Analysis of clinical and pathological characteristics of patients with gastric stromal tumors and construction and validation of a prognostic nomogram model

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Methods: The data of patients with gastric stromal tumors admitted to the 900th hospital of Joint Logistics Support Force from August 2009 to December 2020 were analyzed retrospectively. To analyze the clinical and pathological characteristics of patients with gastric stromal tumors undergoing surgery. The Kaplan Meier method was used to draw the survival curves to analyze the total survival time of patients, and log-rank test was used to analyze the comparison between groups. Logistic regression model and Cox regression model were used for univariate and multivariate analysis. A nomogram prediction model for predicting RFS in patients with gastric stromal tumors was constructed and verified by calibration curve and consistency curve. Results : Among 184 patients with gastric stromal tumor, abdominal pain was the most common clinical symptom, followed by gastrointestinal bleeding. In patients with gastric stromal tumors, the most common location of tumors is the stomach body, followed by the stomach floor and antrum; The diameter of tumor is 2.1 ~ 5 cm and the number of mitosis is ≤5/50 HPF. The 5-year recurrence rate of patients who regularly took imatinib for 3 years after operation was significantly lower than that of patients who did not take imatinib (14.16% vs. 43.80%, P<0.05), while the 5-year RFS was higher than that of patients who did not take imatinib (73.30% vs. 55.10%, P<0.05). Multivariate Logistic regression analysis showed that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P<0.05). Multivariate Cox regression analysis showed that the modified NIH criteria and oral imatinib treatment were independent influencing factors for postoperative RFS of gastric stromal tumors (P<0.05). Kaplan-meier method was used to calculate DFS and draw the survival curve of the correlation between the modified NIH criteria and oral imatinib treatment with the prognosis of gastric stromal tumor patients. The results showed that patients with higher modified NIH criteria and those without oral imatinib treatment had shorter DFS and worse prognosis. The factors (age, gender, tumor diameter, mitotic index, tumor rupture, tumor necrosis, modified NIH criteria, gastrointestinal bleeding, oral imatinib treatment, and surgical method) that will affect patients' RFS were selected to construct a nomogram for predicting RFS, and the consistency index (C-index) was 0.828 and 0.881, and the external verification C-index was 0.837. The calibration curve indicates that the nomogram has relatively accurate prediction ability. Conclusions : The first clinical symptoms of patients with gastric stromal tumor are abdominal pain and gastrointestinal bleeding. Patients with higher risk of modified NIH criteria, tumor necrosis and no oral imatinib treatment are prone to relapse. The higher the risk of modified NIH criteria and the shorter the RFS of patients who have not received oral imatinib treatment, the worse the prognosis of patients. For patients with medium and high risk gastric stromal tumor, it is recommended to carry out imatinib adjuvant therapy for 3 years or more after operation, which can effectively improve the prognosis of patients. In addition, the nomogram prediction model based on the factors affecting patients' RFS can effectively predict the 3-and 5-year recurrence-free survival rate, which is conducive to individualized diagnosis and treatment of patients' prognosis in clinic. Biological sciences/Cancer Health sciences/Diseases Health sciences/Gastroenterology Health sciences/Medical research Health sciences/Oncology Health sciences/Risk factors Health sciences/Signs and symptoms gastric stromal tumor recurrence risk factors recurrence free survival nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Gastrointestinal stromal tumor (GIST), as a common mesenchymal tumor in the gastrointestinal tract, originated from Cajal cells in the gastrointestinal tract, accounting for 1% of gastrointestinal tumors, with an annual incidence of about 10–15/1 million cases, especially in people aged 50–70 1 . Most GIST have gene activation mutations of KIT or platelet-derived growth factor receptor-α (PDGFR-α) 2 . GIST can occur in any part of the gastrointestinal tract, among which the stomach is the most common (60%), followed by the small intestine (30%), while the colon and other parts (such as esophagus and omentum) only account for about 10% 3 . The clinical manifestations of gastric stromal tumors are not specific, and the symptoms are hidden. Most of them are in the middle and late stage when diagnosed, which leads to poor prognosis, and the postoperative recurrence rate and metastasis rate of patients are also at a high level (about 50% of patients have tumor recurrence after surgery). It is reported that the median survival time of patients with postoperative recurrence of GIST is less than 2 years 4 . Therefore, it is very important to accurately evaluate the condition of GIST patients to improve their prognosis. In 2002, the National Institutes of Health (NIH) made a consensus on recurrence risk assessment, and then revised and improved in 2008 as the modified NIH criteria 5 to assess the risk of GIST. However, this classification method also has some defects, such as less indicators and insufficient accuracy 6 . Therefore, it is very important for medical staff to explore some effective and simple means to predict the recurrence risk of GIST patients and make appropriate treatment plans accurately according to their corresponding risks, so as to improve the prognosis of patients. In recent years, nomograms have been continuously used to evaluate the prognosis of patients with digestive tract tumors 7 , 8 . Based on this, this study retrospectively analyzed the clinical manifestations and pathological features of patients with gastric stromal tumors, and analyzed the risk factors of recurrence and recurrence free survival of patients with gastric stromal tumors, and constructed and verified the nomogram model of RFS of patients with gastric stromal tumors, aiming at effectively guiding the clinical diagnosis and treatment of gastric stromal tumors. 2. Materials and Methods 2.1 Data collection The data of patients with gastric stromal tumors admitted to the 900th hospital of Joint Logistics Support Force from August 2009 to December 2020 were analyzed retrospectively. Treatment methods include open surgery and Laparoscopic surgery; The tumor sites include gastric fundus, gastric body and gastric antrum. Inclusion criteria: (1) The patient was pathologically diagnosed as gastric stromal tumor; (2) Clinicopathological data are complete (including general medical history data, pathological and immunohistochemical test results and other related indicators). Exclusion criteria: (1) Patients with tumors in other parts; (2) Patients receiving radiotherapy, chemotherapy and biotherapy before operation; (3) patients who refuse to undergo surgery; (4) The medical records are missing or lost. This study was conducted according to the ethical principles of medical research involving human subjects in Helsinki Declaration. Patients and their families signed informed consent before operation. 2.2 Data collection Including age, gender, tumor location, tumor diameter, the first clinical symptom (abdominal pain, gastrointestinal bleeding, abdominal mass, other symptoms, asymptomatic), Ki67, CD34, CD117, DOG-1, mitotic index (the number of mitoses per 50 HPF), tumor rupture, modified NIH criteria 9 , tumor necrosis, oral imatinib, surgical method and recurrence. 2.3 Follow-up All postoperative patients were followed up regularly every 3–6 months, using outpatient service, SMS, telephone, email, network communication tools and other means to follow up. Recurrence free survival (RFS) is defined as the time from the first day after operation to the recurrence of gastric stromal tumor, and the dead patients without recurrence are calculated as the time of death, and the surviving patients without recurrence are calculated as the last follow-up date. The deadline for follow-up is January 10, 2024 or the patient relapses or dies. 2.4 Statistical analysis SPSS 26.0, GraphPad Prism 8.0 and R (version 4.4.0) software were used for statistical analysis, and the count data was expressed by in the form of percentage n(%). Kaplan-meier method was used to calculate RFS and draw survival curve. log-rank test was used to test the differences between groups. Logistic regression analysis was used to analyze the related influencing factors of gastric stromal tumor recurrence. Cox regression analysis was used to analyze the influencing factors of RFS in patients with gastric stromal tumors, and hazard ratio (HR) and corresponding 95% CI were calculated. According to the above independent influencing factors, the corresponding nomogram prediction model 10 is constructed and drawn by using the "rms" package in R software (version 4.4.0), and verified by calibration curve and consistency index (C-index) to determine the prediction accuracy and discrimination ability of the model. The greater the C-index, the more accurate the prognosis prediction, with α = 0.05 as the test level 11 , 12 . 3. Results 3.1 General data According to the inclusion criteria, a total of 201 patients who underwent surgical treatment of gastric stromal tumors were included. According to the exclusion criteria, 184 patients were finally included in this study, including 90 males (48.91%) and 94 females (51.09%), with an average age of 58.38 10.80 years (range: 24.0–83.0 years). All 184 patients were discharged from the hospital without death. All patients were followed up for a median of 68 months, of which 52 cases (28.26%) relapsed and 132 cases (71.74%) did not relapse. The recurrence-free survival rates at 3 and 5 years after operation were 91.20% and 76.10%, respectively, as shown in Fig. 1 . 3.2 Analysis of clinical and pathological characteristics of patients Among the patients with gastric stromal tumors, abdominal pain was the most common clinical symptom, accounting for 96 cases (52.17%), followed by gastrointestinal bleeding in 42 cases (22.83%), abdominal mass in 24 cases (13.04%), other symptoms (such as fatigue, dizziness, etc.) in 14 cases (7.61%), and asymptomatic cases in 8 cases (4.35%), as shown in Table 1 . The results of pathological features of gastric stromal tumors showed the tumor locations of patients with gastric stromal tumors include: 85 cases of gastric body (46.20%), 55 cases of gastric fundus (29.89%) and 44 cases of gastric antrum (23.91%); The most common tumor diameter is between 2.1 ~ 5 cm, and the most common number of mitotic index is ≤ 5/50 HPF. The positive cases of CD117 were 173 (94.02%), CD34 was 165 (89.67%) and DOG-1 was 158 (85.87%). There were 146 cases (79.35%) with cell proliferation index Ki67 ≤ 5%, and 38 cases (20.65%) with Ki67 > 5%. Tumor necrosis occurred in 23 cases of gastric stromal tumors, accounting for 12.50%, and 23 cases with bleeding, accounting for 22.28%, as shown in Table 2 . Table 1 Clinical characteristics of gastric stromal tumor (n). NIH risk classification Abdominalgia Gastrointestinal bleeding Abdominal mass Other symptoms Asymptomatic Very low risk 35 6 10 4 4 Low risk 18 8 5 2 1 Medium risk 26 13 6 2 1 High risk 17 15 3 6 2 Total 96 42 24 14 8 Table 2 Pathological features of gastric stromal tumors (n). NIH risk classification Tumor location Tumor diameter (cm) Mitotic index (/50 HPF) fundus of stomach gastric body gastric antrum ≤ 2 2.1ཞ5 5.1ཞ10 > 10 ≤ 5 6ཞ10 > 10 Very low risk 18 27 14 42 15 2 0 57 1 1 Low risk 10 17 7 6 18 10 0 33 1 0 Medium risk 14 21 13 3 20 22 3 28 15 5 High risk 13 20 10 1 23 5 14 13 21 9 Total 55 85 44 52 76 39 17 131 38 15 Table 2 (continued). NIH risk classification Immunohistochemical Cell proliferation index Ki67 Tumor necrosis Gastrointestinal bleeding CD117 (+) CD34 (+) DOG-1 (+) ≤ 5% > 5% No Yes No Yes Very low risk 56 54 51 55 4 58 1 54 5 Low risk 31 32 29 31 3 32 2 26 8 Medium risk 45 42 38 40 8 40 8 35 13 High risk 41 37 40 20 23 31 12 28 15 Total 173 165 158 146 38 161 23 143 41 3.3 Univariate and multivariate Logistic regression analysis of postoperative recurrence in patients with gastric stromal tumor The results of univariate Logistic regression analysis indicated that gender, tumor diameter, modified NIH criteria, tumor necrosis and oral imatinib were the influencing factors for postoperative recurrence of gastric stromal tumors (P < 0.05). The univariate meaningful indexes were analyzed by multivariate Logistic regression model. The results showed that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P < 0.05), as shown in Table 3 . Table 3 Univariate and multivariate Logistic regression analysis on the recurrence of gastric stromal tumors. Characteristics Univariate analysis Multivariate analysis OR (95% CI ) P value OR (95% CI ) P value Age (year) 0.057 ≤ 60 1 > 60 1.883 (0.981–3.615) Gender 0.016 0.718 Male 1 1 Female 2.267 (1.164–4.413) 1.172 (0.495–2.777) Tumor diameter (cm) 0.030 0.637 ≤ 5 1 1 > 5 2.114 (1.077–4.149) 1.396 (0.349–5.587) Ki67 0.483 ≤ 5% 1 > 5% 0.743 (0.325–1.702) CD34 0.383 Positive 1 Feminine 0.643 (0.238–1.735) CD117 0.540 Positive 1 Feminine 0.672 (0.188–2.399) DOG-1 0.870 Positive 1 Feminine 1.081 (0.425–2.748) Mitotic index 0.276 ≤ 5 1 > 5 1.467 (0.736–2.925) Tumor rupture 0.999 No 1 Yes 4442555878 (-) Modified NIH criteria 0.018 0.010 Very low risk 1 1 Low risk 2.656 (0.931–7.581) 2.928 (0.986–8.694) Medium risk 3.187 (1.224–8.299) 14.272 (2.819–72.246) High risk 4.590 (1.757–11.994) 6.965 (1.394–34.792) Gastrointestinal bleeding 0.182 No 1 Yes 1.653 (0.791–3.456) Tumor necrosis < 0.001 0.012 No 1 1 Yes 13.447 (4.656–38.838) 5.987 (1.478–24.248) Oral imatinib < 0.001 < 0.001 No 1 1 Yes 0.088 (0.026–0.298) 0.025 (0.006–0.109) Surgical method 0.400 Open surgery 1 Laparoscopic surgery 0.737 (0.362–1.501) 3.4 Cox regression analysis of influencing RFS in patients with gastric stromal tumor Univariate analysis showed that age, gender, tumor diameter, mitotic index, tumor rupture, modified NIH criteria, tumor necrosis and oral imatinib treatment were the influencing factors of RFS in patients with gastric stromal tumors (P < 0.05). The univariate significant indexes were included in the multivariate Cox regression model for analysis, and the results showed that the modified NIH criteria and oral imatinib treatment were independent influencing factors of RFS in patients with gastric stromal tumors (P < 0.05), as shown in Table 4 . In addition, we will draw the corresponding survival curves of the independent risk factors affecting RFS in patients with gastric stromal tumors by Kaplan-Meier method, and display them visually, as shown in Fig. 2 respectively. Table 4 Univariate and multivariate Cox regression analysis of influencing RFS in patients with gastric stromal tumors. Characteristics Univariate analysis Multivariate analysis OR (95% CI ) P value OR (95% CI ) P value Age (year) 0.030 0.289 ≤ 60 1 1 > 60 1.855 (1.063–3.235) 1.377 (0.762–2.490) Gender 0.011 0.650 Male 1 1 Female 2.093 (1.180–3.712) 1.157 (0.616–2.173) Tumor diameter (cm) 0.001 0.238 ≤ 5 1 1 > 5 2.565 (1.457–4.516) 1.665 (0.714–3.884) Ki67 0.929 ≤ 5% 1 > 5% 0.968 (0.471–1.991) CD34 0.684 Positive 1 Feminine 0.847 (0.381–1.884) CD117 0.480 Positive 1 Feminine 0.692 (0.248–1.925) DOG-1 0.913 Positive 1 Feminine 1.045 (0.470–2.328) Mitotic index 0.015 0.572 ≤ 5 1 1 > 5 2.065 (1.150–3.710) 1.274 (0.549–2.956) Tumor rupture 0.003 0.625 No 1 1 Yes 4.730 (1.696–13.194) 1.393 (0.369–5.256) Modified NIH criteria < 0.001 < 0.001 Very low risk 1 1 Low risk 2.338 (0.919–5.945) 2.783 (1.079–7.177) Medium risk 4.072 (1.680–9.872) 11.751 (3.631–38.028) High risk 6.493 (2.720–15.500) 10.799 (2.543–45.850) Gastrointestinal bleeding 0.072 No 1 Yes 1.741 (0.952–3.185) Tumor necrosis < 0.001 0.333 No 1 1 Yes 5.466 (3.061–9.762) 1.426 (0.696–2.923) Oral imatinib 0.001 < 0.001 No 1 1 Yes 0.129 (0.040–0.416) 0.038 (0.011–0.134) Surgical method 0.292 Open surgery 1 Laparoscopic surgery 0.719 (0.389–1.328) 3.5 Relationship between oral imatinib treatment and prognosis of patients with gastric stromal tumor Among the 91 patients with high-risk gastric stromal tumors, 50 patients (54.95%) were treated with imatinib orally (the dosage was 400 mg/d), among which 19 patients took the medicine for one year, among which 5 patients were treated with sunitinib because of postoperative recurrence (the dosage was 37.5 mg/d), and 3 patients stopped taking the medicine for personal reasons. The other 31 patients all took drugs for 3 years. For patients who took drugs regularly for 3 years after operation, the 5-year recurrence rate was significantly lower than that of patients who did not take drugs (14.16% vs. 43.80%, P < 0.05), while the 5-year RFS was higher than that of patients who did not take drugs (73.30% vs. 55.10%, P < 0.05). 3.6 Prediction effect of nomogram prediction model on RFS in gastric stromal tumors We screened out the independent influencing factors of RFS by multivariate Cox regression analysis (P < 0.05), and combined with the factors that clinically affect the prognosis of patients (RFS), all of them were included in the nomogram prediction model. The results indicate that the C-index of the nomogram prediction model of RFS is 0.828(0.799–0.857), as shown in Fig. 3 . In addition, we use calibration curve to test the prediction ability of the model, and the results show that the calibration curve of RFS is very close to the ideal curve, which shows that the predicted RFS of nomogram model is consistent with the actual observation results in the research queue, suggesting that the nomogram has relatively accurate prediction ability, as shown in Fig. 4 . 3.7 Verification of nomogram prediction model In the internal verification queue, the C index is 0.881(0.868–0.894) by bootstrapping validation analysis, which shows that the model has good discrimination. The calibration curve also performed well in the verification set, as shown in Fig. 5 . Similarly, we conducted external verification by collecting the relevant data of 138 patients with gastric stromal tumors who visited the Affiliated Hospital of Qinghai University from August 2009 to December 2020. The verification C index was 0.837(0.797–0.878), and the calibration curve also performed well in the verification set, as shown in Fig. 6 . 4. Discussion Gastric stromal tumor, as a common mesenchymal tumor in digestive tract, has certain malignant potential, and it is common in the elderly (average age is 50 ~ 50 years old), with no obvious gender difference, and the tumor prone sites include gastric body, gastric fundus and gastric antrum respectively 13 , 14 . In this study, the average age of patients with gastric stromal tumors was 58.38 years old, and women accounted for a little more than men (51.09% vs. 48.91%), with no statistical significance (P > 0.05). Most tumors were located in the stomach body (46.20%), which was consistent with the literature reports. The clinical manifestations of gastric stromal tumors are not specific, and the symptoms are hidden. About 50% of patients with gastric stromal tumors take abdominal pain as the first symptom, followed by gastrointestinal bleeding (about 35%) 3 . In this study, 96 patients (52.17%) presented with abdominal pain as the initial clinical symptom, followed by gastrointestinal bleeding in 42 cases (22.83%), which was consistent with the literature reports. Among them, gastrointestinal bleeding is more serious than patients with abdominal pain, abdominal mass and other initial symptoms, and it is reported that about 10%-15% patients may have hemorrhagic shock 15 . However, there is still controversy over whether gastrointestinal bleeding indicates tumor rupture and affects patient prognosis. In this study, there is no significant correlation between gastrointestinal bleeding and tumor recurrence and patients' RFS. In addition, in this study, the most common tumor diameter was 2.1 ~ 5 cm, and the number of mitotic index was mostly ≤ 5/50 HPF, which is consistent with relevant research results 16 . In addition, it has been reported that DOG-1, CD117, and CD34 are highly expressed in gastric stromal tumors, and they are obviously related to the prognosis of patients. The comprehensive detection of gastrointestinal stromal tumor specimens using immunohistochemical markers such as DOG-1, CD117, and CD34 can provide more accurate disease and prognosis judgment basis for clinical doctors, which is conducive to timely development of correct treatment plans to ensure patient efficacy and prognosis 17 . In this study, the positive rates of CD117, CD34 and DOG-1 were 94.02%, 89.67% and 85.87% respectively, which were at a high level, which was consistent with the literature reports. Ki67, as an important index, can accurately reflect the proliferation activity of gastric stromal tumor cells and is positively correlated with risk classification and mitosis index. It is also related to the development, metastasis and prognosis of various tumors 18 . In this study, 146 cases (79.35%) had a cell proliferation index of Ki67 ≤ 5%, while 38 cases (20.65%) had a Ki67 > 5%, which was at a relatively low level. In addition, in this study, there were 23 cases of gastric stromal tumor with tumor necrosis, accounting for 12.50%, and 23 cases with bleeding, accounting for 22.28%. According to the analysis, gastric stromal tumor belongs to a well-defined type of tumor, and some tumors may be accompanied by bleeding and necrosis. It has been reported in the literature that as long as tumor necrosis is combined, the prognosis of GIST is poor regardless of tumor location, tumor size or patient race, and the risk of disease progression of GIST patients with tumor necrosis is increased by about 7 times 19 , 20 . Radical resection (negative margin) is the first choice for treatment, but there is still a high recurrence rate, which often leads to poor prognosis of patients 9 , 21 . Therefore, accurate risk stratification of gastric stromal tumors is very important for the choice of treatment options and prognosis evaluation of patients. In the past, the risk grading evaluation system has been adjusted many times. At present, the most commonly used standard is the National Institutes of Health (NIH) risk grading standard 9 . This classification method mainly refers to GIST tumor diameter, primary tumor site, mitotic index and whether the tumor is ruptured. It is divided into four levels based on postoperative recurrence risk: extremely low risk, low risk, medium risk, and high risk, However, this classification method also has certain shortcomings, such as insufficient inclusion of indicators and insufficient accuracy 6 . Joensuu et al 4 . pointed out that although the Modified NIH criteria is the best standard to identify a single high-risk population and formulate corresponding adjuvant treatment, the index is too single, even if the risk classification of gastric stromal tumors is the same, the prognosis of patients will be different. Therefore, it is very important for medical staff to explore simple, effective and accurate parameters to predict the postoperative recurrence risk of patients with gastric stromal tumors and to carry out individualized treatment to improve the prognosis of patients. Tian Xiaowen et al. 22 collected the clinical data of 90 patients with gastric stromal tumor and established nomogram to predict the probability of postoperative recurrence, and obtained satisfactory expected results. However, it contains too few indicators and collects fewer samples. Based on this, this study retrospectively analyzed the risk factors of recurrence and RFS of patients with gastric stromal tumors, and constructed and verified the nomogram model of RFS of patients with gastric stromal tumors, aiming at serving the clinic. In this study, we found that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P < 0.05). The results of univariate and multivariate Cox regression analysis showed that the modified NIH criteria and oral imatinib treatment were the independent influencing factors of RFS in patients with gastric stromal tumors (P < 0.05). Analysis of the reasons mainly includes the following points: on the one hand, it is the most commonly used risk classification standard for the modified NIH criteria, which is divided into four levels according to the risk of postoperative recurrence: extremely low risk, low risk, medium risk, and high risk 9 , 23 , 24 ; In addition, the 2013 edition of China Consensus on Diagnosis and Treatment of Gastrointestinal Stromal Tumors 6 also takes NIH risk criteria as the main index to evaluate the risk of Gastrointestinal Stromal Tumors. On the other hand, oral imatinib is a first-line targeted drug approved by Food and Drug Administration (FDA) for the treatment of advanced gastric stromal tumors. By analyzing the clinical data of 397 patients with high-risk gastric stromal tumors, Joensuu et al. 4 found that the patients treated with oral imatinib had better overall survival and relapse-free survival than those treated with oral imatinib. In this study, by comparing the 5-year prognosis of patients who regularly took drugs for 3 years after operation with those who did not take drugs, it was found that the 5-year recurrence rate was significantly lower than that of patients who did not take drugs (14.16% vs. 43.80%, P < 0.05), while the 5-year RFS was higher than that of patients who did not take drugs (73.30% vs. 55.10%, P < 0.05), which further verified the accuracy of the results. In addition, tumor necrosis is an independent risk factor for postoperative recurrence of gastric stromal tumors, which is consistent with the research conclusion of Yi et al. 25 . GIST patients with tumor necrosis have higher risk of disease progression and recurrence, and worse prognosis (the risk of disease progression of GIST patients with tumor necrosis is increased by about 7 times) 19 . Therefore, clinicians should pay more attention to the treatment of GIST patients with tumor necrosis found by preoperative CT or pathological examination 26 . Finally, the independent influencing factors of RFS screened by multivariate Cox regression analysis and the factors that have an impact on patients' prognosis (RFS) are all included in the nomogram prediction model, and the risk probability of RFS in patients with gastric stromal tumors is predicted and evaluated by counting the scores of each variable in the model. The accuracy of nomogram is evaluated by C-index and calibration chart, which proves that nomogram has high predictive value. The nomogram can transform a complex regression equation into a simple visual graph, and medical staff can predict the prognosis of patients with gastric stromal tumors more quickly and conveniently according to the nomogram, which is beneficial to the prevention work in advance and has high clinical application value. This study has some limitations. On the one hand, it is a retrospective study of small samples, which has certain and inevitable bias; On the other hand, some high-risk GIST patients in this study failed to receive standard adjuvant treatment after operation, and different postoperative treatment schemes can affect the prognosis of GIST patients to some extent. Therefore, we hope to have a more rigorous multi-center and large-sample prospective study to verify our research results. To sum up, the first clinical symptoms of patients with gastric stromal tumors are abdominal pain and gastrointestinal bleeding. Patients with higher risk of modified NIH criteria, tumor necrosis and no oral imatinib treatment are prone to relapse. The higher the risk of modified NIH criteria and the shorter the RFS of patients who have not received oral imatinib treatment, the worse the prognosis of patients. For patients with medium and high risk gastric stromal tumor, it is recommended to carry out imatinib adjuvant therapy for 3 years or more after operation, which can effectively improve the prognosis of patients. In addition, the nomogram prediction model based on the factors affecting patients' RFS has high clinical application value, which can effectively predict the 3-and 5-year recurrence-free survival rate and is conducive to individualized diagnosis and treatment of patients' prognosis. Declarations Acknowledgements The authors are grateful for the invaluable support and useful discussions with other members of the general surgery department. Ethical approval This study was conducted according to the ethical principles of medical research involving human subjects in the Declaration of Helsinki and have been approved by the biomedical ethics committee of the 900th hospital of Joint Logistics Support Force (Number: 2024-126). Patients and their families signed informed consent before operation. Declaration of Competing Interest The authors declare no conflicts of interest. Reserch involving human participants and/or animals This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication Funding This work was supported by the General Project of Fujian Natural Science Foundation (2023J01206), Key project of the 900th Hospital (2023zs01); Top-notch project of the PLA Medical Science and Technology Youth Training Program (21QNPY138); Special research project of training injury prevention and treatment in the 900th Hospital (2023XL02). Informed consent Informed consent has been received from the subject. References Akahoshi K, Oya M, Koga T, Shiratsuchi Y. Current clinical management of gastrointestinal stromal tumor. World J Gastroenterol. 2018 Jul 14;24(26):2806-2817. doi: 10.3748/wjg.v24.i26.2806. Joensuu H, Hohenberger P, Corless CL. 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China Expert Consensus on Standardized Surgical Treatment of Gastrointestinal Stromal Tumors (2018 Edition) [J]. China Journal of Practical Surgery, 2018,38 (9): 965-973. DOI: 10.19538/j.cjps.issn1005-2208.2018.09.01 Yu C, Zhang Y. Establishment of prognostic nomogram for elderly colorectal cancer patients: a SEER database analysis. BMC Gastroenterol. 2020 Oct 20;20(1):347. doi: 10.1186/s12876-020-01464-z. Wu J, Zhang H, Li L, Hu M, Chen L, Xu B, Song Q. A nomogram for predicting overall survival in patients with low-grade endometrial stromal sarcoma: A population-based analysis. Cancer Commun (Lond). 2020 Jul;40(7):301-312. doi: 10.1002/cac2.12067. Epub 2020 Jun 18. Cannella R, Tabone E, Porrello G, Cappello G, Gozzo C, Incorvaia L, et al. Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors. Eur Radiol. 2021 Nov;31(11):8554-8564. doi: 10.1007/s00330-021-07961-3. Epub 2021 Apr 21. 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Asian Consensus Guidelines for the Diagnosis and Management of Gastrointestinal Stromal Tumor. Cancer Res Treat. 2016 Oct;48(4):1155-1166. doi: 10.4143/crt.2016.187. Epub 2016 Jun 24. Yang Z, Wang F, Liu S, Guan W. Comparative clinical features and short-term outcomes of gastric and small intestinal gastrointestinal stromal tumours: a retrospective study. Sci Rep. 2019 Jul 11;9(1):10033. doi: 10.1038/s41598-019-46520-1. Naito Y, Nishida T, Doi T. Current status of and future prospects for the treatment of unresectable or metastatic gastrointestinal stromal tumours. Gastric Cancer. 2023 May;26(3):339-351. doi: 10.1007/s10120-023-01381-6. Epub 2023 Mar 13. Alghamdi HM, Amr SS, Shawarby MA, Sheikh SS, Alsayyah AA, Alamri AM, Ismail MH, Almarhabi A, Alrefaee MA, Ahmed MI. Gastrointestinal stromal tumors. A clinicopathological study. Saudi Med J. 2019 Feb;40(2):126-130. doi: 10.15537/smj.2019.2.23913. Yang C, Zhang J, Ding M, Xu K, Li L, Mao L, Zheng J. Ki67 targeted strategies for cancer therapy. Clin Transl Oncol. 2018 May;20(5):570-575. doi: 10.1007/s12094-017-1774-3. Epub 2017 Oct 20. Charville GW, Longacre TA. Surgical Pathology of Gastrointestinal Stromal Tumors: Practical Implications of Morphologic and Molecular Heterogeneity for Precision Medicine. Adv Anat Pathol. 2017 Nov;24(6):336-353. doi: 10.1097/PAP.0000000000000166. Yamamoto H, Oda Y. Gastrointestinal stromal tumor: recent advances in pathology and genetics. Pathol Int. 2015 Jan;65(1):9-18. doi: 10.1111/pin.12230. Epub 2014 Nov 20. Sharma AK, Kim TS, Bauer S, Sicklick JK. Gastrointestinal Stromal Tumor: New Insights for a Multimodal Approach. Surg Oncol Clin N Am. 2022 Jul;31(3):431-446. doi: 10.1016/j.soc.2022.03.007. Epub 2022 May 31. Tian Xiaowen, Liang Xiaobo, Wang Zhenhua, et al. Risk factors of postoperative recurrence of gastrointestinal stromal tumors and the application value of nomogram [J]. Chinese Journal of Gastrointestinal Surgery, 2017,16 (1): 71-76. DOI: 10.3760/cma.j.issn.1673-9752.2017.01.014. Huang W, Yuan W, Ren L, Liang H, Du X, Sun X, Fang Y, Gao X, Fu M, Sun Y, Shen K, Hou Y. Clinicopathological and therapeutic analysis of PDGFRA mutated gastrointestinal stromal tumor. Pathol Res Pract. 2022 Nov;239:154138. doi: 10.1016/j.prp.2022.154138. Epub 2022 Sep 19. Xu SJ, Zhang SY, Dong LY, Lin GS, Zhou YJ. Dynamic survival analysis of gastrointestinal stromal tumors (GISTs): a 10-year follow-up based on conditional survival. BMC Cancer. 2021 Nov 1;21(1):1170. doi: 10.1186/s12885-021-08828-y. Yi M, Xia L, Zhou Y, Wu X, Zhuang W, Chen Y, Zhao R, Wan Q, Du L, Zhou Y. Prognostic value of tumor necrosis in gastrointestinal stromal tumor: A meta-analysis. Medicine (Baltimore). 2019 Apr;98(17):e15338. doi: 10.1097/MD.0000000000015338. Xia Shengwei, Yu Jie, Lin Xizhou, et al. CT imaging of gastric neuroendocrine tumors Characteristics [J]. Chinese Journal of Digestive Surgery, 2020,19 (9): 995-1000. DOI: 10.3760/cma.j.cn115610-20200814-00551. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5339667","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377198374,"identity":"f184899b-e799-44f6-a331-07a72249d5cd","order_by":0,"name":"Zhicheng Huang","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine (No.900 Hospital of PLA)","correspondingAuthor":false,"prefix":"","firstName":"Zhicheng","middleName":"","lastName":"Huang","suffix":""},{"id":377198375,"identity":"807e04ff-4e58-4594-ba37-764099ce0bbd","order_by":1,"name":"Baohua Zheng","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine (No.900 Hospital of PLA)","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Zheng","suffix":""},{"id":377198376,"identity":"6c3f04ff-3e0a-42ab-9bb7-bf936f4ec36f","order_by":2,"name":"Zhiwei Wang","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine (No.900 Hospital of PLA)","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Wang","suffix":""},{"id":377198377,"identity":"30fa253f-91aa-4be9-aa9f-fa330dd62e44","order_by":3,"name":"Xiaobin Chen","email":"","orcid":"","institution":"Fujian University of Traditional Chinese Medicine (No.900 Hospital of PLA)","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Chen","suffix":""},{"id":377198378,"identity":"d23a60d2-4716-4a7d-8801-ddc34538f481","order_by":4,"name":"Yu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBAC9nYGhg8JDAdAbMbHYCFm5ga8WngOMzDOgGphNmZgMABSjERoYYBoYZMGa2EgpIWZx7DhwZ87if2z269VF1T8ieZvB2r5UbENv5bEtmeJM+6cKbs944xB7ozDjA2MPWdu49Riz8xj/iCx4XBuw42ctNu8bQa5DUAtzIxtuLWAbUn4czh3PlBLMUjLfOK0sB3O3XAj/RgzSMsGwlrYCoF+OVy/8UYOszTPGePcjUAtB/H5hYe9eWPjjz+HjeVupD/8zFMhlzvv/OGDD35U4NbCwMBhANNtABc7gEc9ELA/QGeMglEwCkbBKEAFAFTuYCqcZzOUAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian University of Traditional Chinese Medicine (No.900 Hospital of PLA)","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-27 04:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5339667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5339667/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71144549,"identity":"4cada7fd-2273-4395-89a1-f38712e5b959","added_by":"auto","created_at":"2024-12-11 14:11:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27852,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curve of patients with gastric stromal tumor.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/505c8a19b9a5339c694d27bc.png"},{"id":71144553,"identity":"c638d204-da91-4e22-9d08-394e05f564f5","added_by":"auto","created_at":"2024-12-11 14:11:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197901,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of survival curve of RFS in patients with gastric stromal tumor by modified NIH criteria and oral imatinib treatment.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/1364d30514bb15ae796bb3b5.png"},{"id":71145341,"identity":"09a9cdd4-c5fa-4e84-b115-989d431d9cd6","added_by":"auto","created_at":"2024-12-11 14:19:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120133,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model of RFS in patients with gastric stromal tumor.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/d8fcb953284dfead37e1d022.png"},{"id":71144550,"identity":"8532c45b-2249-4575-b0d2-91310b1bb9a2","added_by":"auto","created_at":"2024-12-11 14:11:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64866,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of RFS in patients with gastric stromal tumor. a: 3-year, b: 5-year.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/d677de425a438acbc59e47c4.png"},{"id":71144554,"identity":"362b7e4a-b04c-4570-84b6-1cf7f3283014","added_by":"auto","created_at":"2024-12-11 14:11:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62383,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of centralized RFS in patients with gastric stromal tumor (internal verification). a: 3-year, b: 5-year.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/3adb637789a0690c058bcb3c.png"},{"id":71144551,"identity":"d0184080-2c7a-4b38-bc70-975e80dd73a3","added_by":"auto","created_at":"2024-12-11 14:11:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":61002,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of centralized RFS in patients with gastric stromal tumor (external verification). a: 3-year, b: 5-year.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/b16b1ba07ef508211954d52d.png"},{"id":84907670,"identity":"f0966979-afa6-4a39-b8cd-3937ce14d52e","added_by":"auto","created_at":"2025-06-18 16:16:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1922931,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5339667/v1/cb1b140e-663c-458f-9fff-07fba902d232.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of clinical and pathological characteristics of patients with gastric stromal tumors and construction and validation of a prognostic nomogram model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastrointestinal stromal tumor (GIST), as a common mesenchymal tumor in the gastrointestinal tract, originated from Cajal cells in the gastrointestinal tract, accounting for 1% of gastrointestinal tumors, with an annual incidence of about 10\u0026ndash;15/1\u0026nbsp;million cases, especially in people aged 50\u0026ndash;70 \u003csup\u003e1\u003c/sup\u003e. Most GIST have gene activation mutations of KIT or platelet-derived growth factor receptor-α (PDGFR-α) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. GIST can occur in any part of the gastrointestinal tract, among which the stomach is the most common (60%), followed by the small intestine (30%), while the colon and other parts (such as esophagus and omentum) only account for about 10% \u003csup\u003e3\u003c/sup\u003e. The clinical manifestations of gastric stromal tumors are not specific, and the symptoms are hidden. Most of them are in the middle and late stage when diagnosed, which leads to poor prognosis, and the postoperative recurrence rate and metastasis rate of patients are also at a high level (about 50% of patients have tumor recurrence after surgery). It is reported that the median survival time of patients with postoperative recurrence of GIST is less than 2 years \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, it is very important to accurately evaluate the condition of GIST patients to improve their prognosis.\u003c/p\u003e \u003cp\u003eIn 2002, the National Institutes of Health (NIH) made a consensus on recurrence risk assessment, and then revised and improved in 2008 as the modified NIH criteria \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e to assess the risk of GIST. However, this classification method also has some defects, such as less indicators and insufficient accuracy \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, it is very important for medical staff to explore some effective and simple means to predict the recurrence risk of GIST patients and make appropriate treatment plans accurately according to their corresponding risks, so as to improve the prognosis of patients. In recent years, nomograms have been continuously used to evaluate the prognosis of patients with digestive tract tumors \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Based on this, this study retrospectively analyzed the clinical manifestations and pathological features of patients with gastric stromal tumors, and analyzed the risk factors of recurrence and recurrence free survival of patients with gastric stromal tumors, and constructed and verified the nomogram model of RFS of patients with gastric stromal tumors, aiming at effectively guiding the clinical diagnosis and treatment of gastric stromal tumors.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eThe data of patients with gastric stromal tumors admitted to the 900th hospital of Joint Logistics Support Force from August 2009 to December 2020 were analyzed retrospectively. Treatment methods include open surgery and Laparoscopic surgery; The tumor sites include gastric fundus, gastric body and gastric antrum. Inclusion criteria: (1) The patient was pathologically diagnosed as gastric stromal tumor; (2) Clinicopathological data are complete (including general medical history data, pathological and immunohistochemical test results and other related indicators). Exclusion criteria: (1) Patients with tumors in other parts; (2) Patients receiving radiotherapy, chemotherapy and biotherapy before operation; (3) patients who refuse to undergo surgery; (4) The medical records are missing or lost. This study was conducted according to the ethical principles of medical research involving human subjects in Helsinki Declaration. Patients and their families signed informed consent before operation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eIncluding age, gender, tumor location, tumor diameter, the first clinical symptom (abdominal pain, gastrointestinal bleeding, abdominal mass, other symptoms, asymptomatic), Ki67, CD34, CD117, DOG-1, mitotic index (the number of mitoses per 50 HPF), tumor rupture, modified NIH criteria \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, tumor necrosis, oral imatinib, surgical method and recurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Follow-up\u003c/h2\u003e \u003cp\u003eAll postoperative patients were followed up regularly every 3\u0026ndash;6 months, using outpatient service, SMS, telephone, email, network communication tools and other means to follow up. Recurrence free survival (RFS) is defined as the time from the first day after operation to the recurrence of gastric stromal tumor, and the dead patients without recurrence are calculated as the time of death, and the surviving patients without recurrence are calculated as the last follow-up date. The deadline for follow-up is January 10, 2024 or the patient relapses or dies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 26.0, GraphPad Prism 8.0 and R (version 4.4.0) software were used for statistical analysis, and the count data was expressed by in the form of percentage n(%). Kaplan-meier method was used to calculate RFS and draw survival curve. log-rank test was used to test the differences between groups. Logistic regression analysis was used to analyze the related influencing factors of gastric stromal tumor recurrence. Cox regression analysis was used to analyze the influencing factors of RFS in patients with gastric stromal tumors, and hazard ratio (HR) and corresponding 95% CI were calculated. According to the above independent influencing factors, the corresponding nomogram prediction model \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e is constructed and drawn by using the \"rms\" package in R software (version 4.4.0), and verified by calibration curve and consistency index (C-index) to determine the prediction accuracy and discrimination ability of the model. The greater the C-index, the more accurate the prognosis prediction, with α\u0026thinsp;=\u0026thinsp;0.05 as the test level \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General data\u003c/h2\u003e \u003cp\u003eAccording to the inclusion criteria, a total of 201 patients who underwent surgical treatment of gastric stromal tumors were included. According to the exclusion criteria, 184 patients were finally included in this study, including 90 males (48.91%) and 94 females (51.09%), with an average age of 58.38 10.80 years (range: 24.0\u0026ndash;83.0 years). All 184 patients were discharged from the hospital without death. All patients were followed up for a median of 68 months, of which 52 cases (28.26%) relapsed and 132 cases (71.74%) did not relapse. The recurrence-free survival rates at 3 and 5 years after operation were 91.20% and 76.10%, respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of clinical and pathological characteristics of patients\u003c/h2\u003e \u003cp\u003eAmong the patients with gastric stromal tumors, abdominal pain was the most common clinical symptom, accounting for 96 cases (52.17%), followed by gastrointestinal bleeding in 42 cases (22.83%), abdominal mass in 24 cases (13.04%), other symptoms (such as fatigue, dizziness, etc.) in 14 cases (7.61%), and asymptomatic cases in 8 cases (4.35%), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results of pathological features of gastric stromal tumors showed the tumor locations of patients with gastric stromal tumors include: 85 cases of gastric body (46.20%), 55 cases of gastric fundus (29.89%) and 44 cases of gastric antrum (23.91%); The most common tumor diameter is between 2.1\u0026thinsp;~\u0026thinsp;5 cm, and the most common number of mitotic index is \u0026le;\u0026thinsp;5/50 HPF. The positive cases of CD117 were 173 (94.02%), CD34 was 165 (89.67%) and DOG-1 was 158 (85.87%). There were 146 cases (79.35%) with cell proliferation index Ki67\u0026thinsp;\u0026le;\u0026thinsp;5%, and 38 cases (20.65%) with Ki67\u0026thinsp;\u0026gt;\u0026thinsp;5%. Tumor necrosis occurred in 23 cases of gastric stromal tumors, accounting for 12.50%, and 23 cases with bleeding, accounting for 22.28%, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of gastric stromal tumor (n).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIH risk classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbdominalgia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGastrointestinal bleeding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbdominal mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOther symptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAsymptomatic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathological features of gastric stromal tumors (n).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNIH risk classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eTumor diameter (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eMitotic index (/50 HPF)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003efundus of stomach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003egastric body\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003egastric antrum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1ཞ5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1ཞ10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6ཞ10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e(continued).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNIH risk classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eImmunohistochemical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCell proliferation index Ki67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTumor necrosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eGastrointestinal bleeding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD117 (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD34 (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDOG-1 (+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate and multivariate Logistic regression analysis of postoperative recurrence in patients with gastric stromal tumor\u003c/h2\u003e \u003cp\u003eThe results of univariate Logistic regression analysis indicated that gender, tumor diameter, modified NIH criteria, tumor necrosis and oral imatinib were the influencing factors for postoperative recurrence of gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The univariate meaningful indexes were analyzed by multivariate Logistic regression model. The results showed that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Logistic regression analysis on the recurrence of gastric stromal tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.883 (0.981\u0026ndash;3.615)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.267 (1.164\u0026ndash;4.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.172 (0.495\u0026ndash;2.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.114 (1.077\u0026ndash;4.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.396 (0.349\u0026ndash;5.587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.743 (0.325\u0026ndash;1.702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.643 (0.238\u0026ndash;1.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.672 (0.188\u0026ndash;2.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOG-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.081 (0.425\u0026ndash;2.748)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotic index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.467 (0.736\u0026ndash;2.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor rupture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4442555878 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified NIH criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.656 (0.931\u0026ndash;7.581)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.928 (0.986\u0026ndash;8.694)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.187 (1.224\u0026ndash;8.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.272 (2.819\u0026ndash;72.246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.590 (1.757\u0026ndash;11.994)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.965 (1.394\u0026ndash;34.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.653 (0.791\u0026ndash;3.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor necrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.447 (4.656\u0026ndash;38.838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.987 (1.478\u0026ndash;24.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral imatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088 (0.026\u0026ndash;0.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025 (0.006\u0026ndash;0.109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.737 (0.362\u0026ndash;1.501)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Cox regression analysis of influencing RFS in patients with gastric stromal tumor\u003c/h2\u003e \u003cp\u003eUnivariate analysis showed that age, gender, tumor diameter, mitotic index, tumor rupture, modified NIH criteria, tumor necrosis and oral imatinib treatment were the influencing factors of RFS in patients with gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The univariate significant indexes were included in the multivariate Cox regression model for analysis, and the results showed that the modified NIH criteria and oral imatinib treatment were independent influencing factors of RFS in patients with gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In addition, we will draw the corresponding survival curves of the independent risk factors affecting RFS in patients with gastric stromal tumors by Kaplan-Meier method, and display them visually, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Cox regression analysis of influencing RFS in patients with gastric stromal tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e(95%\u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.855 (1.063\u0026ndash;3.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.377 (0.762\u0026ndash;2.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.093 (1.180\u0026ndash;3.712)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.157 (0.616\u0026ndash;2.173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.565 (1.457\u0026ndash;4.516)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.665 (0.714\u0026ndash;3.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.968 (0.471\u0026ndash;1.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.847 (0.381\u0026ndash;1.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692 (0.248\u0026ndash;1.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDOG-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeminine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.045 (0.470\u0026ndash;2.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMitotic index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.065 (1.150\u0026ndash;3.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.274 (0.549\u0026ndash;2.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor rupture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.730 (1.696\u0026ndash;13.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.393 (0.369\u0026ndash;5.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified NIH criteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.338 (0.919\u0026ndash;5.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.783 (1.079\u0026ndash;7.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.072 (1.680\u0026ndash;9.872)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.751 (3.631\u0026ndash;38.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.493 (2.720\u0026ndash;15.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.799 (2.543\u0026ndash;45.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.741 (0.952\u0026ndash;3.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor necrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.466 (3.061\u0026ndash;9.762)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.426 (0.696\u0026ndash;2.923)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral imatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.129 (0.040\u0026ndash;0.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038 (0.011\u0026ndash;0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpen surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719 (0.389\u0026ndash;1.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Relationship between oral imatinib treatment and prognosis of patients with gastric stromal tumor\u003c/h2\u003e \u003cp\u003eAmong the 91 patients with high-risk gastric stromal tumors, 50 patients (54.95%) were treated with imatinib orally (the dosage was 400 mg/d), among which 19 patients took the medicine for one year, among which 5 patients were treated with sunitinib because of postoperative recurrence (the dosage was 37.5 mg/d), and 3 patients stopped taking the medicine for personal reasons. The other 31 patients all took drugs for 3 years. For patients who took drugs regularly for 3 years after operation, the 5-year recurrence rate was significantly lower than that of patients who did not take drugs (14.16% vs. 43.80%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the 5-year RFS was higher than that of patients who did not take drugs (73.30% vs. 55.10%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Prediction effect of nomogram prediction model on RFS in gastric stromal tumors\u003c/h2\u003e \u003cp\u003eWe screened out the independent influencing factors of RFS by multivariate Cox regression analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and combined with the factors that clinically affect the prognosis of patients (RFS), all of them were included in the nomogram prediction model. The results indicate that the C-index of the nomogram prediction model of RFS is 0.828(0.799\u0026ndash;0.857), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In addition, we use calibration curve to test the prediction ability of the model, and the results show that the calibration curve of RFS is very close to the ideal curve, which shows that the predicted RFS of nomogram model is consistent with the actual observation results in the research queue, suggesting that the nomogram has relatively accurate prediction ability, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Verification of nomogram prediction model\u003c/h2\u003e \u003cp\u003eIn the internal verification queue, the C index is 0.881(0.868\u0026ndash;0.894) by bootstrapping validation analysis, which shows that the model has good discrimination. The calibration curve also performed well in the verification set, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Similarly, we conducted external verification by collecting the relevant data of 138 patients with gastric stromal tumors who visited the Affiliated Hospital of Qinghai University from August 2009 to December 2020. The verification C index was 0.837(0.797\u0026ndash;0.878), and the calibration curve also performed well in the verification set, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGastric stromal tumor, as a common mesenchymal tumor in digestive tract, has certain malignant potential, and it is common in the elderly (average age is 50\u0026thinsp;~\u0026thinsp;50 years old), with no obvious gender difference, and the tumor prone sites include gastric body, gastric fundus and gastric antrum respectively \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In this study, the average age of patients with gastric stromal tumors was 58.38 years old, and women accounted for a little more than men (51.09% vs. 48.91%), with no statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Most tumors were located in the stomach body (46.20%), which was consistent with the literature reports. The clinical manifestations of gastric stromal tumors are not specific, and the symptoms are hidden. About 50% of patients with gastric stromal tumors take abdominal pain as the first symptom, followed by gastrointestinal bleeding (about 35%) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In this study, 96 patients (52.17%) presented with abdominal pain as the initial clinical symptom, followed by gastrointestinal bleeding in 42 cases (22.83%), which was consistent with the literature reports. Among them, gastrointestinal bleeding is more serious than patients with abdominal pain, abdominal mass and other initial symptoms, and it is reported that about 10%-15% patients may have hemorrhagic shock \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, there is still controversy over whether gastrointestinal bleeding indicates tumor rupture and affects patient prognosis. In this study, there is no significant correlation between gastrointestinal bleeding and tumor recurrence and patients' RFS. In addition, in this study, the most common tumor diameter was 2.1\u0026thinsp;~\u0026thinsp;5 cm, and the number of mitotic index was mostly\u0026thinsp;\u0026le;\u0026thinsp;5/50 HPF, which is consistent with relevant research results \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition, it has been reported that DOG-1, CD117, and CD34 are highly expressed in gastric stromal tumors, and they are obviously related to the prognosis of patients. The comprehensive detection of gastrointestinal stromal tumor specimens using immunohistochemical markers such as DOG-1, CD117, and CD34 can provide more accurate disease and prognosis judgment basis for clinical doctors, which is conducive to timely development of correct treatment plans to ensure patient efficacy and prognosis \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, the positive rates of CD117, CD34 and DOG-1 were 94.02%, 89.67% and 85.87% respectively, which were at a high level, which was consistent with the literature reports. Ki67, as an important index, can accurately reflect the proliferation activity of gastric stromal tumor cells and is positively correlated with risk classification and mitosis index. It is also related to the development, metastasis and prognosis of various tumors \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In this study, 146 cases (79.35%) had a cell proliferation index of Ki67\u0026thinsp;\u0026le;\u0026thinsp;5%, while 38 cases (20.65%) had a Ki67\u0026thinsp;\u0026gt;\u0026thinsp;5%, which was at a relatively low level. In addition, in this study, there were 23 cases of gastric stromal tumor with tumor necrosis, accounting for 12.50%, and 23 cases with bleeding, accounting for 22.28%. According to the analysis, gastric stromal tumor belongs to a well-defined type of tumor, and some tumors may be accompanied by bleeding and necrosis. It has been reported in the literature that as long as tumor necrosis is combined, the prognosis of GIST is poor regardless of tumor location, tumor size or patient race, and the risk of disease progression of GIST patients with tumor necrosis is increased by about 7 times \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Radical resection (negative margin) is the first choice for treatment, but there is still a high recurrence rate, which often leads to poor prognosis of patients \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Therefore, accurate risk stratification of gastric stromal tumors is very important for the choice of treatment options and prognosis evaluation of patients.\u003c/p\u003e \u003cp\u003eIn the past, the risk grading evaluation system has been adjusted many times. At present, the most commonly used standard is the National Institutes of Health (NIH) risk grading standard \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This classification method mainly refers to GIST tumor diameter, primary tumor site, mitotic index and whether the tumor is ruptured. It is divided into four levels based on postoperative recurrence risk: extremely low risk, low risk, medium risk, and high risk, However, this classification method also has certain shortcomings, such as insufficient inclusion of indicators and insufficient accuracy \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Joensuu et al \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. pointed out that although the Modified NIH criteria is the best standard to identify a single high-risk population and formulate corresponding adjuvant treatment, the index is too single, even if the risk classification of gastric stromal tumors is the same, the prognosis of patients will be different. Therefore, it is very important for medical staff to explore simple, effective and accurate parameters to predict the postoperative recurrence risk of patients with gastric stromal tumors and to carry out individualized treatment to improve the prognosis of patients. Tian Xiaowen et al. \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e collected the clinical data of 90 patients with gastric stromal tumor and established nomogram to predict the probability of postoperative recurrence, and obtained satisfactory expected results. However, it contains too few indicators and collects fewer samples. Based on this, this study retrospectively analyzed the risk factors of recurrence and RFS of patients with gastric stromal tumors, and constructed and verified the nomogram model of RFS of patients with gastric stromal tumors, aiming at serving the clinic.\u003c/p\u003e \u003cp\u003eIn this study, we found that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results of univariate and multivariate Cox regression analysis showed that the modified NIH criteria and oral imatinib treatment were the independent influencing factors of RFS in patients with gastric stromal tumors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Analysis of the reasons mainly includes the following points: on the one hand, it is the most commonly used risk classification standard for the modified NIH criteria, which is divided into four levels according to the risk of postoperative recurrence: extremely low risk, low risk, medium risk, and high risk \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e; In addition, the 2013 edition of China Consensus on Diagnosis and Treatment of Gastrointestinal Stromal Tumors \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e also takes NIH risk criteria as the main index to evaluate the risk of Gastrointestinal Stromal Tumors. On the other hand, oral imatinib is a first-line targeted drug approved by Food and Drug Administration (FDA) for the treatment of advanced gastric stromal tumors. By analyzing the clinical data of 397 patients with high-risk gastric stromal tumors, Joensuu et al. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e found that the patients treated with oral imatinib had better overall survival and relapse-free survival than those treated with oral imatinib. In this study, by comparing the 5-year prognosis of patients who regularly took drugs for 3 years after operation with those who did not take drugs, it was found that the 5-year recurrence rate was significantly lower than that of patients who did not take drugs (14.16% vs. 43.80%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the 5-year RFS was higher than that of patients who did not take drugs (73.30% vs. 55.10%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which further verified the accuracy of the results. In addition, tumor necrosis is an independent risk factor for postoperative recurrence of gastric stromal tumors, which is consistent with the research conclusion of Yi et al. \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. GIST patients with tumor necrosis have higher risk of disease progression and recurrence, and worse prognosis (the risk of disease progression of GIST patients with tumor necrosis is increased by about 7 times) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, clinicians should pay more attention to the treatment of GIST patients with tumor necrosis found by preoperative CT or pathological examination \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, the independent influencing factors of RFS screened by multivariate Cox regression analysis and the factors that have an impact on patients' prognosis (RFS) are all included in the nomogram prediction model, and the risk probability of RFS in patients with gastric stromal tumors is predicted and evaluated by counting the scores of each variable in the model. The accuracy of nomogram is evaluated by C-index and calibration chart, which proves that nomogram has high predictive value. The nomogram can transform a complex regression equation into a simple visual graph, and medical staff can predict the prognosis of patients with gastric stromal tumors more quickly and conveniently according to the nomogram, which is beneficial to the prevention work in advance and has high clinical application value.\u003c/p\u003e \u003cp\u003eThis study has some limitations. On the one hand, it is a retrospective study of small samples, which has certain and inevitable bias; On the other hand, some high-risk GIST patients in this study failed to receive standard adjuvant treatment after operation, and different postoperative treatment schemes can affect the prognosis of GIST patients to some extent. Therefore, we hope to have a more rigorous multi-center and large-sample prospective study to verify our research results.\u003c/p\u003e \u003cp\u003eTo sum up, the first clinical symptoms of patients with gastric stromal tumors are abdominal pain and gastrointestinal bleeding. Patients with higher risk of modified NIH criteria, tumor necrosis and no oral imatinib treatment are prone to relapse. The higher the risk of modified NIH criteria and the shorter the RFS of patients who have not received oral imatinib treatment, the worse the prognosis of patients. For patients with medium and high risk gastric stromal tumor, it is recommended to carry out imatinib adjuvant therapy for 3 years or more after operation, which can effectively improve the prognosis of patients. In addition, the nomogram prediction model based on the factors affecting patients' RFS has high clinical application value, which can effectively predict the 3-and 5-year recurrence-free survival rate and is conducive to individualized diagnosis and treatment of patients' prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful for the invaluable support and useful discussions with other members of the general surgery department.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the ethical principles of medical research involving human subjects in the Declaration of Helsinki and have been approved by the biomedical ethics committee of the 900th hospital of Joint Logistics Support Force (Number: 2024-126). Patients and their families signed informed consent before operation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of Competing Interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReserch involving human participants and/or animals \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the General Project of Fujian Natural Science Foundation (2023J01206), Key project of the 900th Hospital (2023zs01); Top-notch project of the PLA Medical Science and Technology Youth Training Program (21QNPY138); Special research project of training injury prevention and treatment in the 900th Hospital (2023XL02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed consent\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent has been received from the subject.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkahoshi K, Oya M, Koga T, Shiratsuchi Y. Current clinical management of gastrointestinal stromal tumor. World J Gastroenterol. 2018 Jul 14;24(26):2806-2817. doi: 10.3748/wjg.v24.i26.2806.\u003c/li\u003e\n\u003cli\u003eJoensuu H, Hohenberger P, Corless CL. Gastrointestinal stromal tumour. Lancet. 2013 Sep 14;382(9896):973-83. doi: 10.1016/S0140-6736(13)60106-3. Epub 2013 Apr 24.\u003c/li\u003e\n\u003cli\u003eBlay JY, Kang YK, Nishida T, von Mehren M. Gastrointestinal stromal tumours. Nat Rev Dis Primers. 2021 Mar 18;7(1):22. doi: 10.1038/s41572-021-00254-5.\u003c/li\u003e\n\u003cli\u003eJoensuu H, Vehtari A, Riihim\u0026auml;ki J, Nishida T, Steigen SE, Brabec P, Plank L, Nilsson B, Cirilli C, Braconi C, Bordoni A, Magnusson MK, Linke Z, Sufliarsky J, Federico M, Jonasson JG, Dei Tos AP, Rutkowski P. Risk of recurrence of gastrointestinal stromal tumour after surgery: an analysis of pooled population-based cohorts. Lancet Oncol. 2012 Mar;13(3):265-74. doi: 10.1016/S1470-2045(11)70299-6. 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Cancer Commun (Lond). 2020 Jul;40(7):301-312. doi: 10.1002/cac2.12067. Epub 2020 Jun 18.\u003c/li\u003e\n\u003cli\u003eCannella R, Tabone E, Porrello G, Cappello G, Gozzo C, Incorvaia L, et al. Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors. Eur Radiol. 2021 Nov;31(11):8554-8564. doi: 10.1007/s00330-021-07961-3. Epub 2021 Apr 21.\u003c/li\u003e\n\u003cli\u003eHarrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996 Feb 28;15(4):361-87. doi: 10.1002/(SICI)1097-0258(19960229)15:4\u0026lt;361::AID-SIM168\u0026gt;3.0.CO;2-4.\u003c/li\u003e\n\u003cli\u003eHuitzil-Melendez FD, Capanu M, O\u0026apos;Reilly EM, Duffy A, Gansukh B, Saltz LL, Abou-Alfa GK. Advanced hepatocellular carcinoma: which staging systems best predict prognosis? J Clin Oncol. 2010 Jun 10;28(17):2889-95. doi: 10.1200/JCO.2009.25.9895. Epub 2010 May 10.\u003c/li\u003e\n\u003cli\u003eKRAMER AA, ZIMMERMAN JE. Assessing the calibration of mortality benchmarks in critical care: The Hosmer-Lemeshow test revisited[J]. Crit Care Med, 2007, 35(9): 2052-2056.\u003c/li\u003e\n\u003cli\u003eHall M, Krishnanandan VA, Cheung MC, Coburn NG, Haas B, Chan KKW, Raphael MJ. An Evaluation of Sex- and Gender-Based Analyses in Oncology Clinical Trials. J Natl Cancer Inst. 2022 Aug 8;114(8):1186-1191. doi: 10.1093/jnci/djac092.\u003c/li\u003e\n\u003cli\u003eKoo DH, Ryu MH, Kim KM, Yang HK, Sawaki A, Hirota S, et al. Asian Consensus Guidelines for the Diagnosis and Management of Gastrointestinal Stromal Tumor. Cancer Res Treat. 2016 Oct;48(4):1155-1166. doi: 10.4143/crt.2016.187. Epub 2016 Jun 24.\u003c/li\u003e\n\u003cli\u003eYang Z, Wang F, Liu S, Guan W. Comparative clinical features and short-term outcomes of gastric and small intestinal gastrointestinal stromal tumours: a retrospective study. Sci Rep. 2019 Jul 11;9(1):10033. doi: 10.1038/s41598-019-46520-1.\u003c/li\u003e\n\u003cli\u003eNaito Y, Nishida T, Doi T. Current status of and future prospects for the treatment of unresectable or metastatic gastrointestinal stromal tumours. Gastric Cancer. 2023 May;26(3):339-351. doi: 10.1007/s10120-023-01381-6. Epub 2023 Mar 13.\u003c/li\u003e\n\u003cli\u003eAlghamdi HM, Amr SS, Shawarby MA, Sheikh SS, Alsayyah AA, Alamri AM, Ismail MH, Almarhabi A, Alrefaee MA, Ahmed MI. Gastrointestinal stromal tumors. A clinicopathological study. Saudi Med J. 2019 Feb;40(2):126-130. doi: 10.15537/smj.2019.2.23913.\u003c/li\u003e\n\u003cli\u003eYang C, Zhang J, Ding M, Xu K, Li L, Mao L, Zheng J. Ki67 targeted strategies for cancer therapy. Clin Transl Oncol. 2018 May;20(5):570-575. doi: 10.1007/s12094-017-1774-3. Epub 2017 Oct 20.\u003c/li\u003e\n\u003cli\u003eCharville GW, Longacre TA. Surgical Pathology of Gastrointestinal Stromal Tumors: Practical Implications of Morphologic and Molecular Heterogeneity for Precision Medicine. Adv Anat Pathol. 2017 Nov;24(6):336-353. doi: 10.1097/PAP.0000000000000166.\u003c/li\u003e\n\u003cli\u003eYamamoto H, Oda Y. Gastrointestinal stromal tumor: recent advances in pathology and genetics. Pathol Int. 2015 Jan;65(1):9-18. doi: 10.1111/pin.12230. Epub 2014 Nov 20.\u003c/li\u003e\n\u003cli\u003eSharma AK, Kim TS, Bauer S, Sicklick JK. Gastrointestinal Stromal Tumor: New Insights for a Multimodal Approach. Surg Oncol Clin N Am. 2022 Jul;31(3):431-446. doi: 10.1016/j.soc.2022.03.007. Epub 2022 May 31.\u003c/li\u003e\n\u003cli\u003eTian Xiaowen, Liang Xiaobo, Wang Zhenhua, et al. Risk factors of postoperative recurrence of gastrointestinal stromal tumors and the application value of nomogram [J]. Chinese Journal of Gastrointestinal Surgery, 2017,16 (1): 71-76. DOI: 10.3760/cma.j.issn.1673-9752.2017.01.014.\u003c/li\u003e\n\u003cli\u003eHuang W, Yuan W, Ren L, Liang H, Du X, Sun X, Fang Y, Gao X, Fu M, Sun Y, Shen K, Hou Y. Clinicopathological and therapeutic analysis of PDGFRA mutated gastrointestinal stromal tumor. Pathol Res Pract. 2022 Nov;239:154138. doi: 10.1016/j.prp.2022.154138. Epub 2022 Sep 19.\u003c/li\u003e\n\u003cli\u003eXu SJ, Zhang SY, Dong LY, Lin GS, Zhou YJ. Dynamic survival analysis of gastrointestinal stromal tumors (GISTs): a 10-year follow-up based on conditional survival. BMC Cancer. 2021 Nov 1;21(1):1170. doi: 10.1186/s12885-021-08828-y.\u003c/li\u003e\n\u003cli\u003eYi M, Xia L, Zhou Y, Wu X, Zhuang W, Chen Y, Zhao R, Wan Q, Du L, Zhou Y. Prognostic value of tumor necrosis in gastrointestinal stromal tumor: A meta-analysis. Medicine (Baltimore). 2019 Apr;98(17):e15338. doi: 10.1097/MD.0000000000015338.\u003c/li\u003e\n\u003cli\u003eXia Shengwei, Yu Jie, Lin Xizhou, et al. CT imaging of gastric neuroendocrine tumors Characteristics [J]. Chinese Journal of Digestive Surgery, 2020,19 (9): 995-1000. DOI: 10.3760/cma.j.cn115610-20200814-00551.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gastric stromal tumor, recurrence, risk factors, recurrence free survival, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-5339667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5339667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eExplore the clinical and pathological characteristics of patients with gastric stromal tumors and the factors influencing postoperative recurrence, and establish a nomogram model to predict the recurrence free survival (RFS) of patients with gastric stromal tumors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e The data of patients with gastric stromal tumors admitted to the 900th hospital of Joint Logistics Support Force from August 2009 to December 2020 were analyzed retrospectively. To analyze the clinical and pathological characteristics of patients with gastric stromal tumors undergoing surgery. The Kaplan Meier method was used to draw the survival curves to analyze the total survival time of patients, and log-rank test was used to analyze the comparison between groups. Logistic regression model and Cox regression model were used for univariate and multivariate analysis. A nomogram prediction model for predicting RFS in patients with gastric stromal tumors was constructed and verified by calibration curve and consistency curve.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eAmong 184 patients with gastric stromal tumor, abdominal pain was the most common clinical symptom, followed by gastrointestinal bleeding. In patients with gastric stromal tumors, the most common location of tumors is the stomach body, followed by the stomach floor and antrum; The diameter of tumor is 2.1 ~ 5 cm and the number of mitosis is ≤5/50 HPF. The 5-year recurrence rate of patients who regularly took imatinib for 3 years after operation was significantly lower than that of patients who did not take imatinib (14.16% vs. 43.80%, P\u0026lt;0.05), while the 5-year RFS was higher than that of patients who did not take imatinib (73.30% vs. 55.10%, P\u0026lt;0.05). Multivariate Logistic regression analysis showed that the modified NIH criteria, tumor necrosis and oral imatinib treatment were independent influencing factors for postoperative recurrence of gastric stromal tumors (P\u0026lt;0.05). Multivariate Cox regression analysis showed that the modified NIH criteria and oral imatinib treatment were independent influencing factors for postoperative RFS of gastric stromal tumors (P\u0026lt;0.05). Kaplan-meier method was used to calculate DFS and draw the survival curve of the correlation between the modified NIH criteria and oral imatinib treatment with the prognosis of gastric stromal tumor patients. The results showed that patients with higher modified NIH criteria and those without oral imatinib treatment had shorter DFS and worse prognosis. The factors (age, gender, tumor diameter, mitotic index, tumor rupture, tumor necrosis, modified NIH criteria, gastrointestinal bleeding, oral imatinib treatment, and surgical method) that will affect patients' RFS were selected to construct a nomogram for predicting RFS, and the consistency index (C-index) was 0.828 and 0.881, and the external verification C-index was 0.837. The calibration curve indicates that the nomogram has relatively accurate prediction ability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eThe first clinical symptoms of patients with gastric stromal tumor are abdominal pain and gastrointestinal bleeding. Patients with higher risk of modified NIH criteria, tumor necrosis and no oral imatinib treatment are prone to relapse. The higher the risk of modified NIH criteria and the shorter the RFS of patients who have not received oral imatinib treatment, the worse the prognosis of patients. For patients with medium and high risk gastric stromal tumor, it is recommended to carry out imatinib adjuvant therapy for 3 years or more after operation, which can effectively improve the prognosis of patients. In addition, the nomogram prediction model based on the factors affecting patients' RFS can effectively predict the 3-and 5-year recurrence-free survival rate, which is conducive to individualized diagnosis and treatment of patients' prognosis in clinic.\u003c/p\u003e","manuscriptTitle":"Analysis of clinical and pathological characteristics of patients with gastric stromal tumors and construction and validation of a prognostic nomogram model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 14:11:51","doi":"10.21203/rs.3.rs-5339667/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b4e2c22e-4701-46bc-8c24-d59cd044f81c","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40154375,"name":"Biological sciences/Cancer"},{"id":40154376,"name":"Health sciences/Diseases"},{"id":40154377,"name":"Health sciences/Gastroenterology"},{"id":40154378,"name":"Health sciences/Medical research"},{"id":40154379,"name":"Health sciences/Oncology"},{"id":40154380,"name":"Health sciences/Risk factors"},{"id":40154381,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2025-07-30T06:38:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-11 14:11:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5339667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5339667","identity":"rs-5339667","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}