US-12620490-B2 - System and method for detecting recurrence of a disease

Abstract

A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.

Inventors

• Soumya Ghose
• FIONA GINTY
• Cynthia Elizabeth Landberg Davis
• Sanghee Cho
• Sunil S. Badve
• Yesim Gokmen-Polar

Assignees

• GE Precision Healthcare LLC
• THE TRUSTEES OF INDIANA UNIVERSITY

Dates

Publication Date

20260505

Application Date

20220325

Claims (16)

1. 1 . A method for determining a recurrence of a disease in a patient, the method comprising: generating a medical image of an organ of the patient; extracting an invasive edge around an area of interest in the medical image; obtaining a plurality of radiomics features from the invasive edge; and determining the recurrence of the disease based on the plurality of radiomics features, wherein the disease defines a breast cancer, wherein determining the recurrence of the disease comprises: ranking the plurality of radiomics features based on their ability to predict the recurrence of the cancer using a machine learning classifier; and providing high ranking radiomics features to a deep neural network for classifying the medical image as a disease recurrent medical image or a disease non-recurrent medical image, and wherein the deep neural network is trained on radiomics features that have been selected to predict the recurrence of the disease based on a plurality of training data pairs comprising noisy and pristine medical images.
2. 2 . The method of claim 1 , wherein the medical image comprises an X-ray image, a computed tomography (CT) image, a magnetic resonance image (MRI) or an ultrasound image.
3. 3 . The method of claim 1 , wherein the area of interest includes a tumor region.
4. 4 . The method of claim 1 , wherein extracting the invasive edge comprises segmenting the area of interest from the medical image.
5. 5 . The method of claim 4 , wherein the invasive edge includes an edge surrounding the area of interest having a width.
6. 6 . The method of claim 5 , wherein the width of the invasive edge is dependent on the nature of the disease and a resolution of the medical image.
7. 7 . The method of claim 5 , wherein the width of the invasive edge is in the range of 0.5 mm to 1 cm.
8. 8 . The method of claim 1 , wherein the plurality of radiomics features comprises shape, texture, intensity, and gradient magnitude of the invasive edge.
9. 9 . The method of claim 1 , wherein the disease comprises triple negative breast cancer, receptor positive breast cancer, or HER-2 positive breast cancer.
10. 10 . The method of claim 1 further comprising extracting an area in the tumor region along with the invasive edge around the tumor region and determining the recurrence of the disease based on the radiomics features in both the invasive edge and the tumor region area.
11. 11 . A system comprising: a memory storing a machine learning model including a classifier and a deep learning network; a display device; and a processor communicably coupled to the memory and configured to: receive a medical image of an organ of the patient; rank a plurality of radiomics features based on their ability to predict the recurrence of the cancer using the classifier; providing high ranking radiomics features of an invasive edge around an area of interest in the medical image to the deep learning network; classify the medical image using the deep neural network as a disease recurrent image or a disease non-recurrent image based on the high ranking radiomics features; and display the disease recurrent image via the display device, wherein the disease defines a breast cancer, and wherein the deep neural network is trained on radiomics features that have been selected to predict the recurrence of the disease based on a plurality of training data pairs comprising noisy and pristine medical images.
12. 12 . The system of claim 11 , wherein the processor is further configured to extract the invasive edge by segmenting the area of interest from the medical image.
13. 13 . The system of claim 11 , wherein the invasive edge includes an edge surrounding the area of interest having a width.
14. 14 . The system of claim 13 , wherein the width of the invasive edge is in the range of 0.5 mm to 1 cm.
15. 15 . The system of claim 11 , wherein the plurality of radiomics features comprises shape, texture, intensity, and gradient magnitude of the invasive edge.
16. 16 . The system of claim 11 , wherein the disease comprises triple negative breast cancer, receptor positive breast cancer, or HER-2 positive breast cancer.

Description

FIELD Embodiments of the subject matter disclosed herein relate to medical imaging, and more particularly to breast cancer detection using medical imaging. BACKGROUND Breast cancer is the most common cancer in women worldwide, affecting an estimated 1.5 million women around the world each year. It is also a leading cause of cancer-related death in women. Early detection of breast cancer can reduce mortality and the intensity of treatment required. Among the breast cancer subtypes, Triple negative breast cancer (TNBC) is the most aggressive and heterogeneous breast cancer subtype and accounts for 10-20% of newly diagnosed early breast cancers. The lack of hormone receptors and human epidermal growth factor receptor 2 (HER2) prevent TNBCs from being treated with therapies against these targets. Recurrences of TNBC occurs in about 25% of patients and is observed within the first few years after diagnosis. Early detection of recurrence from routinely collected mammograms would allow early intervention and a better treatment procedure. In many cases, the cancer tumor in a breast is detected by a medical imaging procedure such as a Mammography. In digital mammography, a scout or pre-shot image may be taken of a patient to determine an x-ray technique (e.g., x-ray tube current and voltage, exposure time) to acquire images of the patient having a sufficient brightness. Upon determination of the x-ray technique, one or more x-ray images of the patient may be acquired. In some examples, multiple x-ray images may be acquired at different view angles and/or at different energy levels. Although, an existing cancer tumor may be detected using the mammography technique, predicting recurrence in TNBC is difficult from routinely collected clinical data including biopsy samples, clinical information, and mammograms. Therefore, there is a need for an improved system and method to determine recurrence for triple negative breast cancer patients. BRIEF DESCRIPTION In accordance with an embodiment of the present technique, a method for determining a recurrence of a disease in a patient is presented. The method includes generating a medical image of an organ of the patient and extracting an invasive edge around an area of interest in the medical image. The method further includes obtaining a plurality of radiomics features from the invasive edge. Finally, the method includes determining the recurrence of the disease based on the plurality of radiomics features. In accordance with another embodiment of the present technique, a system including a memory storing a machine learning model is presented. The system further includes a display device and a processor that is communicably coupled to the memory and configured to receive a medical image of an organ of the patient. The processor is further configured to classify the medical image using the machine learning model network as a disease recurrent image or a disease non-recurrent image based on the radiomics features of an invasive edge around an area of interest in the medical image and display the disease recurrent image via the display device. It should be understood that the brief description above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS The present disclosure will be better understood from reading the following description of non-limiting embodiments, with reference to the attached drawings, wherein below: FIG. 1 is a schematic illustration of an exemplary x-ray system, in accordance with an embodiment of the present technique; FIG. 2 is a flowchart representing a method for detecting a recurrence of a disease from medical images, in accordance with an embodiment of the present technique; FIGS. 3A and 3B are pictorial views showing extraction of an invasive edge around an area of interest in the medical image, in accordance with an embodiment of the present technique; FIG. 3C is a pictorial view of an image of the extracted invasive edge from the medical image, in accordance with an embodiment of the present technique; FIG. 4 is a schematic diagram illustrating an image processing system for detecting a recurrence of a disease using a deep neural network, according to an exemplary embodiment; and FIGS. 5A and 5B are pictorial views showing validation results of an experimental study for peritumoral region and intra-tumoral region, in accordance with an embodiment of the present technique DETAILED DESCRIPTION One or more specific embodiments of the present disclosure are described below. These described embodiments are only examples of the sys