To Investigate the Predictive Efficiency of Staging by Processing Tomography Images in Esophageal and Stomach Malignancies
Conditions: Gastrointestinal Cancer
Sponsor: Sebahattin Celik MD

 
 
 
 
 
 
Artificial Intelligence and Cancer Staging 
in Upper Gastrointestinal Malignancies 
Study protocol with statistical analysis 
 
11-10-2021 
 

INTRODUCTION 
Esophageal and stomach cancers, which constitute cancers of the upper region of the 
digestive system, are cancers that are frequently observed and unfortunately have a low rate of 
cured patients [1]. Due to the fact that the esophagus and stomach are hollow organs and their 
capacity to stretch, patients' complaints about nutrition usually occur when the tumor grows and 
occludes the lumen. Naturally, the tumor is at an advanced stage when diagnosed [2]. The stage 
of cancer at diagnosis is very important for two reasons; First, the stage of the cancer is directly 
related to the survival time. Secondly, treatment is planned according to the stage. Different 
treatments are applied to patients at different stages [3,4]. 
Currently, the TNM staging (Tumor, Lymph Node and Metastases) system is the accepted 
one worldwide. According to this system, the depth of the cancer in the tissue (T), spread to lymph 
nodes and spread to distant organs (M) are evaluated and categorized from stage 1 to stage 4[5]. 
Despite many advanced technology tools used in staging (Computed Tomogrphy, Magnetic 
Resonance Imaging, Endoscopic Ultrsonograph), there are still difficulties in correct staging 
before surgery or before-after neoadjuvant therapy[6]. The Endoscopic Ultrsonograph (EUS) 
gives the best accuracy (71 - 99%) in “T” staging of esophageal and gastric cancers [6]. However, 
the number of centers where EUS is located and the number of experienced gastroenterologists 
who will perform EUS are low. Computed Tomography (CT), Magnetic Resonance Imaging (MRI) 
are used frequently, but the accuracy rates are reported to be 55-91% [6]. It is certain that the 
diagnosis and treatment will be better with the increase in the accuracy of cancer staging. 
Artificial intelligence techniques are increasingly used in the field of health, especially in 
the diagnosis and treatment of cancers [7-9]. Obtaining cancer details in radiological images, 
which cannot be noticed by the human eye, by analyzing big data with the help of algorithms gave 
rise to the application area of "radiomics" [10]. It is stated that with Radiomiks, there will be 
improvements in both the diagnosis and staging of cancers and, accordingly, in the treatment. 

While there are studies on the use of endoscopic methods with artificial intelligence for the early 
diagnosis of esophageal cancers, a limited number of studies have been conducted on stage 
estimation from radiological images [11,13]. In particular, there are not enough studies on the 
investigation of changes in tumor size after chemotherapy with artificial intelligence and the 
estimation of staging. 
In this study, it was aimed to investigate the predictive efficiency of staging by processing 
tomography images in a region where esophageal cancers are endemic. 
METHOD-MATERIAL 
Design 
The study, which included patients with histopathological proven diagnosis of esophageal cancer, 
was designed prospectively between the years 2021-2022. This research is the first pilot study of 
an ongoing project of artificial intelligence applications in the staging of upper gastrointestinal tract 
cancers. The study was carried out in 2 different centers, which are 3rd level training and research 
hospitals. 
Approval was obtained from two separate local ethics committees (Van Training and Research 
Hospital Ethics Committee 26/12/2019, 2019/22 and Van Yuzuncu Yıl University Ethics 
Committee 15.12.2021, 2021/17). 
In both centers participating in the study, all cancer patients are evaluated weekly by the oncology 
council. Tomography images of esophageal or stomach cancers that come to these councils are 
recorded. During the council, the “T” stage of the tumor is specified and recorded by the 
radiologist. According to this pre-treatment clinical staging, which is also confirmed by oncology 
and general surgery specialists, the treatment protocol that the patients will receive is determined. 

In this study, tumors were marked on tomography images of patients who were admitted to the 
council at the time of new diagnosis or after neoadjuvant therapy (Figure-1). After marking, these 
images were taught to algorithms developed with the technique described in detail below. Thus, 
algorithm training is completed with these images. 
 
 
Duration of study/number of patients 
This study, in which artificial intelligence applications are planned to be developed through 
tomography images of patients, is planned to last two years. The more data inputs into the study, 
the better the training of algorithms will be, and the admission of patients will continue throughout 
the study period. After the last patient is recruited, the final revision of the developed algorithms 
will be made. The current pilot study was evaluated with data from 50 patients. 
Inclusion criteria: 
Each patient meeting the following criteria will be included in the study. 
1- Being diagnosed with esophageal cancer (adenocarcinoma or squamous cancer) 
histopathologically 
2- Being over 18 years old 
3- Having a tomography image before or after chemotherapy. 
4- Giving informed consent to participate in the study. 
5- Having final pathological staging after surgery. 
 
Exclusion criteria: 

A participant with any of the following criteria will be excluded from the study: 
1- Previous thoracic surgery. 
2- Having a recurrent tumor 
3- Inability to perform clinical staging due to technical reasons 
4- Drawings cannot be made due to poor tomography quality. 
 
Primary outcomes: 
The accuracy of the algorithm developed with artificial intelligence to predict the “T” staging in 
esophageal cancer 
Secondary outcomes: 
  In addition to the demographic data of the patients, post-treatment mortality, morbidity rates, 
complication rates. 
 
Tomography shooting technique and drawing technique of images: 
CT examinations were performed with a 128 slice CT [Siemens Definition AS +(Plus) ), Siemens 
AG, Munich, Germany] device. The acquisition parameters (mAs, Kv, slicetickness) were as 
follows, respectively: ThoraxCT (110, 120, 3 mm) and abdomen CT (210, 120, 3 mm). CT scans 
were performed after 12 hours of fasting, and 1500 ml of water was drunk within 60 minutes of all 
patients before the extraction to distend the esophagus, stomach and intestines. Iokexol 
(Omnipaque or Opaxol), which is a non-ionic contrast agent, was administered 100 ml with an 
automatic injector at a rate of 3 ml/s by IV route. In ThoraxCT, imaging was performed from the 
distal neck to the upper abdomen, and in abdominal CT, from the diaphragm to the 

symphysispubis level. All images are available in our PACS system and were evaluated by 
consensus by two radiologists with 15 and 17 years of thoracoabdominal CT experience. Tumors 
were also drawn on images by two radiologists. Readers were blind to both the pathology result 
and the AI prediction result. 
Pathological evaluation: 
The specimens were fixed in 10% formalin solution for 24 hours. Tissue sections taken after 
routine tissue macroscopic examination embedded in paraffin blocks. Then, from the prepared 
paraffin blocks, 4 mm thick sections were taken and stained with hematoxylin-eosin (HE). 
Microscopic examination of the primary tumor in the evaluation. Pathological staging was done 
according to American Joint Committee on Cancer as TX, T0, Tis, T1(a/b), T2, T3 and T4(a /b 
[14]. 
 
 
Predicting “T” stage of esophageal and gastric cancers in computed tomography images 
Measurements parameters:  
1- Patient’s CT images 
2- Patient’s pathology “T” stages 
 
 
 
 
 

Details:  
 We will collect all CT images, start from cervical region to upper abdomen. Images from DICOM 
will be converted to JPEG. Then these images will be classified with help of artificial intelligence 
(AI) algorithms. Training of algorithm will be performed with data previously collected and testing 
with new patients when available. New patient images will be evaluated for “T” stage by artificial 
intelligence algorithms. Then these predictions will be compared with pathological results and 
parameters below will be measured:  
1- positive prediction value  
2- false positive value  
3- accuracy rate 
4- sensitivity and specificity  
 
 
Outcome measures:   
1- prediction of “T” stage: there are 5 stages 
T0 → sensitivity, specificity, accuracy 
T1 → sensitivity, specificity, accuracy 
T2 → sensitivity, specificity, accuracy 
T3 → sensitivity, specificity, accuracy 
T4 → sensitivity, specificity, accuracy     
 
 

 
 
Comparing AI success to pathology results: 
 
 
PATHOLOGY 
T0 
PATHOLOGY 
T1 
PATHOLOGY 
T2 
PATHOLOGY 
T3 
PATHOLOGY 
T4 
TOTAL 
AI-T0 
A 
B 
C 
D 
E 
A+B+C+D+E 
AI-T1 
F 
G 
H 
I 
J 
F+G+H+I+J 
AI-T2 
K 
L 
M 
N 
O 
K+L+M+N+O 
AI-T3 
P 
Q 
R 
S 
T 
P+Q+R+S+T 
AI-T4 
U 
V 
W 
X 
Y 
U+V+W+X+Y 
TOTAL 
A+F+K+P+U 
B+G+L+Q+V 
C+H+M+R+W 
D+I+N+S+X 
E+J+O+T+Y 
A+B+C+…+X+Y 
 
• 
Sensitivity of AI-T1 = 
𝐺
𝐵+𝐺+𝐿+𝑄+𝑉 
• 
Accuracy of AI-T1 = 
𝐺+(𝐴+𝑀+𝑆+𝑌)
𝐴+𝐵+𝐶+⋯+𝑋+𝑌 
- 
for us 5% prediction precision will be important 
• 
Sample size formula =  
𝑍1−𝛼2
⁄
2
×𝑆𝑁×(1−𝑆𝑁)
𝐿2×𝑃𝑟𝑒𝑣𝑎𝑙𝑒𝑛𝑐𝑒 
 
 

 
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