images were excluded from this study.

A total of 2179 laryngoscopy images, including 288 images of malignant
lesions and 1891 images of benign lesions from 2179 patients were
qualified for inclusion. Each image only displayed a patient's glottic
lesion or tumor. The benign lesion images were composed of 998 vocal
polyps, 169 vocal cysts, 276 nodules, 356 vocal leukoplakia, 80 vocal
process granuloma, and 12 vocal papillomae. Of the 288 malignant
lesions, there were 22 cases of T3 tumors, 62 of T2 tumors, and 204 of
T1 tumors. The images were retrospectively collected in 6 hospitals with
5 endoscopic systems including First hospital (n=783, XION EndoSTROB D
and Olympus ENF), Second hospital (n=622, Olympus ENF), Third hospital
(n=531, Olympus VISER Pro and XION EndoSTROB D), Forth hospital (n=89,
XION LCD), Fifth hospital (n=78, Olympus CV170), and Sixth hospital
(n=76, Olympus ENF). All images were taken between January 1, 2015, and
December 31, 2018. The related information is described in Table 1. All
patients in this study underwent the white light laryngoscopic scan and
were diagnosed by two experienced doctors. The images were distributed
into malignant and benign groups by pathology.

\textbf{2.2 Data Preparation}

The dataset was randomly split into two parts: 1450 images for training
and 729 images for testing. For training, the number of images in the
malignant and benign groups was 199 and 1251, respectively. For the
testing group, 89 were malign and 640 were benign (Table 1). Two senior
otolaryngologists used the labeling software Label Tool to interpret the
laryngoscope images and label the exact region of the biopsy.

\textbf{2.3 Outcome Measures}

The automatic detection of laryngeal carcinoma was carried out using
Faster R-CNN, which is one of the most popular two-stage detection
networks used for many medical image detection situations. The
architecture of Faster R-CNN generally consists of the region proposal
network (RPN) and Fast R-CNN detector, as shown in Figure 1. RPN is a
deep fully convolutional network using simultaneous object boundaries
and object scores prediction at each detection for generating
high-quality region proposals. It should be highlighted that RPNs are
introduced to share convolutional layers with the Fast R-CNN detector to
a more effective detection network's computation (Figure
1).\textsuperscript{15} Fast R-CNN includes region of interest (RoI)
pooling, which produces the fixed-size feature maps from non-uniform
inputs by doing max-pooling on the inputs, aiming to refine the
proposals and process the sketch map.\textsuperscript{20}

\textbf{Results}

After the training procedure described above, the CNN-based classifier
was used to detect laryngeal lesions. Comparisons were conducted of
laryngoscopic images detected by the CNN-based classifier with that by
the pathological results to determine the diagnostic potential of the
classification system for the testing dataset.

Among 89 cases of the malignant group, the classifier was able to
accurately evaluate the laryngeal carcinoma in 66 cases (74.16\%,
sensitivity). For the benign lesions, the classifier was able to
accurately assess lesions in 503 cases of the 640 cases in the benign
group (78.59\%, specificity). Furthermore, the CNN-based classifier
achieved an overall accuracy of 78.05\% on the testing dataset. The
results for the classifier are summarized in Table 2 and Table 3.

\textbf{Discussion}

For years, the laryngoscope has been recommended as an essential tool
for assessing vocal fold lesions. However, laryngoscopes have
limitations in making diagnoses owing to subtle differences in mucosal
tissue. Distinguishing different mucosa can be difficult, especially for
early-stage laryngeal carcinoma. Insufficient experience and excessive
workload can also lead to missed diagnosis and misdiagnosis. Therefore,
an assistive system for identifying laryngeal carcinoma is needed to
improve and standardize clinicians' diagnostic capacity.

The Faster R-CNN detection system has the potential to analyze the
characteristics, extract the images' commonalities, and then classify
the data. In our study, a 78.05\% accurate rate was achieved. Our
results confirmed that the model we have established could offer
valuable clinical assistance to preliminarily screen patients with
laryngeal carcinoma and identify patients with benign lesions. This
system would be able to help clinicians make more efficient and accurate
diagnoses and enable standardized evaluation.

In this study, 2179 laryngoscope images taken were from different
endoscopic systems between six hospitals. We did not impose special
restrictions on equipment or images. Neither did we have restrictions on
age or gender. Such processing maximized the natural diversity of
different laryngoscopic appearances in various conditions to reflect
whether AI technology could cope with complex situations in the real
world.

Our results indicate that the automatic classifier has a sensitivity of
74.16\%, a specificity of 78.59\%, and 78.05\% accuracy for detecting
laryngeal carcinoma, which outperformed physicians in laryngeal
carcinoma recognitions as demonstrated in several articles. In Ren's
study, twelve human experts only achieved an overall accuracy of 54\% in
malignancy detection in a set of 706 laryngoscopic testing
images.\textsuperscript{19} Additionally, a recent meta-analysis showed
that only 65\% of the tested population was correctly identified as not
having cancer by ENT doctors, even with
videostroboscopy.\textsuperscript{8} Generally speaking, an automated
system could help physicians make more confident determinations to
exclude the malignant lesion, reducing the burden of endoscopists and
patients' waiting time, while it could raise red flags when a patient
tests positive, reminding doctors to complete further clinical
investigation to determine if the patient is truly positive.

However, two recent studies using the CNN-based technique obtained over
90\% accuracy for malignancy lesions detection, which outperformed our
classifier. A possible explanation for this might be that our
laryngoscope images were from different endoscopic systems in several
hospitals. The diverse laryngoscope equipment could attribute to
complicated commonality extractions, leading to the limited quality of
data classification. This situation would be a common problem for
clinical application, which is an obstacle that needs to be overcome in
future research. Additionally, increasing the training dataset size may
be another approach to improving our classifier's sensitivity and
specificity.

Further research is necessary to include high-quality laryngoscope
images with NBI from multiple centers. Since the sensitivity of NBI has
been found to be superior to white light
endoscopy,\textsuperscript{21}the CNN-based classifier may provide more
efficient and accurate information with NBI.

\textbf{Conclusion}

The fiber-optic laryngoscope has been a routine method to diagnose
laryngeal lesions; however, physicians still have difficulty
distinguishing early-stage cancer. In this study, an automatic
classifier for laryngeal carcinoma based on Faster R-CNN was established
for multiple clinics and multiple laryngoscopic systems. An accuracy of
78.05\% was obtained. This diagnostic system could offer promising
applicability to daily medical practice, which may improve clinical
diagnosis quality and benefit patients.

\textbf{REFERENCES}

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3. Ramroth H, Schoeps A, Rudolph Eet al. Factors predicting survival
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Figure 1 Schematic diagram of Fast R-CNN detector\selectlanguage{english}
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