CN113034436A - Breast cancer molecular typing change prediction device based on mammary gland MR imaging omics - Google Patents
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Abstract
The invention discloses a breast cancer molecular typing change prediction device based on breast MR imaging omics, which comprises: the acquisition module is used for acquiring an MR image of a breast cancer patient; the segmentation module is used for extracting an enhanced image in the MR image and sketching the edge of the tumor in the enhanced image to obtain a marked focus area; the first prediction module is used for extracting a focus region to obtain an image to be processed, preprocessing the image to be processed and inputting the preprocessed image into the deep learning model to obtain a first prediction result of breast cancer molecular typing; the second prediction module is used for extracting the characteristic data in the enhanced image and analyzing the characteristic data to obtain a second prediction result of breast cancer molecular typing; and the verification module is used for verifying the robustness of the first prediction result and the second prediction result. According to the embodiment of the invention, robustness verification is carried out through the first prediction result and the second result, and the accuracy of breast cancer molecule typing prediction can be effectively improved.
Description
Technical Field
The invention relates to the technical field of computer medical image information processing, in particular to a breast cancer molecular typing change prediction device based on breast MR imaging omics.
Background
Mammary gland imaging omics are mainly applied at present: identification of benign and malignant tumors, prediction of the effect of neoadjuvant therapy, correlation with prognostic factors (including lymph node metastasis status, correlation with Ki67 expression, etc.), correlation with molecular typing and clinical staging, evaluation of risk of relapse, etc. The imaging omics is used for researching molecular typing or immunohistochemical expression of breast cancer, is the correlation research of baseline molecular typing or immunohistochemical, has limited value for clinical diagnosis and treatment at the present stage under the condition of conventional immunohistochemistry of puncture pathology before new auxiliary chemotherapy, but proves that the magnetic resonance imaging omics has the capability of distinguishing different molecular typing breast cancers, and provides a foundation for the imaging omics research in each molecular typing group. The curative effect evaluation of the imaging omics for breast cancer neoadjuvant therapy mainly focuses on prediction of pathological complete remission (pCR), and in addition, the research specially aiming at HER-2 positive breast cancer and subtype prognosis thereof and the research on patient prognosis insensitive to neoadjuvant therapy both obtain more ideal prediction models, and the research of the breast cancer imaging omics has certain technical basis and experience. The existing breast cancer molecule typing change prediction device only adopts a deep learning method to carry out breast cancer molecule typing prediction analysis, but the deep learning method does not have causal logicality, so that the prediction result is inaccurate.
Disclosure of Invention
The invention provides a breast cancer molecular typing change prediction device based on breast MR imaging omics, which aims to solve the technical problem that the prediction result is inaccurate as the existing breast cancer molecular typing change prediction device only adopts a deep learning method to carry out breast cancer molecular typing prediction analysis.
The embodiment of the invention provides a breast cancer molecular typing change prediction device based on breast MR imaging omics, which comprises:
the acquisition module is used for acquiring an MR image of a breast cancer patient;
the segmentation module is used for extracting an enhanced image in the MR image and sketching the edge of the tumor in the enhanced image to obtain a marked focus area;
the first prediction module is used for extracting the focus region to obtain an image to be processed, preprocessing the image to be processed and inputting the preprocessed image into a deep learning model to obtain a first prediction result of breast cancer molecular typing;
the second prediction module is used for extracting the characteristic data in the enhanced image and analyzing the characteristic data to obtain a second prediction result of breast cancer molecular typing;
and the verification module is used for verifying the robustness of the first prediction result and the second prediction result.
Further, the segmentation module is specifically configured to:
and deriving a breast cancer MR image in a DICOM format, carrying out desensitization treatment on the breast cancer MR image, extracting an enhanced image in the MR image, and delineating the edge of the tumor in the enhanced image to obtain a marked lesion region.
Further, the extracting the focus area to obtain an image to be processed specifically includes:
labeling and copying the focus region of the MR image in DCE imaging, and extracting the focus region through T1 weighted imaging, T2 weighted imaging and diffusion weighted imaging respectively to obtain an image to be processed.
Further, the image to be processed is input into a deep learning model after being subjected to image preprocessing, so as to obtain a first prediction result of breast cancer molecular typing, specifically:
the method comprises the steps of carrying out image preprocessing and data enhancement processing on an image to be processed to obtain image data to be input, inputting the image data to be input into a deep learning model, and carrying out molecular typing change prediction according to tumor tissue information in the image to be input to obtain a first prediction result of breast cancer molecular typing.
Further, the tumor tissue information includes, but is not limited to, volume information and texture information.
Further, the second prediction module is specifically configured to:
extracting feature data in the enhanced image, constructing a multivariate logistic regression model, training the multivariate logistic regression model according to the feature data to obtain optimal parameters, and optimizing the multivariate logistic regression model by using the optimal parameters to obtain an optimal prediction model; and inputting the characteristic data into the optimal prediction model to obtain a second prediction result of breast cancer molecular typing.
Further, the feature data includes tumor overall feature data, 3D feature data, and main feature data, and the extracting the feature data in the enhanced image specifically includes:
extracting feature information without specific labels in the image to be processed as tumor overall feature data;
extracting the 3D characteristic data by constructing a tumor 3D model;
and analyzing the characteristic coefficient in the picture to be processed by constructing an LASSO model, and screening out main characteristics according to the characteristic coefficient to obtain main characteristic data.
According to the embodiment of the invention, the first prediction result of the breast cancer molecular typing change is obtained based on deep learning detection, the second prediction result of the breast cancer molecular typing change is obtained based on feature extraction, robustness verification is carried out on the first prediction result and the second prediction result, the first prediction result and the second prediction result can be verified mutually, and the final prediction result is determined according to the verification result, so that the accuracy of breast cancer molecular typing prediction is improved, and further, a theoretical basis can be provided for clinical individualized accurate diagnosis and treatment of a new breast cancer adjuvant therapy patient.
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FIG. 1 is a schematic structural diagram of an apparatus for predicting molecular typing changes of breast cancer according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for predicting molecular typing changes of breast cancer according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
Referring to fig. 1, an embodiment of the present invention provides a breast cancer molecular typing change prediction device based on breast MR imaging omics, including:
the acquisition module 1 is used for acquiring an MR image of a breast cancer patient;
optionally, acquiring MR images of breast cancer patients by inclusion and exclusion criteria, wherein the inclusion and exclusion criteria include case inclusion and exclusion criteria and MR inclusion and exclusion criteria, the case inclusion and exclusion criteria include case inclusion criteria and case exclusion criteria, and the case inclusion criteria include 1) all patients undergoing new adjuvant therapy in the hospital; 2) puncture pathological results and specimens with baselines are obtained, and immunohistochemical detection is carried out; 3) the pathological result and specimen of the operation after the new adjuvant therapy exist, and the immunohistochemical detection is carried out; case exclusion criteria included 1) non-primary breast malignancy patients; 2) patients with recurrent breast cancer; 3) patients with multicentric breast cancer of different molecular typing; MR inclusion criteria include: 1) patients who meet the case inclusion criteria (1); 2) there is a hospital MRI original image; the MR exclusion standard comprises that the MRI original image does not meet the quality control standard; 2) MRI images lack DCE data; 3) a breast tissue marker is placed within the tumor in the MRI image.
As a specific embodiment, the information of the breast MR image acquisition includes: the axial T1WI-DCE sequence shows the blood supply information of the focus; the axial T1WI scout, T2WI scout liposuction and T1WI augmentation delay period sequences show anatomical and spatial information of the lesion; axial DWI sequences and ADC maps show functional information of the lesions.
The segmentation module 2 is used for extracting an enhanced image in the MR image and sketching the edge of the tumor in the enhanced image to obtain a marked focus area;
the first prediction module 3 is used for extracting a focus region to obtain an image to be processed, preprocessing the image to be processed and inputting the preprocessed image into the deep learning model to obtain a first prediction result of breast cancer molecular typing;
the second prediction module 4 is used for extracting feature data in the enhanced image and analyzing the feature data to obtain a second prediction result of breast cancer molecular typing;
and the verification module 5 is used for verifying the robustness of the first prediction result and the second prediction result.
According to the embodiment of the invention, the first prediction result and the second prediction result can be mutually verified by performing robustness verification on the first prediction result and the second prediction result, and the final prediction result is determined according to the verification result, so that the accuracy of breast cancer molecular typing prediction is improved, and further, a theoretical basis can be provided for clinical individualized accurate diagnosis and treatment of a breast cancer newly-assisted treatment patient.
As a specific implementation manner of the embodiment of the present invention, the segmentation module 2 is specifically configured to:
and deriving a breast cancer MR image in a DICOM format, carrying out desensitization treatment on the breast cancer MR image, extracting an enhanced image in the MR image, and delineating the edge of the tumor in the enhanced image to obtain a marked lesion region.
In the embodiment of the invention, a breast cancer MR image in a DICOM format is derived from a PACS system, desensitization processing is carried out on the breast cancer MR image, a 120 th cross-sectional position T1W enhanced image of a DCE sequence in the breast cancer MR image is extracted and is led into a 3D-Slicer, and a marked lesion region is obtained by segmenting the cross-sectional position T1W enhanced image in the 3D-Slicer, wherein the image segmentation comprises manual segmentation and automatic segmentation, and the manual segmentation specifically comprises the step of realizing segmentation of the lesion region by delineating a tumor edge in a cross-sectional position T1W enhanced image in the 3D-Slicer; the automatic segmentation comprises the steps of utilizing a segmentation model to combine two-dimensional and three-dimensional characteristics of an image, and jointly optimizing in-chip and out-chip characteristics through a mixed characteristic fusion layer to realize accurate segmentation of a focus area.
Furthermore, the optimized automatic segmentation model is adopted for segmentation, and compared with the traditional three-dimensional convolution model, the method can effectively reduce parameters and calculation cost, so that the training overhead can be effectively reduced.
As a specific implementation manner of the embodiment of the present invention, the extracting of the lesion area to obtain the image to be processed specifically includes:
and marking and copying a focus region of the MR image in DCE imaging, and extracting the focus region through T1 weighted imaging, T2 weighted imaging and diffusion weighted imaging respectively to obtain an image to be processed.
As a specific implementation manner of the embodiment of the present invention, an image to be processed is input into a deep learning model after being subjected to image preprocessing, so as to obtain a first prediction result of breast cancer molecular typing, which specifically is:
the method comprises the steps of carrying out image preprocessing and data enhancement processing on an image to be processed to obtain image data to be input, inputting the image data to be input into a deep learning model, and carrying out molecular typing change prediction according to tumor tissue information in the image to be input to obtain a first prediction result of breast cancer molecular typing.
In the embodiment of the invention, the deep learning model based on the convolutional neural network is constructed, and the deep learning model has a deeper network layer number, so that the data depicting capability of the neural network can be improved, more complex functions can be fitted, and the prediction accuracy can be effectively improved. Furthermore, a convolution module is added in the deep learning model, so that the accuracy of MR image processing is improved.
As a specific implementation manner, the embodiment of the present invention constructs an encoder-decoder architecture based on a deep learning model of a convolutional neural network, where the encoder is configured to extract image features, and the decoder is configured to restore the extracted features to the size of an original image and output a final segmentation result. Optionally, in consideration of predicting breast cancer molecular typing by using three-dimensional data, the embodiment of the invention constructs a three-dimensional convolutional neural network model, and further improves the segmentation effect by using three-dimensional space characteristics. Specifically, a rough liver segmentation result is obtained by using a simple Resnet, two-dimensional image features are effectively extracted by using a two-dimensional convolution neural network model, three-dimensional image features are extracted by using a three-dimensional convolution neural network, and finally a mixed feature fusion layer is designed to jointly optimize the two-dimensional and three-dimensional features. The applicability of the neural network model in the prospective sample is evaluated by using an AUC value or a decision curve, a calibration curve and a nomogram, and the model is adjusted according to the segmentation result, so that the accuracy of the segmentation result of the model can be further improved.
As a specific implementation of an embodiment of the present invention, the tumor tissue information includes, but is not limited to, volume information and texture information.
In the embodiment of the invention, the breast cancer molecule typing change prediction is carried out according to the tumor volume and the tumor texture in the input image, so that the prediction accuracy can be effectively improved.
As a specific implementation manner of the embodiment of the present invention, the second prediction module 4 is specifically configured to:
extracting feature data in the enhanced image, constructing a multivariate logistic regression model, training the multivariate logistic regression model according to the feature data to obtain optimal parameters, and optimizing the multivariate logistic regression model by using the optimal parameters to obtain an optimal prediction model; and inputting the characteristic data into the optimal prediction model to obtain a second prediction result of breast cancer molecular typing.
In the embodiment of the invention, the first prediction result and the second prediction result can be mutually verified by performing robustness verification on the first prediction result and the second prediction result, and the final prediction result is determined according to the verification result, so that the accuracy of breast cancer molecular typing prediction is improved, and further, a theoretical basis can be provided for clinical individualized accurate diagnosis and treatment of a breast cancer newly-assisted treatment patient.
As a specific implementation manner of the embodiment of the present invention, the feature data includes tumor whole feature data, 3D feature data, and main feature data, and the feature data in the enhanced image is extracted specifically as follows:
extracting feature information without specific labels in the image to be processed as tumor overall feature data;
extracting 3D characteristic data by constructing a tumor 3D model;
and analyzing the characteristic coefficient in the picture to be processed by constructing an LASSO model, and screening out main characteristics according to the characteristic coefficient to obtain main characteristic data.
Fig. 2 is a schematic flow chart of a breast cancer molecular typing change prediction method based on breast MR imaging omics according to an embodiment of the present invention.
The embodiment of the invention has the following beneficial effects:
according to the embodiment of the invention, the first prediction result and the second prediction result can be mutually verified by performing robustness verification on the first prediction result and the second prediction result, and the final prediction result is determined according to the verification result, so that the accuracy of breast cancer molecular typing prediction is improved, and further, a theoretical basis can be provided for clinical individualized accurate diagnosis and treatment of a breast cancer newly-assisted treatment patient.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.
Claims (7)
1. A breast cancer molecular typing change prediction device based on breast MR imaging omics is characterized by comprising:
the acquisition module is used for acquiring an MR image of a breast cancer patient;
the segmentation module is used for extracting an enhanced image in the MR image and sketching the edge of the tumor in the enhanced image to obtain a marked focus area;
the first prediction module is used for extracting the focus region to obtain an image to be processed, preprocessing the image to be processed and inputting the preprocessed image into a deep learning model to obtain a first prediction result of breast cancer molecular typing;
the second prediction module is used for extracting the characteristic data in the enhanced image and analyzing the characteristic data to obtain a second prediction result of breast cancer molecular typing;
and the verification module is used for verifying the robustness of the first prediction result and the second prediction result.
2. The breast cancer molecular typing change prediction apparatus according to claim 1 based on breast MR imaging omics wherein said segmentation module is specifically configured to:
and deriving a breast cancer MR image in a DICOM format, carrying out desensitization treatment on the breast cancer MR image, extracting an enhanced image in the MR image, and delineating the edge of the tumor in the enhanced image to obtain a marked lesion region.
3. The breast cancer molecular typing change prediction device based on breast MR imaging omics as defined in claim 1, wherein said extracting the lesion area yields an image to be processed, specifically:
labeling and copying the focus region of the MR image in DCE imaging, and extracting the focus region through T1 weighted imaging, T2 weighted imaging and diffusion weighted imaging respectively to obtain an image to be processed.
4. The breast cancer molecular typing change prediction device based on breast MR imaging omics as set forth in claim 1, wherein the image to be processed is input into the deep learning model after being subjected to image preprocessing, so as to obtain a first prediction result of breast cancer molecular typing, specifically:
the method comprises the steps of carrying out image preprocessing and data enhancement processing on an image to be processed to obtain image data to be input, inputting the image data to be input into a deep learning model, and carrying out molecular typing change prediction according to tumor tissue information in the image to be input to obtain a first prediction result of breast cancer molecular typing.
5. The breast cancer molecular typing change prediction apparatus according to claim 1 wherein said tumor tissue information includes but is not limited to volume information and texture information.
6. The breast cancer molecular typing change prediction device based on breast MR imaging omics as set forth in claim 1, wherein said second prediction module is specifically configured to:
extracting feature data in the enhanced image, constructing a multivariate logistic regression model, training the multivariate logistic regression model according to the feature data to obtain optimal parameters, and optimizing the multivariate logistic regression model by using the optimal parameters to obtain an optimal prediction model; and inputting the characteristic data into the optimal prediction model to obtain a second prediction result of breast cancer molecular typing.
7. The breast cancer molecular typing change prediction device based on breast MR imaging omics as claimed in claim 6, wherein the feature data comprises tumor global feature data, 3D feature data and main feature data, and the extracting of the feature data in the enhanced image comprises:
extracting feature information without specific labels in the image to be processed as tumor overall feature data;
extracting the 3D characteristic data by constructing a tumor 3D model;
and analyzing the characteristic coefficient in the picture to be processed by constructing an LASSO model, and screening out main characteristics according to the characteristic coefficient to obtain main characteristic data.
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| CN116664563A (en) * | 2023-07-27 | 2023-08-29 | 浙江杜比医疗科技有限公司 | New adjuvant chemotherapy curative effect evaluation system, equipment and medium |
| CN116664563B (en) * | 2023-07-27 | 2023-11-24 | 浙江杜比医疗科技有限公司 | New adjuvant chemotherapy curative effect evaluation system, equipment and medium |
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