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\begin{document}

\title{Differentiation of eosinophilic and non-eosinophilic chronic
rhinosinusitis on preoperative computed tomography using deep learning}



\author[1]{Hongli Hua}%
\author[1]{Song Li}%
\author[1]{Yu Xu}%
\author[1]{Shiming Chen}%
\author[1]{Yonggang Kong}%
\author[1]{Rui Yang}%
\author[1]{Yu-Qin Deng}%
\author[1]{Zezhang Tao}%
\affil[1]{Renmin Hospital of Wuhan University}%


\vspace{-1em}



  
  \date{May 08, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Objective: This study aimed to develop deep learning (DL) models for
differentiating between eosinophilic chronic rhinosinusitis (ECRS) and
non-eosinophilic chronic rhinosinusitis (NECRS) on preoperative computed
tomography (CT). Methods: A total of 878 chronic rhinosinusitis (CRS)
patients undergoing nasal endoscopic surgery were included. Axial spiral
CT images were pre-processed and used to build the dataset. Two semantic
segmentation models based on U-net and Deeplabv3 were trained to segment
sinus area in CT images. All patient images were segmented using the
better-performing segmentation model and used for training and
validation of the transferred efficientnet\_b0, resnet50,
inception\_resnet\_v2, and Xception neural networks. Additionally, we
evaluated the performances of the models trained using each image and
each patient as a unit. The precision of each model was assessed based
on the receiver operating characteristic curve. Further, we analyzed the
confusion matrix, accuracy, and interpretability of each model. Results:
The Dice coefficients of U-net and Deeplabv3 were 0.953 and 0.961,
respectively. The average area under the curve and mean accuracy values
of the four networks were 0.848 and 0.762 for models trained using a
single image as a unit, while the corresponding values for models
trained using each patient as a unit were 0.853 and 0.893, respectively.
The generated Grad-Cams showed good interpretability. Conclusion:
Combining semantic segmentation with classification networks could
effectively distinguish between patients with ECRS and NECRS based on
preoperative sinus CT images. Furthermore, labeling each patient to
build a dataset for classification may be more reliable than labeling
each medical image.%
\end{abstract}%



\sloppy


\textbf{Abstract}

\textbf{Objective} : This study aimed to develop deep learning (DL)
models for differentiating between eosinophilic chronic rhinosinusitis
(ECRS) and non-eosinophilic chronic rhinosinusitis (NECRS) on
preoperative computed tomography (CT).

\textbf{Methods} : A total of 878 chronic rhinosinusitis (CRS) patients
undergoing nasal endoscopic surgery were included. Axial spiral CT
images were pre-processed and used to build the dataset. Two semantic
segmentation models based on U-net and Deeplabv3 were trained to segment
sinus area in CT images. All patient images were segmented using the
better-performing segmentation model and used for training and
validation of the transferred efficientnet\_b0, resnet50,
inception\_resnet\_v2, and Xception neural networks. Additionally, we
evaluated the performances of the models trained using each image and
each patient as a unit. The precision of each model was assessed based
on the receiver operating characteristic curve. Further, we analyzed the
confusion matrix, accuracy, and interpretability of each model.

\textbf{Results} : The Dice coefficients of U-net and Deeplabv3 were
0.953 and 0.961, respectively. The average area under the curve and mean
accuracy values of the four networks were 0.848 and 0.762 for models
trained using a single image as a unit, while the corresponding values
for models trained using each patient as a unit were 0.853 and 0.893,
respectively. The generated Grad-Cams showed good interpretability.

\textbf{Conclusion} : Combining semantic segmentation with
classification networks could effectively distinguish between patients
with ECRS and NECRS based on preoperative sinus CT images. Furthermore,
labeling each patient to build a dataset for classification may be more
reliable than labeling each medical image.

\textbf{Keywords:} deep learning; eosinophil; computed tomography;
rhinosinusitis; differentiation

\textbf{Key points:}

\begin{enumerate}
\tightlist
\item
  Accurate preoperative prediction of the CRS type is necessary for
  predicting postoperative outcomes and administering personalized
  treatment
\item
  We combined semantic segmentation with classification network for
  sinus region segmentation and differentiating between ECRS and NECRS
  on preoperative CT
\item
  The segmentation model could accurately segment the sinus region in CT
  images
\item
  The classification model could effectively distinguish between
  patients with ECRS and NECRS based on preoperative sinus CT images
\item
  Labeling each patient to build a dataset for classification may be
  more reliable than labeling each medical image.
\end{enumerate}

\textbf{1. Introduction}

Chronic rhinosinusitis (CRS), which is characterized by inflammatory
dysregulation of the nasal and parasinus mucosa for [?]12 consecutive
weeks, is among the most common otolaryngological diseases, with a
prevalence rate of 8\% in the general population {[}1{]}. Currently, CRS
is classified into two types based on the presence or absence of nasal
polyps; further, it can be classified as eosinophilic sinusitis (ECRS)
and non-eosinophilic sinusitis (NECRS) based on the eosinophilic
infiltration level in the nasal mucosa or polyp.~There has been recent
progress in the elucidation of the pathogenesis of CRS.~ There are
several limitations in the traditional phenotypic classification of CRS
into CRSwNP and CRSsNP. First, this classification does not account for
pathophysiological differences; moreover, immune system cells are
crucially involved in inflammatory mechanisms {[}2-4{]}. Many patients
with extensive tissue eosinophil infiltration do not present polyp-like
degeneration, with the incidence rate being approximately 27.5\%
{[}5{]}. Further, even patients with polypoid changes may predominantly
present non-eosinophilic inflammation {[}6{]}. Classification of CRS
into ECRS and NECRS is more likely to reflect the underlying
inflammatory process. Additionally, the level of eosinophil infiltration
in CRS lesions is strongly associated with postoperative outcomes {[}7,
8{]}.

ECRS has a recurrence rate as high as 98.5\%; additionally, it is the
main cause of refractory sinusitis recurrence {[}9{]}.~The gold standard
for ECSR diagnosis is histopathological examination; however, it is
invasive. Recently, the main treatments for ECSR and NECRS are contoured
endoscopic nasal surgery and functional endoscopic nasal surgery,
respectively {[}10, 11{]}.~Given the high postoperative recurrence rate
of ECRS and differences in the surgical methods for both CRS types,
accurate preoperative prediction of the CRS type is necessary for
predicting postoperative outcomes and administering personalized
treatment. Eosinophil levels in the peripheral blood are associated with
the eosinophil infiltration degree in the nasal sinus mucosa
{[}12{]}.~However, allergies, autoimmune diseases, drug reactions,
parasitic infections, and corticosteroid treatment can alter circulating
eosinophil levels.~Additionally, increased eosinophil levels in
peripheral blood do not necessarily reflect an increase in tissue
eosinophils; moreover, the predictive utility of blood eosinophil levels
for ECRS remains limited {[}13, 14{]}. There has been extensive research
on the preoperative predictive utility of exhaled nitric oxide levels,
serum total immunoglobulin E (IgE), specific IgE, and skin prick tests
for ECRS and NECRS; however, they have low sensitivity and specificity
{[}15, 16{]}.~There are several differences in sinus computed tomography
(CT) findings between patients with ECRS and NECRS. Early-stage ECRS
often presents as ethmoid sinus lesions and mild maxillary sinus
lesions, while NECRS often presents with maxillary sinus lesions.
However, these characteristics do not effectively distinguish ECRS from
NECRS {[}8, 17{]}.

Considering the strong feature extraction and screening ability of
artificial intelligence, applying artificial intelligence technology to
sinus CT-assisted ECRS diagnosis may allow accurate preoperative
prediction of ECRS.~CRS lesions are limited to the nasal cavity and
paranasal sinus areas in each disease type; moreover, the sinus area
comprises a small part of the sinus CT images, with the surrounding
tissue structure being complex. Therefore, noise information in the
whole image increases the required training data.~Manually delimiting
the regions of interest (ROIs) is inconsistent with the original
intention of artificial intelligence; further, its huge sketching
workload greatly reduces its clinical application value.~Therefore, we
aimed to develop a segmentation model that allowed automatic
segmentation of the sinus region. Moreover, since different networks
have certain data ''preferences,'' we used four common classification
networks to train the segmented images.

\textbf{2. Methods}

Patients

We included 878 patients with CRS who underwent nasal endoscopic surgery
in the Department of Otolaryngology, Head and Neck Surgery of our
hospital, from October 2016 to June 2021. The study protocol was
approved by the institutional review board, which waived the requirement
for informed consent. The inclusion criteria were as follows: diagnosis
of chronic rhinosinusitis based on the European Position Paper on
Rhinosinusitis and Nasal Polyps 2020 and having undergone a sinus CT
scan within 2 preoperative weeks. The exclusion criteria were as
follows: fungal sinusitis identified on pathological examination; sinus
cystic fibrosis; unclear CT images; posterior nostril obstruction; a
history of radiotherapy for the head and neck.

2.2 Histological examination

Intraoperative nasal polyps and pathological nasal mucosal tissues were
selected for fixation, embedding, and sectioning. Sections underwent
conventional hematoxylin-eosin staining, followed by observation under a
high-magnification field (HPF) of 400 \selectlanguage{ngerman}×. In each field, ten fields were
randomly selected for observation and the eosinophils and inflammatory
cells were counted.~The mean counts of eosinophils and inflammatory
cells were calculated for each field. ECRS was diagnosed when the
eosinophil to inflammatory cell count ratio (Eos\%) was \selectlanguage{english}[?] 10\%, and
NECRS was diagnosed when the Eos\% was \textless{}10\%{[}18{]}.

2.3 Image collection and Pre-processing

The patients underwent sinus scanning with 64-slice spiral CT using the
following parameters: tube voltage, 120 kV and tube current, 200
mA.~There was a soft tissue window (window width: 300--350 HU, window
position: 30--50 HU) and bone window (window width: 1000--2000 HU,
window position: 300--350 HU). Exported CT images were saved in the
DICOM format and converted to the PNG format for segmentation and
classification model training; additionally, the slices of axial CT
images with lesions were used to build the dataset.

To establish segmentation dataset, 1,365 images were randomly selected
from patients with ECRS and NECRS; additionally, the nasal cavity and
sinus regions in the image were marked using the ITK-SNAP software. We
defined the nasal cavity and sinus regions as follows: the inferior side
is the plane where the bilateral inferior turbinates begin to appear,
while the superior side is the plane where the straight gyrus and
orbital gyrus~of the frontal lobe begin to appear. At the maxillary
level, the anterior side is the anterior nostril or nasal bone, the
lateral sides are the anterior lateral wall and posterior wall of the
maxillary or lateral wall of the sphenoid, and the posterior side is the
posterior nostril or posterior wall of the sphenoid. At the ethmoid
level, the anterior side is the nasal or frontal bone, the lateral sides
are the lateral walls of the ethmoid and sphenoid, and the posterior
side is the posterior wall of the posterior ethmoid or sphenoid (Figure
1).

The dataset of the classification model comprised 56,892 images,
including ECRS (343 patients and 22,671 images) and NECRS (535 patients
and 34,221 images). Patients in each category were allocated to the
training and validation cohorts at a ratio of 4:1. Since all sinus CT
slices did not show between-disease differences and we sought to achieve
accurate classification, we constructed two datasets using each patient
and each image as a unit. When using individual images, each image was
labeled and input into the classification network for learning. When
using patients as units, we labeled each patient, set the average
probability value of all the images obtained from each patient as the
patient's probability value, and input it into the model for learning.

2.4 Network Architecture

Our compiling platform was based on the Pytorch library (version 1.9.0)
with CUDA (version 10.0) for GPU (NVIDIA T4) acceleration on a Windows
operating system (Server 2019 data center version 64 bit). We
transformed the U-Net and Deeplabv3 networks to build semantic
segmentation models. Additionally, 1,365 images were used to construct
datasets, which were randomly divided into the training and validation
cohorts at a ratio of 4:1. The model was trained using the RMSprop
optimizer, with the batch size and initial learning rate set at 32 and
0.001, respectively. Both semantic segmentation models were trained for
20 epochs. We selected the model with the best performance and used a
rectangular segmentation method to segment the nasal cavity and sinus
areas on the CT images (Figure 2).

Since different neural networks may have different preferences for the
data distribution, type, and dataset size, we used four common
pre-trained classification networks for model building, including
efficientnet\_b0, resnet50, inception\_resnet\_v2, and Xception neural
networks, to avoid model inclination. These networks were trained using
the SGD optimizer with a batch size of 32; furthermore, each model was
trained for 40 epochs.

Statistical Analyses

Statistical analyses were performed using SPSS22.0 statistical
software.~Normally distributed measurement data are expressed as
(\emph{X +- S} ) and were analyzed using an independent sample t-test.
Counting data are expressed as frequencies and were analyzed using the
chi-square test.~Statistical significance was set at \emph{P}\textless{}
0.05. Segmentation model performance was evaluated using Dice similarity
coefficients, and classification model was evaluated using the ROC
curve, accuracy, and confusion matrix; moreover, Grad-Cams were
generated by extracting feature maps from the final convolutional layers
to verify the reliability of the model.

\textbf{3. Results}

3.1 Clinical Characteristics of the Study Population

After screening, we enrolled 878 eligible patients in the training (n =
702) and validation (n = 176) cohorts of the classification model. There
were no significant between-cohort differences in the general
information.

3.2 Results of the Semantic Segmentation Models

After training for 20 epochs, the performance of U-net and Deeplabv3
quickly stabilized; moreover, they had Dice coefficients of 0.953 and
0.961, respectively (Figure 4). Deeplabv3 performed slightly better than
U-net. We used the Deeplabv3 network trained to segment the sinus region
in CT images.

3.3 Performance of the Classification Models

When trained using a single image as a unit, the areas under the curve
(AUCs) of the efficientnet\_b0, resnet50, inception\_resnet\_v2, and
Xception networks were 0.84, 0.86, 0.83, and 0.86, respectively. When
trained using each patient as a unit, the AUCs of the four neural
networks increased to 0.89, 0.90, 0.88, and 0.90, respectively (Figure
5). The accuracy of the training process reflects the overall
classification performance of the DL models (Figure: 6).

We incorporated a confusion matrix for class-wise comparison to evaluate
whether the model could reliably detect and classify objects. As shown
in Figures 7 and 8, the specificity and sensitivity of the four networks
were higher when using each patient, rather than single images, as
units.

The presentation of Grad-Cams allows elucidation of how the DL network
captured image features for prediction and resolves doubts regarding the
network's ability to learn in the appropriate direction. Yellow areas
shown in the Grad-Cams had the strongest correlation with the
classification. For patients with ECRS, the yellow areas represent
characteristics associated with an increased risk of ECRS. The
efficientnet\_b0 network was used as an example. Figures 9, 10, 11, and
12 show Grad-Cams for both the bone window and soft tissue window images
of the patients. For both diseases, yellow areas were concentrated in
areas with lesions, which is consistent with our medical experience.

\textbf{4. Discussion}

The recent advances in computer vision technology have allowed rapid
development of artificial intelligence technology for image processing,
automatic recognition, classification, and segmentation. This has led to
more efficient extraction of large amounts of feature information from
medical images.~Medical image evaluation is not limited to qualitative
disease diagnosis; rather, it also includes the acquisition and analysis
of rich quantitative information to provide data regarding disease
severity, optimal treatment options, and patient outcomes. In our study,
we introduced semantic segmentation and classification networks to
achieve effective classification of ECRS and NECRS based on preoperative
sinus CT images. In contrast to patients with NECRS, patients with ECRS
require repeated administration of corticosteroid therapy and multiple
revision surgeries to achieve disease control {[}19{]}. Specifically,
the therapeutic strategy involves local treatment through high-volume
corticosteroid irrigation in a widely open surgical cavity {[}20, 21{]}.
Given the high postoperative recurrence rate and drug resistance of
ECRS, targeted biotherapeutics targeting the TH2 inflammatory mediators
interleukin (IL)-5, IL-4, and IgE have been recently developed as a
potential therapeutic approach {[}22, 23{]}.~Increased eosinophil
infiltration in the nasal polyps is an important biomarker for asthma
development after nasal endoscopy {[}24{]}. Given the differences in
surgical modalities and therapeutic agents between ECRS and NECRS, as
well as the risk of postoperative asthma and recurrence, accurate
preoperative diagnosis of ECRS is crucial for determining the optimal
treatment plan. Our model showed satisfactory performance and could
provide valuable information for accurate diagnosis and treatment.

We introduced a semantic segmentation model that could automatically
segment the sinus and nasal area from a complex image for classification
model learning.~Medical image reading requires systematic anatomical
knowledge. Accordingly, an excellent radiologist should master the
anatomical and disease characteristics, including hidden prior knowledge
regarding medical images.~Traditional machine learning classification
models, including the classic cat and dog recognition model, label
images and input them into the network for training. Here, pixels
representing the animal can appear anywhere in an image; further, the
disease distribution is usually located in the corresponding anatomical
region.~Identifying the corresponding region for model learning can
eliminate surrounding interference factors and prevent failure resulting
from the lack of prior knowledge. We previously confirmed that a
training method based on anatomical partitioning could effectively
improve model performance and interpretability when the dataset was
reduced {[}25{]}.~Additionally, rectangular segmentation was used as the
segmentation method.~Here, irregular images should be filled with ''0''
pixels around them and they visually appear as black.~Compared with
irregular segmentation, rectangular segmentation retains the structure
around the sinus, which is more consistent with the real situation.

Previous studies on DL application in the medical field have mostly
labeled single images and input them into the network for learning.
However, medical images are unique. Specifically, for a patient with a
certain classification feature, not all image slices contain information
for classification. For example, since tumors are heterogeneous, the
patient's prognosis or risk of metastasis cannot be attributed~to each
lesion slice.~Similarly, the features in some CT image slices might not
show differences between patients with ECRS and NECRS. The
classification results of a single image may have insufficient
predictive utility. Each patient has multiple images; moreover, image
classifications may differ for the same patient, which affects the
outcomes. We used the traditional learning method of labeling individual
images and attempted to label each patient. Our findings demonstrated
that the dataset composed of each patient as a unit allowed
significantly better model performance than the dataset composed of a
single image as a unit.~This confirms the hypothesis that not all slices
show between-disease differences; moreover, it demonstrates the
correctness and reliability of constructing datasets for model learning
based on each patient as a unit.

\textbf{5. Limitations}

First, since we could only obtain information regarding the extent of
tissue eosinophil infiltration from postoperative patient specimens, the
data only included patients who underwent nasal endoscopic surgery,
while patients with well-managed CRS without surgical interventions were
excluded. Second, although our internal validation results showed
excellent model performance, we did not perform external validation to
confirm the universality of the model.

\textbf{6. Conclusion}

Our findings demonstrated that combining semantic segmentation with
classification networks could effectively distinguish between patients
with ECRS and NECRS based on preoperative sinus CT images. This may
facilitate preoperative prediction of postoperative recurrence and
selection of optimal treatment strategies. Moreover, labeling each
patient to build a dataset for classification may be more reliable than
labeling individual images.

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