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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
To ascertain the advantages of AI in patient care, Vollmer et al offer 20 implementation, statistical methodology, and repeatability-related issues [94]. Patients and
healthcare systems may benefit from a practical framework that uses a common
technical vocabulary and relies on empirical research. AI may also take human and
emotional judgment out of computer-aided diagnosis decision-making.
10.4 Role of AI from endoscopic diagnosis to treatment
A guide for using ML in clinical endoscopy to diagnose gastrointestinal diseases
accurately is an idea that Van Der Sommen et al [95] have put forth. Each medical
professional must have a good technical foundation in order to adequately
comprehend the influence of ML on gastrointestinal diagnosis [95]. In terms of
anatomy, the stomach is different from other gastrointestinal organs such the colon
and esophagus [96]. Clinicians need several in-depth searches to prevent any
omissions due to the broader bent lumen of the device, which may necessitate
more tedious observations [96].
Infection with H. pylori is another factor which masks the early signs of EGC [96],
leading to variation in endoscopic diagnosis [9, 97]. As a result, adopting AI from
colon cancer to abdominal cancer may be inadvisable. Endoscopy, such as EMR
(endoscopic mucosal excision for EGC), is another alternative for treating tumors in
the stomach [98]. EMR is renowned in Japan and the West due to its low risk of
metastasis lymph node cancer [98–100]. However, local lesions greater than 15 mm
may increase difficulty in assessing tumor depth and recurrence. ESD (endoscopic
submucosal dissection) is a formidable opponent to open/laparoscopic surgery for
treating EGC [98].
To improve endoscopic resection in clinical practice, Zhu et al developed a highly
accurate and specific CNN-CAD system [101]. However, AI’s specific role in
endoscopic resection procedures remains limited. While AI-based detection systems
can predict the depth of tumor invasion and reduce unnecessary gastrectomy, they
are unable to manage the resection procedure or activate alarms for high-risk
consequences including bleeding, perforation, and peritonitis. ESD-related complications remain a very difficult problem in GC, with a 3.5% rate [102]. The creation
and training of AI-based approaches, particularly those comprising ML or DL that
require adequate data training, should be assigned to hospitals with significant
patient volumes. In a clinical guide on the use of AI in endoscopy [101], Namikawa
et al gathered its applications in stomach-related disciplines such as clinical
detection, classification, and blind spot monitoring. Additionally, they expected
that in the future, AI might be fully taught to differentiate between stomach
neoplastic and non-plastic tumors, contributing more significantly [96]. However,
the use of AI in the management of stomach cancer is still in its infancy. In contrast
to endoscopic diagnosis of GC, which is mostly based on image interpretation, AI in
chemo radiotherapy could require multimodal data interpretation, such as genetic
characterization, immuno-histochemistry results, mutation analysis, or insensitivity
prediction. DeepIC50, a 1D CNN model, was created by Joo et al that reliably
predicts drug responsiveness in GC patients and cell lines [95].
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
10.5 Artificial intelligence in surgery
AI’s application in surgery will involve computer-assisted improvements to human
performance [103]. Proper surgical education and evaluation are essential components of the medical field. Fard et al utilized ML techniques to evaluate robotic
surgery skills, and a future date [15], AI is anticipated to be employed throughout
and after surgical operations [14]. A patient-specific surgical risk assessment and
postoperative results may be established using AI analysis of preoperative clinical
data. In order to forecast postoperative problems in patients with stomach cancer,
Chien et al employed artificial neural networks (ANNs) [18]. During surgery, EMR
data can be an integrating operational data for real-time direction and adverse event
prevention. Autonomous robots capable of performing surgical procedures under
human supervision might be developed in the future. To improve cancer care,
postoperative data might be combined with hospitalization data [18].
10.6 Molecules and genes
The usage of molecular and genetic approaches is growing to diagnose and predict
tumors. Early intervention may be possible if high-risk stomach cancer patients are
identified. For localized GC patients receiving treatment, detecting circulating
tumor DNA may aid in the facilitation of tailored neo-adjuvant treatment to
increase survival in patients at high risk of resurgence [104]. To maximize efficacy
and avoid overtreatment, comprehensive molecular signatures can be exploited to
personalize therapy to each patient [105]. In this field, AI is frequently applied
(table 10.1). In order to direct medical care and forecast prognosis, a classifier can
discriminate between the gene expression patterns of different subtypes of GC [106].
Various algorithms may be utilized to develop a comprehensive data mining model
for the aim of detecting biomarkers based on gene expression data and biological
aspects of stomach cancer based on gene characteristics from the prediction model
[107]. Due to the intricacy of cancer, current targeted therapies are built on ideas
that have undergone experimental verification and explain one potential mechanism
of carcinogenesis while neglecting other disease-related facts [5, 6]. Patients may
have severe adverse effects as well as unintended effects on healthy tissues [7, 8].
Table 10.1. The use of AI in genetics.
Authors Year Disease Algorithm Identifying object
Yan et al
[107]
Ishii et al
[106]
2013 GC DM and
ML
2013 GC (2
subtypes)
Bayesian
network
Feature genes 216 Sn,>90%;
The pattern of
expression of
genes
classifier,100%
10-9
No.
of
cases Results
Sp,>90%
46 Accuracy of
the
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Interactome data can be used to better understand the molecular causes of cancer,
which can be represented as network structures with components representing
biological entities (e.g. genes, proteins, mRNAs, and metabolites) and edges
representing their associations/interactions (e.g. gene co-expression, signaling transduction, gene regulation, and physical interaction between proteins) [9–14]. AI
algorithms can effectively process biological network data for classification [15],
clustering, and prediction tasks, improving our understanding of carcinogenesis and
exploring new cancer-fighting targets [16]. We have witnessed rapid progress during
the last few decades regarding biology analysis algorithms. On the one hand,
network-based biology analysis algorithms offer a number of different network
methodologies for identifying cancer targets. Furthermore, distinct network-based
biology analysis algorithms may look at network data from different angles, they
can compensate for each other to produce accurate biological explanations [108].
High-performance, diverse, and complicated molecular data may be handled using
ML-based biology analysis in an effective manner, and biological networks can be
mined for features or relationships. Increasing the number of algorithms will enable
more accurate target identification and cancer medication development [108–110].
In recent years, two of the most important parts of AI biological analysis have
been to uncover potential oncology targets [114–116] and the fast development of
cancer-associated techniques [111 –113]. These technologies are divided into five
categories in figure 10.2 epigenetic, genomics, proteomics, metabolomics, and
multiomics integration analysis. Epigenetics is the study of DNA and DNA-related
protein alterations that modify gene expression without altering DNA sequence
(figure 10.2)[54]. AI is essential for investigating epigenetic data and designing
targeted therapeutics. As an illustration, regulatory networks relating to histone
lysine demethylation may be studied using transcriptome and epigenetic data [116].
Figure 10.2. The discovery of cancer treatment targets using AI to combine multiomics data (such as
epigenetics, genomics, proteomics and metabolomics).
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
They established the importance of epigenetic regulators such as KDM1A,
KDM3A, EZH2, and DOT1L in oncogenesis and drug resistance, emphasizing
their importance in mitogenic control and therapeutic potential [114].
Genome-scale experiments, such as sequencing, are used in genomics to investigate the function of every genetic component in an organism [107]. Applications
include mapping genomic areas with high biochemical activity, discovering biomarkers for patient classification, predicting gene function, and establishing genotype–phenotype relationships. In order to identify cancer subtypes and therapeutic
targets, comparative genomics analysis of molecular datasets has been greatly
enhanced by recent network-based biology analysis approaches [109]. Medi et al
[117], for example, incorporated gene expression patterns into genome-scale
molecular associations to find therapeutic targets for cervical cancer, comprising
receptors, micro RNAs, transcription factors, proteins (such as CRYAB, CDK1,
PARP1, WNK1, and KAT2B), and metabolites (arachidonic acids). Cantini et al
[118] used a network-based biology analysis methodology to merge several genomic
layers into a biological network to uncover cancer driver genes such as F11R,
HDGF, PRCC, ATF3, BTG2, and CD46 as oncogenes and potential indicators for
pancreatic cancer. Following that, they implemented a consensus clustering
approach.
Proteomics is the study of proteins. Proteomic investigations are carried out to
mark up and compare genomic patterns, estimate protein abundance, find modifications after translation, and discover protein–protein interactions (PPIs) [119].
PPIs are often employed for the processing of proteomics data [120] and serve key
roles in organizing and modulating biological processes. Vinayagam et al [114], for
example, used control theory to examine the human PPI interaction network in
order to discover essential proteins that impact the network’s controllability [121].
By varying the number of driver nodes in the network in response to the removal of
that protein, the hub may be classed as ‘indispensable,’‘neutral,’ or ‘dispensable,’
which correlates with increasing, no impact, or reducing the number of driver nodes
in the network in response to the removal of the key protein. The findings show that
these critical proteins are the primary targets of drugs, viruses, and disease-causing
mutations. In addition, intelligent network controllability analysis of data from 1547
discovered 46 additional cancer-associated genes in addition to 56 essential genes
across nine malignancies. According to a network-based biological evaluation
framework, there are significant changes in gene expression for disorders whose
proteins are close to phenolic targets but not for those whose proteins are distant to
polyphenol targets [120]. This network link offers a way to determine how
polyphenols affect illnesses as well as a foundation for finding new anticancer
targets.
By analyzing the metabolites present in bodily fluids, cells, and tissues, the study
of metabolism is frequently employed to identify biomarkers [122]. The sensitivity of
biotechnology allows for the detection of subtle changes in metabolic pathways,
providing understanding of the processes behind cancer and diverse physiological
states. In order to do metabolomic studies and give systems-level knowledge of the
function of metabolites in cancer, researchers are currently using biological networks
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
[122]. To analyze flow control and find driver responses in metabolic networks, for
instance, Basler et al [123] suggested a network-based paradigm. Escherichia coli
driver responses were shown to be subject to intricate cellular regulation, pointing to
their crucial function in aiding cellular control. According to correlation data, the
driven response is a viable therapeutic target since it slows cancer development.
The handling of integrated omics information and the intricacy of tumor–host
relationships are necessary for multiomics integration analysis [124]. Multiomics
data offers researchers related molecular profiles to analyse carcinogenesis in
comparison to single omics investigations [124]. In order to properly understand
the intricate interlayer regulatory connections in cancer progression, integrated
multiomics information in a hierarchical design to AI biology study has become an
effective tool. With the use of this strategy, we may take advantage of earlier data
that can be condensed and displayed as networks, giving us insights into the process
of carcinogenesis as a whole [125]. Gov et al [126] undertook a comparative study of
transcriptome data to uncover biomolecules such as genes, receptors, membrane
proteins, TFs, and miRNAs. They then used the links between these molecules to
build a tissue-specific network for ovarian cancer, and identified GATA2 and miR124–3p as potential biomarkers.
10.7 AI models’ function in prognosis prediction
GC patients are divided into several risk categories using the TNM staging method.
Patients having a similar TNM stage, however, might have varying chances of
surviving. The accepted technique for determining risk variables for prognosis is the
Cox proportional hazard (CPH) model. The prognostic parameters which were
previously shown to have been more accurate for predicting survival have been
corroborated by a nomogram based on CPH. However, the nomogram technique’s
predictive power has its own constraints when taking linear analysis into account.
The complexity of the human body includes several nonlinear elements that may
affect survival. As illustrated in table 10.2, nonlinear statistical models that use
ANN have proven to be more accurate at forecasting patients’ chances of surviving
stomach cancer. Biglarian et al predicted the survival of stomach cancer patients by
comparing an ANN to the CPH model [127]. When Amiri et al evaluated the
weights in the ANN using a variety of hidden nodes [128], they observed that five
nodes provided the best accuracy.
Nilsaz-Dezfouli et al developed the system [129] using a single-time-point ANN
model and the ability to handle filtered input. ANNs were surpassed by Bayesian
neural networks, according to Korhani Kangi and Bahrampour in terms of
predicting survival [130]. TNM staging was not as effective as the survival recurrent
network (SRN) [130]. Prior to surgery, it offered a trustworthy prognosis for longterm survival of GC that was statistically superior to cTNM and pTNM, or clinical
and pathological TNM, respectively [129]. Unquestionably, much more data is
needed to continue improving ANN models. Jiang et al’s[131] prognostic classifier
was created by applying SVM to survival analysis. The fi ndings showed that overall
survival and disease-free survival could be predicted with greater accuracy than the
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
ANN,90.3% for BNN),
specificity (95.4% for ANN,
90.9% for BNN)
135 cases Cancer centre ANN Accuracy (93%)
specificity (96.1%)
Sensitivity (71%),
quantification
neural network
developingnet
works
Hospital SVM classifier Accuracy (up to 94.19%)
Samples
Accuracy (72.73%)
technique
4302 cases Cancer centre QUEEN
Table 10.2. Utilisation of AI in stomach cancer prognosis based on several research populations.
Authors Year Country/region Number of cases Study population Methods Results
Jiang et al [131] 2018 China 786 cases Hospital SVM classifier AUCs (up to 0.834)
2018 Iran 339 patients Hospital ANN, BNN Sensitivity (88.2% for
and
Lu et al [132] 2017 China 939 patients Hospital MMHG Accuracy (69.28%)
Korhani Kangi
2004 Germany
Bahrampour
[130]
Zhang et al [133] 2020 China 669 cases Hospital ML AUCs (up to 0.831)
Bollschweiler et al
Japan
[135]
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Jagric et al [136] 2010 Slovenia 213 cases Cancer centre Vertex
Liu et al [134] 2018 China 432 GC tissue
Japan
Hensler et al [42] 2005 Germany
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
American Joint Committee on Cancer’s tumor-node-metastasis staging classification. Additionally, the suggested SVM classification of stomach cancer was utilized
to forecast the efficacy of adjuvant chemotherapy, enabling the treatment of
stomach cancer on an individual basis. One of the major reasons for mortality
was recurrence for people with stomach cancer, Therefore, in the course of routine
therapeutic activity, a precise estimate of the risk of recurrence was crucial. Recent
findings state that the AI-assisted recurrence prediction method outperformed
conventional statistical techniques. The radiomic fingerprints of advanced stomach
cancer in 669 people in a row were extracted from CT scans using ML techniques by
Zhang et al [133]. They subsequently developed a CT-based radiomic model to
predict the recurrence of advanced stomach cancer. The SVM classifier was created
by Liu et al [134] with the intent to predict resurgence in patients with stomach
cancer.
Lymph node metastases from stomach cancer are a highly reliable indicator [64].
The use of AI-assisted prediction tools has made it possible to more accurately assess
the metastasis risk due to the absence of reliable ways to forecast the metastasis of
GC. ANNs were shown to significantly improve the lymph node metastasis
prediction accuracy by Bollschweiler et al [41]. Hensler et al [42] described a unique
ANN approach for detecting lymph node metastases before surgery. The proposed
model surpassed the Maruyama Diagnostic System established at the National
Cancer Centre in Tokyo in terms of accuracy and dependability. Using the
expression of gene profiling dataset GSE26253, they discovered that a variety of
characteristic genes, including PLCG1, PRKACA, and TGFBR1, may be linked to
the reappearance of GC [63]. Using the GSE26253 gene expression profile dataset, a
collection of feature genes, including PLCG1, PRKACA, and TGFBR1, were
discovered to possibly be associated with GC relapse. GC lymph node metastases
were a major predictive factor. The use of AI-assisted prediction tools has made it
possible to more accurately assess the metastasis risk due to the absence of reliable
ways to forecast the spread of GC. Bollschweiler et al [135] introduced a novel ANN
technique for the preoperative evaluation of lymph node metastasis and demonstrated how ANNs may considerably increase the prognostic accuracy of lymph
node metastasis. When compared to the Maruyama Diagnostic System developed at
the National Cancer Centre in Tokyo, the proposed model displayed improved
accuracy and reliability. It was also demonstrated that the possibility of liver
metastases could significantly reduce a patient’s long-term prognosis for stomach
cancer. Jagric et al [136] developed a learning vector quantization network to predict
postoperative liver metastases in patients with GC, and it produced a remarkably
high predictive value.
10.7.1 Metastasis and staging prediction
The capacity to anticipate lymph node metastases (LNMs) is crucial for clinical
decision-making, that might involve endoscopic mucosa excision, neo-adjuvant
chemotherapy, or major surgery. Currently, the lymph nodes’ dimensions, contours,
and densities serve as the primary determinants of the imaging diagnosis of LNMs.
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
The N status pre-treatment screening was frequently insufficient. Recent studies
have shown that ANNs can predict LNM with a markedly higher degree of accuracy
[135]. Furthermore, neural network-based liver metastasis prediction has been
shown to have a strong negative predictive value and a respectably high sensitivity
[136]. The early diagnosis of peritoneal metastases was enhanced by DL method for
ascites cytopathology evaluation [137].
An ANN model that included clinical, pathological, and genetic polymorphism
data correctly predicted the preoperative stage of GC 81.82% of the time [138]
(table 10.3). To increase the predicted accuracy of the ANN models, it will be
important to combine clinical, pathological, and biological data with biological
markers.
10.7.2 AI aided treatment decisions
Advanced GC (AGC) patients have been recommended to have adjuvant chemotherapy and targeted molecular treatment; resection is the preferred curative
treatment for EGC. Additionally, adjuvant immunotherapy has been included in
preoperative treatment regimens. Some of the uses of AI in the management of GC
are compiled in table 10.4. Several researches [139–146] investigated the use of AI
approaches in resection surgery, chemotherapy, and molecular drug decisionmaking, while other studies employed clinico-pathologic characteristics, CT,
immuno-histochemical stain, and lymph-node WSIs to assess the outcome of
treatment. These applications showed how AI may be used in various GC therapy
modalities.
10.7.3 Clinical massive data analysis and prognostic prediction
AI is often employed in clinical big data analysis and prognosis prediction, similar to
how patient history, clinical nursing data, pathology, and imaging data have been
integrated and used for data analysis and mining (table 10.5). Complex conditions
should be treated using multidisciplinary methods that combine gastrointestinal,
radiology, pathology, medicine, surgery, and radiation oncology [147]. For instance,
AI has been used to predict complications after gastrectomy to significantly lower
postoperative mortality and morbidity [135], reinforce early detection and screening
to enhance the long-term survival and standard of life of EGC patients, predict the
preoperative staging of tumors through the use of clinico-pathological datasets and
genetic susceptibility tests, and predict tumor recurrence in patients with carcinoma
of the stomach to develop [147].
10.8 Survival analysis
The prognosis determines the malignancy of the tumor and forecasts patient
survival. A major prognostic factor for GC is TNM staging. It is nonetheless
constrained because people with different stages may have varying survival rates.
The typical model for survival analysis is Cox regression. Age, sex, histology, depth
of the tumor, the number of metastatic and examined lymph nodes, the presence of
distant metastases, and the amount of the resection were the eight criteria included
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
developing sample is
71%; specificity is
96.1%. The sensitivity
of the test sample is
66.7%; the specificity
accuracy, about 10%
greater sensitivity, and
approximately 18%
better specificity over
93%; MCP, 42% to
70%
0.9541
MDS
is 97.1%
Patient count
for validation Results
135 Accuracy: ANN, 64% to
position, and Borrmann
classification, T category
Tumor dimensions,
computer programme
(MCP) and the ANN
LNM The maruyama
CT images 100 mAP, 0.7801; AUC,
may detect perigastric
metastatic lymph
nodes.
34 QUEEN, 72.73%
invasive depth, Bormann
classification, tumor size,
Age, gender, tumor type,
diagnostic system
(MDS) and QUEEN
LNM The maruyama
transverse and concentric
locations, and tumor size
73 Sensitivity for a
histological type,
adjuvant chemotherapy
and radiation treatment,
Size of the tumor, Lauren
neural networks,
forecast liver
metastases
DL vector quantization
metastasis
Liver
TNM N position, UICC
stage, number of positive
lymph nodes, and
percentage of positive
nodes among all nodes
121 Accuracy: 81.82%
removed
Diagnostic data,
pathological information,
and genetic variations
using ANN
(2004) [135]
Table 10.3. For individuals with stomach cancer, a method for predicting metastases.
Authors Goal Prediction Variables
Bollschweiler et al
Gao et al (2019) [54] LNM Using ANN, a CT scan
[42]
Hensler et al (2005)
[136]
Jagric et al (2010)
Lai et al (2008) [138] Staging staging before surgery
CT: computed tomography, ANN: artificial neural network; QUEEN: quality assured efficient engineering of feed forward neural networks with supervised learning.
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
(Log rank)
(image), 89.5% (video)
(undifferentiated)
AUC: 0.844–0.852 (five-year survival)
1244 images and ESD videos U-Net þþ IoU: 67.6% (image), 70.4% (video); Sen.: 81.7%
margin for EGC
Delineate resection 1
U-net, Res Net Hazard ratio: 2.04 (univariable), C-index: 0.694
pathological images
Prognosis prediction 1164 patients; lymph node
Prognosis prediction 1615 patients; CT S-net C-index: 0.719 (DFS), 0.724 (OS)
1670 images and ESD videos U-Net þ þ Acc: 82.7% (differentiated), 88.1%
margin for EGC
Delineate resection
Google Net Hazard ratio: 1.273 (Cox), 1.234 (Uno), 1.149
TMAs
Prognosis prediction 248 patients; IHC-stained
Prognosis prediction 640 patients; CT Res Net C-index: 0.78 (OS)
network
Five-layer neural
pathologic factors
Prognosis prediction 1549 patients; clinico-
GDSC, CCLE, TGGA dataset DeepIC50
116 patients Delta radiomics Acc: 0.728–0.828
response
Predict molecular drug
Predict chemotherapy
response
Table 10.4. AI’s use in making treatment decisions for GC.
Authors Aim Data Method Result
[139]
An et al (2020)
(2017) [145]
(2020) [143]
(2021) [140]
Wang et al
Jiang et al
[142]
(2021) [141]
Ling et al (2020)
Meier et al
(2020) [144]
Zhang et al
Hyung et al
[95]
Joo et al (2019)
[146]
Tan et al (2020)
Acc, accuracy Area under the receiver-operating characteristic curve is known as AUC; concordance index is known as C-index; and computed tomography is known
as CT. The term ‘disease-free survival’ ESD, or endoscopic sub-mucosal dissection, stands for early stomach cancer. The term immuno-histochemistry; IoU stands for
Intersection over Union. The total survival rate; Sen., sensibility the distinctiveness of; the tissue microarray.
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