A New Perspective on the Treatment of Ovarian Cancer: Deep
Learning Algorithms-Based Prediction
Yan Yan
a
Intelligent Medical Engineering, Nankai University, Tianjin, China
Keywords: Deep Learning, Ovarian Cancer, Medical Diagnosis, Precision Oncology.
Abstract: Ovarian cancer is one of the most common gynaecological cancers worldwide, because the initial symptoms
are not obvious, they are often discovered in the late stages, resulting in poor treatment effects and poor
prognosis. This paper explores various applications of deep learning (DL) in early detection and personalized
treatment of ovarian cancer. The construction process of the deep learning model including data collection,
preprocessing, model training and evaluation is introduced in detail. In the application of Convolutional
Neural Network (CNN), this paper discusses how to reduce and learn parameters through image enhancement
and feature mapping, and how to use SoftMax and cross-entropy loss functions in the later stages of data
preprocessing and classification to improve the recognition accuracy of the model. For Long Short Term
Memory (LSTM), this paper analyses its significant advantages in handling irregular time series data,
especially in handling missing value patterns and complex time dependencies. In addition, this study also
explored the clinical challenges of DL models when dealing with ovarian cancer-related issues, such as the
“black box” nature of decision-making, generalization capabilities, and privacy issues of sensitive data. The
comprehensive results indicate that the DL method will become an effective way to advance the development
of the oncology field.
1 INTRODUCTION
Ovarian cancer is one of the most common
gynecological cancers worldwide, its mortality rate
ranks first among the three major malignant tumors
of the reproductive system, so it is called the silent
killer (Wang, 2019). It is difficult to detect in the
early stages of the disease, and there is no effective
and reliable screening method for early-stage ovarian
cancer, therefore clinical surgical treatment,
radiotherapy, and chemotherapy have poor efficacy,
has seriously threatened women's health and lives.
Ovarian cancer occurs in the ovarian tissue deep
in the pelvic cavity. Early symptoms are vague and
easily misdiagnosed. More than 75% of ovarian
cancer patients are diagnosed in stages III-IV (late
stage). When diagnosed, patients already have typical
symptoms such as pelvic pain, abdominal distension,
abdominal distension, or loss of appetite. At this time,
the tumor cells have spread, and the five-year relative
survival rate is only 29% (Gao, 2022). Mortality rates
a
https://orcid.org/0009-0003-9945-1230
from ovarian cancer are particularly concerning, so it
is crucial to find effective treatment strategies and
diagnostic methods, and thus more effective
monitoring indicators and interventions.
In recent years, the application of deep learning
has become more and more widespread and has
demonstrated its unique advantages in various fields
(Liu, 2021; Qiu, 2022; Zhao, 2023), which is
particularly significant in medical applications. Deep
learning, through its complex multi-level computing
structure, ability to deeply analyze image data and
capture its core features (Thompson, 2018). Based on
this, deep learning can learn these features and make
more intelligent, accurate and personalized decisions
for image analysis, early diagnosis, and drug
treatment. As a model that is efficient, accurate, and
does not completely rely on doctors’ subjective
judgment, deep learning shows great potential in the
diagnosis and treatment of ovarian cancer.
The combination of deep learning and ovarian
cancer research is currently showing huge advantages.
Yan, Y.
A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction.
DOI: 10.5220/0012939300004508
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 417-421
ISBN: 978-989-758-713-9
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
417
Based on the advantages of artificial intelligence and
machine learning algorithms, deep learning
algorithms can independently mine key features from
medical images. It then identifies and differentiates
potential pathological variations that are difficult to
detect by clinicians, greatly improving the accuracy
and efficiency of early diagnosis in the process
(Sadeghi, 2024). This facilitates earlier and more
accurate diagnosis. This model is more efficient and
accurate than traditional methods such as ultrasound,
Computed Tomography (CT), and Magnetic
Resonance Imaging (MRI), greatly improving the
efficiency of tumor detection and feature analysis.
Taken together, deep learning, as a transformative
technology, can assist clinicians in making more
informed decisions, thereby promoting progress in
the field of ovarian cancer treatment, and ultimately
improving patients' treatment effects and quality of
life (Sadeghi, 2024).
The core goal of this study is to deepen the
understanding of the application of deep learning in
the field of ovarian cancer and explore new
possibilities for intelligent treatment. It will provide
an in-depth exploration of how deep learning methods
can improve diagnostic efficiency, improve the
efficiency and accuracy of medical imaging, and
provide a basis for identifying new therapeutic targets.
It also provides new perspectives and strategies for
the diagnosis and treatment of ovarian cancer, brings
new hope to patients, and provides experience and
methods for the research of other types of tumors.
This review provides an in-depth exploration of deep
learning methods in the field of ovarian cancer
(Section 2), including key steps such as data
collection, preprocessing, and model training, and
introduces different models used in ovarian cancer
research through examples, including convolutional
neural networks (CNN), long short-term memory
networks (LSTM) and other technologies. Next, in
Section 3, this paper discusses the limitations of
existing research in deep learning research on ovarian
cancer and provides solutions to these problems.
Finally, in Section 4, this paper summarizes the main
contents of this review and provide ideas for future
research. This article highlights the unique potential
of deep learning in the field of ovarian cancer and
explores its application prospects in clinical practice.
2 METHODS
2.1 Framework of Deep
Learning-Based Ovarian Cancer
Prediction
Deep learning processes complex data by simulating
the working mechanism of the human brain. In
ovarian cancer prediction research, it can
autonomously learn from pathological images,
genetic information, and patients’ electronic medical
records to identify key disease indicators.
The effectiveness of deep learning technology
depends largely on the quality and quantity of data.
Ideally, high-quality data should include ovarian
cancer images and corresponding clinical information
from different perspectives and times. At the same
time, in real life, different ethnic groups and different
types of lesions will affect the adaptability of the
model, so the diversity of data is particularly
important. In published studies, multiple research
groups have used different data collection methods to
obtain data for ovarian cancer prediction. For instance,
Gao et al. (Gao, 2022) extracted data from the
ultrasound image database of 10 hospitals in their
study. They classified the data according to whether
women were single or not and used rectal ultrasound
and transvaginal ultrasound images separately;
Thomas et al. (Buddenkotte, 2023) used 451 scan data
from four different institutions and two countries.
Although the sources of these data are very wide,
considering that most of the data (380 samples) come
from the United Kingdom, they chose to use this part
of the data for model training and evaluation.
In the process of establishing and evaluating deep
learning models, data preprocessing, model selection,
training, evaluation, and testing are closely related. In
the data preprocessing stage, the data is processed to
adapt to the requirements of the model, including
standardization, denoising and enhancement of
images, and processing of missing data; for non-
image data, it needs to be converted into a structured
format and normalized. Depending on the
characteristics of the data and the prediction task,
select an appropriate deep learning model, such as a
CNN for image recognition, a LSTM for sequence
data. In the model training stage, a large amount of
labeled data is used to adjust the weights of the
network to minimize the gap between the predicted
output and the actual output, and appropriate loss
functions and optimizers are used. Finally, the
performance of the model is evaluated on an
independent test set, and the generalization ability of
the model is verified through indicators such as
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accuracy, recall, and F1 scores, and is visualized
through qualitative analysis such as Gradient-
weighted Class Activation Mapping (Grad-CAM)
technology for model decision-making process.
Finally, the developed deep learning model can be
deployed in clinical settings to assist doctors in more
accurate diagnosis and treatment planning. When
deployed, ensure that the model can integrate
seamlessly with existing healthcare record systems
and maintain accuracy and efficiency while
protecting patient privacy.
2.2 CNN-Based Ovarian Cancer
Prediction
Among deep learning methods, CNN, is one of the
commonly used architectures. Convolutional neural
network (CNN) is a very broad structure in deep
learning technology. CNN consists of different types
of processing layers, including convolutional layers,
fully connected layers (FC), and pooling layers.
Among them, the convolution layer generates an
output feature map through a 2D convolution kernel
operation with the input feature map, which is the
core part of CNN. The pooling layer downsamples the
feature map after the convolutional layer and selects
the average or maximum value to summarize the
characteristics of each block. Finally, the fully
connected layer performs the classification task,
which is similar to traditional artificial neural
networks (ANN) (Banerjee, 2019; Ghoniem, 2020).
The rapid development of deep learning models such
as CNN is of great significance in the field of medical
diagnosis and has gradually shown outstanding
potential (Ghoniem, 2021).
In previous studies, many scholars have studied
the application of CNN-based methods in ovarian
cancer prediction. For example, Ziyambe et al.
(Ziyambe, 2023) proposed a novel CNN algorithm
for prediction and diagnosis of ovarian cancer. This
study used a dataset consisting of 200 images of
serum ovarian cancer and non-cancer samples,
extended to 11,040 images by augmentation methods
for use with deep learning architectures. After
preprocessing the data set, it is processed using a
convolution operation. After preprocessing the data,
convolution operation is used for preliminary
processing. The data is then further processed through
the ReLU nonlinear activation function, and on this
basis, the pooling layer is used for data simplification.
Through this process, the size of the feature map and
the number of future learning parameters are reduced.
The data is then flattened and transformed into a one-
dimensional vector in preparation for data input to the
fully connected layer. In the final stage, the author
uses SoftMax as the classifier and cross-entropy as
the loss function. After training, the CNN model
showed an accuracy of up to 94% and was able to
identify cancer samples with an accuracy of 95.12%,
it can also distinguish healthy cells with an accuracy
of 93.02%.
2.3 LSTM-Based Ovarian Cancer
Prediction
Long short-term memory network is an extension of
the basic recurrent neural network (RNN) and aims to
solve the gradient disappearance and gradient
explosion problems of traditional RNN (Ghoniem,
2020; Datta, 2020). The LSTM network introduces
memory units (cells) and gating mechanisms to
effectively process time series data. In this process, it
is necessary to control the memory unit (c) of the long
short-term memory network (LSTM) by using a set
of gate control networks, including the forget gate (f),
the input gate (i) and the output gate (o)
(Ghoniem,2021). This built-in gating mechanism can
also avoid computational problems of vanishing or
exploding gradients. It can be adapted to handle
irregular time series data with missing values,
inconsistent time intervals and complex time
dependencies. Based on these advantages of LSTM
and being very suitable for TM data containing
missing data and irregular intervals between sequence
tests, this method has become an ideal algorithm for
processing actual data sets (Wu,2022).
Researchers such as Wu et al. (Wu,2022) applied
the LSTM model to an incomplete large-scale TM
data set to create a tool for predicting cancer risk,
which is undoubtedly a pioneering act. This tool
adopts simple missing value processing strategies
including directly filling gaps with 0 or KNN, MICE
interpolation technology, etc. The design includes a
hidden layer configured with 100 units. On this basis,
a standard S-shaped activation function was used, and
a 10-round training cycle was carried out. This study
highlights the potential ability of the LSTM model to
handle irregular time series problems including
missing values, varying time intervals, and complex
dependencies. It also avoids the limitations of
gradient disappearance or excessive growth. Through
single-point verification of TM test data, the model
showed higher accuracy than single threshold
analysis, with an AUROC value of 0.831. And in
another model, by analyzing data at up to four time
points, the AUROC value increased to 0.931. This
series of tools greatly improves the efficiency of the
A New Perspective on the Treatment of Ovarian Cancer: Deep Learning Algorithms-Based Prediction
419
further screening process for individuals and helps to
detect potential tumor risks at an early stage.
3 DISCUSSIONS
3.1 Limitations and Challenges
1) Transparency and Explainability: The potential
of deep learning models to improve the accuracy of
ovarian cancer diagnosis cannot be ignored, however,
their use in clinical decision-making processes has
been severely limited, in part due to the so-called
“black box” problem. This means that the model’s
decision-making process is opaque, although the
model can make predictions by learning patterns in
large amounts of data, its internal logic is invisible,
which is a fatal problem in cancer diagnosis. Medical
decision-making requires a high degree of accuracy
and interpretability, if doctors and patients have
difficulty understanding how the model derives a
specific diagnosis from the input data, trust in the
model will be reduced, this limits the application of
deep learning tools in clinical practice. 2)
Limitations on Generalization Ability: Ideally, a
good model can not only perform well on the training
set, but also show good prediction capabilities on
unseen data sets, which is the generalization ability of
the model. In practical applications, deep learning
models often have difficulty achieving this. For
ovarian cancer, the data comes from a wide range of
sources, including different populations, geographic
locations, and devices, so the data may vary
significantly, the diversity of data, differences in data
quality between different devices, and the complexity
of the model itself make it challenging for the model
to maintain performance on new data. Such problems
may cause the model to overfit the distribution of
training data and fail to fully cover all potential
situations. 3) Private issues: Deep learning models
require large amounts of data to train and optimize. In
the medical field, this means collecting and analyzing
sensitive information such as patients personal
records, pathology images, and genetic information.
As a gynecological disease, ovarian cancer will be
more serious. Thereforeif not handled properly, it
may result in unauthorized privacy disclosure. In
addition, existing regulations and laws may not be
able to keep up with the development of science and
technology, making privacy protection measures
insufficient and leading to more serious
consequences. For healthcare organizations, data
breaches can result in reputational damage, financial
losses, and legal liability. For individuals, it may lead
to social discrimination, insurance fraud and even
security risks. Such leaks may also erode public trust
in the field of smart healthcare and affect the further
development of future research projects.
3.2 Solution Strategy
For interpretability, by utilizing techniques such as
Shapely Additive Explanations (SHAP) and Local
Interpretable Model-agnostic Explanations (LIME) to
quantify the contribution of each input feature to
model prediction, it can provide more intuitive use of
the model. In addition, visualizing networks in key
regions using activation maps and Class Activation
Mapping (CAM) techniques is particularly useful for
medical image analysis; For limitations on
generalization capabilities, data enhancement and
transfer learning can be used to increase the diversity
and breadth of model training. Furthermore,
regularization techniques can reduce overfitting,
thereby improving model performance on unseen
data; As a distributed machine learning method,
federated learning allows models to be trained on
local devices, which means that only the weights and
biases of the model are shared during the training
process, rather than the training data itself. This is an
effective method to reduce data transmission and
protect data privacy, especially suitable for handling
sensitive medical information in diseases such as
ovarian cancer. With the continuous advancement of
deep learning technology and the deepening of
artificial intelligence in the medical field, in the
future, the application of deep learning in early
diagnosis of ovarian cancer, treatment plan
formulation, and patient prognosis assessment will
become more and more extensive and accurate.
4 CONCLUSIONS
This review highlights the challenges posed by
ovarian cancer as a common gynecological
malignancy and the revolutionary role of deep
learning in early diagnosis and treatment. In it, this
paper elaborates on the architecture of deep learning
in ovarian cancer diagnosis and discuss the process of
training and validating models on different datasets.
Additionally, the application of deep learning
techniques was explored, specifically CNN and
LSTM, to ovarian cancer prediction and diagnosis,
and how these models can learn from medical images
and genetic data and assist clinical decision-making.
Although deep learning models achieve remarkable
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accuracy and performance in experiments, this paper
also identify and discuss the limitations of these
methods in real-world applications. At the same time,
it can be foreseen that with the maturity and
advancement of explainable artificial intelligence
technology and the strengthening of privacy
protection measures, the application of deep learning
in the field of ovarian cancer will become more
extensive and efficient. Future work will need to
focus on more reliable model evaluation tools and
algorithms for handling diverse and irregular data sets.
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