CN112420200A - Early malignant change prediction system for papillary mucous tumor in pancreatic duct - Google Patents
Early malignant change prediction system for papillary mucous tumor in pancreatic duct Download PDFInfo
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- 206010028980 Neoplasm Diseases 0.000 title claims abstract description 65
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- 230000008859 change Effects 0.000 title abstract description 6
- 201000004754 pancreatic intraductal papillary-mucinous neoplasm Diseases 0.000 claims abstract description 54
- 206010073365 Intraductal papillary mucinous carcinoma of pancreas Diseases 0.000 claims abstract description 53
- 201000011510 cancer Diseases 0.000 claims abstract description 26
- 230000036210 malignancy Effects 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 11
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- 238000007477 logistic regression Methods 0.000 claims abstract description 6
- 238000003384 imaging method Methods 0.000 claims description 13
- 206010011732 Cyst Diseases 0.000 claims description 9
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- 210000002966 serum Anatomy 0.000 claims description 6
- 206010064912 Malignant transformation Diseases 0.000 claims description 4
- 210000000013 bile duct Anatomy 0.000 claims description 4
- 210000001953 common bile duct Anatomy 0.000 claims description 4
- 230000036212 malign transformation Effects 0.000 claims description 4
- 208000022669 mucinous neoplasm Diseases 0.000 claims description 4
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 3
- 238000000585 Mann–Whitney U test Methods 0.000 claims description 3
- 206010033645 Pancreatitis Diseases 0.000 claims description 3
- 238000000546 chi-square test Methods 0.000 claims description 3
- 206010012601 diabetes mellitus Diseases 0.000 claims description 3
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- 230000003908 liver function Effects 0.000 claims description 3
- 238000007427 paired t-test Methods 0.000 claims description 3
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- 206010027476 Metastases Diseases 0.000 description 3
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- 238000001764 infiltration Methods 0.000 description 3
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- 238000004393 prognosis Methods 0.000 description 2
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Abstract
The invention relates to a system for predicting early malignant change of papillary mucous tumors in pancreatic ducts, which comprises the following components: the device comprises a collection module, a grouping module, an extraction module, an analysis modeling module and a prediction module. The analysis modeling module compares the indexes of the risk factors affecting the malignancy and the goodness of the patient tumor between the LGD group and the HGD/pT1a group, and between the HGD/pT1a group and the pT1a group except for the I-IPMC according to a single-factor analysis method, selects candidate variables from the indexes, performs logistic regression on each index of the risk factors affecting the malignancy and the selected candidate variables one by one to screen out final variables, and establishes a prediction model according to the final variables. The method can screen potential early-stage malignant IPMN patients from 'benign' IPMN.
Description
Technical Field
The invention relates to the technical field of computer-aided diagnosis, in particular to a system for predicting early malignant change of papillary mucus tumors in pancreatic ducts.
Background
The definition of IPMN is: involvement of mucinogenic tumors of the main or branch pancreatic ducts, together with the lack of ovarian-like stroma characteristic of MCN (Mucinous cystic adenoma). IPMN has a broad histological spectrum ranging from low grade atypical hyperplasia (LGD) to invasive Intraductal Papillary Mucinous Carcinoma (IPMC). IPMN is divided into three types according to lesion involvement: main pancreatic duct type (MD), Branch pancreatic duct type (BD), and Mixed Type (MT) when MD and BD are simultaneously involved. Research reports show that aggressive IPMC has similar characteristics to Pancreatic Ductal Adenocarcinoma (PDAC), including potential Lymph Node (LN) or distant metastasis, postoperative recurrence and low survival. Malignant IPMN was defined as HGD and associated invasive carcinoma. Surgical decisions for IPMN should predict malignancy by assessing various risk factors.
Currently, consensus opinions are also made internationally for preoperative assessment of HGD-IPMN and/or invasive IPMC malignancy risk for patient clinical features and imaging outcomes, such as International Consensus Guidelines (ICG) published in 2012, where predictors of malignancy are classified as high risk and alarming signs. However, subsequent studies showed that Negative Predictive Value (NPV) and Positive Predictive Value (PPV) for HGD IPMN and invasive IPMC diagnosis were higher using the guidelines, and lower. Thus, ICG was revised in 2017 to a smaller extent. However, studies have found that median survival following tumor resection in HGD-IPMN patients is similar to that of LGD patients and significantly superior to that of IPMN patients. Therefore, early identification of HGD-IPMN is of great clinical significance in improving patient prognosis, and it is necessary to predict risk factors separately from invasive IPMC.
Recent studies have shown that the malignant potential of IPMN depends on the presence and extent of invasive cancer. However, more studies show that the 5-year survival rate of invasive IPMC is about 36% -90%, and there is a big difference. This may be due to heterogeneity of I-IPMC, including differences in biological behavior and size of invasive components. Research results show that the postoperative survival period of a patient with the invasion component less than or equal to 0.5cm is close to non-invasive IPMN, and the prognosis of the advanced invasive IPMC is close to PDAC. Furthermore, progressive invasive IPMC is often associated with LN metastasis, requiring LN excision during surgery, while LN metastasis is not seen in patients with early infiltration ≦ 5 mm. Therefore, the 2019WHO subdivides pT1 (early infiltration. ltoreq.2 cm) into pT1a, in particular, the infiltration component is less than or equal to 5 mm.
It is therefore reasonable to assume that HGD and pT1a can be considered as early malignant IPMN. Clinically, it is generally easier to distinguish non-invasive IPMN from progressive invasive IPMN by clinical data and imaging examination, and it is very challenging to identify early malignant IPMN from benign IPMN. Therefore, it is necessary to find a simple and objective method for identifying HGD/pT1a and LGD-IPMN.
Disclosure of Invention
The invention aims to provide a system for predicting early malignant change of papillary mucous tumors in pancreatic ducts, which can screen potential early malignant IPMN patients from benign IPMN.
The technical scheme adopted by the invention for solving the technical problems is as follows: provided is a pancreatic ductal papillary mucinous tumor early malignant transformation prediction system, including: the collection module is used for collecting imaging data and pathological results of confirmed IPMN patients before and after surgery; a grouping module for grouping the contents collected by the collection module into an LGD group, an HGD/pT1a group and an I-IPMC excluding pT1a group according to the pathological results of the IPMN operation; the extraction module is used for extracting risk factor indexes influencing the malignancy and the benign of the tumor of the patient from the collected contents of the collection module; the analysis modeling module is used for carrying out difference comparison on risk factor indexes which influence the malignancy and the goodness of the patient tumor between the LGD group and the HGD/pT1a group and between the HGD/pT1a group and the pT1a group except for the I-IPMC according to a single-factor analysis method, selecting candidate variables from the risk factor indexes, carrying out logistic regression on each risk factor index which influences the malignancy and the selected candidate variables one by one to screen out final variables, and establishing a prediction model according to the final variables; and the prediction module predicts the patient by using the established prediction model.
The extraction module extracts risk factor indexes affecting the malignancy and the malignancy of the tumor of the patient, and comprises the following steps: age, sex, BMI, diabetes, smoking, alcohol consumption, history of pancreatitis, clinical symptoms, tumor imaging typing, location, tumor diameter, number of tumors, cyst wall nodules, parenchymal components, cyst boundaries, pancreatic duct and bile duct maximum diameters, whether pancreatic parenchyma is atrophied, vascular involvement, laboratory examinations including CA199, CEA, CA125, AFP and preoperative liver function, mode of surgery and surgical pathology results.
When the analysis modeling module carries out difference comparison on risk factor indexes affecting the malignancy and the malignancy of the patient tumor between an LGD group and an HGD/pT1a group and between an HGD/pT1a group and an I-IPMC except pT1a group according to a single-factor analysis method, a chi-square test or a Fisher exact probability method is adopted, and a Mann-Whitney U test or a paired t test is adopted for continuous variables.
The predictive model established by the analysis modeling module is as follows:
wherein, P is a predicted value, MD/MT represents a main pancreatic duct type/mixed type IPMN, BD-IPMN represents a branch type IPMN, mural nodes represents cyst wall nodules, solid components represent actual components, CBD differentiation represents common bile duct expansion, serum CEA is a tumor index CEA value, and serum CA125 is a tumor index CA125 value.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: according to the invention, by comparing the clinical and imaging results of HGD/pT1a-IPMN and LGD-IPMN patients, Logistic regression analysis is carried out, a prediction model for predicting HGD/pT1a is established, and potential early malignant IPMN patients can be screened from 'benign' IPMN through the prediction model.
Drawings
FIG. 1 is a block diagram of the architecture of an embodiment of the present invention;
FIG. 2 is a model diagnosis IPMN ROC curve of a prediction model established in an embodiment of the present invention in a training sample;
FIG. 3 is a graphical illustration of a model diagnostic IPMN ROC curve in a test sample for a predictive model established in an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a system for predicting early malignant change of papillary mucinous tumors in pancreatic ducts, which is shown in figure 1 and comprises the following components: the device comprises a collection module, a grouping module, an extraction module, an analysis modeling module and a prediction module.
The collection module is used for collecting imaging data and pathological results of confirmed IPMN patients before and after surgery.
And a grouping module for grouping the contents collected by the collection module into an LGD group, an HGD/pT1a group and an I-IPMC excluding pT1a group according to the pathological results of the IPMN operation.
And the extraction module is used for extracting risk factor indexes influencing the malignancy and the goodness of the tumor of the patient from the collected contents of the collection module. In this embodiment, the extracting module extracts risk factor indicators affecting the malignancy and malignancy of the tumor of the patient, including: age, sex, BMI, diabetes, smoking, alcohol consumption, history of pancreatitis, clinical symptoms, tumor imaging typing, location, tumor diameter, number of tumors, cyst wall nodules, parenchymal components, cyst boundaries, pancreatic duct and bile duct maximum diameters, whether pancreatic parenchyma is atrophied, vascular involvement, laboratory examinations including CA199, CEA, CA125, AFP and preoperative liver function, mode of surgery and surgical pathology results.
And the analysis modeling module is used for performing difference comparison on the risk factor indexes affecting the malignancy and the goodness of the patient tumor between the LGD group and the HGD/pT1a group and between the HGD/pT1a group and the pT1a group except for the I-IPMC according to a single-factor analysis method, selecting candidate variables from the risk factor indexes, performing logistic regression on each risk factor index affecting the malignancy and the selected candidate variables one by one to screen out final variables, and establishing a prediction model according to the final variables. Wherein, the single-factor analysis method adopts chi-square test or Fisher exact probability method, and Mann-Whitney U test or paired t test is adopted for continuous variables.
The prediction model is obtained by calculating the area under the final variable Receiver Operating Characteristic (ROC) curve (AUC), making the ROC curve, selecting the optimal critical value, and calculating the sensitivity, the specificity, the Positive Prediction Value (PPV) and the Negative Prediction Value (NPV). The analysis modeling module establishes a predictionThe measurement model is as follows:
wherein P is a predicted value, MD/MT represents a main pancreatic duct type/hybrid IPMN, BD-IPMN represents a branch type IPMN, and both the main pancreatic duct type/hybrid IPMN and the branch type IPMN are the result of tumor imaging typing, and the value is 1 when the tumor imaging typing is MD/MT and 2 when the tumor imaging typing is BD-IPMN; mural nodule represents a cyst wall nodule with 1 and 0; the solid component represents an actual component, and is 0 if 1 is present; CBD diagnosis represents the expansion of common bile duct, if 1 is not 0, whether the expansion of the common bile duct exists or not can be determined according to the maximum diameter of the bile duct, serum CEA is the value of tumor index CEA, and serum CA125 is the value of tumor index CA 125.
The model diagnosis IPMN ROC curve of the prediction model in a training sample is shown in FIG. 2, and the diagnosis sensitivity, specificity and accuracy are 83.8%, 84.8% and 84.2%, respectively. The model diagnosis IPMN ROC curve of the prediction model in the training sample is shown in FIG. 3, and the diagnosis sensitivity, specificity and accuracy are 68.8%, 77.3% and 75.4%, respectively.
And the prediction module is used for predicting the patient by using the established prediction model, substituting the risk factor indexes of the patient, which influence the malignancy and the malignancy of the tumor of the patient, into the prediction model during specific prediction, and when the obtained prediction value P is greater than a threshold value, indicating that the patient is a potential early-stage malignant IPMN patient.
It is not difficult to discover that the invention carries out Logistic regression analysis and establishes a prediction model for predicting HGD/pT1a by comparing the clinical and imaging results of HGD/pT1a-IPMN patients and LGD-IPMN patients, potential early malignant IPMN patients can be screened from 'benign' IPMN by the prediction model, and the prediction model has ideal sensitivity, specificity and accuracy, and can be popularized and applied in clinic.
Claims (4)
1. A pancreatic ductal papillary mucinous tumor early malignant transformation prediction system is characterized by comprising: the collection module is used for collecting imaging data and pathological results of confirmed IPMN patients before and after surgery; a grouping module for grouping the contents collected by the collection module into an LGD group, an HGD/pT1a group and an I-IPMC excluding pT1a group according to the pathological results of the IPMN operation; the extraction module is used for extracting risk factor indexes influencing the malignancy and the benign of the tumor of the patient from the collected contents of the collection module; the analysis modeling module is used for carrying out difference comparison on risk factor indexes which influence the malignancy and the goodness of the patient tumor between the LGD group and the HGD/pT1a group and between the HGD/pT1a group and the pT1a group except for the I-IPMC according to a single-factor analysis method, selecting candidate variables from the risk factor indexes, carrying out logistic regression on each risk factor index which influences the malignancy and the selected candidate variables one by one to screen out final variables, and establishing a prediction model according to the final variables; and the prediction module predicts the patient by using the established prediction model.
2. The system for predicting early malignant changes of papillary mucinous tumors in pancreatic ducts of claim 1, wherein the extracting module extracts risk factor indexes affecting the malignancy and well of tumors of a patient comprises: age, sex, BMI, diabetes, smoking, alcohol consumption, history of pancreatitis, clinical symptoms, tumor imaging typing, location, tumor diameter, number of tumors, cyst wall nodules, parenchymal components, cyst boundaries, pancreatic duct and bile duct maximum diameters, whether pancreatic parenchyma is atrophied, vascular involvement, laboratory examinations including CA199, CEA, CA125, AFP and preoperative liver function, mode of surgery and surgical pathology results.
3. The system for predicting early malignant transformation of papillary mucoid tumors in pancreatic ducts of claim 1, wherein the analysis modeling module compares the indexes of risk factors affecting the malignancy and malignancy of tumors of patients according to a single-factor analysis method between LGD group and HGD/pT1a group, and between HGD/pT1a group and the pT1a group excluding I-IPMC, and uses Mann-Whitney U test or paired t test for continuous variables by using Chi-Square test or Fisher exact probability method.
4. The pancreatic intraductal papillary mucinous tumor early malignant transformation prediction system of claim 1The method is characterized in that the predictive model established by the analysis modeling module is as follows:
wherein, P is a predicted value, MD/MT represents a main pancreatic duct type/mixed type IPMN, BD-IPMN represents a branch type IPMN, mural nodes represents cyst wall nodules, solid components represent actual components, CBD differentiation represents common bile duct expansion, serum CEA is a tumor index CEA value, and serum CA125 is a tumor index CA125 value.
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