Imaging systems and methods for particle-driven, knowledge-based, and predictive cancer radiogenomics
Described herein are particle-driven radiogenomics systems and methods that can be used to identify imaging features for prediction of intratumoral and interstitial nanoparticle distributions in cancers (e.g., in low grade and/or high-grade brain cancers (e.g., gliomas, e.g., primary gliomas)). In certain embodiments, the systems and methods described herein extract and combine quantitative multi-dimensional data generated from structural, functional, and/or metabolic imaging. In certain embodiments, the combined multidimensional data is linked to intratumoral and interstitial nanoparticle distributions. For example, this linked data can be used to determine quantitative functional-metabolic multimodality particle-based imaging features and to predict treatment efficacy. These techniques provide an improved quantitative ability to measure treatment response and determine tumor progressions compared to traditional size-based imaging methods.
1 . An in vivo method for determining an intratumoral and/or interstitial nanoparticle distribution within a tumor and/or a tumor interstitium of a subject, the method comprising the steps of:
administering to the subject a single probe species that consists of a plurality of individual silica-based nanoparticles of the same species;
following the administering step, obtaining a plurality of in vivo images of the subject;
producing, by a processor of a computing device, one or more segments from at least one of the plurality of in vivo images;
extracting, by the processor, one or more features from at least one of the one or more segments;
accessing, by the processor, functional and/or metabolic imaging data from at least one of the plurality of in vivo images and extracting and combining quantitative multi-dimensional data generated from the functional and/or metabolic imaging data; and
determining the intratumoral and/or interstitial nanoparticle distribution within the tumor and/or the tumor interstitium of the subject using the extracted one or more features and the accessed functional and/or metabolic imaging data.
2 . The method of claim 1 , wherein the tumor comprises a metastatic disease, and wherein the metastasis is in the brain.
3 . The method of claim 1 , wherein the tumor comprises a primary glioma.
4 . The method of claim 1 , wherein the tumor comprises a low-grade glioma or high-grade glioma.
5 . The method of claim 1 , wherein the nanoparticles have an average diameter no greater than 20 nm.
6 . The method of claim 1 , wherein a radioisotope is attached directly or indirectly to each nanoparticle.
7 . The method of claim 1 , wherein a therapeutic is attached directly or indirectly to each nanoparticle.
8 . The method of claim 1 , wherein the plurality of in vivo images comprises a member selected from the group consisting of a positron emission tomography (PET) images(s), X-ray images(s), magnetic resonance imaging (MRI) images(s), Computed Tomography (CT) images(s), Single-Photon Emission Computed Tomography (SPECT) images(s), PET-CT images(s), and ultrasound image(s).
9 . The method of claim 1 , wherein the plurality of in vivo images comprises a combination of two or more of PET images(s), X-ray images(s), MRI images(s), CT images(s), SPECT images(s), PET-CT images(s), and ultrasound image(s).
10 . The method of claim 1 , wherein the one or more features are one or more texture features comprising Gabor edge features and/or Visually Accessible Rembrandt features.
11 . The method of claim 1 , wherein the functional and/or metabolic imaging data comprises one or more of the following: diffusion-weighted imaging data, diffusion tensor imaging data, and/or dynamic contrast enhanced T1 perfusion imaging data.
12 . A system comprising:
a single probe species that consists of a plurality of individual silica-based nanoparticles of the same species;
one or more imaging devices;
a processor; and
a nontransitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by the processor, cause the processor to:
(i) produce one or more segments from at least one of a plurality of in vivo images obtained using the one or more imaging devices;
(ii) extract one or more features from at least one of the one or more segments;
(iii) access functional and/or metabolic imaging data from at least one of the plurality of in vivo images, and extract and combine quantitative multi-dimensional data generated from the functional and/or metabolic imaging data;
(iv) determine an intratumoral and/or interstitial nanoparticle distribution within a tumor and/or a tumor interstitium of a subject using the extracted one or more features and the accessed functional and/or metabolic imaging data; and
(v) link the determined intratumoral and/or interstitial silica based nanoparticle distribution within the tumor interstitium to the extracted and combined multi-dimensional data; and
(vi) cause display of a graphical representation of the intratumoral and/or interstitial nanoparticle distribution within the tumor and/or the tumor interstitium superimposed on an image of the tumor and/or tumor interstitium captured by the one or more imaging devices.
13 . The system of claim 12 , wherein the one or more features are one or more texture features, and wherein the one or more texture features comprise Gabor edge features and/or Visually Accessible Rembrandt features.
14 . The system of claim 12 , wherein the functional and/or metabolic imaging data comprises one or more of the following: diffusion-weighted imaging data, diffusion tensor imaging data, and/or dynamic contrast enhanced T1 perfusion imaging data.
15 . The system of claim 12 , wherein the instructions further cause the processor to determine a measure of treatment efficacy using the extracted one or more features.
16 . The system of claim 15 , wherein the one or more features are functional and/or structural features.
17 . The system of claim 12 , wherein the instructions cause the processor to extract one or more features by identifying quantitative functional magnetic resonance (MR) texture features.
18 . The system of claim 12 , wherein a molecular inhibitor is attached directly or indirectly to each nanoparticle.
19 . The system of claim 15 , wherein the instructions cause the processor to determine the measure of treatment efficacy using high-dimensional data from one or more radiomic analysis of MR diffusion and/or perfusion functional images to predict inhibitor treatment efficacy.
20 . The system of claim 15 , wherein the instructions, when executed by the processor, further cause the processor to determine a measure of glioma heterogeneity using the extracted one or more features.
21 . The system of claim 12 , wherein step (i) comprises determining a multi-level overall minimizing energy criteria for characterization.
22 . The system of claim 20 , wherein the instructions cause the processor to determine a measure of glioma heterogeneity by determining hemodynamic metrics using a computation of the number of interface junctions inside a tumor region, and/or determining an MR vascular signature.
23 . The system of claim 12 , wherein the nanoparticles have an average diameter no greater than 20 nm.
24 . The system of claim 23 , wherein the nanoparticles have an average diameter no greater than 10 nm.
25 . The system of claim 12 , wherein a therapeutic is attached directly or indirectly to each nanoparticle.
26 . The system of claim 12 , wherein a radioisotope is attached directly or indirectly to each nanoparticle.
27 . The system of claim 12 , wherein each nanoparticle is a dual-modality cRGDY-PEG-C dot.
28 . The system of claim 12 , wherein the one or more imaging devices is selected from MR, PET, SPECT, CT, ultrasound, X-ray, and a combination thereof.
29 . The system of claim 15 , wherein a molecular inhibitor is attached to each nanoparticle and wherein the measure is a prediction of treatment efficacy in a low-grade glioma treated with the mutation specific inhibitor therapy.
30 . The system of claim 16 , wherein the features comprise quantitative functional magnetic resonance (MR) texture features.
31 . The system of claim 15 , wherein the instructions cause the processor to determine the measure of treatment efficacy using the extracted one or more features in addition to data regarding genetic mutations and/or disease history of the subject.
32 . The method of claim 1 , further comprising linking the determined intratumoral and/or interstitial nanoparticle distribution within the tumor and/or the tumor interstitium to the extracted and combined multi-dimensional data; and
causing a display of a graphical representation of the intratumoral and/or interstitial nanoparticle distribution within the tumor and/or the tumor interstitium superimposed on an image of the tumor and/or tumor interstitium.
33 . The method of claim 1 , wherein the one or more features are one or more texture features comprising Gabor edge features; and wherein the tumor comprises a primary glioma.
34 . The system of claim 12 , wherein the one or more features are one or more texture features comprising Gabor edge features; and wherein the tumor comprises a primary glioma.