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Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II and III Colon Cancer Who Underwent Xelox Chemotherapy after Laparoscopic Radical Resection

Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II... Hindawi Journal of Oncology Volume 2022, Article ID 7742035, 8 pages https://doi.org/10.1155/2022/7742035 Research Article Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II and III Colon Cancer Who Underwent Xelox Chemotherapy after Laparoscopic Radical Resection Qiang Sun, Kai Xu, Shifeng Teng, Wenqiang Wang, Wei Zhang, Xinxing Li , and Zhiqian Hu Department of General Surgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China Correspondence should be addressed to Xinxing Li; 41807020@xs.ustb.edu.cn and Zhiqian Hu; huzhiq163@163.com Received 9 August 2022; Revised 23 August 2022; Accepted 30 August 2022; Published 30 September 2022 Academic Editor: Recep Liman Copyright © 2022 Qiang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. To construct a nomogram-based prediction model for the clinical prognosis of patients with stage II and III colon cancer who underwent Xelox chemotherapy after laparoscopic radical resection based on large data sets. Methods. A total of 7,832 patients with colorectal cancer who received postoperative Xelox-based chemotherapy were screened from the Surveillance, Epidemiology, and End Results database (USA) as the training data set. In addition, 348 domestic patients were screened as the validation data set. Multivariate Cox regression analysis was performed to identify variables for inclusion in the nomogram-based prediction model. The predictive accuracy of the model was assessed using C-index and calibration curve. Results. Age, cell differentiation, nerve invasion, T and N stages of tumours, number of dissected lymph nodes, and carcinoembryonic antigen (CEA) level were found to influence the efficacy of postoperative chemotherapy. The nomogram-based prediction model was successfully constructed. The C-index of both the training set and validation set were higher than those of the 7th edition of TNM staging system published by the American Joint Commission on Cancer (C − index of training data set = 0:728, C − index of validation data set = 0:734). The prediction results of the model in the calibration curve showed a good fit with the actual situation. Conclusion. We successfully constructed a nomogram-based model to predict the clinical prognosis of patients with colorectal cancer receiving postoperative Xelox-based chemotherapy after laparoscopic radical resection, which showed good clinical application value for predicting the efficacy of postoperative Xelox-based chemotherapy in patients with colorectal cancer. 1. Introduction Comprehensive Cancer Network guidelines recommend Xelox-based chemotherapy (Oxaliplatin: 130 mg/m , intra- Colorectal cancer is a common malignant tumour in venically given, 2 h, d1; Capecitabine: 1800 mg/(m ·d), two oral cycles, d1-14, every 21 days) as the first-line regimen China and is associated with a high mortality rate. Cur- rently, surgical resection supplemented with chemotherapy after surgery for colorectal cancer. It is a widely used chemo- therapy regimen in clinical settings owing to the ease of is the main treatment modality for colorectal cancer. Most patients undergoing surgery have advanced stage disease administration and high efficacy [2]. Despite the advances and are at a high risk of postoperative recurrence and/or in surgery and chemotherapy regimens, a large proportion metastasis. Therefore, chemotherapy is typically used to of patients with colorectal cancer develop postoperative achieve disease control in clinical settings [1]. The National recurrence and metastasis, leading to poor prognosis. Thus, 2 Journal of Oncology identification of prognostically relevant clinical factors and defined as the time from diagnosis to the first tumour pro- their use to predict the treatment outcomes may help individ- gression, death, or the end of follow-up. ualise the treatment plan and improve the prognosis of 2.2.3. Statistical Analysis. Data were sorted and analysed patients [3]. Nomograms assign scores for various influenc- ing factors calculated by the statistical model; the obtained using SPSS Statistics 26 (IBM) and R language 3.6.2 (Bell total score of individual risk can help predict the risk of mor- Laboratories). The categorical variables were expressed as bidity. Therefore, in this study, a nomogram-based model percentage (%) and between-group differences assessed was constructed to predict the prognosis of patients with using the chi-squared test. For the analysis of prognostic factors, univariate analysis was performed with the log- colorectal cancer receiving postoperative Xelox-based che- motherapy by analysing the relevant data. rank χ test. Variables that showed a significant associa- tion with prognosis on univariate analysis (P <0:05) were included in multivariate Cox regression analysis to identify 2. Subjects and Methods the factors influencing OS and PFS. Finally, the 2.1. Subjects. Data pertaining to patients with colorectal can- nomogram-based prediction model was constructed using cer recorded in the Surveillance, Epidemiology, and End variables screened by the multivariate analysis. The accu- Results (SEER) database (USA) from 2011 to 2016 was used racy of the model was verified by Harrell’s C-statistic as the training data set. The inclusion criteria were as fol- and calibration curve. Two-tailed P <0:05 were considered lows: age ≥ 18 years; primary tumour, located at the colorec- indicative of statistical significance. Calibration, which tum (code: C18.0, C18.2–C18.7, C19.0, C20.0, C20.X01); refers to how closely the predicted probabilities by the pathological diagnosis: adenocarcinoma (code: M81400); nomogram agree with the observed survival probabilities, patients who underwent surgery (code: 20~80) and received was visually assessed by plotting actual survival probabilities postoperative Xelox-based chemotherapy. The exclusion against predicted survival probabilities for each group. The criteria were as follows: patients with incomplete clinically horizontal and vertical axes of the calibration plot showing relevant data, including age, gender, tumour stage and grade, the predicted versus the observed probability of the 5-year laboratory examination results, and follow-up data. Accord- overall survival and progression-free survival. The gray line ing to the inclusion and exclusion criteria, 7,832 patients represents the optimal line in case of complete concordance were finally screened as the training data set. Simulta- between predicted and observed progression-free survival. neously, a validation data set was established. From 2014 Decision curve analysis (DCA) was used to evaluate the clin- to 2016, a total of 348 patients who underwent colorectal ical benefits and utility of the nomogram compared with an resection and Xelox-based chemotherapy were identified American Joint Council on Cancer (AJCC) staging system from the electronic medical record system at our hospital. alone. Complete clinical information was available for all patients. Identical inclusion and exclusion criteria were adopted for 3. Results both the training and validation data sets. 3.1. Baseline Data of Patients. The 7,832 patients in the 2.2. Methods training data set included 3,822 males and 4,010 females (mean age: 54:7±8:9 years); the OS was 30.4 (10.3–36) 2.2.1. Data Collection. Detailed clinical data were retrieved months and PFS was 18.3 (6.2–31.8) months. The 348 for patients in both the training and validation data sets, patients included in the validation data set included 172 including gender, age, tumour location, tumour stage, cell males and 176 females (mean age: 53:2±8:5 years; the OS differentiation, depth of cancer invasion, lymph node metas- was 29.8 (10.1–36) months and PFS was 18.4 (5.9–32.7) tasis, and carcinoembryonic antigen (CEA) level. months). The baseline data in the two data sets are com- 2.2.2. Follow-Up. Complete follow-up data was available pared in Table 1. for all patients in the training data set. All patients in the validation set were followed up for 3 years; the 3.2. Factors Influencing the Efficacy of Postoperative patients were followed up once a month in the first year, Chemotherapy. On univariate analysis, age, cell differentia- every three months in the second year, and at six-month tion, nerve invasion, T and N stages of tumours, number intervals in the third year. Follow-up data of patients were of dissected lymph nodes, and CEA level were found to have obtained mainly through face-to-face interview in the doc- a significant influence on OS and PFS (P <0:05). Multivari- tors’ office or through telephonic contact. If the patient ate Cox regression analysis showed that the above variables could not be contacted, the relevant information was were independent predictors of OS and PFS (P <0:05) obtained from the patient’s family or community doctors. (Table 2). A follow-up record was established for every patient to document the detailed prognosis of patients after dis- 3.3. Construction and Validation of Nomogram-Based charge. According to the follow-up results, the overall sur- Prediction Model. Cox regression analysis identified seven vival (OS) and progression-free survival (PFS) were variables that influenced the prognosis of patients with colo- calculated and a detailed list was made, which were used rectal cancer receiving Xelox-based chemotherapy. The as the end points of the study. OS was defined as the time nomogram-based prediction model was constructed; on from diagnosis to death or the end of follow-up; PFS was the basis of the model, individualised risk scoring was Journal of Oncology 3 Table 1: Comparison of baseline data of patients. Training data set (n = 7832) Validation data set (n = 348) Variable χ P n (%) n (%) Gender 0.052 0.819 Male 3822 (48.8) 172 (49.4) Female 4010 (51.2) 176 (50.6) Age 0.167 0.682 ≤60 3305 (42.2) 143 (41.1) >60 4527 (57.8) 205 (58.9) Tumour location 0.146 0.703 Rectum 5325 (68.0) 240 (69.0) Colon 2507 (32.0) 108 (31.0) Cell differentiation 0.496 0.920 High 407 (5.2) 16 (4.6) Middle 5864 (74.9) 266 (76.4) Low 1253 (16.0) 53 (15.2) Undifferentiated 308 (3.9) 13 (3.7) Nerve invasion 0.001 0.976 Invasive 6657 (85.0) 296 (85.1) Noninvasive 1175 (15.0) 52 (14.9) T staging 0.407 0.939 T1 289 (3.7) 12 (3.4) T2 971 (12.4) 46 (13.2) T3 4825 (61.6) 216 (62.1) T4 1747 (22.3) 74 (21.3) Number of lymphadenectomy 0.250 0.883 None 110 (1.4) 6 (1.7) 1~3 71 (0.9) 3 (0.9) ≥4 7651 (97.7) 339 (97.4) N staging 5.009 0.082 N0 4104 (52.4) 163 (46.8) N1 2318 (29.6) 109 (31.3) N2 1410 (18.0) 76 (21.8) CEA level 0.618 0.432 Rise 3344 (42.7) 156 (44.8) Normal 4488 (57.3) 192 (55.2) performed, and the 3-year and 5-year survival rates (OS and end-point indicators OS and PFS, the results of the predic- PFS) were predicted (Figures 1 and 2). tion model showed a good fit with the actual situation; this For OS, the C-index of the training data set and vali- suggested high discriminative ability and accuracy of the predic- dation data set was 0.792 and 0.753, respectively. For tion model constructed in this study (Figure 3). The 5-year PFS, the C-index was 0.783 and 0.761, respectively. All DCA curves also revealed that the nomogram had better clinical these values were higher than those of the 7th edition of performance than the AJCC staging system among all study TNM staging system published by the American Joint subjects (Figure 4). Commission on Cancer (AJCC) (C − index of training data set = 0:728, C − index of validation data set = 0:734). The 4. Discussion results suggested a slightly more accurate prediction ability of the model compared with the traditional staging method. 4.1. Application of Xelox Regimen in Patients with Colorectal In addition, the calibration curve was drawn using the sur- Cancer after Surgery. Currently, colorectal cancer is one of vival rate predicted by the model as the horizontal ordinate the common malignant tumours of the digestive tract and and the actual survival as the longitudinal ordinate. For the is associated with high mortality and poor prognosis. 4 Journal of Oncology Table 2: Analysis of influencing factors of OS and PFS in colorectal cancer patients during training data set. OS PFS Variable Single-factor analysis Multifactor analysis Single-factor analysis Multifactor analysis OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P Gender — 0.137 ——— 0.231 —— Age ≤60 1.00 —— — 1.00 —— — >60 2.17 (1.97~2.64) 0.002 2.56 (1.98~2.79) <0.001 2.31 (1.86~2.53) 0.003 2.43 (1.91~2.68) <0.001 Tumour location — 0.261 ——— 0.164 —— Differentiation High 1.00 —— — 1.00 —— — Middle 1.34 (1.13~1.56) 0.005 1.11 (1.03~1.23) <0.001 1.23 (1.13~1.54) <0.001 1.14 (1.03~1.21) <0.001 Low 1.53 (1.38~1.87) <0.001 1.25 (1.12~1.37) 0.004 1.47 (1.31~1.81) <0.001 1.28 (1.18~1.33) 0.001 Undifferentiated 2.16 (1.76~2.68) <0.001 1.36 (1.26~1.48) <0.001 2.23 (1.85~2.45) 0.017 1.34 (1.21~1.48) 0.021 Nerve invasion Invasive 1.00 —— — 1.00 —— — Noninvasive 2.11 (1.82~2.41) 0.014 1.37 (1.21~1.51) <0.001 2.25 (1.91~2.42) 0.001 1.41 (1.24~1.67) <0.001 T staging T1 1.00 —— — 1.00 —— — T2 1.23 (1.14~1.42) <0.001 1.13 (1.04~1.25) <0.001 1.22 (1.11~1.55) <0.001 1.15 (1.04~1.26) <0.001 T3 1.42 (1.31~1.63) 0.002 1.31 (1.21~1.54) <0.001 1.67 (1.42~1.89) <0.001 1.41 (1.28~1.61) <0.001 T4 2.43 (2.01~2.83) <0.001 1.54 (1.36~1.78) 0.006 2.43 (1.93~2.75) 0.004 1.57 (1.35~1.81) 0.017 Number of lymphadenectomy None 1.23 (0.92~2.12) 0.164 1.42 (0.97~2.18) 0.761 1.23 (0.93~2.01) 0.182 1.51 (0.94~2.12) 0.687 1~3 1.51 (1.02~2.24) 0.031 1.83 (1.15~2.63) 0.003 1.41 (1.12~2.31) 0.042 1.91 (1.32~2.28) 0.003 ≥4 1.00 —— — 1.00 —— — N staging N0 1.00 —— — 1.00 —— — N1 1.91 (1.62~2.15) <0.001 1.61 (1.37~1.83) 0.006 1.82 (1.59~2.03) <0.001 1.72 (1.47~1.93) 0.002 N2 CEA level Normal 1.00 —— — 1.00 —— — Rise 2.57 (2.28~2.87) <0.001 1.98 (1.69~2.18) 0.021 2.47 (2.18~2.73) 0.029 2.01 (1.83~2.34) 0.017 Journal of Oncology 5 0 10 20 30 40 50 60 70 80 90 100 Score >60 Age ≤60 Middle Undifferentiated Differentiation High Low Invasive Nerve invasion Noninvasive None Number of lymphadenectomy ≥4 1~3 T2 T4 T staging T1 T3 N1 N staging N0 N2 Rise CEA level Normal Total score 0 50 100 150 200 250 300 350 400 450 500 3-year OS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 5-year OS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Figure 1: OS nomogram of 3-year and 5-year prognoses for colorectal cancer patients. Surgery is the only potential curative treatment recognised itate individualised treatment decision-making and help in clinical practice [1]. However, owing to the lack of improve the prognosis of patients. obvious symptoms in the early stage of the disease, patients with colorectal cancer are typically diagnosed in 4.2. Factors Influencing the Efficacy of Postoperative the middle and late stages; most of these patients are past Chemotherapy in Patients with Colorectal Cancer. In this the optimal time to achieve radical cure. Moreover, there study, age, cell differentiation, nerve invasion, T and N is a high risk of postsurgical recurrence and metastasis stages of tumours, number of dissected lymph nodes, and CEA level were found to influence OS and PFS. Our results [4]. Therefore, postoperative chemotherapy is typically administered to patients with colorectal cancer who undergo are consistent with those of previous studies, but not exactly surgery. However, patients with colorectal cancer often have the same. digestive dysfunction, physical weakness, and multiple In our study, age was the most important determinant comorbid conditions. All these factors contribute to chemo- of prognosis. The older the patient, the worse was the therapy intolerance; therefore, selection of the appropriate prognosis. Therefore, the benefit of surgical treatment for chemotherapy regimen is a key imperative for these patients. older patients should be carefully considered based on Xelox-based chemotherapy (also known as the CapeOX individualised analysis and assessment of the general con- regimen) consists of oxaliplatin injection administered in dition of the patient [7]. For elderly patients with poor tol- combination with oral Xeloda. Owing to its efficacy and ease erance, the risk of surgery may outweigh the benefits. of administration, it is used as the main postoperative adju- Additionally, dissection of 1–3 lymph nodes was found vant chemotherapy regimen for patients with colorectal can- to be more dangerous than no dissection; therefore, clini- cer in clinical settings [5]. However, approximately 50% of cians should consider increasing the number of dissected patients who received postoperative Xelox-based chemo- lymph nodes in patients scheduled to undergo lymph node therapy were found to develop recurrence and metastasis dissection [7, 8]. The prognostic value of cell differentia- at different time points after surgery; in addition, the prog- tion, nerve invasion, and tumour stage was in line with nosis of these patients is still not very ideal [6]. Therefore, that found in previous studies [9–11]. In this study, levels construction of statistical models based on appropriate clin- of CEA were included in the model as factors influencing ical indicators to predict the prognosis of patients can facil- the prognosis. The final results showed that all three 6 Journal of Oncology 0 10 20 30 40 50 60 70 80 90 100 Score >60 Age ≤60 Middle Undifferentiated Differentiation High Low Invasive Nerve invasion Noninvasive None Number of lymphadenectomy ≥4 1~3 T2 T4 T staging T1 T3 N1 N staging N0 N2 Rise CEA level Normal Total score 0 50 100 150 200 250 300 350 400 450 500 3-year PFS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 5-year PFS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Figure 2: PFS nomogram of 3-year and 5-year prognoses for colorectal cancer patients. factors were independent predictors of prognosis. As a by the AJCC; our model showed higher prediction ability proteoglycan compound of the digestive system, CEA is in both the training and validation data sets. Visual anal- a commonly used tumour marker; the correlation of the ysis of the calibration curve showed a good fit of the pre- CEA level with the prognosis of patients with colorectal diction of the training data set with the actual situation; however, the fit of the verification data set showed a cer- cancer is well documented [12–15]. tain deviation. This deviation may be attributable to bias caused by insufficient sample size, ethnic differences, and 4.3. Advantages of the Prediction Model Constructed in variable selection of the verification data set. The DCA This Study. Nomogram-based prediction models provide results also demonstrated that our nomogram provided visual representation of individual risk assessment. It greater clinical value than the AJCC grading system. employs multiple clinical indicators, scores the value of each indicator, and finally predicts the corresponding situ- ation of patients according to the total score of individ- 4.4. Limitations and Reflection. The prediction model con- uals. Use of nomograms to predict the incidence and structed in this study effectively predicted the efficacy of post- prognosis is a current research hot spot. It can intuitively operative chemotherapy in patients with colorectal cancer; and accurately display complex mathematical formulas in however, some limitations of the study should be considered the form of images and has high clinical application value while interpreting the results. Firstly, due to the limitations [16]. However, the prediction model for the efficacy of of SEER data, the grouping criteria for some indicators were postoperative chemotherapy in patients with colorectal different from those used in actual clinical practice. For exam- cancer has rarely been reported. ple, for the grouping of the number of dissected lymph nodes, In this study, we constructed a nomogram-based pre- acut-off value of 12 lymph nodes is used in clinical settings; diction model using variables identified on multivariate however, four lymph nodes were used as the cut-off value in analysis; the prediction model was found to accurately the database [17]. Secondly, there were inevitable limitations predict individual prognosis. The model showed high dis- during data acquisition owing to the retrospective nature of criminative ability and accuracy in the validation cohort. the study. Moreover, there may be a certain bias in the selec- In addition, we compared our nomogram-based model tion of variables. Further studies are required to confirm our with the 7th edition of the TNM staging system published results and to further improve the prediction model. Journal of Oncology 7 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted probability of 5-years OS Predicted probability of 5-years PFS (a) (b) 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted probability of 5-years PFS Predicted probability of 5-years OS (c) (d) Figure 3: Fitting curve (OS and PFS) between prediction model and actual survival of patients: (a, b) training data set; (c, d) validation data set. Decision curve model for OS and PFS. The model showed good clinical application value for predicting the efficacy of postoperative 1.0 Xelox-based chemotherapy in patients with colorectal can- cer. Both the training and validation data sets showed higher 0.8 predictive ability when compared with the 7th edition of the TNM staging system published by the AJCC. 0.6 Data Availability 0.4 The experimental data used to support the findings of this study are available from the corresponding authors upon 0.2 request. Conflicts of Interest 0.0 The authors declared that they have no conflicts of interest 0.0 0.2 0.4 0.6 0.8 1.0 regarding this work. Nomogram All AJCC stage None Authors’ Contributions Figure 4: Decision curve analysis for the nomogram and the AJCC Qiang Sun, Kai Xu, and Shifeng Teng are common first stage. authors. References 5. Conclusion [1] E. Dekker, P. J. Tanis, J. Vleugels, P. M. Kasi, and M. B. Wal- Based on the SEER database and our institutional medical lace, “Colorectal cancer,” The Lancet, vol. 394, no. 10207, record database, we successfully constructed a prediction pp. 1467–1480, 2019. 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Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II and III Colon Cancer Who Underwent Xelox Chemotherapy after Laparoscopic Radical Resection

Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II and III Colon Cancer Who Underwent Xelox Chemotherapy after Laparoscopic Radical Resection

Abstract

<i>Objective</i>. To construct a nomogram-based prediction model for the clinical prognosis of patients with stage II and III colon cancer who underwent Xelox chemotherapy after laparoscopic radical resection based on large data sets. <i>Methods</i>. A total of 7,832 patients with colorectal cancer who received postoperative Xelox-based chemotherapy were screened from the Surveillance, Epidemiology, and End Results database (USA) as the training data set. In addition, 348 domestic patients were screened as the validation data set. Multivariate Cox regression analysis was performed to identify variables for inclusion in the nomogram-based prediction model. The predictive accuracy of the model was assessed using C-index and calibration curve. <i>Results</i>. Age, cell differentiation, nerve invasion, T and N stages of tumours, number of dissected lymph nodes, and carcinoembryonic antigen (CEA) level were found to influence the efficacy of postoperative chemotherapy. The nomogram-based prediction model was successfully constructed. 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We successfully constructed a nomogram-based model to predict the clinical prognosis of patients with colorectal cancer receiving postoperative Xelox-based chemotherapy after laparoscopic radical resection, which showed good clinical application value for predicting the efficacy of postoperative Xelox-based chemotherapy in patients with colorectal cancer.

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Publisher
Hindawi Publishing Corporation
ISSN
1687-8450
eISSN
1687-8469
DOI
10.1155/2022/7742035
Publisher site
See Article on Publisher Site

Abstract

Hindawi Journal of Oncology Volume 2022, Article ID 7742035, 8 pages https://doi.org/10.1155/2022/7742035 Research Article Construction of Nomogram-Based Prediction Model for Clinical Prognosis of Patients with Stage II and III Colon Cancer Who Underwent Xelox Chemotherapy after Laparoscopic Radical Resection Qiang Sun, Kai Xu, Shifeng Teng, Wenqiang Wang, Wei Zhang, Xinxing Li , and Zhiqian Hu Department of General Surgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai 200092, China Correspondence should be addressed to Xinxing Li; 41807020@xs.ustb.edu.cn and Zhiqian Hu; huzhiq163@163.com Received 9 August 2022; Revised 23 August 2022; Accepted 30 August 2022; Published 30 September 2022 Academic Editor: Recep Liman Copyright © 2022 Qiang Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. To construct a nomogram-based prediction model for the clinical prognosis of patients with stage II and III colon cancer who underwent Xelox chemotherapy after laparoscopic radical resection based on large data sets. Methods. A total of 7,832 patients with colorectal cancer who received postoperative Xelox-based chemotherapy were screened from the Surveillance, Epidemiology, and End Results database (USA) as the training data set. In addition, 348 domestic patients were screened as the validation data set. Multivariate Cox regression analysis was performed to identify variables for inclusion in the nomogram-based prediction model. The predictive accuracy of the model was assessed using C-index and calibration curve. Results. Age, cell differentiation, nerve invasion, T and N stages of tumours, number of dissected lymph nodes, and carcinoembryonic antigen (CEA) level were found to influence the efficacy of postoperative chemotherapy. The nomogram-based prediction model was successfully constructed. The C-index of both the training set and validation set were higher than those of the 7th edition of TNM staging system published by the American Joint Commission on Cancer (C − index of training data set = 0:728, C − index of validation data set = 0:734). The prediction results of the model in the calibration curve showed a good fit with the actual situation. Conclusion. We successfully constructed a nomogram-based model to predict the clinical prognosis of patients with colorectal cancer receiving postoperative Xelox-based chemotherapy after laparoscopic radical resection, which showed good clinical application value for predicting the efficacy of postoperative Xelox-based chemotherapy in patients with colorectal cancer. 1. Introduction Comprehensive Cancer Network guidelines recommend Xelox-based chemotherapy (Oxaliplatin: 130 mg/m , intra- Colorectal cancer is a common malignant tumour in venically given, 2 h, d1; Capecitabine: 1800 mg/(m ·d), two oral cycles, d1-14, every 21 days) as the first-line regimen China and is associated with a high mortality rate. Cur- rently, surgical resection supplemented with chemotherapy after surgery for colorectal cancer. It is a widely used chemo- therapy regimen in clinical settings owing to the ease of is the main treatment modality for colorectal cancer. Most patients undergoing surgery have advanced stage disease administration and high efficacy [2]. Despite the advances and are at a high risk of postoperative recurrence and/or in surgery and chemotherapy regimens, a large proportion metastasis. Therefore, chemotherapy is typically used to of patients with colorectal cancer develop postoperative achieve disease control in clinical settings [1]. The National recurrence and metastasis, leading to poor prognosis. Thus, 2 Journal of Oncology identification of prognostically relevant clinical factors and defined as the time from diagnosis to the first tumour pro- their use to predict the treatment outcomes may help individ- gression, death, or the end of follow-up. ualise the treatment plan and improve the prognosis of 2.2.3. Statistical Analysis. Data were sorted and analysed patients [3]. Nomograms assign scores for various influenc- ing factors calculated by the statistical model; the obtained using SPSS Statistics 26 (IBM) and R language 3.6.2 (Bell total score of individual risk can help predict the risk of mor- Laboratories). The categorical variables were expressed as bidity. Therefore, in this study, a nomogram-based model percentage (%) and between-group differences assessed was constructed to predict the prognosis of patients with using the chi-squared test. For the analysis of prognostic factors, univariate analysis was performed with the log- colorectal cancer receiving postoperative Xelox-based che- motherapy by analysing the relevant data. rank χ test. Variables that showed a significant associa- tion with prognosis on univariate analysis (P <0:05) were included in multivariate Cox regression analysis to identify 2. Subjects and Methods the factors influencing OS and PFS. Finally, the 2.1. Subjects. Data pertaining to patients with colorectal can- nomogram-based prediction model was constructed using cer recorded in the Surveillance, Epidemiology, and End variables screened by the multivariate analysis. The accu- Results (SEER) database (USA) from 2011 to 2016 was used racy of the model was verified by Harrell’s C-statistic as the training data set. The inclusion criteria were as fol- and calibration curve. Two-tailed P <0:05 were considered lows: age ≥ 18 years; primary tumour, located at the colorec- indicative of statistical significance. Calibration, which tum (code: C18.0, C18.2–C18.7, C19.0, C20.0, C20.X01); refers to how closely the predicted probabilities by the pathological diagnosis: adenocarcinoma (code: M81400); nomogram agree with the observed survival probabilities, patients who underwent surgery (code: 20~80) and received was visually assessed by plotting actual survival probabilities postoperative Xelox-based chemotherapy. The exclusion against predicted survival probabilities for each group. The criteria were as follows: patients with incomplete clinically horizontal and vertical axes of the calibration plot showing relevant data, including age, gender, tumour stage and grade, the predicted versus the observed probability of the 5-year laboratory examination results, and follow-up data. Accord- overall survival and progression-free survival. The gray line ing to the inclusion and exclusion criteria, 7,832 patients represents the optimal line in case of complete concordance were finally screened as the training data set. Simulta- between predicted and observed progression-free survival. neously, a validation data set was established. From 2014 Decision curve analysis (DCA) was used to evaluate the clin- to 2016, a total of 348 patients who underwent colorectal ical benefits and utility of the nomogram compared with an resection and Xelox-based chemotherapy were identified American Joint Council on Cancer (AJCC) staging system from the electronic medical record system at our hospital. alone. Complete clinical information was available for all patients. Identical inclusion and exclusion criteria were adopted for 3. Results both the training and validation data sets. 3.1. Baseline Data of Patients. The 7,832 patients in the 2.2. Methods training data set included 3,822 males and 4,010 females (mean age: 54:7±8:9 years); the OS was 30.4 (10.3–36) 2.2.1. Data Collection. Detailed clinical data were retrieved months and PFS was 18.3 (6.2–31.8) months. The 348 for patients in both the training and validation data sets, patients included in the validation data set included 172 including gender, age, tumour location, tumour stage, cell males and 176 females (mean age: 53:2±8:5 years; the OS differentiation, depth of cancer invasion, lymph node metas- was 29.8 (10.1–36) months and PFS was 18.4 (5.9–32.7) tasis, and carcinoembryonic antigen (CEA) level. months). The baseline data in the two data sets are com- 2.2.2. Follow-Up. Complete follow-up data was available pared in Table 1. for all patients in the training data set. All patients in the validation set were followed up for 3 years; the 3.2. Factors Influencing the Efficacy of Postoperative patients were followed up once a month in the first year, Chemotherapy. On univariate analysis, age, cell differentia- every three months in the second year, and at six-month tion, nerve invasion, T and N stages of tumours, number intervals in the third year. Follow-up data of patients were of dissected lymph nodes, and CEA level were found to have obtained mainly through face-to-face interview in the doc- a significant influence on OS and PFS (P <0:05). Multivari- tors’ office or through telephonic contact. If the patient ate Cox regression analysis showed that the above variables could not be contacted, the relevant information was were independent predictors of OS and PFS (P <0:05) obtained from the patient’s family or community doctors. (Table 2). A follow-up record was established for every patient to document the detailed prognosis of patients after dis- 3.3. Construction and Validation of Nomogram-Based charge. According to the follow-up results, the overall sur- Prediction Model. Cox regression analysis identified seven vival (OS) and progression-free survival (PFS) were variables that influenced the prognosis of patients with colo- calculated and a detailed list was made, which were used rectal cancer receiving Xelox-based chemotherapy. The as the end points of the study. OS was defined as the time nomogram-based prediction model was constructed; on from diagnosis to death or the end of follow-up; PFS was the basis of the model, individualised risk scoring was Journal of Oncology 3 Table 1: Comparison of baseline data of patients. Training data set (n = 7832) Validation data set (n = 348) Variable χ P n (%) n (%) Gender 0.052 0.819 Male 3822 (48.8) 172 (49.4) Female 4010 (51.2) 176 (50.6) Age 0.167 0.682 ≤60 3305 (42.2) 143 (41.1) >60 4527 (57.8) 205 (58.9) Tumour location 0.146 0.703 Rectum 5325 (68.0) 240 (69.0) Colon 2507 (32.0) 108 (31.0) Cell differentiation 0.496 0.920 High 407 (5.2) 16 (4.6) Middle 5864 (74.9) 266 (76.4) Low 1253 (16.0) 53 (15.2) Undifferentiated 308 (3.9) 13 (3.7) Nerve invasion 0.001 0.976 Invasive 6657 (85.0) 296 (85.1) Noninvasive 1175 (15.0) 52 (14.9) T staging 0.407 0.939 T1 289 (3.7) 12 (3.4) T2 971 (12.4) 46 (13.2) T3 4825 (61.6) 216 (62.1) T4 1747 (22.3) 74 (21.3) Number of lymphadenectomy 0.250 0.883 None 110 (1.4) 6 (1.7) 1~3 71 (0.9) 3 (0.9) ≥4 7651 (97.7) 339 (97.4) N staging 5.009 0.082 N0 4104 (52.4) 163 (46.8) N1 2318 (29.6) 109 (31.3) N2 1410 (18.0) 76 (21.8) CEA level 0.618 0.432 Rise 3344 (42.7) 156 (44.8) Normal 4488 (57.3) 192 (55.2) performed, and the 3-year and 5-year survival rates (OS and end-point indicators OS and PFS, the results of the predic- PFS) were predicted (Figures 1 and 2). tion model showed a good fit with the actual situation; this For OS, the C-index of the training data set and vali- suggested high discriminative ability and accuracy of the predic- dation data set was 0.792 and 0.753, respectively. For tion model constructed in this study (Figure 3). The 5-year PFS, the C-index was 0.783 and 0.761, respectively. All DCA curves also revealed that the nomogram had better clinical these values were higher than those of the 7th edition of performance than the AJCC staging system among all study TNM staging system published by the American Joint subjects (Figure 4). Commission on Cancer (AJCC) (C − index of training data set = 0:728, C − index of validation data set = 0:734). The 4. Discussion results suggested a slightly more accurate prediction ability of the model compared with the traditional staging method. 4.1. Application of Xelox Regimen in Patients with Colorectal In addition, the calibration curve was drawn using the sur- Cancer after Surgery. Currently, colorectal cancer is one of vival rate predicted by the model as the horizontal ordinate the common malignant tumours of the digestive tract and and the actual survival as the longitudinal ordinate. For the is associated with high mortality and poor prognosis. 4 Journal of Oncology Table 2: Analysis of influencing factors of OS and PFS in colorectal cancer patients during training data set. OS PFS Variable Single-factor analysis Multifactor analysis Single-factor analysis Multifactor analysis OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P Gender — 0.137 ——— 0.231 —— Age ≤60 1.00 —— — 1.00 —— — >60 2.17 (1.97~2.64) 0.002 2.56 (1.98~2.79) <0.001 2.31 (1.86~2.53) 0.003 2.43 (1.91~2.68) <0.001 Tumour location — 0.261 ——— 0.164 —— Differentiation High 1.00 —— — 1.00 —— — Middle 1.34 (1.13~1.56) 0.005 1.11 (1.03~1.23) <0.001 1.23 (1.13~1.54) <0.001 1.14 (1.03~1.21) <0.001 Low 1.53 (1.38~1.87) <0.001 1.25 (1.12~1.37) 0.004 1.47 (1.31~1.81) <0.001 1.28 (1.18~1.33) 0.001 Undifferentiated 2.16 (1.76~2.68) <0.001 1.36 (1.26~1.48) <0.001 2.23 (1.85~2.45) 0.017 1.34 (1.21~1.48) 0.021 Nerve invasion Invasive 1.00 —— — 1.00 —— — Noninvasive 2.11 (1.82~2.41) 0.014 1.37 (1.21~1.51) <0.001 2.25 (1.91~2.42) 0.001 1.41 (1.24~1.67) <0.001 T staging T1 1.00 —— — 1.00 —— — T2 1.23 (1.14~1.42) <0.001 1.13 (1.04~1.25) <0.001 1.22 (1.11~1.55) <0.001 1.15 (1.04~1.26) <0.001 T3 1.42 (1.31~1.63) 0.002 1.31 (1.21~1.54) <0.001 1.67 (1.42~1.89) <0.001 1.41 (1.28~1.61) <0.001 T4 2.43 (2.01~2.83) <0.001 1.54 (1.36~1.78) 0.006 2.43 (1.93~2.75) 0.004 1.57 (1.35~1.81) 0.017 Number of lymphadenectomy None 1.23 (0.92~2.12) 0.164 1.42 (0.97~2.18) 0.761 1.23 (0.93~2.01) 0.182 1.51 (0.94~2.12) 0.687 1~3 1.51 (1.02~2.24) 0.031 1.83 (1.15~2.63) 0.003 1.41 (1.12~2.31) 0.042 1.91 (1.32~2.28) 0.003 ≥4 1.00 —— — 1.00 —— — N staging N0 1.00 —— — 1.00 —— — N1 1.91 (1.62~2.15) <0.001 1.61 (1.37~1.83) 0.006 1.82 (1.59~2.03) <0.001 1.72 (1.47~1.93) 0.002 N2 CEA level Normal 1.00 —— — 1.00 —— — Rise 2.57 (2.28~2.87) <0.001 1.98 (1.69~2.18) 0.021 2.47 (2.18~2.73) 0.029 2.01 (1.83~2.34) 0.017 Journal of Oncology 5 0 10 20 30 40 50 60 70 80 90 100 Score >60 Age ≤60 Middle Undifferentiated Differentiation High Low Invasive Nerve invasion Noninvasive None Number of lymphadenectomy ≥4 1~3 T2 T4 T staging T1 T3 N1 N staging N0 N2 Rise CEA level Normal Total score 0 50 100 150 200 250 300 350 400 450 500 3-year OS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 5-year OS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Figure 1: OS nomogram of 3-year and 5-year prognoses for colorectal cancer patients. Surgery is the only potential curative treatment recognised itate individualised treatment decision-making and help in clinical practice [1]. However, owing to the lack of improve the prognosis of patients. obvious symptoms in the early stage of the disease, patients with colorectal cancer are typically diagnosed in 4.2. Factors Influencing the Efficacy of Postoperative the middle and late stages; most of these patients are past Chemotherapy in Patients with Colorectal Cancer. In this the optimal time to achieve radical cure. Moreover, there study, age, cell differentiation, nerve invasion, T and N is a high risk of postsurgical recurrence and metastasis stages of tumours, number of dissected lymph nodes, and CEA level were found to influence OS and PFS. Our results [4]. Therefore, postoperative chemotherapy is typically administered to patients with colorectal cancer who undergo are consistent with those of previous studies, but not exactly surgery. However, patients with colorectal cancer often have the same. digestive dysfunction, physical weakness, and multiple In our study, age was the most important determinant comorbid conditions. All these factors contribute to chemo- of prognosis. The older the patient, the worse was the therapy intolerance; therefore, selection of the appropriate prognosis. Therefore, the benefit of surgical treatment for chemotherapy regimen is a key imperative for these patients. older patients should be carefully considered based on Xelox-based chemotherapy (also known as the CapeOX individualised analysis and assessment of the general con- regimen) consists of oxaliplatin injection administered in dition of the patient [7]. For elderly patients with poor tol- combination with oral Xeloda. Owing to its efficacy and ease erance, the risk of surgery may outweigh the benefits. of administration, it is used as the main postoperative adju- Additionally, dissection of 1–3 lymph nodes was found vant chemotherapy regimen for patients with colorectal can- to be more dangerous than no dissection; therefore, clini- cer in clinical settings [5]. However, approximately 50% of cians should consider increasing the number of dissected patients who received postoperative Xelox-based chemo- lymph nodes in patients scheduled to undergo lymph node therapy were found to develop recurrence and metastasis dissection [7, 8]. The prognostic value of cell differentia- at different time points after surgery; in addition, the prog- tion, nerve invasion, and tumour stage was in line with nosis of these patients is still not very ideal [6]. Therefore, that found in previous studies [9–11]. In this study, levels construction of statistical models based on appropriate clin- of CEA were included in the model as factors influencing ical indicators to predict the prognosis of patients can facil- the prognosis. The final results showed that all three 6 Journal of Oncology 0 10 20 30 40 50 60 70 80 90 100 Score >60 Age ≤60 Middle Undifferentiated Differentiation High Low Invasive Nerve invasion Noninvasive None Number of lymphadenectomy ≥4 1~3 T2 T4 T staging T1 T3 N1 N staging N0 N2 Rise CEA level Normal Total score 0 50 100 150 200 250 300 350 400 450 500 3-year PFS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 5-year PFS 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Figure 2: PFS nomogram of 3-year and 5-year prognoses for colorectal cancer patients. factors were independent predictors of prognosis. As a by the AJCC; our model showed higher prediction ability proteoglycan compound of the digestive system, CEA is in both the training and validation data sets. Visual anal- a commonly used tumour marker; the correlation of the ysis of the calibration curve showed a good fit of the pre- CEA level with the prognosis of patients with colorectal diction of the training data set with the actual situation; however, the fit of the verification data set showed a cer- cancer is well documented [12–15]. tain deviation. This deviation may be attributable to bias caused by insufficient sample size, ethnic differences, and 4.3. Advantages of the Prediction Model Constructed in variable selection of the verification data set. The DCA This Study. Nomogram-based prediction models provide results also demonstrated that our nomogram provided visual representation of individual risk assessment. It greater clinical value than the AJCC grading system. employs multiple clinical indicators, scores the value of each indicator, and finally predicts the corresponding situ- ation of patients according to the total score of individ- 4.4. Limitations and Reflection. The prediction model con- uals. Use of nomograms to predict the incidence and structed in this study effectively predicted the efficacy of post- prognosis is a current research hot spot. It can intuitively operative chemotherapy in patients with colorectal cancer; and accurately display complex mathematical formulas in however, some limitations of the study should be considered the form of images and has high clinical application value while interpreting the results. Firstly, due to the limitations [16]. However, the prediction model for the efficacy of of SEER data, the grouping criteria for some indicators were postoperative chemotherapy in patients with colorectal different from those used in actual clinical practice. For exam- cancer has rarely been reported. ple, for the grouping of the number of dissected lymph nodes, In this study, we constructed a nomogram-based pre- acut-off value of 12 lymph nodes is used in clinical settings; diction model using variables identified on multivariate however, four lymph nodes were used as the cut-off value in analysis; the prediction model was found to accurately the database [17]. Secondly, there were inevitable limitations predict individual prognosis. The model showed high dis- during data acquisition owing to the retrospective nature of criminative ability and accuracy in the validation cohort. the study. Moreover, there may be a certain bias in the selec- In addition, we compared our nomogram-based model tion of variables. Further studies are required to confirm our with the 7th edition of the TNM staging system published results and to further improve the prediction model. Journal of Oncology 7 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted probability of 5-years OS Predicted probability of 5-years PFS (a) (b) 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Predicted probability of 5-years PFS Predicted probability of 5-years OS (c) (d) Figure 3: Fitting curve (OS and PFS) between prediction model and actual survival of patients: (a, b) training data set; (c, d) validation data set. Decision curve model for OS and PFS. The model showed good clinical application value for predicting the efficacy of postoperative 1.0 Xelox-based chemotherapy in patients with colorectal can- cer. Both the training and validation data sets showed higher 0.8 predictive ability when compared with the 7th edition of the TNM staging system published by the AJCC. 0.6 Data Availability 0.4 The experimental data used to support the findings of this study are available from the corresponding authors upon 0.2 request. Conflicts of Interest 0.0 The authors declared that they have no conflicts of interest 0.0 0.2 0.4 0.6 0.8 1.0 regarding this work. Nomogram All AJCC stage None Authors’ Contributions Figure 4: Decision curve analysis for the nomogram and the AJCC Qiang Sun, Kai Xu, and Shifeng Teng are common first stage. authors. References 5. Conclusion [1] E. Dekker, P. J. Tanis, J. Vleugels, P. M. Kasi, and M. B. Wal- Based on the SEER database and our institutional medical lace, “Colorectal cancer,” The Lancet, vol. 394, no. 10207, record database, we successfully constructed a prediction pp. 1467–1480, 2019. 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Journal of OncologyHindawi Publishing Corporation

Published: Sep 30, 2022

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