Method and apparatus for classifying core biopsy specimens with optical coherence tomography
11226189 · 2022-01-18
Assignee
Inventors
Cpc classification
A61B10/0283
HUMAN NECESSITIES
G01N21/4795
PHYSICS
G01B9/02091
PHYSICS
A61B5/7264
HUMAN NECESSITIES
G01N33/4833
PHYSICS
International classification
A61B10/02
HUMAN NECESSITIES
A61B10/00
HUMAN NECESSITIES
Abstract
Described herein are an apparatus and method by which at least one core specimen is obtained from a patient. The specimen is optionally placed on a tray, in a holder, or in another device designed to hold the tissue specimen; images of the specimens are acquired with optical coherence tomography, optical coherence tomography image data and, optionally, data from an additional imaging or analysis method, and when analyzed with the tissue classification process yield information on one or more of: the adequacy of the specimens obtained; the likelihood that they contain abnormal or malignant tissue; the regions and/or specimens most likely to contain diagnostic tissue; the approximate dimensions, area, or volume of the abnormal tissue; and the probable type of abnormality.
Claims
1. A method for classifying core biopsy specimens, the method comprising: obtaining a core biopsy specimen with a core needle; transferring the core biopsy specimen to a specimen holder coupled to the core needle, wherein the specimen holder comprises a plurality of segments and transferring the core biopsy specimen to the specimen holder comprises transferring the core biopsy specimen to a first segment of the plurality of segments, and wherein the first segment is located adjacent to the core needle; acquiring image data from the core biopsy specimen with an optical coherence tomography system coupled to the specimen holder; applying a tissue classification process to the acquired image data; providing feedback to a user based on the tissue classification process; acquiring one or more of patient demographic data, clinical data, and prior imaging data from patient records; assessing the acquired data in combination with the acquired optical coherence tomography image data from the core biopsy specimen; moving the specimen holder so that the first segment is no longer adjacent to the core needle and a second segment of the plurality of segments is adjacent to the core needle; obtaining a second core biopsy specimen with the core needle; transferring the second core biopsy specimen to the second segment; and repeating the acquiring, applying, and providing steps for the second core biopsy specimen.
2. The method of claim 1, wherein moving the specimen holder so that the first segment is no longer adjacent to the core needle and a second segment of the plurality of segments is adjacent to the core needle comprises rotating the specimen holder about an axis that is parallel to a longitudinal axis of the core needle.
3. The method of claim 1, further comprising using an output from the tissue classification process to spatially direct the acquisition of additional biopsy specimens.
Description
DRAWINGS
(1) The foregoing features will be more readily understood by reference to the following detailed description, taken with reference to the accompanying drawings, in which:
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DESCRIPTION
(9) The term “image” as used herein shall refer to any multidimensional representation, whether in tangible or otherwise perceptible form, or otherwise, whereby a value of some characteristic (amplitude, phase, etc.) is associated with each of a plurality of locations corresponding to dimensional coordinates of an object in physical space, though not necessarily mapped one-to-one thereonto. Thus, for example, the graphic display of the spatial distribution of some field, either scalar or vectorial, such as brightness or color, constitutes an image. So, also, does an array of numbers, such as a 3D holographic dataset, in a computer memory or holographic medium. Similarly, “imaging” refers to the rendering of a stated physical characteristic in terms of one or more images.
(10) The term “specimen” as used herein shall refer to a tangible, non-transitory physical object capable of being rendered as an image.
(11) Core needle biopsy procedures described herein may include vacuum-assisted needle biopsy procedures or any other tissue specimens acquired with a needle or similar device. The resulting tissue specimens are alternatively referred to as “cores,” “core specimens,” “core biopsy specimens,” or “core needle specimens.”
(12) According to an embodiment, a core biopsy needle is inserted into the breast of a patient and a specimen is acquired through the needle channel, the specimen being pulled by a vacuum into a cylindrical specimen holder having multiple slots for specimen storage and a reference reflective surface located on the outermost extent of the cylindrical specimen holder. Each specimen slot is numbered so that information on a specific specimen may be tracked and reported.
(13) In an embodiment, after specimen entry into the holder, a 3D optical coherence tomography data set is acquired from the entire specimen (i.e., such that data from all relevant tissue structures are captured). The image data are acquired using imaging optics located centrally to the specimen holder, with the imaging beam extending radially outward from the optics toward the reference surface. The image data are fed to a graphical processing unit-based interferometric synthetic aperture microscopy (ISAM) process (e.g., carried out by software executed by logic circuitry) to improve out-of-focus image resolution.
(14) According to an embodiment, the 3D ISAM image data, and optionally patient history and previously-acquired mammography data, are then processed locally or sent to cloud-based remote processing hardware that carries out a classification process. The processing hardware may include one or more computer processors or graphical processing units and may have the architecture described in conjunction with
(15) In an embodiment, the classification process (i.e., the processing hardware executing software) measures the refractive index and attenuation of the core biopsy specimens by determining the apparent distance and intensity of the reference surface, respectively. Optionally, the classification process can also assess the spectral response of the sample. The classification process uses learned characteristics from historical specimen pathology diagnoses and their corresponding 3D ISAM image data, patient history data, mammography data, refractive index data, and/or attenuation data to determine the likelihood that the specimen contains malignant tissue and the likelihood that the specimen contains adequate tissue for a subsequent pathology diagnosis. An alternative approach includes parametric analysis and classification (e.g., by the processing hardware).
(16) According to an embodiment, the classification process (e.g., the processing hardware) reports the findings to a biopsy needle controller (i.e., logic circuitry integrated with or in communication with the biopsy needle). If the specimen is found to contain insufficient tissue for a subsequent pathology diagnosis, the needle will rotate and acquire an additional specimen in an effort to locate more appropriate tissue. If the initial specimen contained adequate tissue for a subsequent pathology diagnosis, the needle will acquire additional specimens from the same location in an effort to completely remove all accessible malignant tissue. After each tissue removal, data will be sent to the cloud computing hardware and the classification process will update, returning feedback to the biopsy needle controller.
(17) In an embodiment, upon completion of the tissue removal process, the classification process will automatically issue a final report, including a malignancy likelihood score, an imaging disposition score, similar to a BIRADS score, and pathology guidance. The pathology guidance includes information on the most important specimens to target for histology analysis and those specimens that would benefit from immunohistochemical analysis.
(18) In an embodiment, histology analysis is performed on the numbered specimens, the results of which, when linked to the corresponding OCT imaging and other classification algorithm input data, are used as data for improvement of the classification algorithm. In an embodiment, the classification algorithm incorporates one or both of an artificial intelligence algorithm and a machine learning algorithm. In the case of an artificial intelligence or machine learning algorithm, these data form a feedback loop for continuous learning.
(19) Alternatively to the specimen holder described, the specimens may enter a tray or basket external to the biopsy device. In this case, OCT image acquisition would be performed outside the biopsy needle device where the tray may be moved to an imaging device, (e.g., as a slide is place onto a microscope) or samples may be placed directly into an imaging device. As will be discussed below in further detail,
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(27) Example implementations of the memory 504 include a non-transitory computer-readable medium (such as solid-state memory or magnetic storage memory). The logic circuitry 502 is a circuit (a type of electronic hardware) designed to perform complex functions defined in terms of mathematical logic. Example implementations of the logic circuitry 502 include a microprocessor, a graphics processing unit, a field-programmable gate array, a controller, or an application-specific integrated circuit.
(28) The embodiments described above are intended to be merely exemplary; numerous variations and modifications will be apparent to those skilled in the art. All such variations and modifications are intended to be within the scope of these descriptions.