CN114494232A - Breast cancer breast-conserving operation margin state prediction model based on image omics - Google Patents
Breast cancer breast-conserving operation margin state prediction model based on image omics Download PDFInfo
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Abstract
The invention provides a breast cancer breast-conserving operation margin state prediction model based on image omics, belonging to the technical field of biology. The construction method of the prediction model comprises the following steps: (1) dividing breast cancer breast-conserving operation patients meeting the standard into a training set and a testing set, and collecting breast MRI images of the breast cancer breast-conserving operation patients meeting the standard; (2) delineating a target lesion of the breast MRI on a T1 enhanced sequence of the MRI image, and extracting the imaging features on a T1 enhanced sequence, a T1 flat scan sequence and a T2 flat scan sequence; (3) dimensionality reduction is carried out on the iconography characteristics by using a dimensionality reduction strategy for backward recursion characteristic elimination, and then an XGboost algorithm is used for constructing a breast cancer breast-protecting operation margin state prediction model based on the iconography. The prediction model is established by using the preoperative MRI image of the patient receiving the breast protection operation and adopting an analysis method of the image omics, can predict the incisal margin state of the breast protection operation of the patient and provides corresponding reference for the preparation of an operation plan.
Description
Technical Field
The invention belongs to the technical field of biology, and particularly relates to a breast cancer breast-conserving operation margin state prediction model based on image omics.
Background
In China, breast cancer is the cancer with the highest incidence rate and the sixth highest fatality rate in women. Globally, breast cancer accounts for approximately 24% of the number of new cancers worldwide in women each year, and is also the first to women. Of all new-onset breast cancers, about 15.7% is stage I, 44.9% is stage II, and 18.7% is stage III, and can be treated by surgical operation. Radical breast cancer surgery (breast conservation surgery) to preserve the breast is one of the standard treatments for early stage breast cancer. Several large-scale clinical studies have shown that breast conserving surgery plus standard radiotherapy has the same survival benefit as patients who traditionally received whole breast resection. Successful breast conservation surgery must achieve surgical margin negativity. Previous studies have shown that margin positivity is significantly associated with increased local recurrence of breast cancer. Due to the positive margin after breast conservation surgery, about 20-30% of patients need to undergo secondary surgery, which may increase patient anxiety and increase surgical costs. The ASBS consensus provides several methods for reducing the secondary operation rate of breast-conserving operations, including intraoperative freezing, fine needle puncture and the like. However, none of these methods predict the risk of margin positivity preoperatively. The preoperative accurate estimation of the risk of the positive margin of breast protection operation has reference significance for clinical decision. According to literature reports, several clinical predictive models have been developed to predict the probability of cut-edge positivity. However, most predictive models contain only clinical pathology variables that are not strong predictors of edge states, and thus have poor ability to predict edge states. Furthermore, the sample size of these studies was too small to draw convincing conclusions. Therefore, a reliable model for preoperatively predicting the incisal margin of breast conserving surgery is needed.
Imaging omics perform quantitative studies by extracting high-throughput imaging features from medical images. In the aspect of breast cancer imaging omics research, the method is applied to the diagnosis of breast cancer, the prediction of axillary lymph node metastasis, the prediction of treatment effect, the prediction of survival and the like.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a breast cancer breast conservation surgery margin state prediction model based on imagery omics.
In order to achieve the purpose, the invention adopts the technical scheme that: a construction method of a breast cancer breast-conserving operation margin state prediction model based on imagery omics comprises the following steps:
(1) dividing breast cancer breast-conserving operation patients meeting the standard into a training set and a testing set, and collecting breast MRI images of the breast cancer breast-conserving operation patients meeting the standard;
(2) delineating a target lesion of the breast MRI on a T1 enhanced sequence of the MRI image, and extracting the imaging features on a T1 enhanced sequence, a T1 flat scan sequence and a T2 flat scan sequence;
(3) dimensionality reduction is carried out on the iconography characteristics by using a dimensionality reduction strategy for backward recursion characteristic elimination, and then an XGboost algorithm is used for constructing a breast cancer breast-protecting operation margin state prediction model based on the iconography.
The invention uses the preoperative MRI image of a patient receiving the breast protection operation, adopts the analysis method of the image omics to establish a prediction model so as to realize the prediction of the incisal margin state of the breast protection operation before the operation and provide corresponding reference for the preparation of an operation plan.
The preferred embodiment of the method for constructing the breast cancer breast conservation surgery incisal marginal state prediction model based on the imageomics further comprises the following steps (5): and testing the model by adopting the test set to obtain the optimal parameters of the model.
As a preferred embodiment of the method for constructing the breast cancer breast-conserving operation margin state prediction model based on the imaging omics, after the breast cancer breast-conserving operation margin state prediction model based on the imaging omics is constructed, a verification set is adopted to verify the prediction result of the breast cancer breast-conserving operation margin state prediction model based on the imaging omics.
As a preferred embodiment of the construction method of the breast cancer breast-conserving surgery incisal marginal state prediction model based on the imaging omics, the verification comprises area AUC under ROC curve, sensitivity and specificity.
As a preferred embodiment of the construction method of the breast cancer breast-conserving operation margin state prediction model based on the imaging omics, the target focus comprises the range of 0-10mm of tumor parenchyma and tumor margin.
As a preferred embodiment of the method for constructing the breast cancer breast conserving surgery incisal margin state prediction model based on the imagery omics, the imagery characteristics comprise Shape Features (Shape Features); first Order Features (First Order Features); gray level co-occurrence matrix features (GLCM); a gray scale dimension area matrix feature (GLSZM); gray run length matrix features (GLRLM); adjacent gray tone difference matrix features (NGTDM); gray-level dependent matrix Features (GLDM) and Wavelet Features (Wavelet-based Features).
As a preferred embodiment of the method for constructing the mammary cancer breast-conserving surgery margin state prediction model based on the imaging omics, the criteria comprise inclusion criteria and exclusion criteria; the selection standard is that the patient is pathologically diagnosed as invasive breast cancer, receives breast cancer breast conservation surgery (including patients who are subjected to breast cutting surgery because the breast cutting surgery can not be changed due to negative margin of cut) and receives breast MRI examination within 1-2 weeks before surgery, and the breast MRI examination is stored in a DICOM format, wherein the breast MRI comprises a T1 enhancing sequence, a T1 horizontal scanning sequence and a T2 horizontal scanning sequence; the exclusion criteria are that the tumor has undergone neoadjuvant chemotherapy, neoadjuvant endocrine therapy, preoperative anti-tumor therapy, bilateral breast cancer, multifocal/multicentric breast cancer, poor MRI imaging quality (e.g., motion artifacts, etc.), or has undergone resection biopsy prior to MRI imaging.
The invention also provides a breast cancer breast conservation operation margin state prediction model based on the imagomics, which is constructed by the construction method.
The invention also provides application of the breast cancer breast-conserving operation margin state prediction model based on the imagomics in predicting the breast cancer patient breast-conserving operation margin state. According to the breast cancer breast-preservation operation margin state prediction model based on the imaging omics, the corresponding model score can be obtained only by inputting the magnetic resonance image data of a breast cancer patient into the model, the breast cancer breast-preservation operation margin state of the breast cancer patient can be predicted according to the score, and the breast-preservation operation strategy is determined.
As a preferred embodiment of the use according to the invention, low risk of margin positive is indicated when the model score of a breast cancer patient is less than 0.2 min; a high risk of margin positive is scored when the model score for breast cancer patients is greater than 0.6.
The invention has the beneficial effects that: the invention provides a breast cancer breast preservation operation margin state prediction model based on imagery omics, which is established by using a preoperative MRI image of a patient receiving a breast preservation operation and adopting an analysis method of the imagery omics, can predict the breast cancer breast preservation operation margin state of the patient and provides corresponding reference for the preparation of an operation plan.
Drawings
Fig. 1 is a test set ROC graph of a breast cancer breast conservation surgery incisal marginal state prediction model based on imagery omics.
Fig. 2 is a ROC graph of breast cancer breast conserving surgery margin state prediction model based on imaging omics for validation set 1.
Fig. 3 is a graph of ROC (breast cancer breast preservation surgery margin state prediction model) based on the imaging omics of the validation set 2.
Detailed Description
In order to more concisely and clearly demonstrate technical solutions, objects and advantages of the present invention, the following detailed description of the technical solutions of the present invention is provided with reference to specific embodiments and accompanying drawings.
Example 1
The method for constructing the breast cancer breast conservation operation margin state prediction model based on the imaging omics comprises the following steps:
(1) the baseline data and the breast MRI raw data of the patients in a plurality of centers are collected, and the total number of the patients is 576 patients meeting the inclusion standard, wherein 304 patients are admitted to the Sun-Yi Xian memorial hospital of Zhongshan university, 131 patients are admitted to the tumor prevention and treatment center of Zhongshan university, and 141 patients are admitted to the people's hospital of Tangshan city. The inclusion and exclusion criteria include inclusion criteria and exclusion criteria. The inclusion criteria were (1) invasive breast cancer as diagnosed pathologically; (2) receiving breast cancer breast protection operation (including a breast cutting operation patient who changes breast after breast protection operation because the breast protection operation cannot reach negative margin); (3) breast MRI, which contained a T1 enhancement sequence, a T1 pan sequence, and a T2 pan sequence, was examined 1-2 weeks prior to surgery and stored in DICOM format. Patients who met one of the following exclusion criteria were excluded: (1) preoperative antitumor treatments such as neoadjuvant chemotherapy and neoadjuvant endocrine therapy are received; (2) bilateral breast cancer; (3) multifocal/multicentric breast cancer; (4) poor MRI imaging quality (e.g., motion artifacts, etc.); (5) tumors had undergone resection biopsy prior to MRI.
(2) According to 7: 3 into training and testing sets. The patients of the center for tumor prevention and treatment of Zhongshan university are verification set 1, and the patients of people's hospital in Tangshan City are verification set 2.
(3) Using 3D-Slicer software to delineate a target lesion of breast MRI on a T1 enhanced sequence from magnetic resonance image data and extract an iconomics feature, wherein the target lesion comprises: the tumor parenchyma and the tumor margin range from 0 to 10 mm. The extracted imagery Features include Shape Features (Shape Features); first Order Features (First Order Features); gray level co-occurrence matrix features (GLCM); a gray scale dimension area matrix feature (GLSZM); gray run length matrix features (GLRLM); adjacent gray tone difference matrix features (NGTDM); gray-level dependent matrix Features (GLDM) and Wavelet Features (Wavelet-based Features).
(4) And performing statistical analysis by using an R language, firstly performing dimensionality reduction by using a dimensionality reduction strategy for eliminating backward recursion characteristics, and then constructing a breast cancer breast-preserving operation margin state prediction model based on the image omics by using an XGboost algorithm.
(5) And (3) performing parameter adjustment on the constructed breast cancer breast-conserving operation margin state prediction model based on the imagomics by adopting the test set.
(6) And (3) verifying the breast cancer breast-conserving operation incisal marginal state prediction model based on the image omics by adopting the verification set 1 and the verification set 2, drawing an ROC curve, and calculating a corresponding AUC value.
The ROC curves of the training set, the validation set 1 and the validation set 2 in this embodiment are shown in fig. 1 to 3, where AUC of the training set, AUC of the validation set 1 and AUC of the validation set 2 are 0.79, 0.82 and 0.76, respectively, which indicates that the breast cancer breast conservation surgery incisal marginal state prediction model based on the imagery omics has better accuracy.
Example 2
Magnetic resonance imaging data of 576 patients were input into the mammary cancer breast-conserving surgery margin state prediction model based on the imaging group in example 1, and a model score was calculated. Statistical analysis shows that when the score is less than 0.2, the cut edge positive rate is less than 10%, and when the score is greater than 0.6, the cut edge positive rate is greater than 50%. Therefore, the following breast conserving surgery planning strategy can be derived from the predictive scoring of the patient by the model: (1) when the score is predicted to be less than 0.2 minutes through preoperative mammary gland MRI, the incisal edge positive rate is very low, an assessment method of intraoperative freezing can be omitted, and the operation time is reduced; (2) when the score predicted by preoperative breast MRI is greater than 0.6 score, the margin positive rate using the conventional width is higher, and the surgical margin width can be appropriately enlarged to realize margin negative. (3) When the score is between 0.2 and 0.6, the capability of the model for predicting the cutting edge is insufficient, and a conventional breast-protecting operation strategy is used.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A construction method of a breast cancer breast-conserving operation margin state prediction model based on image omics is characterized by comprising the following steps:
(1) dividing breast cancer breast-conserving operation patients meeting the standard into a training set and a testing set, and collecting breast MRI images of the breast cancer breast-conserving operation patients meeting the standard;
(2) delineating a target lesion of the breast MRI on a T1 enhanced sequence of the MRI image, and extracting the imaging features on a T1 enhanced sequence, a T1 flat scan sequence and a T2 flat scan sequence;
(3) dimensionality reduction is carried out on the iconography characteristics by using a dimensionality reduction strategy for backward recursion characteristic elimination, and then an XGboost algorithm is used for constructing a breast cancer breast-protecting operation margin state prediction model based on the iconography.
2. The method of claim 1, further comprising the step (5): and testing the model by adopting the test set to obtain the optimal parameters of the model.
3. The method according to claim 1, wherein after constructing the mammomic-based breast cancer breast conserving surgery margin state prediction model, the prediction result of the mammomic-based breast cancer breast conserving surgery margin state prediction model is verified using a verification set.
4. The method of claim 3, wherein said validation comprises area under ROC curve AUC, sensitivity and specificity.
5. The method of claim 1, wherein the target lesion comprises tumor parenchyma and tumor margin in the range of 0-10 mm.
6. The method of claim 1, wherein the imaging features comprise shape features, first order features, gray level co-occurrence matrix features, gray scale size region matrix features, gray level run length matrix features, adjacent gray level hue difference matrix features, gray level dependency matrix features, and wavelet features.
7. The method of claim 1, wherein the criteria comprise inclusion criteria and exclusion criteria; the selection standard is that the patient is pathologically diagnosed as invasive breast cancer, receives breast cancer breast conservation operation and breast MRI examination before operation within 1-2 weeks, and is stored in a DICOM format; the exclusion criteria were the receiving of neoadjuvant chemotherapy, neoadjuvant endocrine therapy, preoperative anti-tumor therapy, bilateral breast cancer, multifocal/multicentric breast cancer, poor MRI imaging quality, or resection biopsy of the tumor prior to MRI filming.
8. A breast cancer breast conservation surgery margin state prediction model based on imagery omics and constructed according to the method of any one of claims 1 to 7.
9. The use of the mammomic-based breast cancer breast conservation surgery margin state prediction model of claim 8 for predicting breast cancer patient breast conservation surgery margin state.
10. The use of claim 9, wherein a low risk of positive margin is indicated when the breast cancer patient has a model score of less than 0.2 points; a high risk of margin positive is scored when the model score for breast cancer patients is greater than 0.6.
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