Cancer Detec-Lung Cancer Diagnosis Support System: First Insights

Nelson Faria

, Sofia Campelos

and Vítor Carvalho

2Ai - School of Technology, IPCA, Barcelos, Portugal

Pathology Laboratory, IPATIMUP - Institute of Pathology and Molecular Immunology, University of Porto, Porto, Portugal

Keywords: Lung Cancer, Digital Pathology, Deep Learning, Convolutional Neural Networks, Whole-Slide Imaging.

Abstract: Lung cancer is the type of cancer that causes most deaths worldwide and as sooner it is discovered as more

possibilities there are for the patient to be treated. An accurate histological classification of tumours is

essential for lung cancer diagnosis and adequate patient management. Whole-slide images (WSI) generated

from tissue samples can be analysed using Deep Learning techniques to assist pathologists. In this study it is

given an overview of the lung cancer exploring the different types of implementations undertaken until the

present. These methods show a two-step implementation in which the tasks consist primarily of the detection

of the tumour and after on the histologic classification of the tumour. To detect the neoplastic cells, the WSI

is split in patches, and then a convolutional neural network is applied to identify and generate a heatmap

highlighting the tumour regions. In the next step, features are extracted from the neoplasic regions and

submitted in a classifier to determine the histologic type of tumour present in each patch. Moreover, in this

paper, it is proposed a possible approach based on the literature review to surpass the limitations found in the

actual models, and with better performance and accuracy, that could be used as an aid in the pathological

diagnosis of the lung cancer.

1 INTRODUCTION

On a global scale, in 2020, lung cancer was the

malignant neoplasia that caused the highest number

of deaths and the second most common in terms of

new cases, appearing more frequently in older people

(Society, 2021; World Health Organization (WHO),

2021). The early detection of the lung cancer is

crucial to reduce the death risk. In the initial

evaluation of a possible lung cancer, several imaging

and surgical procedures are needed such as chest X-

ray, computed tomography (CT), positron emission

tomography (PET), magnetic resonance imaging

(MRI), bronchoscopy, transthoracic needle biopsy

(TNB), fine needle aspiration (FNA),

mediastinoscopy, and endobronchial ultrasound-

guided needle aspiration. The radiologic detection of

a suspected tumour nodule must be followed by a

confirmatory pathologic diagnosis usually made on

small biopsy and cytology samples (Keith, 2020).

When the used methods for diagnosis are in the

radiology scope such as CT, several computer-aided

design (CAD) systems are being tested using a four

steps approach: lung segmentation, nodule detection,

nodule segmentation and nodule diagnosis (El-Baz et

al., 2013). Within the Pathology field, the

microscopic glass slides can be directly observed by

a pathologist on a brightfield microscope, or they can

be scanned to produce digital slides (whole slide

images-WSI). With the evolution of technology, the

computerized image processing has shown that can be

a helper in decision support to histopathological

evaluations but, at the moment, the studies for lung

cancer diagnosis using microscopic images are very

premature (Yu et al., 2016).

This paper aims to carry out a literature review

and it is organized in 4 chapters. In the second

chapter, Literature Review, it starts briefly with the

physiopathology of lung cancer and it is focused on

the lung biopsy since the histopathological

examination is the gold standard for the diagnosis of

cancer (Aeffner et al., 2017). Then, it will be explored

the image processing techniques applied so far to

microscopy images of biopsy tissue samples being

followed by the analysis of the application of artificial

intelligence to the diagnosis of lung cancer. After

that, in chapter three, Proposed Approach, it is

described a strategy to implement a system to

effectively detect and classify the lung cancer in its

two most frequent subtypes. Finally, in chapter 4,

Faria, N., Campelos, S. and Carvalho, V.

Cancer Detec-Lung Cancer Diagnosis Support System: First Insights.

DOI: 10.5220/0010767800003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 3: BIOINFORMATICS, pages 81-88

ISBN: 978-989-758-552-4; ISSN: 2184-4305

Final Remarks, are presented the main conclusions

and described the next project steps.

2 LITERATURE REVIEW

This chapter is organized in two sections: lung cancer

etiology, classification and detection methodologies,

and artificial intelligence applied to lung cancer.

2.1 Lung Cancer: Etiology,

Classification and Detection

Methodologies

According to the WHO, the lung cancer remains the

main cause of deaths in the world and, every year, the

number of deaths is increasing mostly due to smoking

(World Health Organization, 2021). In Portugal, as

globally, this continues to be the biggest cause of

death and the third on the list of new cancer cases with

a number of 5284 cases per year, being responsible

for 20% of deaths by cancer (CUF, 2017; The Global

Cancer Observatory, 2020). Lung cancer is originated

in the lungs but, worryingly, can metastasize to other

organs in the body and normally appears after the fifth

decade (Nasim et al., 2019). The main cause of this

neoplasia is smoking, but other risk factors have been

described such as previous respiratory diseases,

exposure to occupational carcinogens (arsenic,

asbestos, chromium, nickel, and radon), polycyclic

aromatic hydrocarbons, human immunodeficiency,

virus infection, and alcohol consumption (Bade &

Dela Cruz, 2020; Duma et al., 2019).

Most lung cancers are carcinomas, and the most

frequent histological subtypes are adenocarcinoma

(ADC), squamous cell carcinoma (SCC), large cell

carcinoma (LCC) and small cell lung carcinoma

(SCLC). Historically, carcinomas of the lung were

divided into two large groups: SCLC and NSCLC

because there were no therapeutic implications in a

more specific subdivision. However, the

developments in recent years, such as the discovery

of specific mutations in different subtypes of NSCLC,

make the histological classification of the tumour

subtype essential for therapeutic guidance (Board,

2021; Collins et al., 2007; Duma et al., 2019; Goebel

et al., 2019). Usually, the SCLC is detected in

smokers and represents 12% to 15% of lung cancer

cases. In the SCLC, the tumour has a quicker growth,

it is aggressive and expands earlier to other body parts

(Pulmão, 2017). The NSCLC is responsible for more

than 85% of lung cancer cases and the two more

frequent subtypes are the ADC and SCC. The ADC is

more common in non-smokers and arises from

alveolar cells located in the smaller airway epithelium

(Duma et al., 2019).

The current methodologies to detect the lung

cancer are chest X-ray, computed tomography (CT),

positron emission tomography (PET), magnetic

resonance imaging (MRI), bronchoscopy,

transthoracic needle biopsy (TNB), fine needle

aspiration (FNA), mediastinoscopy, and

endobronchial ultrasound-guided needle aspiration.

However, in more than 50% of the new cases, the

patients are diagnosed when the tumour has already

metastasized to different parts of the body. The

reasons of late detection could be the lack of

symptoms at early-stage, incorrect diagnosis of the

symptoms such as cough and wheezing as well as

limited economic situation to access the detection

methods of last generation. In fact, the detection of

lung cancer in an early stage is extremely important

because as sooner it is detected as greater are the

chances of effective treatment and survival (El-Baz et

al., 2013; Goebel et al., 2019).

The gold standard for lung cancer diagnosis is the

histopathological examination. The material

available for pathological diagnosis (histological or

cytological biopsies) is very scarce and the growing

need for additional studies, such as

immunohistochemistry and molecular studies, makes

the careful management of the available samples

essential for a complete and accurate diagnosis.

Formalin-fixed paraffin-embedded (FFPE) tissues

from histologic biopsies are processed to originate

glass slides routinely stained by hematoxylin and

eosin (H&E stain) which is the most used stain for

light microscopy, since it is simple to use and

contains the ability to demonstrate a wide range of

both normal and abnormal cell and tissue

components. These glass slides can then be directly

observed on a brightfield microscope, or they can be

scanned to produce digital slides, and a morphologic

diagnose will be made by the pathologist. Additional

special techniques are frequently used, such as

immunohistochemistry, for a more accurate diagnosis

on the specific subtypes (Kleczek et al., 2020). The

capacity to extract a high-resolution digital scan from

a microscopic slide has become known as digital

pathology which are named as WSI (Hanna et al.,

2020). The acquired images can be in two dimensions

or z-stacks, and each one may contain up to forty

gigabytes of uncompressed data (Bankhead et al.,

2017). From WSI, it is possible to count, measure

sizes and density of the present objects, and apply

algorithms of image processing to detect lesions or

cancer (Bioscience, 2017). An example of a WSI is

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms

shown in figure 1 (adapted from (Pavlisko & Roggli,

2020)).

2.2 Artificial Intelligence Applied to

Lung Cancer

Figure 1: Demonstration of a WSI of an adenocarcinoma

stained with haematoxylin and eosin (H&E) [original

magnification ×200] (adapted from (Pavlisko & Roggli,

2020)).

Artificial Intelligence (AI) techniques are

increasing its presence in our daily lives, whether in

the autonomous driving, processing large amounts of

data in real-time, personalized advertisements,

detecting fraud and diseases such as breast cancer

(Helm et al., 2020).

Over the years, several definitions have emerged

to describe the term AI. The different definitions can

be organized into four perspectives: “Thinking

Humanly”, “Thinking Rationally”, “Acting

Humanly” and “Acting Rationally” (Figure 2).

"Thinking Humanly" states that a system understands

how humans think, however, obtaining one correct

answer from an algorithm does not guarantee that it is

simulating human thinking as there is great difficulty

in defining the model of the human thinking.

“Thinking Rationally” intends to solve problems and

create models of thought processes but it has

obstacles such as the difficulty in defining informal

knowledge using logical notation and the difference

between solving a theoretical and practical problem.

The idea of "Acting Humanly" is to find an

operational way to define intelligence and ensure a

human-level performance in all cognitive tasks,

however, it has as constraints the inability to learn or

deal with new situations and be focused on the

behavior. “Acting Rationally” maximizes the

expectation of reaching the desired goals based on the

available information and the rational behavior

involves taking the correct decision with an implicit

rational decision (Russell & Norvig, 2021).

Figure 2: Definitions of AI to the four categories (Russell

& Norvig, 2021).

AI encompasses Machine Learning (ML) and

Deep Learning (DL) which are composed of AI

algorithms that are implemented in systems to make

predictions, rankings based on input data, image

analysis, and decision making (Greenfield, 2019;

Russell & Norvig, 2021).

In the Medicine field, with the increase amount of

data generated by clinical systems and computational

capacity, the use of artificial intelligence is being

enhanced with the aim of benefiting patients and

physicians by making diagnosis simpler (Greenfield,

2019). Specifically, for cancer detection, the most

common field of AI used is the DL, which consists in

deep neural networks that have several layers that are

refined as the system responds to a specific type of

problem. Like the human brain, they create

"neuronal" connections from "dendritic" connections

at various levels of hierarchical data (Helm et al.,

2020). Nowadays, the most used type of network to

perform image data analysis, such as tumor detection

in the pathology images of breast cancer, is the

convolutional neural network (CNN) (D. Wang et al.,

2016).

The early detection of lung cancer plays an

important role since it can determine the survival of

the patient. The application of an artificial

intelligence methodology in the diagnosis process can

help the pathologist to reduce the time detecting and

classifying the tumour, obtain more accurate results,

making possible to move faster to the final diagnosis.

Usually, the process to diagnose lung cancer is

performed in two parts: Detection and Classification.

In figure 3 is shown the process used by Wang and

his team to detect and classify the lung cancer (Xi

Wang et al., 2020). The studies mentioned next had

Cancer Detec-Lung Cancer Diagnosis Support System: First Insights

Figure 3: The process for the lung cancer detection. “(a) Discriminative patch prediction. A patch-based CNN is used to find

discriminative regions. (b) Context-aware feature selection and aggregation. By imposing spatial constraint, features from

discriminative blocks are selected and aggregated for the WSI classification” (Xi Wang et al., 2020).

as main task the detection of the tumour present in the

evaluated sample and determinate if it is malignant or

non-malignant.

After a pathologist has manually labelled the

Regions of Interest (ROI), Wang et al. developed a

CNN model to analyse ADC WSIs. To train and test,

they extracted 267 images from the National Lung

Screening Trial (NLST) dataset and 457 images from

The Cancer Genome Atlas (TCGA) dataset. This

model segments the haematoxylin and eosin (H&E)

images, by classifying each pixel as nucleus centroid,

non-nucleus, or nucleus boundary. The authors

applied a sliding window in patches of 300 by 300

pixels in the WSI, generating a heatmap to detect the

tumour regions. Spatial distribution in the tumour

microenvironment, nuclear morphology and textural

features were extracted and used as predictors in a

recurrent prediction model (S. Wang et al., 2019;

Xiangxue Wang et al., 2017). This approach

facilitates the detection of the tumour and the study of

its distribution, shape, and boundary features (S.

Wang et al., 2018). The result of the classification

accuracy obtained in the testing set was 89.8%.

Li and team divided the WSI with objective

magnifications of 20 times into 256-pixel-by-256-

pixel portions, cropping them with a stride of 196

pixels, to ensure sufficient overlapping between

adjacent patches. Then, they compared the

performance between different CNNs, being them

AlexNet (Krizhevsky et al., 2017), VGG (Chatfield et

al., 2014), ResNet (He et al., 2016) and SqueezeNet

(Iandola et al., 2016). To test the CNNs, they applied

two different training schemas, which were training

from scratch and pre-trained networks. They recruited

33 lung patients to test the efficiency of their method,

having as result a better accuracy using AlexNet in

training from scratch strategy (97%) and the ResNet

in pre-trained networks (93%). However, the number

of samples used is lower than the used by Wang and

this is a point to take into account (Li et al., 2018).

According to the study of Yu and team, they

followed the same steps as the authors above, that is,

divided the WSI into tiles with 1000 by 1000 pixels,

with a 50% overlap to avoid crop losses, evaluating

the AlexNet (Krizhevsky et al., 2017), GoogLeNet

(Szegedy et al., 2015), VGG (Chatfield et al., 2014)

and ResNet (He et al., 2016) networks that were fine-

tuned from pretrained ImageNet classification

models (Yu et al., 2020). They processed WSIs of

ADC and SCC from TCGA, resulting in an accuracy

of 93.5%. (Yu et al., 2020). Also, Coundray trained a

CNN where the 1635 slides (ADC, non-malignant

and SCC) extracted from the TCGA dataset were tiled

by non-overlapping 512 x 512 pixels patches,

BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms

resulting in a accuracy of 87% for the biopsies

(Coudray et al., 2018).

One relevant limitation for the implementations

done by most of the authors is that they required a

pathologist to do annotations in the patches in order

to get a better result. To overcome this constraint,

Chen and team created a technique to train standard

CNNs with WSIs as inputs, that is, without dividing

the input image or feature maps into patches.

Although, the authors pointed as a limitation the used

memory in the host to process images larger than

20,000 x 20,000 pixels. To address this problem, they

suggested first use a magnification of x4 to locate

important regions and then x40 images of those

regions for the final image recognition task (Chen et

al., 2021). The result of accuracy for ADC and SCC

WSIs is nearly 93%.

When the heatmaps are already generated from

the WSI, it is possible to extract features like

distribution, shape, and boundary features to be

analysed and classified by applying morphological

operations as erosion and dilation (S. Wang et al.,

2018). To those extracted features, it is possible to use

models on them to classify the lung cancer type such

as an ADC, non-malignant or small cell lung cancer.

In the study of Wang and team, they selected the

features that were significantly associated with

survival outcomes and used an univariate Cox

proportional hazard model with a penalty to avoid

overfitting (S. Wang et al., 2018). As aim to classify

the types of the tumour, Yu and colleagues applied

Naive Bayes classifiers (Friedman et al., 1997),

Support Vector Machines (SVM) with Gaussian,

linear, and polynomial kernels (Cortes & Vapnik,

1995), bagging, random forest with conditional

inference trees (Strobl et al., 2008) and Breiman’s

random forest (Liaw & Wiener, 2002). These

algorithms received as input the extracted features

from whole-slide histopathology images of ADC and

SCC received from TCGA and give as output the

predicted diagnosis groups in which SVM with

Gaussian kernel, random forest utilizing conditional

inference trees, and Breiman’s random forest were

the best algorithms obtained an approximate accuracy

of 85% (Yu et al., 2016).

3 PROPOSED APPROACH

After reviewing the literature, it was possible to

identify the limitation of the majority of different

models that are the need of a pathologist to label the

WSIs or the patches. One of the main goals of this

work is to identify and locate the presence of

carcinoma on each sample in order to help the

pathologist saving time and proceed faster to the

following required techniques needed for final

diagnosis.

Following the authors approach and as shown in

figure 4, this work will split the process in two phases:

Figure 4: Diagram of the proposed approach to detect and classify the Lung Cancer according to a given WSI.

Cancer Detec-Lung Cancer Diagnosis Support System: First Insights

1) Image Processing and Tumours Detection, and 2)

Classification of the Lung Cancer type. For the Image

Processing step it will be used a pretrained neural

network such as ResNet since it will facilitate the

detection and the generation of the heatmap for an

image. Since the technology is evolving, the WSI will

be segmented in patches of 512 by 512 pixels. Then,

features like shape, color and perimeter will be

extracted from the highlighted regions and analyzed

by a SVM algorithm. In order to train, test and

validate the implementation, data sets of

histopathological images without or with few

annotations from repositories like Digital Pathology

Association (DIGITAL PATHOLOGY

ASSOCIATION, 2020) and Genomic Data

Commons Data Portal (GDC, n.d.) will be used.

4 FINAL REMARKS

In this paper, studies applied to histopathological

images using artificial intelligence were reviewed and

it was observed the implementation of deep learning

neural networks that are giving satisfactory results for

the tumour detection and lung cancer type

classification tasks. However, one limitation is that

there are no annotated datasets with size and breadth

of scenarios large enough to be able to develop

algorithms with such high performance that they can

be validated for clinical use. Also, this paper proposes

a high-level approach based on the available studies,

but with the aim of assisting the pathologist in the first

morphological approach to the lesion in order to

optimize the diagnostic process. In this proposed

implementation, the WSI is transformed into patches

and a neural network is applied to the generated

heatmap that contains all the tumour regions

highlighted. After that, features are extracted to a

classifier that, according to the feature’s information,

will indicate to the pathologist the presence of lung

carcinoma and its subtype. Further steps will include,

among others, a deep analysis of the AI

methodologies applied and the development of

algorithms to evaluate the presence/non presence of

tumour in the analysed images.

ACKNOWLEDGEMENTS

The authors would like to thank to the National Lung

Screening Trial, The Cancer Genome Atlas and the

Genomic Data Commons Data Portal for the lung

cancer datasets availability and to FCT – Fundação

para a Ciência e Tecnologia and FCT/MCTES in the

scope of the project UIDB/05549/2020 for funding.

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BIOINFORMATICS 2022 - 13th International Conference on Bioinformatics Models, Methods and Algorithms