A nomogram based on imaging features and serological indicators for predicting efficacy of neoadjuvant chemotherapy in gastric cancer

doi:10.21203/rs.3.rs-2447108/v1

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A nomogram based on imaging features and serological indicators for predicting efficacy of neoadjuvant chemotherapy in gastric cancer

https://doi.org/10.21203/rs.3.rs-2447108/v1

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Background Neoadjuvant chemotherapy (NAC) has been proven to be a powerful therapeutic choice for the advanced gastric cancer. However, the overall response rate is only 20-40% and there is a lack of sensitive indicators to predict the efficacy of the therapy. In this study, we aimed to construct a nomogram to predict the efficacy of NAC for gastric cancer.

Method The study comprised 60 gastric cancer patients who underwent NAC. Patients were classified into effective (TRG 0-2) and ineffective (TRG 3) groups based on the Tumor Regression Grade (TRG). Clinical data were compared between the two groups, and binary logistic regression analysis was used to screen the independent factors that could predict efficacy. Then, a new nomogram was created and validated.

Result In this study, a total of 33 patients (33/60, 55%) were successfully treated with NAC. Platelet Distribution Width, Adenosine Deaminase, Urea, and clinical T-stage of tumor were independent factors for predicting the efficacy of NAC for gastric cancer treatment (P < 0.05). The consistency index of the constructed Nomogram was 0.923 (95% CI: 0.851-0.995).

Conclusion The nomogram developed in this study has a high degree of clinical utility, calibration and discrimination, which can help clinicians accurately predict the efficacy of NAC for gastric cancer patients.

Gastric cancer

Neoadjuvant chemotherapy

Nomogram

Efficacy

Gastric Cancer (GC) is one of the most common malignancies and the third leading cause of cancer-related deaths.[1] Despite improvements in surgical methods and procedures, the 5-year overall survival rate for patients with GC is still below 25%.[2]

Neoadjuvant Chemotherapy (NAC) has been shown to reduce tumor stage and increase the likelihood of radical surgery in GC patients in recent years.[1] Numerous NAC regimens have emerged with the continuous research on oncology drugs, however, the overall clinical remission rate of NAC is only 20 to 45%.[3] Therefore, it is critical to predict efficacy before selecting NAC. Nevertheless, there are currently no standardized predictors of NAC in GC. Therefore, it is important to develop predictive models of NAC efficacy in GC to offer the best treatment options for patients.

Currently, radiological features derived from CT scans during the portal phase and some clinical markers are the key building blocks of the NAC prediction model.[4–7] These methods have limited use in clinical practice because they are too expensive and sophisticated to accurately predict NAC efficacy. In clinical work, peripheral blood indicators within the normal range are often disregarded by investigators but may have significant clinical implications. In 2020, a study also used peripheral blood indicators to predict the efficacy of NAC in patients with GC.[4] However, the peripheral blood indicators included in this study were not comprehensive. Furthermore, the predictive model constructed in this study had weak predictive power. To compensate for its shortcomings, this study collected all routine peripheral blood indicators and clinical data from GC patients before NAC and then analyzed the relationship between these factors and NAC efficacy. The objective is to develop a predictive model for NAC in GC using economical and simple clinical parameters. It will facilitate surgeons to precisely select patients who may benefit from NAC. Up to date, no similar predictive models have been proposed.

Clinical Information

The patients with GC who had NAC at the Department of Gastrointestinal Surgery at the Third Affiliated Hospital of Soochow University between January 2019 and June 2022 were retrospectively examined in this study. A total of 78 clinical data were collected. Patients' biological data included age, gender, and body mass index (BMI). Tumor-related data were tumor clinical stage, clinical T stage (cT stage), clinical lymph node stage (cN Stage), chemotherapy regimen and chemotherapy cycle. Patient's laboratory data included their blood routine and type, liver function, kidney function, electrolyte concentration, blood glucose, lipid, coagulation function, and tumor indicators, among others. The BMI was calculated as weight (kg) divided by height squared (m²). The tumor clinical stage, cT stage and cN stage were judged by professional imaging doctors. The Tumor Regression Grade (TRG) was assessed by two pathologists.

Inclusion Criteria

(1) Gastric adenocarcinoma was diagnosed by gastroscopic biopsy pathology. (2) The NAC was decided after multidisciplinary deliberations. (3) Complete imaging data and laboratory tests are available. (4) Completion of NAC in GC patients. (5) Radical GC resection (D2) within one month after NAC.

Exclusion Criteria

(1) Radiotherapy history before NAC. (2) Patients receiving additional radiotherapy or immunotherapy during chemotherapy. (3) Failure to complete the prescribed chemotherapy regimen; (4) Incomplete imaging, pathology or hematology findings; (5) Patients with acute disease (such as acute infection, and acute organ insufficiency, among others) (6) Patients with chronic disease (such as chronic renal insufficiency, chronic hepatic insufficiency, chronic coagulopathy, chronic hematologic insufficiency, among others) that is not under control and stable. (7) Patients who have previously had other malignant tumors.

Grouping

Currently, healthcare professionals concur that the TRG is an important indicator of the efficacy of NAC.[8] The effectiveness of NAC was assessed in this study using the TRG of the 8th edition of the AJCC criteria:[9] no surviving cancer cells (TRG = grade 0); residual single or small clusters of tumor cells (TRG = grade 1); fibrosis with a small number of cancer cells remaining (TRG = grade 2); and the presence of a substantial number of cancer cells without a significant tumor regression response (TRG = grade 3). This way, patients were divided into the effective NAC group (TRG = grade 0–2) and the ineffective NAC group (TRG = grade 3).[10]

Screening Predictors

Clinical data between the effective and ineffective groups of NAC was analyzed. Factors with P < 0.1 in the chi-square test and Fisher's exact probability method were retained and included in the multivariate analysis of binary logistic regression to avoid missing possible predictors. The indicators with P < 0.05 on both sides were considered independent influencing factors affecting the efficacy of NAC. The results of the analysis were expressed as odds ratio (OR) with 95% Confidence Intervals (CIs).

Construction of Nomogram and Effectiveness Evaluation

A Nomogram model for predicting the efficacy of NAC was developed using the "rms" package in R language based on the results of binary logistic multivariate regression analysis. The Consistency index (C-index) was used to assess the differentiation of the model. The SPSS software was used to plot the Receiver Operating Characteristic curve (ROC) of each predictor. To evaluate the predictive ability of each index and Nomogram model, Area Under Curve (AUC) was used. The Bootstrap (B = 1000) method was used to internally validate the developed model as well as draw a calibration diagram. The "rmda" package was used for Decision Curve Analysis (DCA) to evaluate the clinical benefits of the model application.

Statistical Analysis

Statistical analysis was done using SPSS 27.0 and R 4.2.1. The chi-square test and Fisher's exact probability method were used to compare the variables between groups after the continuous numerical variables were transformed into categorical variables by selecting the mean or median as the cutoff point, based on whether the continuous numerical variables conformed to a normal distribution.

Patient Inclusion and Grouping

Figure 1 shows the flow chart of this study. A total of 136 patients with GC treated by NAC were included in this study. Of the total, 34 patients changed chemotherapy drugs during chemotherapy, 23 patients had incomplete imaging or peripheral blood data, 13 patients added immunotherapy during chemotherapy, 5 patients were admitted for surgery after discontinuing NAC due to gastrointestinal bleeding, and 1 patient underwent palliative treatment with a gastrojejunostomy for pyloric obstruction before NAC. Eventually, only 60 patients were included in this study. In this study sample, 33 patients (55%) with GC had NAC that was effective, and 27 patients (45%) of patients had ineffective NAC. There were 7 patients with TRG = 0 (11.7%), 10 patients with TRG = 1(16.7%), 16 patients with TRG = 2 (26.7%), and 27 cases with TRG = 3 (45%) based on pathologists after reading the specimen.

Comparison of Clinical Data of Patients between the Effective and Ineffective Groups of NAC

Table 1 shows the relationship between the relevant indicators of the baseline data and the efficacy of NAC in 60 patients. The chi-square test was used to determine whether there were any differences in patients' gender, age, and BMI between the effective and ineffective NAC groups for GC. All indicators were P > 0.1, implying that there was no significant difference between the above indicators in the two groups.

Table 1

Comparison of clinical data of patients between the effective and ineffective groups of NAC
	Tumor regression grade		NAC response rate	c2	p
	TRG ≤ 2	TRG = 3	NAC response rate	c2	p
Gender
Male	26	23	53.1%	0.091	0.763^#
Female	7	4	63.6%	0.091	0.763^#
Age(years)
≤ 65	15	16	48.4%	1.133	0.287
༞65	18	11	62.1%	1.133	0.287
BMI(kg/m²)
≤ 22.82	17	14	54.8%	0.001	0.979
༞22.82	16	13	55.2%	0.001	0.979
Note: ^# Continuity-corrected chi-square test was used. BMI = weight (kg) divided by square of height (m²).

Comparison of Tumor Pathological Characteristics between the Effective and Ineffective NAC Groups

In this study, the chi-square test and Fisher's exact probability method were used to evaluate the relationships between tumor staging, degree of tumor differentiation, clinical stage of the tumor (pre-NAC), cT stage, cN stage and NAC efficacy (the data are shown in Table 2). The findings revealed that different cT stages were associated with NAC efficacy of patients (P = 0.036), whereas tumor staging, degree of tumor differentiation, clinical stage (before NAC), and cN stage were not significantly correlated with NAC efficacy (P > 0.1).

Table 2

Comparison of tumor pathological characteristics between the effective and ineffective NAC groups
	Tumor regression grade		NAC response rate	c2	P
	TRG ≤ 2	TRG = 3	NAC response rate	c2	P
Type
Upper 1/3	9	11	45%	-	0.461^§
Medium 1/3	6	3	66.7%
Lower 1/3	18	13	60%
Differentiation
Poorly differentiated	28	23	54.9%	0.000	1.000^#
Moderately differentiated	5	4	55.6%	0.000	1.000^#
Clinical stage
Stage IIa	2	2	50%	-	0.822^§
Stage Ⅱb	4	1	80%
Stage Ⅲa	10	7	58.8%
Stage Ⅲb	14	14	50%
Stage Ⅲc	3	3	50%
clinical T stage
cT₂	7	1	87.5%	-	0.036^§
cT₃	20	14	58.8%
cT₄	6	12	33.3%
clinical N stage
cN₁	4	3	57.1%	-	0.803^§
cN₂	10	6	62.5%
cN₃	19	18	51.4%
Note: ^§ Statistical tests were performed using Fisher's exact probability method.
^# Chi-square test with continuity correction were used.

Comparison of NAC Regimen and Chemotherapy Cycles between the Two Groups

The details of the NAC regimens and chemotherapy cycles of 60 patients are shown in Table 3. The results showed that no significant differences were observed between the effective and ineffective NAC groups of GC with various chemotherapy regimens and chemotherapy cycles (P > 0.05).

Table 3

Comparison of NAC regimen and chemotherapy cycles between the two groups
	Tumor regression grade		NAC response rate	c2	P
	TRG ≤ 2	TRG = 3	NAC response rate	c2	P
Chemotherapy regimen
SOX	23	17	57.5%	-	0.842^§
DOS	7	8	4.7%
Other^★	3	2	60%
chemotherapy cycle(n)
< 4	21	16	57.9%	0.12	0.729
≥ 4	12	11	52.2%	0.12	0.729
Note: § Statistical tests were performed using Fisher's exact probability method. ^★SOX (Oxaliplatin + Tegafur), DOS (Oxaliplatin + Docetaxel + Tegafur), DCF (Cisplatin + Docetaxel + 5-FU), XELOX (Oxaliplatin + Capecitabine), FLOT4 (Docetaxel + Oxaliplatin + Calcium Folinate + 5-FU).

Comparison of Serological Parameters between the Effective and Ineffective Groups of NAC

Select the mean or median as the cut-off point, depending on whether the data conform to the normal distribution. The chi-square test was used to assess the differences in serological markers prior to NAC between the chemotherapy-effective and chemotherapy-ineffective groups (Additional file 1: Supplementary table 1). The variables with P < 0.1 in the chi-square test were retained in this study to avoid the omission of possible predictors, and positive results are presented in Table 4. The pre-NAC Hemoglobin (HB) was different between the two groups (P = 0.069) in the chi-square test and patients with higher HB were prone to be treated effectively with NAC. The Platelet Distribution Width (PDW) also varied between the two groups (P = 0.004). Concerning liver function, patients with lower Adenosine Deaminase (ADA) were more likely to have better NAC efficacy (P = 0.04). In renal function indicators, high Urea values had a higher chance to benefit from NAC treatment (P < 0.001). Among tumor indicators, patients with low Carbohydrate Antigen 199 (CA199) potentially had better NAC efficacy (P = 0.069). For the coagulation action test, the Activated Partial Thromboplasting Time (APTT) ratio varied between the two groups, and patients with a higher APTT ratio could have better NAC efficacy (P = 0.061).

Table 4

Comparison of the peripheral blood indices between the effective and ineffective groups of NAC
	Tumor regression grade		NAC response rate	c2	P
	TRG ≤ 2 (n)	TRG = 3 (n)	NAC response rate	c2	P
Blood routine
Hemoglobin (g/L)
≤ 116	13	17	43.33%	3.3	0.069
> 116	20	10	66.67%	3.3	0.069
PDW (fL)
≤ 11.85	22	8	73.33%	8.148	0.004
> 11.85	11	19	36.67%	8.148	0.004
Liver function
ADA (U/L)
≤ 11.065	21	10	67.74%	4.207	0.04
༞11.065	12	17	41.38%	4.207	0.04
Kidney function
Urea(mmol/L)
≤ 4.915	10	20	33.3%	11.38	༜0.001
༞4.915	23	7	76.7%	11.38	༜0.001
Tumor markers
CA199(kU/L)
≤ 10.535	20	10	66.67%	3.3	0.069
༞10.535	13	17	43.33%	3.3	0.069
Coagulation action test
APTT ratio
≤ 1.067	14	18	43.75%	3.506	0.061
༞1.067	19	9	67.86%	3.506	0.061
Note: PDW: Platelet Distribution Width, ADA: Adenosine Deaminase, CA199: Carbohydrate Antigen 199, APTT: Activated Partial Thromboplasting Time.

A Binary Logistic Multifactorial Analysis to Predict the Efficacy of NAC In Patients with GC

By using a chi-square test, it was discovered that the successful and ineffective groups of NAC for GC had distinct tumor cT stage, HB, PDW, ADA, Urea, CA199, and APTT ratios. These indicators were used in the binary logistic multifactorial regression analysis (see Table 5 for details). The cT stage (P = 0.035), PDW (P = 0.011), ADA (P = 0.019), and Urea (P = 0.001) were observed to be independent factors affecting the efficacy of NAC. Whereas the chi-square test showed that HB, CA199, and APTT ratios varied between the two groups, none of these indicators could be an independent factor affecting the efficacy of NAC in the binary logistic multifactorial analysis (P > 0.05).

Table 5

Binary logistic multifactor analysis for predicting the efficacy of NAC in patients with gastric cancer
		B	P	OR	95%CI of OR
		B	P	OR	lower limit	upper limit
cT stage			0.035
	cT2	5.340	0.013	208.425	3.116	13941.829
	cT3	2.904	0.031	18.248	1.305	255.173
	cT4	0		1
HB(g/L)	≤ 116	0		1
HB(g/L)	> 116	1.396	0.170	4.040	0.551	29.618
PDW(fL)	≤ 11.85	2.963	0.011	19.349	1.982	188.899
PDW(fL)	> 11.85	0		1
ADA (U/L)	≤ 11.065	2.443	0.019	11.504	1.497	88.428
ADA (U/L)	> 11.065	0		1
Urea (mmol/L)	≤ 4.915	0		1
Urea (mmol/L)	> 4.915	3.456	0.001	31.701	3.805	264.136
CA199(kU/L)	≤ 10.535	0.988	0.309	2.687	0.400	18.058
CA199(kU/L)	> 10.535	0		1
APTT ratio	≤ 1.067	0		1
APTT ratio	> 1.067	0.927	0.340	2.527	0.376	16.993
Note: HB: Hemoglobin, PDW: Platelet Distribution Width, ADA: Adenosin
Deaminase, CA199: Carbohydrate Antigen 199, APTT ratio: Activated Partial
Thromboplasting Time ratio.

Construction of A Nomogram Model to Predict the Efficacy of NAC

In this study, a functional model was created to create a Nomogram model for predicting the effectiveness of NAC using the independent factors (cT stage, PDW, ADA, and Urea) tested in the multivariate analysis of binary logistic regression (Fig. 2).

Validation and Evaluation of Predictive Models

Discrimination Evaluation

The C-index was used in this study to evaluate the discrimination of this nomogram. The C-index of the aforementioned Nomogram model calculated by R language was 0.923 (95% CI: 0.851–0.995), implying that it had strong discrimination and stability. The study also assesses the predictive power of each indicator and Nomogram model and plots the ROC curves of each predictor (Fig. 3). The findings showed that the AUC for cT stage, PDW, ADA, and Urea to predict NAC efficacy were 0.325, 0.315, 0.367, and 0.719. The AUC of the Nomogram prediction model, however, was 0.923. In contrast, the Nomogram model outperformed the individual predictors in terms of predictive power, indicating that its prediction model is highly discriminatory.

Consistency Analysis

The prediction model and calibration graph in this study were internally validated using the bootstrap method (Fig. 4). The fitted calibration curve matches well with the 45° slope line, indicating that the predicted probability of the model is in agreement with the actual probability.

Decision Curve Analysis

This study included a new evaluation criterion, Decision Curve Analysis, to evaluate the clinical benefit of the model application because ROC-AUC mainly focuses on the discrimination of the prediction model, but has shortcomings in the evaluation of false positive and false negative misclassification results.[11] Fig. 5 shows that the net benefit of the model over a wide threshold probability range is > 0. It was found that the Nomogram model constructed in this study had a better net additional benefit when the threshold probability was in the range of 0.08 to 0.92 after extracting the decision curve details using the R language.

In addition to lowering tumor stage and raising the likelihood of curative resection, NAC for GC also eliminates early micrometastases and improves patient survival.[12, 13] Despite the widespread acceptance of NAC in the treatment of progressive GC, a clinical trial found that the rate of patients achieving pathological complete remission (TRG = 0) after receiving NAC was only 10%.[14] Therefore, it is critical to screen for patients who are sensitive to NAC. We analyzed the clinicopathological characteristics and multisystem serological parameters of GC patients and discovered that PDW, ADA, Urea, and cT stage were independent factors influencing the prediction efficacy of NAC, which we used to construct a nomogram of NAC efficacy. The advantages of this nomogram are its low cost, simple operation and wide applicability in clinical applications.

PDW is an important indicator used to assess platelet morphology and activation that measures the variability of platelet volume in the blood.[15, 16] It has been demonstrated that tumor cells can directly activate platelets through G protein-coupled receptor signaling.[17] Therefore, it was discovered that PDW was higher in tumor patients than in the general population for a number of cancers.[16, 18–23] It has been found that TGF-β released from platelet activation prevents tumor cell DNA from being damaged, creates chemoresistance in cancer cells, and encourages tumor cell metastasis.[24, 25] By increasing the sensitivity of chemotherapy, the use of TGF-β inhibitors can promote cancer cell division outside of the G1/S phase.[26] Furthermore, it was discovered in Chen H's study that activated platelet-derived TGF-β could stimulate PI3K/Akt and MEK/Erk signaling in pancreatic cancer cells, hence reducing the sensitivity of chemotherapy.[27] This explains why patients with high PDW levels are less likely to benefit from NAC. Similar results have been reported in breast cancer.[28]

The primary function of ADA is in the metabolism of adenosine, which can hydrolyze and deamination adenosine to produce inosine and dideoxyinosine. Because tumors typically experience hypoxia and necrosis, resulting in a large release of adenosine,[29, 30] they cause a compensatory increase in ADA as well.[31] Thus, in a number of tumors, it has been discovered that ADA is higher in tumor patients than in healthy individuals.[32, 33] The effective anti-tumor immune response is inhibited when the accumulated adenosine binds to adenosine receptors, promoting the proliferation and activation of bone Marrow-Derived Suppressor Cells (MDSCs) and regulatory T cells (Treg cells).[33] The ability of NAC to destroy tumors is compromised in this immunosuppressive microenvironment. Additionally, elevated serum ADA levels indicate rapid malignancy, more lymph node metastasis, and fast malignancy.[34] The effectiveness of NAC will be impacted by local hypoxia and a significant buildup of inflammatory factors in the tumor microenvironment

The body produces urea through the urea cycle in the liver, which is then fused in the blood and primarily by glomerular filtration. Urea was found to be an independent influential factor in predicting the efficacy of NAC in GC in this study. Patients with higher pre-treatment serum Urea levels were more likely to achieve efficacy in GC NAC. However, the underlying mechanism is still not clear. Studies have shown that patients using the drug with low levels of Urea are more likely to develop liver damage early in treatment.[35] Furthermore, liver dysfunction has been identified as a risk factor for a poor prognosis in large cohort studies.[36] Therefore, NAC therapy may be less effective for patients with low urea levels since they may be more likely to experience hepatic impairment. Similar results have been reported in breast cancer patients.[37]

This study discovered that the cT stage had a separate bearing on predicting NAC's effectiveness. The disease is frequently more severe in these patients, and a higher cT stage signifies deeper tumor involvement. Local hypoxia of the tumor and large accumulation of inflammatory factors in the tumor microenvironment can influence the efficacy of NAC. However, the study observation is that the clinical stage of the tumor was not a valid predictor of NAC efficacy. This could be attributed to the fact that the accuracy of enhanced CT examination in determining the cN stage is about 70%,[38, 39] leading to errors in the determination of tumor staging by imaging physicians.

The effect of tumors on the human body is not limited to local tissues and organs, and to some extent, the progress of tumors and the prognosis of patients can be determined by analyzing serological indicators of different human systems. this study finally screened out four independent influencing factors: PDW, ADA, Urea, and cT stage through multivariate regression analysis, which has great advantages in developing new Nomogram prediction model. First, the nomogram created in this study fills a gap in the predictive models that exist for the effectiveness of NAC in treating GC. Second, to construct the current nomogram, radiological features taken from portal phase CT and some clinical signs are primarily used.[4–7] Nonetheless, these radiological features are not easily accessible to clinical practitioners, and the resulting Nomogram is difficult to develop for general use. The nomogram developed in this study was primarily based on common serological indicators and the cT stage of GC patients before treatment. In clinical work, these common parameters are simple and economical to obtain. Thirdly, the correlation between these indicators and NAC for GC was analyzed more comprehensively with a total of 74 clinical parameters being included in this study, covering all routine serological indicators. Moreover, no studies have included parameters such as PDW, ADA, and Urea as predictors in the field of predicting the efficacy of GC NAC. Finally, the consistency index of the Nomogram prediction model developed in this study is greater than that of the previous model (C-index is 0.923), reflecting superior rectification and clinical practicability in verification and evaluation. Although the results of the current study differ from those of some other studies, this variation may be attributed to the different criteria for evaluating the efficacy of NAC. Whereas in other studies only TRG = 0 was included in the group with the efficacy of NAC, the current study included patients with TRG ≤ 2 as well [4].

The study has some shortcomings as well. First, it is a retrospective study introducing selection bias. Second, it has a relatively small sample size and is conducted in a single race. Therefore, a larger sample size is required to make the results more accurate and solid. Finally, the nomogram constructed in this study lacks external validation and will be further evaluated in a multicenter study.

From the routine analysis of the peripheral blood indicators and clinical characteristics of GC patients, the tumor cT stage, PDW, ADA, and Urea of patients before treatment are independent factors in predicting the efficacy of GC NAC. Therefore, for effective screening of patients for NAC treatment of GC, we must focus on these indexes. Based on them, a new nomogram model for predicting the efficacy of NAC in GC was constructed and proved to be highly discriminative, accurate and stable. It gave surgeons a clinical application for predicting the response to NAC in GC.

ADA Adenosine Deaminase ADA

APTT Activated Partial Thromboplasting Time

AUC Area Under Curve

BMI Body Mass Index

cT stage Clinical T Stage

cN stage Clinical Lymph Node Stage

CIs Confidence Intervals

C-Index Consistency index

CA199 Carbohydrate Antigen 199

DCA Decision Curve Analysis

DCF Cisplatin + Docetaxel + 5-FU

DOS Oxaliplatin + Docetaxel + Tegafur

FLOT4 Docetaxel + Oxaliplatin + Calcium Folinate + 5-FU

GC Gastric Cancer

HB Hemoglobin

MDSCs Marrow-Derived Suppressor Cells

NAC Neoadjuvant Chemotherapy

OR Odds Ratio

PDW Platelet Distribution Width

TRG Tumor Regression Grade

ROC Receiver Operating Characteristic curve

SOX Oxaliplatin + Tegafur

Treg Cells Regulatory T Cells

XELOX Oxaliplatin + Capecitabine

Acknowledgements

Besides of all participants in the author list, we also appreciate Dr. Wu, who is a Pathologist of the Third Affiliated Hospital of Soochow University, for the data support.

Authors’Contributions

Jie Zhou collected the original data. Zhaoyu Xin and Zhilin Liu analyzed the data. Haitao Wang and Wei Ding designed and edited the fgures and tables. Yuehua Feng and Haitao Wang reviewed and edited the manuscript. All authors agree to be accountable for the content of this work. All authors contribute to the article and approved the submitted version.

Fundings

This work was supported by Changzhou Applied Basic Research Project (No:CJ20200114)

Data Availability Statement

Data are available from the corresponding author upon reasonable request.

Ethics Statement

This retrospective study was approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University (no. 2020218). All patients were informed and their informed consent was obtained.

Conflicts of Interests

The authors declare that there is no conflict of interest regarding the publication of this article.

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Additionalfile1SupplementaryTable1.docx
Additional file 1: Supplementary table 1. Comparison of all peripheral blood indices between the effective and ineffective groups of NAC.

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A nomogram based on imaging features and serological indicators for predicting efficacy of neoadjuvant chemotherapy in gastric cancer

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Abstract

Figures

Introduction

Materials And Methods

Clinical Information

Inclusion Criteria

Exclusion Criteria

Grouping

Screening Predictors

Construction of Nomogram and Effectiveness Evaluation

Statistical Analysis

Results

Patient Inclusion and Grouping

Comparison of Clinical Data of Patients between the Effective and Ineffective Groups of NAC

Comparison of Tumor Pathological Characteristics between the Effective and Ineffective NAC Groups

Comparison of NAC Regimen and Chemotherapy Cycles between the Two Groups

Comparison of Serological Parameters between the Effective and Ineffective Groups of NAC

A Binary Logistic Multifactorial Analysis to Predict the Efficacy of NAC In Patients with GC

Construction of A Nomogram Model to Predict the Efficacy of NAC

Validation and Evaluation of Predictive Models

Discrimination Evaluation

Consistency Analysis

Decision Curve Analysis

Discussion

Conclusion

Abbreviations

Declarations

Fundings

Conflicts of Interests

The authors declare that there is no conflict of interest regarding the publication of this article.

References

Additional Declarations

Supplementary Files

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