Methods and systems for cancer risk assessment using tissue sound speed and stiffness
A method of analyzing an image of a volume of tissue to determine a risk of developing breast cancer using a volume averaged sound speed within the volume. A method of determining a response to a treatment plan by determining a volume and a volume averaged sound speed of a region of interest within a volume of breast tissue and generating a combined metric from the volume and the volume averaged sound speed over the plurality of instances of time. A method of analyzing an image of a volume of tissue of a breast by applying a spatial filter to at least one ultrasound tomography image at the computing system and generating a stiffness map from the at least one ultrasound tomography image.
1 . A method of characterizing a tissue volume, the method comprising:
receiving a plurality of ultrasound tomography images of the tissue volume at a computing system, wherein the plurality of ultrasound tomography images comprises a set of sound speed data and a set of attenuation data, and wherein the tissue volume is breast tissue;
extracting, at the computing system, a volume averaged sound speed within the tissue volume from the set of sound speed data;
generating, at the computing system, a stiffness map based at least in part on the set of sound speed data and the set of attenuation data;
differentiating, at the computing system, between a first tissue type and a second tissue type based at least in part on at least a first tissue type volume averaged sound speed threshold;
calculating, at the computing system, a volume average stiffness of at least the first tissue type and the second tissue type for a region of the tissue volume based at least in part on the stiffness map; and
determining, at the computing system, a relative stiffness distribution within the region of the tissue volume, wherein determining the relative stiffness distribution comprises determining a distribution percentage of the first tissue type and a distribution percentage of the second tissue type within the region of the tissue volume, based at least in part on the volume average stiffness of at least the first tissue type and the second tissue type for the region of the tissue volume,
thereby providing a characterization of the region of the tissue volume.
2 . The method of claim 1 , wherein the plurality of ultrasound tomography images comprises a plurality of two-dimensional (2D) images, wherein the plurality of 2D images comprise the set of sound speed data; and wherein extracting the volume averaged sound speed comprises:
determining a volume (V) of the tissue volume by a direct pixel count of the plurality of 2D images; and
determining the volume averaged sound speed by summing all sound speed pixel values within the direct pixel count and dividing by the volume (V).
3 . The method of claim 1 , further comprising determining a percent of sound speed tissue of the tissue volume above a threshold from the plurality of ultrasound tomography images of the tissue volume.
4 . The method of claim 3 , wherein determining the percent of sound speed tissue above the threshold comprises creating a mask comprising the sound speed tissue above the threshold.
5 . The method of claim 4 , wherein the plurality of images comprises a sound reflection image, and wherein the mask is created from the sound reflection image.
6 . The method of claim 4 , wherein the mask is created using a k-means segmentation algorithm.
7 . The method of claim 1 , further comprising characterizing the tissue volume over a plurality of instances of time, wherein the plurality of instances of time comprises at least a portion of a time duration during or after which a treatment is provided.
8 . The method of claim 7 , wherein the treatment is a preventative or an adjuvant treatment, and wherein the time duration is during a time period for the preventative or the adjuvant treatment.
9 . The method of claim 7 , wherein the treatment comprises at least one element selected from the group consisting of a chemotherapy treatment, a radiation therapy treatment, a cryotherapy treatment, a radiofrequency ablation treatment, a focused ultrasound treatment, and an electroporation treatment.
10 . The method of claim 7 , wherein the treatment is a preventative treatment.
11 . The method of claim 10 , wherein the treatment comprises use of tamoxifen, raloxifene, other anti-estrogen drugs, a dietary restriction, and/or a lifestyle intervention.
12 . The method of claim 1 , wherein determining the relative stiffness distribution with the region of the tissue volume occurs within 30 days of a start of a treatment plan or later.
13 . The method of claim 12 , wherein determining the relative stiffness distribution within the region of the tissue volume occurs within 14 days of a start of the treatment plan or later.
14 . The method of claim 7 , wherein the treatment plan comprises neoadjuvant chemotherapy.
15 . The method of claim 12 , wherein the plurality of instance of time are during a preventative or an adjuvant time period.
16 . The method of claim 12 , wherein the treatment plan comprises at least one element selected from the group consisting of a chemotherapy treatment, a radiation therapy treatment, a cryotherapy treatment, a radiofrequency ablation treatment, a focused ultrasound treatment and an electroporation treatment.
17 . The method of claim 12 , wherein the treatment plan is a preventative treatment.
18 . The method of claim 17 , wherein the treatment plan comprises use of tamoxifen, raloxifene, other anti-estrogen drugs, a dietary intervention, and/or a lifestyle intervention.
19 . A computing system, wherein the computing system comprises a non-transitory computer-readable medium comprising instructions stored thereon which when executed by a processor are configured to:
receive a plurality of ultrasound tomography images of a tissue volume at a computing system, wherein the plurality of ultrasound tomography images corresponds to a plurality of instances of time, and wherein the plurality of ultrasound tomography images comprises a set of sound speed data, and a set of attenuation data;
extract a volume averaged sound speed within the tissue volume from the set of sound speed data using the computing system;
generate a stiffness map based at least in part on the set of sound speed data and the set of attenuation data using the computing system;
differentiate between a first tissue type and a second tissue type based at least in part on at least a first tissue type volume averaged sound speed threshold using the computing system;
calculate a volume average stiffness of at least the first tissue type and the second tissue type for a region of the tissue volume based at least in part on the stiffness map;
determine a relative stiffness distribution within the region of the tissue volume using the computing system, wherein determining the relative stiffness distribution comprises determining a distribution percentage of the first tissue type and a distribution percentage of the second tissue type within the region of the tissue volume based at least in part on the volume average stiffness of at least the first tissue type and the second tissue type for the region of the tissue volume,
thereby providing a characterization of the region of the tissue volume.
20 . The method of claim 1 , further comprising determining a mass type within the region of the tissue.
21 . The method of claim 1 , wherein the plurality of ultrasound tomography images comprises a set of reflection data, and wherein the plurality of ultrasound tomography images comprises the relative stiffness distribution overlaying the reflection data.
22 . The method of claim 1 , wherein the characterization of the region of the tissue volume is performed with or without filtering.