Development and Validation of a Nomogram Based on Inflammatory indicators and Tumor Markers for Prognosis Prediction of Colorectal Cancer

doi:10.21203/rs.3.rs-3125643/v1

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Development and Validation of a Nomogram Based on Inflammatory indicators and Tumor Markers for Prognosis Prediction of Colorectal Cancer

https://doi.org/10.21203/rs.3.rs-3125643/v1

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Background: Reliable evaluation methods play an important role in improving the prognosis of colorectal cancer patients, guiding the development of treatment plans, and prolonging patient survival. Several preoperative inflammatory indicators and tumor markers were evaluated in this study for predicting colorectal cancer (CRC) prognosis.

Methods: A total of 224 eligible patients with CRC were enrolled in the present study. Patients were divided into a training group (n=150) and a validation group (n=74). The training cohort underwent both the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analyses to discern pivotal prognostic factors, aiming to formulate a nomogram for the prediction of overall survival (OS).

Results: LASSO regression, univariate and multivariate Cox regression analysis revealed that Neutrophil-lymphocyte ratio (NLR), carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) were effective risk factors. The concordance index (C-index) of the nomogram in the training and validation groups were 0.716 and 0.7 respectively. The areas under curve (AUC) of the nomogram for 3-years were 0.748 and 0.776, for 5-years were 0.749 and 0.773 respectively.

Conclusion: NLR, CA199 and CEA were effective supplements to traditional clinical assessment methods. The nomogram incorporating the three preoperative indicators can be effectively and efficiently used to predict the prognosis of CRC patients.

Colorectal cancer

Prognostic signature

Overall survival

Nomogram

CRC, a prevalent gastrointestinal malignant neoplasms continues to maintain its status as the second most prominent contributor to global cancer-related mortality, with an estimated 4 million new cases and 693,900 associated deaths projected by by 2020¹. The integration of curative resection and adjuvant cancer chemotherapy has emerged as a paradigmatic treatment modality, yielding remarkable overall amelioration in the prognosis of patients with CRC. ². Nevertheless, a cascade of alterations in epigenetic and metabolic processes, however, led to a strong transfer and invasion capacity in the cells of colorectal cancer, ultimately resulting in a meager 5-year overall survival rate of about 12% in patients afflicted with metastatic colorectal cancer³. Over the course of decades, the American Joint Commission on Cancer/International Union against Cancer TNM staging system has stood as the benchmark for prognosticating colorectal cancer outcomes. Nonetheless, the considerable variability in clinical prognoses among patients classified under the same stage implies inherent limitations in the predictive efficacy of the TNM staging system for colorectal cancer prognosis⁴. Accordingly, an integrated approach that amalgamates the TNM staging system with other salient prognostic parameters needs to be adopted urgently to direct the clinical decision-making process and uncover individualized therapeutic strategies.

Cancer-associated inflammation can occur throughout the course of the disease. Inflammatory cells play a major role in the tumor microenvironment. Inflammatory cells are mediators of tumorigenesis and progression, and they provide cytokines to cancer cells and enhance cell survival and proliferation⁵. Neutrophils-lymphocyte ratio, a measure of the number of neutrophils per gram of body weight, has been increasing in patients with higher disease stage and more aggressive disease evidence of increased number of tumor stages and metastatic sites and, as a result, constituting a particularly high risk patient population⁶. Consequently, a blood-value assay may be useful to estimate prognosis in patients with cancer.

Nomograms are statistically based models that create individualized risk predictions by taking into account prognostic factors. As the predictor, nomogram has simple graphical displays of the statistic prediction models which produce numeric probability of the clinical event⁷. Compared with conventional TNM staging, nomogram has much higher advantages for treating many kinds of cancers, thus it has been presented as a replacement or even a new standard^{8, 9}. Prognostic biomarkers, which are indicators or prognostic biomarkers predictive of clinical pathological feature and survival rate of CRC patients, are useful in screening, diagnosis, classification, and treatment of CRC¹⁰.

In this investigation, we formulated a prognostic model rooted in clinical parameters intricately associated with the clinical prognosis. The primary objective was to anticipate the prognostic trajectory of individuals diagnosed with colorectal cancer, thereby furnishing valuable insights to guide clinicians in their therapeutic strategies for colorectal cancer patients.

2.1.Data collecting and patient characteristics

From June 2012 to June 2018, 224 cases of surgically resected CRC from the First Affiliated Hospital of Soochow University were included in the retrospective cohort (Fig. 1). All patients underwent surgical resection and followed the standard treatment guidelines outlined during this period. We excluded patients with a prior medical history of other malignancies, including those pertaining to the cervix, uterus, or bladder; recent chemotherapy or radiotherapy; the postoperative complication of severe heart, lung, and kidney; addiction or mental disorder. Patients who did not follow up were also excluded. Prior to data collection, informed consent was obtained from all participants. The study received approval from the Committee for the Ethical Review of Research at the First Affiliated Hospital of Soochow University (Approval No. 125) and was conducted in adherence to institutional guidelines and the principles outlined in the Declaration of Helsinki.

The collected clinical data were randomly divided into validation set (n = 74) and training set (n = 150). The following clinical data were collected and grouped separately: gender, blood type, age, hypertension status, diabetes, prognostic nutritional index (PNI), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), body mass index (BMI), CA199, carbohydrate antigen 72 − 4 (CA72-4), carbohydrate antigen 125 (CA125), CEA, lymphocyte, thrombocyte, hemoglobin, neutrophil, monocyte, alanine aminotransferase (ALT), aspartate aminotransferase (AST), C-reactive protein (CRP), albumin, fibrinogen, tumor site, tumor size, T, N, M, survival time, survival status, systemic immune-inflammation index (SII), postoperative adjuvant therapy, NLR and pathological type. The PNI score was derived from the serum albumin level and lymphocyte count, with the formula defined as follows: PNI = albumin (g/L) + 5 × lymphocyte (10⁹/L).

2.2.Treatment

In the Department of General surgery of the First Affiliated Hospital of Soochow University, patients with colorectal cancer were surgically resected, and the resected tissues were examined pathologically in frozen sections to confirm the diagnosis and negative surgical margin¹¹. According to the postoperative pathological results, adjuvant chemotherapy began within 3 months after the surgery, including oxaliplatin plus capecitabine or oxaliplatin plus capecitabine plus bevacizumab.

2.3.Follow Up

OS is defined as the time from surgery to death in any case¹¹. Follow-up evaluation was performed every 3 months for 5 years, and OS was collected until December 31, 2022. Survival was determined by reviewing medical records, including laboratory data, computed tomography, colonoscopy results, and telephone follow-up¹¹.

2.4.Statistical Analysis

Before starting analysis, we converted non-hierarchical categorical variables into dummy variables and ordinal factor variables into numerical variables.

LASSO analysis was employed to identify prognostic factors. The estimation of the optimal penalty parameter was accomplished through a 10-fold cross-validation procedure performed on the training dataset.

In order to ascertain the statistical significance of the prognostic factors identified above, we conducted univariate Cox proportional hazards regression analysis. This approach allowed us to assess the impact of each factor's value on survival time and calculate the corresponding hazard ratio (HR), 95% confidence interval, and two-sided P-value. Factors with a p-value < 0.05 were included in the construction of the prognostic clinical signature through the multivariate Cox proportional hazards model. Subsequently, we computed individual risk scores for each patient using the following formula: risk score = β₁ (variable1) × value of variable1 + β₂ (variable2) × value of variable2 + ... + βₙ (variablen) × value of variablen, where β represents the regression coefficient obtained from the multivariate Cox proportional hazards regression model. The univariate and multivariate Cox proportional hazards regression analyses were conducted using the R packages "survival" and "survminer", respectively.

We utilized the discerned factors from the training cohort to craft a nomogram employing the rms R package. To evaluate its discriminatory capability, the nomogram underwent bootstrapping validation (1,000 bootstrap resamples) to compute a relatively corrected C-index. The KM survival curves with the log-rank test were employed to assess differences in OS between the high- or low-risk groups. Additionally, time-dependent receiver operating characteristic (ROC) and calibration curves were utilized to evaluate the predictive performance of the nomogram within the training cohort. Furthermore, we employed ROC analysis and decision curve analysis (DCA) to quantitatively assess and compare the clinical utility of the nomogram, PNI score, and standard TNM staging system. These comparative analyses were replicated in the validation group.

All statistical analyses were conducted using R software (version 4.0.0). Comparisons between groups were performed utilizing analysis of variance or the Mann–Whitney U test for continuous variables, and the Chi-square test or Fisher's exact test for categorical variables¹¹. Unless otherwise specified, statistical significance was determined at P < 0.05.

3.1.Clinical Characteristics

The demographic and clinical profiles of 224 suitable participants who underwent radical resection for colorectal cancer are presented in Table 1. This includes a cohort of 150 patients in the training group and 74 patients in the validation group. Patients in both groups exhibited no statistically significant differences in gender, blood type, age, hypertension status, diabetes, PNI, LMR, PLR, BMI, CA199, CA72-4, CA125, CEA, lymphocyte, thrombocyte, hemoglobin, neutrophil, monocyte, ALT, AST, CRP, albumin, fibrinogen, tumor site, tumor size, T stage, N stage, M stage, survival time, survival status, SII, and postoperative adjuvant therapy. However, there were differences in NLR and pathological type between the two groups. The mean NLR in the training group was 2.54, while in the validation groups, it was 2.24, respectively. In the validation group, proportion of the patients with moderate differentiation are slightly less (n = 86%) than the training group (n = 89.2%). The existence of significant differences of pathological type may be due to the unbalanced distribution of very few samples of high/low differentiation. Continuous variables are presented as medians with interquartile ranges, while categorical variables are presented as counts along with their respective proportions.

Table 1

Demographic and clinical characteristics of patients in the test and train cohorts (x̄ ± s).
Items	Test Set(n = 74)	Train Set(n = 150)	p
Gender			0.57
Female	37(50.00%)	68(45.3%)
Male	37(50.00%)	82(54.7%)
Age			0.203
>60	34(45.90%)	83(55.30%)
<60	40(54.10%)	67(44.70%)
Hypertension			0.881
No	50(67.60%)	99(66.00%)
Yes	24(32.40%)	51(34.00%)
Diabetes			0.2
No	68(91.90%)	128(85.30%)
Yes	6(8.10%)	22(14.70%)
Blood Type			0.157
A	15(20.30%)	50(33.30%)
AB	8(10.80%)	12(8.00%)
B	22(29.70%)	45(30.00%)
O	29(39.20%)	43(28.70%)
CA199	9.77[3.78,16.48]	9.89 [4.32, 29.00]	0.276
CA125	11.40[7.40, 15.90]	11.55[8.10,15.35]	0.885
CA72-4	2.26 [1.16, 3.72]	1.75 [0.97, 3.72]	0.350
CEA	3.45 [1.52, 7.39]	3.46 [1.80, 7.45]	0.652
Lymphocyte	1.48 [1.20, 1.92]	1.38 [1.11, 1.78]	0.098
Thrombocyte	211.50[176.25,270.50]	215.50[177.50,275.50]	0.988
Hemoglobin	124.00[107.50,136.25]	128.00[107.00,140.00]	0.394
Neutrophil	3.62 [2.89, 4.54]	3.78 [2.85, 5.00]	0.217
Monocyte	0.39 [0.32, 0.51]	0.38 [0.29, 0.51]	0.283
ALT	13.85 [11.10, 18.00]	15.00 [11.00, 21.00]	0.324
AST	18.45 [15.43, 22.00]	18.65 [16.00, 22.00]	0.676
CRP	2.71 [1.07, 6.42]	2.57 [0.98, 9.41]	0.745
Albumin	40.20 [37.60, 42.88]	40.00 [37.60, 43.68]	0.812
Fibrinogen	3.12 [2.62, 3.60]	3.21 [2.82, 3.66]	0.309
Tumor Site			0.088
Left colon	31(41.90%)	42(28.00%)
Rectal	26(35.10%)	73(48.70%)
Right colon	17(23.00%)	35(23.30%)
Size	4.00 [3.00, 5.00]	4.00 [3.00, 5.00]	0.793
Pathological Type			0.04
high differentiation	1(1.40%)	0(0.00%)
low differentiation	6(8.10%)	8(5.30%)
Moderate differentiation	66(89.20%)	129(86.00%)
mucous gland	1(1.40%)	13(8.70%)
T			0.307
1	5(6.80%)	9(6.00%)
2	7(9.50%)	18(12.00%)
3	56(75.70%)	119(79.30%)
4	6(8.10%)	4(2.70%)
N			0.538
0	37(50.00%)	85(56.70%)
1	24(32.40%)	38(25.30%)
2	13(17.60%)	27(18.00%)
M			0.679
0	63(85.10%)	131(87.30%)
1	11(14.90%)	19(12.70%)
Post operative Adjuvant Therapy			0.458
No	23(31.10%)	55(36.70%)
Yes	51(68.90%)	95(63.30%)
PNI	48.20 [45.23, 52.86]	48.15 [43.99, 51.88]	0.613
LMR	3.85 [2.46, 5.21]	3.65 [2.58, 5.15]	0.713
PLR	145.22 [93.74, 206.87]	153.14[111.41,211.62]	0.197
BMI	23.01 [20.81, 24.94]	23.02 [20.70, 25.22]	0.651
NLR	2.24 [1.62, 3.49]	2.54 [2.01, 3.74]	0.029
SII	495.55[295.91,819.47]	567.07[381.88,946.02]	0.088
Status			0.569
Alive	42(56.80%)	78(52.00%)
Dead	32(43.20%)	72(48.00%)
Time	65.67 [50.67, 70.57]	64.28 [41.59, 71.07]	0.398

3.2. Identification of Prognostic Factors and Nomogram Development

LASSO was first undertaken to identify significant factors related to prognosis. Gender, age, body mass index, tumor diameter, stage, degree of differentiation, surgical procedure, chemotherapy, lymphocyte count, albumin, CA199, hemoglobin, platelets, aspartate aminotransferase, alanine aminotransferase, prealbumin, and C-reactive protein in the training group were included in the Lasso regression analysis. With lambda of 0.084, six pivotal factors were discerned to hold substantial significance in determining the prognosis of colorectal cancer patients, including NLR, CA199, CEA, T, M, N (Figs. 2A, B). Our univariate Cox regression analysis further unveiled the prognostic relevance of all these six factors in relation to patients' outcomes. (Figs. 2C). In the multivariate Cox regression analysis encompassing all variables, a multitude of independent prognostic factors surfaced, prominently featuring NLR (HR = 1.09; p = 0.0044), CA19-9 (HR = 1.85; p = 0.042), M (HR = 1.88; p = 0.031), and N (HR = 3.03; p = 0.0007) (Fig. 2D).

With the goal of screening preoperative inflammatory indicators and enhancing the predictive capacity beyond the standard TNM staging system, we selected three accessible preoperative factors NLR, CEA, and CA199 and integrated them into a nomogram along with the TNM stage. The primary aim was to establish a user-friendly model for predicting the OS of patients who underwent resection for CRC(Fig. 2E).

3.3.Accessing performance of the nomogram

To assess the quantitative predictive performance of the nomogram, we conducted time-dependent ROC analysis. For the training cohort, the AUCs were 0.748 at 3 years and 0.749 at 5 years (Fig. 3A). For the training cohort, the AUCs were 0.776 at 3 years and 0.731 at 5 years (Fig. 3B). The Harrell’s concordance index (C-index) of the nomogram was 0.716 (95%, 0.58–0.82) in the training cohort and 0.70 (95%, 0.50–0.84) in the validation cohort. Moreover, the calibration curve exhibited a commendable concordance between the prognostic predictive model and the ideal reference model. (Fig. 3C, D).

To evaluate the effect of this prognostic model, prognostic scores were calculated in both training and validation cohort using the above formula (Fig. 4A, B). Patients in both the training and validation cohorts were stratified into groups using the optimal cutoff value, and KM curves were constructed for each cohort. The findings revealed that the high-risk group exhibited significantly shorter 5-year OS compared to the low-risk group in both the training cohort (Fig. 4C) and the validation cohort (Fig. 4D).

In summary, the nomogram model proves to be a highly effective and efficient tool for predicting the OS prognosis of CRC patients.

3.4.Clinical Utility

ROC analysis showed that nomogram could also markedly predict 5-year OS of CRC (training group: AUC = 0.82; test group: AUC = 0.81) and was significantly superior to PNI scores (training group: AUC = 0.46, test group: AUC = 0.42; Fig. 5A) and TNM stage (training group: AUC = 0.64, test group: AUC = 0.72; Fig. 5B). Subsequently, a decision curve analysis was conducted for the nomogram, PNI scores, and TNM stage. The decision curve analysis in both the training and test groups demonstrated that when the threshold probability exceeded 0.25, utilizing the developed nomogram for prognostic prediction resulted in greater net benefits compared to PNI scores and the standard TNM staging system. This finding indicates the clinical usefulness of the nomogram (Fig. 5C, D).

Conceivably, the nomogram model had a great improvement and NLR, CA19-9 and CEA were effective supplements to traditional TNM stage.

Currently, CRC is one of the most common malignancies, with colorectal cancer being the third most common malignancy worldwide¹². The TNM staging system is commonly used to assess the prognosis of cancer patients in clinical practice, and although the TNM staging system is considered the gold standard for assessing the clinical outcome of patients, there are significant differences in prognosis even among patients with the same TNM stage¹³. The differences in genetic and biological characteristics of patients at the same stage of disease lead to limited predictive accuracy of the TNM staging system¹⁴. In view of this, a more accurate and convenient method for predicting outcome is needed to guide clinicians in the treatment of CRC patients. Cancer-associated inflammation is considered to be one of the key features in the development of cancer. In this study, we investigated preoperative inflammatory indicators and tumor markers in CRC patients and combined them with TNM staging to create a more accurate and convenient nomogram for predicting OS in CRC patients. Our nomogram showed strong calibration and discrimination in both training and validation groups, and the discrimination, calibration, and clinical validity of our nomogram were superior to the prognostic model of the TNM staging system.

Numerous studies have demonstrated the significant involvement of inflammation in tumor initiation and progression. Several recent studies have shown that systemic inflammatory response markers, including CRP ¹⁵, glasgow prognostic score ¹⁶, PLR¹⁷, LMR¹⁸, are associated with CRC and poor survival in many other cancers¹⁹. In particular, NLR, which is considered one of the markers of systemic inflammatory response, is closely related to the prognosis of patients with various types of cancer^{20, 21}.

Initially, neutrophils were thought to have a protective function against tumors, but previous studies have shown that neutrophils in the tumor microenvironment play a role in promoting growth, invasion, angiogenesis, and metastasis of various cancers^22–24. Circulating neutrophils mediate migration to tumor tissues via CXC motif chemokine ligand 2 - CXC motif chemokine receptor chemotaxis^{25, 26}, termed "tumor-associated neutrophils"²⁷. Lymphocytes play an important role in cancer-specific immune responses, among which tumor-infiltrating lymphocytes are one of the major cells mediating the local immune response to tumors and are phenotypically characterized by CD8⁺ and CD4^+ 28. Thus, tumor-associated lymphocytes receive different stimuli in different tumor microenvironments, leading to a shift in lymphocyte subtypes that can play both a tumor growth-promoting and tumor growth-suppressing role. The presence of a severe inflammatory response in the body is indicated by elevated NLR values.

Our study suggests that NLR is an independent prognostic factor for poor survival in patients with colorectal cancer. This finding is consistent with much of the previous literature. For example, a study by Shin et al.²⁹ showed that preoperative NLR predicted survival in patients with resectable stage T1-2 N0 colorectal cancer. Kubo et al.³⁰ also showed that preoperative and postoperative NLR was a good predictor of long-term survival in colorectal cancer patients. These studies and our study suggest that high NLR values are important for the prognosis of CRC patients. Therefore, we included NLR in our model.

Of course, the survival of cancer patients depends not only on the host systemic inflammatory response but also on the tumor self characteristics^{31, 32}.CEA and CA199 are tumor-related markers widely used for prognosis prediction in CRC patients^{19, 33}. Toiyama et al.³⁴ proposed that elevated CEA is a predictor of decreased OS after preoperative radiotherapy and chemotherapy in patients with rectal cancer. Thirunavukarasu et al.³⁵ demonstrated that serum CEA is an independent prognostic indicator in patients with colorectal cancer with the mean follow-up period was 27 months. Because of the prognostic role of CEA, the American Society of Medical Oncology recommends CEA levels as the gold follow-up standard after CRC treatment^{36, 37}. Therefore, we hypothesized that the inclusion of both tumor markers reflecting tumor characteristics and NLR might be a better way to predict patient survival by including CEA and CA199 in our model as well.

In the training cohort of this study, univariate and multifactorial analyses were performed to identify four prognostic variables including TNM stage, CEA, CA199, and NLR in patients with CRC.

We assigned scores to the patients based on the newly developed model and subsequently categorized them into low-risk and high-risk groups. It is evident that the high-risk group exhibits a notably shorter survival time compared to the low-risk group, providing valuable suggestions for clinicians to make precise and personalized decisions.

In our study, AUC and DCA were evaluated by comparing TNM with PNI using column line plots, and the results showed that nomograms had a higher C-index and a clear calibration curve in predicting OS than TNM or PNI. A total of 224 eligible CRC patients were enrolled in this study. Patients were divided into a training group (n = 150) and a validation group (n = 74). LASSO regression and Cox regression analyses were performed on the training group to identify meaningful prognostic factors and construct a nomogram to predict OS, which showed NLR, CA199 and CEA as valid risk factors. The C-index of the nomogram was 0.716 and 0.7 in the training and validation groups, respectively. AUC of the nomogram was 0.748 and 0.776 at 3 years and 0.749 and 0.773 at 5 years, respectively. When assessing the predictive value of the nomogram, the decision curve analysis indicated superior predictive accuracy compared to the standard TNM staging system.

Our study possesses several limitations. Firstly, this study is retrospective and cannot avoid bias caused by retrospective bias and follow-up compliance. Second, only internal validation was used in this study, which may lead to overfitting of the model. Further external validation is needed to more accurately assess the validity of the model.

In conclusion, this study screened various valuable blood biomarkers and clinicopathological characteristics of patients. And then we constructed and validated a nomogram utilizing these factors to evaluate the inflammatory status and prognosticate overall survival (OS) in CRC patients. The nomogram demonstrates potential enhancements in predictive power, sensitivity, and accuracy when compared to the conventional TNM staging system. As a more convenient and efficient tool, the nomogram holds promise in assisting clinical practitioners with informed decision-making.

Author Contributions: All authors contributed to the study’s conception and design. Study designs were performed by Bo Shi, Haoran Guo, Junjie Chen and Songbing He. Clinical data collection and analysis were performed by Bo Shi, Haoran Guo, Junjie Chen, Zhijian Peng, Qingliang Tai ,Suo Wang, Xinyu Shi and Guoliang Chen. The frst draft of the manuscript was written by Bo Shi and Haoran Guo. The manuscript was revised by Bo Shi, Haoran Guo, Junjie Chen and Songbing He.

Funding: This work was supported by the Gusu Medical Key Talent Project of Suzhou City of China GSWS2020005 (S. He), the New Pharmaceutics and Medical Apparatuses Project of Suzhou City of China SLJ2021007 (S. He), and the Clinical Medical Expert Team Project of Suzhou City of China CSYJTD202101 (S. Wang).

Institutional Review Board Statement: The study protocol was approved by the ethics committee of the First Affiliated Hospital of Soochow University. Written informed consent was obtained from all patients.

Data Availability Statement: The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest: The authors have no conflict of interest to declare.

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No competing interests reported.

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Development and Validation of a Nomogram Based on Inflammatory indicators and Tumor Markers for Prognosis Prediction of Colorectal Cancer

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Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1.Data collecting and patient characteristics

2.2.Treatment

2.3.Follow Up

2.4.Statistical Analysis

3. RESULTS

3.1.Clinical Characteristics

3.2. Identification of Prognostic Factors and Nomogram Development

3.3.Accessing performance of the nomogram

3.4.Clinical Utility

4. DISCUSSION

Declarations

References

Additional Declarations

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