A model based on Kimura-Takemoto Classification: real-time endoscopic risk prediction for gastric cancer

doi:10.21203/rs.3.rs-2129997/v1

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A model based on Kimura-Takemoto Classification: real-time endoscopic risk prediction for gastric cancer

https://doi.org/10.21203/rs.3.rs-2129997/v1

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Background: The Kimura-Takemoto Classification (KTC) is widely used to assess the extent of gastric mucosal atrophy. Our study aimed to develop a real-time endoscopic gastric cancer(GC) risk predictive model based on KTC.

Methods: A total of 1825 patients who underwent endoscopy in Shandong Provincial Hospital were included from January 2020 to December 2021. The logistic regression was performed to select variables and the nomogram was constructed. The discrimination of the model was evaluated using the area under the receiver operating characteristic curve (AUC), the calibration was detected using the calibration curve and the Hosmer-Lemeshow test, and the clinical utility was determined by decision curve analysis (DCA). Internal validation was performed with 1,000 bootstrap resamples.

Results: The model finally included age, sex, family history of GC and the KTC. The AUC exhibited well discrimination and displayed significantly better than KTC alone (AUC 0.781 vs 0.656, P<0.001). The calibration curve and Hosmer-Lemeshow test (P>0.05) showed favorable calibration of the model. The DCA showed good clinical applicability which was better than KTC alone. The population was finally classified into low risk (0-69), medium risk (70-99), and high risk (>100) according to the score of the nomogram. Compared to the low-risk population, those in medium (OR=5.10, 95% CI: 3.53-7.49, P<0.001) and high risk (OR=12.84, 95% CI: 8.39-19.85, P<0.001) had a significantly higher risk of GC.

Conclusions: The model based on KTC can effectively predict the risk of GC and perform real-time endoscopic GC risk stratification to guide biopsy and clinical management strategies.

Kimura-Takemoto Classification

endoscopy

gastric cancer

risk prediction model

risk stratification

Gastric cancer (GC) is a common malignant tumor in the digestive system. The 2020 Global Cancer Statistics Report showed that more than one million persons around the world were diagnosed with GC, accounting for 5.6% of all cancers. China is a country with a very high incidence of GC and the 2020 China Cancer Report showed an estimated 480,000 new GC cases (10.5%) and almost 370,000 GC deaths (12.4%). The five-year survival rate for progressive GC is less than 10%. Fortunately, the rate can be increased to over 90% through endoscopic treatment for early GC. Therefore, to improve the prognosis of GC patients, it is crucial to develop a risk predictive model for the detection of early GC.

As a precancerous lesion of the stomach, gastric mucosal atrophy is an important stage in the development of GC. The most widely accepted pattern is the Correa’s cascade. The normal gastric mucosa gradually progressed to GC through chronic inflammation, atrophy, and dysplasia. It has been shown that the risk of gastric carcinogenesis is associated with the degree and extent of atrophy. Currently, the updated Sydney System and the operative link on gastritis assessment (OLGA) are most commonly used to assess the degree and extent of gastric atrophy. However, the biopsy from five locations under endoscopy is required for these two systems. Firstly, it will increase the patient’s suffering and medical costs. Secondly, it is difficult for physicians to perform in clinical practice. Thirdly, biopsy seems to be limited, and difficult to assess the status of the entire gastric mucosa. Besides histopathology, some serological indicators such as pepsinogen, Helicobacter Pylori(H. pylori) antibodies, gastrin-17, and tumor markers can also be used to assess gastric mucosa atrophy or predict the risk of GC. It has been documented that most of the previous GC risk predictive models were established based on these serological indicators. However, these indicators are susceptible to many factors, such as the testing methods, the intaking of anti-acid drugs, and the comorbidity of other gastric diseases. If the cut-off values vary among different medical institutions, the use of the model will be limited.

The Kimura-Takemoto classification (KTC), proposed by Japanese scholars in 1969, is mainly used for evaluating the extent of gastric mucosal atrophy under the endoscopy. In the early days, this classification was not given much attention due to the limitation of endoscopic technology and the insufficient understanding of gastric mucosal atrophy among endoscopists. Fortunately, with the highly developed techniques of endoscopy, endoscopists can recognize the atrophic gastric mucosa clearly, making the KTC widely used in clinical practice. There are several advantages to the KTC. Firstly, compared with the updated Sydney System and OLGA, the KTC is easy to perform and can assess the entire gastric mucosa with lower suffering and cost. Secondly, unlike serological indicators, the KTC is not susceptible to other factors and does not change much in the short term. Besides, it can be used in clinical practice according to an international uniform standard, making the model more applicable and reliable. Several studies have shown that there is a high diagnostic agreement between the KTC and histopathology, making it an expected tool for the identification of ‘high risk’ endoscopic screening individuals.

All in all, the aforementioned literature review implies that a risk predictive model for GC is very important as the initial screening for asymptomatic subjects for the management of precancerous conditions. However, there is still a lack of a simplified and rapid predictive model only relying on gastroscopic images for endoscopic screening population. Therefore, we aimed to develop a real-time endoscopic risk predictive model for GC in hope of being used to stratify the endoscopic screening individuals for further guidance of biopsy and clinical management strategies.

Ethics statements

This study was approved by the Ethics Committee of the Shandong Provincial Hospital (SWYX: NO.2022 − 326). This study was retrospective and was approved by the Ethics Committee to exempt patients from informed consent.

Study populations

The data were retrospectively collected from patients who underwent endoscopy in Shandong Provincial Hospital between January 2020 and December 2021. A total of 3,262 patients were included according to the inclusion criteria: (1) age between 30 and 90 years; (2) KTC was conducted; (3) complete medical records were available. Then a total of 1,437 patients were excluded according to the exclusion criteria: (1) esophageal cancer; (2) a history of gastric surgery; and (3) gastric submucosal protrusion lesions, gastric polyps, and gastric ulcers were excluded from the non-GC group. A total of 1825 patients were eventually analyzed, including 203 patients with GC and 1622 patients with non-GC. The study flow was shown in Fig. 1.

Data collection

The information was collected from the patients' electronic medical records and endoscopic reports, including sex, age, smoking, alcohol, family history of first-degree relatives with GC, KTC, bile reflux, and xanthoma. The criteria of KTC¹⁰ are as follows. Firstly, the KTC was divided into closed and open types, then each type was further divided into three subtypes. Finally, there were six types in the KTC: (1) C1, the atrophy is confined in the antrum; (2) C2, the atrophy exceeds the angulus but is confined in a small area of the lesser curvature; (3) C3, the atrophy in a large area of the lesser curvature but not exceeds the cardia; (4) O1, the atrophy extends to the cardia and the atrophic border lies between the lesser curvature and the anterior wall; (5) O2, the atrophic border lies in the anterior wall; (6) O3, the atrophic area is widely spread and the atrophic border lies between the anterior wall and the greater curvature; C0 is used for those without atrophy. The findings of gastric atrophy under the endoscopy include the appearance of the capillary network, pallor of the gastric mucosa, and flattening or even disappearance of the mucosal folds. Bile reflux is a condition in which bile produced by the liver, refluxes into the stomach with the visualization of bile pooling under the endoscopy. Gastric xanthoma is a yellow plaque-like lesion under the endoscopy with many foamy histiocytes in the lamina propria on histopathology. The GC was diagnosed according to the Vienna classification for gastrointestinal epithelial neoplasia .

Statistical analysis

IBM SPSS Statistics for Windows (V.25.0) and R for Windows (V.4.2.0) were used for statistical analysis. Continuous variables were described as mean with standard deviation (\(\stackrel{-}{\text{X}}\)±SD). Categorical variables were expressed as absolute frequency and percentage. Binary logistic regression (stepwise method) was used in multivariate analysis to estimate the adjusted odds ratio (adj.OR) and the related 95% confidence interval (CI). Only variables with a p-value of ≤ 0.20 in univariate analysis were included in multivariate analysis. The area under the receiver operating characteristic curve (AUC) was performed to evaluate the discrimination, the Hosmer-Lemeshow test and calibration curve was used to assess the calibration, and decision curve analysis (DCA) was used to detect the clinical benefits and utility of the model. Nomogram based on the logistic regression formula was constructed using the R package 'rms' to present the model. Internal validation of the predictive model was performed with 1,000 bootstrap resamples.

Characteristics of the patients

A total of 1825 patients were included in this study, with a mean age of 58.35 ± 11.37 years, 1062 (58.2%) males, 565 (31%) smokers, 712 (39%) alcohol drinkers, 51 (2.8%) with a family history of GC in first-degree relatives, 77 (4.2%) with bile reflux, and 11 (0.6%) with xanthoma. In the KTC, there were 1306 (71.6%) with C0 type, 101 (5.5%) with C1 type, 225 (12.3%) with C2 type, 105 (5.8%) with C3 type, 44 (2.4%) with O1 type, 30 (1.6%) with O2 type and 14 (0.8%) with O3 type. Baseline characteristics of the included patients are shown in Table 1.

Table 1

Univariate and multivariate logistic analysis of the GC
	Total	Non-GC	GC	Univariate		Multivariate
	Total	Non-GC	GC	OR(95%CI)	P	OR(95%CI)	P
Total	1825	1622	203
sex
female	763 (41.8)	707 (43.6)	56 (27.6)
male	1062 (58.2)	915 (56.4)	147 (72.4)	2.03(1.48–2.82)	< 0.001	1.95(1.39–2.77)	< 0.001
age
mean (SD)	58.35 (11.37)	57.47 (11.33)	65.44 (8.96)	1.07(1.06–1.09)	< 0.001	1.07(1.05–1.09)	< 0.001
smoking
NO	1260 (69.0)	1141 (70.3)	119 (58.6)
YES	565 (31.0)	481 (29.7)	84 (41.4)	1.67(1.24–2.25)	0.001
alcohol
NO	1113 (61.0)	1003 (61.8)	110 (54.2)
YES	712 (39.0)	619 (38.2)	93 (45.8)	1.37(1.02–1.84)	0.036
FamilyHistory
NO	1774 (97.2)	1585 (97.7)	189 (93.1)
YES	51 ( 2.8)	37 ( 2.3)	14 ( 6.9)	3.17(1.63–5.85)	< 0.001	3.58(1.72–7.12)	< 0.001
BileReflux
NO	1748 (95.8)	1550 (95.6)	198 (97.5)
YES	77 ( 4.2)	72 ( 4.4)	5 ( 2.5)	0.54(0.19–1.23)	0.193
Xanthoma
NO	1814 (99.4)	1612 (99.4)	202 (99.5)
YES	11 ( 0.6)	10 ( 0.6)	1 ( 0.5)	0.80(0.04–4.20)	0.830
KTC
C0-C1	1407 (77.1)	1303 (80.3)	104 (51.2)
C2	225 (12.3)	186 (11.5)	39 (19.2)	2.63(1.75–3.89)	< 0.001	2.13(1.40–3.20)	< 0.001
C3-O2	179 ( 9.8)	128 ( 7.9)	51 (25.1)	4.99(3.39–7.28)	< 0.001	3.54(2.36–5.26)	< 0.001
O3	14 ( 0.8)	5 ( 0.3)	9 ( 4.4)	22.55(7.65–74.54)	< 0.001	21.16(6.68–74.60)	< 0.001
GC, gastric cancer; KTC, Kimura-Takemoto Classification; FamilyHistory, family history of first-degree relatives with gastric cancer. OR, odds ratio; CI, confidence interval. Statistical significance was set as P ≤ 0.20 in univariate analysis and P < 0.05 in multivariate analysis.

The KTC was associated with the risk of GC

The univariate logistic regression analysis of the KTC showed that compared to non-atrophy (C0), C2 (OR = 2.70, 95%CI: 1.79–4.02, P < 0.001), C3 (OR = 5.91, 95%CI: 3.69–9.33, P < 0.001), O1 (OR = 3.79, 95%CI: 1.73–7.65, P < 0.001), O2 (OR = 4.69, 95%CI: 1.91–10.42, P < 0.001) and O3 (OR = 23.21, 95%CI: 7.86–76.83, P < 0.001) increased the risk of GC, as shown in Fig. S1. Then we modified the KTC into four types, C0-C1, C2, C3-O2, and O3. The results showed that compared with C0-C1, C2 (OR = 2.63, 95% CI: 1.75–3.89, P < 0.001), C3-O2 (OR = 4.99, 95% CI: 3.39–7.28, P < 0.001), and O3 types all (OR = 22.55, 95% CI: 7.65–74.54, P < 0.001) increased the risk of GC. In addition, we also found that a larger OR was obtained with the higher grade in the KTC, as shown in Fig. S2. Further comparison of the original and modified KTC in predicting the risk of GC showed that the difference was not statistically significant (AUC 0.661 vs 0.656, P = 0.453).

The risk predictive model for GC based on the modified KTC was established

In univariate analysis, seven variables were potentially associated with GC (P ≤ 0.20), including age (OR = 1.07, 95% CI: 1.06–1.09, P < 0.001), sex (OR = 2.03, 95% CI: 1.48–2.82, P < 0.001), smoking (OR = 1.67, 95% CI: 1.24–2.25, P < 0.001), alcohol (OR = 1.37, 95%CI: 1.02–1.84, P = 0.036), family history of GC (OR = 3.17, 95%CI: 1.63–5.85, P < 0.001), bile reflux (OR = 0.54, 95%CI: 0.19–1.23, P = 0.193), and modified KTC. These seven variables were included in multivariate logistic regression analysis. Finally, the results showed that four variables including sex (adjusted OR = 1.95, 95% CI: 1.39–2.77, P < 0.001), age (adjusted OR = 1.07, 95% CI: 1.05–1.09, P < 0.001), family history of GC (adjusted OR = 3.58, 95%CI: 1.72–7.12, P < 0.001) and modified KTC (C2: adj.OR = 2.13, 95%CI 1.40–3.20, P < 0.001; C3-O2: adj.OR = 3.54, 95%CI 2.36–5.26, P < 0.001; O3: adj.OR = 21.16, 95%CI 6.68–74.60, P < 0.001) were independently associated with GC. The results were shown in Table 1 and Fig.S3. The further subgroup analysis of sex showed that there was no statistically significant difference of smoking and alcohol for GC (P > 0.05).

According to the result of multivariate logistic regression analysis, the nomogram of the GC risk predictive model was constructed as shown in Fig. 2. The scores for each variable are as follows: 17 points were assigned to men, 32 points to family history of GC, 0 points to KTC C0-C1, 19 points to C2, 32 points to C3-O2, and 77 points to O3. The age was calculated with the formula (age − 30)*1.67, then the total point was summed. The scale value in risk corresponding to the total score was the predicted probability of GC.

The risk predictive model for GC had well efficacy

The AUC value was 0.656 (95% CI: 0.619–0.693) when the KTC alone was applied to predict the risk of GC, while the AUC value of the model constructed based on the modified KTC was 0.781 (95% CI: 0.750–0.813), suggesting that the model had higher discrimination compared to the KTC alone (p < 0.001), as shown in Fig. 3. And a corrected AUC of 0.777 was obtained by 1,000 bootstrap resamples, which validated the good discrimination of the model. The calibration curve was shown in Fig. 4. The x-axis of the plot was the predicted probabilities and the y-axis was the actual probabilities. The "ideal" curve represented that the predicted probability was exactly equal to the actual probability. The calibration curve of our model was close to the ideal line, which meant favorable calibration of the model. The Hosmer-Lemeshow goodness-of-fit test showed that the difference between the predicted and actual probabilities was not statistically significant (p > 0.05), further confirming the good calibration of the model. The clinical utility of the model was evaluated using decision curve analysis (DCA). The x-axis of the plot was the threshold probability and the y-axis was the net benefit. The two baselines represented two extremes of the clinical benefit: the horizontal line with a y-axis of 0 represented no GC in all patients, and the clinical benefit was 0; the gray curve with a negative slope represented the net clinical benefit if all patients had GC. As shown in Fig. 5, the DCA curve of the model was above the two extreme lines, indicating that additional benefits could be obtained when using the model to make clinical decisions compared to all or no intervention. And the model added more net benefits compared with the KTC alone for almost all threshold probabilities.

The endoscopic screening population could be stratified according to the nomogram

The scores of all patients were calculated according to the nomogram. The cut-off value between the low-risk and medium-high-risk populations was set to 69 according to the value of the Youden index, and the cut-off value for the medium-risk and high-risk populations was set to 99 according to the 90th percentile. The population was finally stratified into low (0–69 points), medium (70–99 points), and high-risk (≥ 100 points), and the OR values were calculated for different risk groups, as shown in Fig. 6. The results showed that compared to the low-risk population, the risk of GC was significantly higher in the medium-risk (OR = 5.10, 95% CI:3.53–7.49, P < 0.001) and high-risk (OR = 12.84, 95% CI:8.39–19.85, P < 0.001) population.

To date, China still has a high mortality rate of GC, which is mainly attributable to delayed diagnosis. In this regard, it is crucial to improve the early detection of the GC. Currently, the most widely accepted risk predictive model for GC in the Chinese population is the Lee's Scale, which is mainly used to predict the risk of GC based on serological indicators in asymptomatic individuals. However, there is no risk predictive model for the endoscopic screening population.

Atrophy, an important stage in the development of the GC, is mainly assessed according to the KTC under the endoscopy. It has been shown that the risk of GC increases with a higher grade of KTC, which can be used to stratify the risk of GC under the endoscopy . Even after H. pylori eradication, the KTC is still an independent risk factor for the development of GC. It has also been documented that the KTC can be used to discriminate the gastric adenoma and early GC. Therefore, the present study explored the relationship between KTC and the risk of GC.

In this work, the results showed that except for the C1 type, all other types increased the risk of GC. Then, we further modified the KTC from a clinical perspective. The boundary between C3 and O1 types is the lesser curvature of the gastric body near the cardia, which is difficult to be recognized in clinical practice, so endoscopists can easily misjudge O1 as C3. Besides, the boundary of the O1 and O2 types is the middle of the gastric fundus. During the endoscopic procedure, the thin mucosa of the gastric fundus is susceptible to hyperinflation, leading to the misjudgments of O1 and O2 types. Therefore, we modified the KTC by combining the C3, O1, and O2 types, which could simplify the clinical practice and increase the accuracy and reliability of the classification. Finally, our results indicated that the modified KTC was associated with the risk of GC, and the risk increased with the higher grade of the modified KTC.

Next, we screened other potential risk factors for GC. Previous studies found that the incidence of GC increased with age progressively and was higher in males. Those with a family history of GC in first-degree relatives had a significantly higher risk of GC. In addition, a cohort study showed that smoking increased the incidence of GC, especially in H. pylori-positive patients.

In this research, the univariate analysis showed that age, sex, smoking, alcohol, and family history of GC were statistically significant, which is consistent with the results of the previous studies above. However, in multivariate analysis, we found that smoking and alcohol were not statistically significant when the variable of sex was included in the regression analysis. The subgroup analysis of sex also showed that there was no statistically significant difference of smoking and alcohol for GC, which might because the proportion of smoking and alcohol was higher in males. In addition, a cohort study of Chinese ethnicity in Singapore showed that only smoking more than 20 packs per year was associated with an increased risk of GC. So, the possible explanation for this discrepancy might lie in the differences in consumption of smoking and alcohol. A further stratified investigation is necessary for understanding the impacts of tobacco and alcohol on GC risk.

A retrospective study showed that bile reflux was associated with GC and the level of bile reflux is positively correlated with the severity of the gastric mucosal lesion. Another study showed that xanthoma was an independent risk factor for GC. But the results of our study indicated that neither bile reflux nor xanthomas were related to GC. Thus, the association of bile reflux and xanthomas with GC maybe controversial. But it has to be mentioned that the bile reflux was not graded in our study and the mutual influence between Hp and bile reflux still lacks co-analysis, which might lead to the discrepancy with the previous study. For gastric xanthomas, it is a relatively rare gastric lesion, some endoscopists may miss the diagnosis because of insufficient understanding of this lesion. Therefore, there was a small number of positive cases in this study which may cause the irrelevance of xanthomas and GC.

Lastly, according to the results of univariate and multivariate analysis, we developed an endoscopic real-time GC risk predictive model based on the KTC, with good discrimination (AUC = 0.781), calibration, and clinical utility. And the nomogram was constructed to present the model. The scores calculated according to the nomogram for all patients further stratified the population into low risk (score 0–69), medium risk (score 70–99), and high risk (score ≥ 100). Referring to the AGA Clinical Practice Update on the Diagnosis and Management of Atrophic Gastritis, and combined with the risk stratification obtained from our study, we established the following clinical management strategies. Firstly, we recommend the biopsy from five points for the high-risk population, and three points for the medium-risk population to further assess the OLGA and operative link for gastric intestinal metaplasia assessment (OLGIM), even if no mucosal lesions are found under the endoscopy. Secondly, the follow-up time can be reduced to 6 months for the high-risk population and 12 months for the medium-risk population. For those in low risk, it can be appropriately extended to 2–3 years. However, reassessment of the risk of GC and adjustment of the strategies after every endoscopy is needed. Moreover, many studies have found that atrophic gastritis can be reversed. For example, a 10-year follow-up in Korea found that H. pylori eradication treatment could reverse atrophic gastritis. A multi-center cohort study in China showed that the lamb's tripe extract and vitamin B12 capsule could reverse both atrophy and intestinal metaplasia. And several studies have shown that Weifuchun (WFC) alone, WFC combined with Rebamipide and WFC combined with Tepronone could improve the pathological grading or clinical outcomes in patients with chronic atrophic gastritis. Therefore, for the medium and high-risk populations, the eradication of H. pylori is strongly recommended. After H. pylori eradication, the drugs mentioned above can be appropriately used to decrease the extent and degree of atrophy and further reduce the risk of GC as much as possible.

Compared with existing models, our model has unique advantages. Firstly, the model can predict GC risk only based on the KTC and patients’ basic characteristics, which is applicable for real-time guidance of biopsy and clinical management under the endoscopy with less burden for clinicians, lower suffering and cost for patients. Secondly, neither serological testing nor pathological examination is needed in the model, which can reduce the influence of other factors and improve the accuracy and reliability of the model. Thirdly, the KTC is widely used in clinical practice and there is an international uniform standard for it, which will make the model widely used. Nonetheless, there are still some deficiencies in this model. Firstly, the data of our study were collected from a single center, and further validation is needed to confirm its generalization. Secondly, only inpatients were selected and patients for physical examinations were not included, so there might be selection bias in our study. Thirdly, this study retrospectively collected the information of the patients, so the variables that could be collected were limited. In the future, other endoscopic findings such as diffuse redness, regular arrangement of collecting venules (RAC) and endoscopic grading of the gastric intestinal metaplasia (EGGIM) can be combined to improve the efficacy of the model.

In conclusion, the Kimura-Takemoto Classification was associated with the risk of GC. The GC risk predictive model based on the modified KTC has good discrimination, calibration and clinical utility, which can effectively and rapidly predict the risk of GC and stratify the endoscopic screening population for further guidance of biopsy and clinical management strategies. However, a larger endoscopic screening population is needed to validate its feasibility in the future.

KTC: Kimura-Takemoto Classification; GC: Gastric cancer; OLGA: the operative link on gastritis assessment; H. pylori: HelicobacterPylori; OR: odds ratio; CI: confidence interval; AUC: area under the receiver operating characteristic curve; DCA: decision curve analysis; OGIM: operative link for gastric intestinal metaplasia assessment; WFC: Weifuchun; RAC: regular arrangement of collecting venules; EGGIM: endoscopic grading of the gastric intestinal metaplasia.

Acknowledgements

We thank Professor Jianfeng Li for English language editing.

Authors’ contributions

HWX, QZ and YDL designed the research. YDL, XXM, TYZ, BH, MYL and YJS collected the data. SL and YDL analyzed the data. YDL and QQX wrote the manuscript. HWX, YDL and QQX revised the final manuscript. All authors read and approved the final manuscript

Funding

This research was financially supported by the National Natural Science Foundation of China (Nos. 82170650).

Availability of data and materials

Additional data and materials may be requested from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Shandong Provincial Hospital (SWYX: NO.2022-326). This study was retrospective and was approved by the Ethics Committee to exempt patients from informed consent.

Consent for publication

Not applicable.

Competing interest

All authors declare no competing interests.

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No competing interests reported.

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A model based on Kimura-Takemoto Classification: real-time endoscopic risk prediction for gastric cancer

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Abstract

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Background

Methods

Ethics statements

Study populations

Data collection

Statistical analysis

Results

Characteristics of the patients

The KTC was associated with the risk of GC

The risk predictive model for GC based on the modified KTC was established

The risk predictive model for GC had well efficacy

The endoscopic screening population could be stratified according to the nomogram

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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