Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

Lu, Cheng; Shiradkar, Rakesh; Liu, Zaiyi

doi:10.21147/j.issn.1000-9604.2021.05.03

Cited by 32 publications

(28 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another discipline in which radiomics is now increasingly being applied is pathology [ 23 ]. The idea to genetically classify tumors without biopsy using non-invasive extraction of image information promises support in diagnostics, individualized prognosis and therapy planning.…”

Section: Discussionmentioning

confidence: 99%

Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy

Reinert¹,

Perl²,

Faul³

et al. 2022

JCM

View full text Add to dashboard Cite

The goal of this study was to investigate the value of CT-textural features and volume-based PET parameters in comparison to serologic markers for response prediction in patients with diffuse large B-cell lymphoma (DLBCL) undergoing cluster of differentiation (CD19)-chimeric antigen receptor (CAR)-T cell therapy. We retrospectively analyzed the whole-body (WB)-metabolic tumor volume (MTV), the WB-total lesion glycolysis (TLG) and first order textural features derived from 18F-FDG-PET/CT, as well as serologic parameters (C-reactive protein [CRP] and lactate dehydrogenase [LDH], leucocytes) prior and after CAR-T cell therapy in 21 patients with DLBCL (57.7 ± 14.7 year; 7 female). Interleukin 6 (IL-6) and IL-2 receptor peaks were monitored after treatment onset and compared with patient outcome judged by follow-up 18F-FDG-PET/CT. In 12/21 patients (57%), complete remission (CR) was observed, whereas 9/21 patients (43%) showed partial remission (PR). At baseline, WB-MTV and WB-TLG were lower in patients achieving CR (35 ± 38 mL and 319 ± 362) compared to patients achieving PR (88 ± 110 mL and 1487 ± 2254; p < 0.05). The “entropy” proved lower (1.81 ± 0.09) and “uniformity” higher (0.33 ± 0.02) in patients with CR compared to PR (2.08 ± 0.22 and 0.28 ± 0.47; p < 0.05). Patients achieving CR had lower levels of CRP, LDH and leucocytes at baseline compared to patients achieving PR (p < 0.05). In the entire cohort, WB-MTV and WB-TLG decreased after therapy onset (p < 0.01) becoming not measurable in the CR-group. Leucocytes and CRP significantly dropped after therapy (p < 0.01). The IL-6 and IL-2R peaks after therapy were lower in patients with CR compared to PR (p > 0.05). In conclusion, volume-based PET parameters derived from PET/CT and CT-textural features have the potential to predict therapy response in patients with DLBCL undergoing CAR-T cell therapy.

show abstract

Section: Discussionmentioning

confidence: 99%

Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy

Reinert¹,

Perl²,

Faul³

et al. 2022

JCM

View full text Add to dashboard Cite

show abstract

“…Typically, region- or volume-averaged features are extracted and linked to available data on survival outcomes ( 37 ). Radiomic analyses can also be performed on subregions of the tumor (habitats).…”

Section: Discussionmentioning

confidence: 99%

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I−III colorectal cancer

Huang

Chen

et al. 2022

Chinese Journal of Cancer Research

Self Cite

View full text Add to dashboard Cite

Objective This study aimed to establish a method to predict the overall survival (OS) of patients with stage I−III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods We retrospectively identified 161 consecutive patients with stage I−III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction. Results The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433−12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646−4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289−8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804−0.822 in the training cohort; 0.758, 95% CI: 0.751−0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722−0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness. Conclusions This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I−III CRC patients.

show abstract

“…Classical parallel beam projection tomography reconstructs image planes Y ⊂ R 2 via the Radon transform generated from sinograms indexed over a single space dimension dimension Z ⊂ R 1 arising from idealized line integrals [47]. Define the set of oriented lines in R 2 parametetrized by their angles (θ) and offsets from the origin (z), L θ (z) = {(y (1) , y (2) )} ⊂ R 2 with…”

Section: Optical Sectioning Pet and Parallel Beam Tomographymentioning

confidence: 99%

“…The past decade has ushered an “omics” revolution into biomedical research, with high yields of data ranging from microscopic to macroscopic scales. Modern machine learning methods coupled with image processing have enabled the integration of such “pathomics” data extracted from digital pathology technologies with “radiomics” data extracted from lower resolution imaging technologies such as magnetic resonance imaging (MRI) in a number of niche applications, such as those within the domain of cancer diagnostics and prognostics [1, 2]. However, approaches remain widely varied across applications and often require particularities in image acquisition and image type, such as block face imaging [3], to facilitate alignment between imaging modalities [4].…”

Section: Introductionmentioning

confidence: 99%

Projective LDDMM: Mapping Molecular Digital Pathology with Tissue MRI

Stouffer

Tward

Miller

2022

Preprint

View full text Add to dashboard Cite

Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. We describe a new family of diffeomorphic mapping technologies called Projective LDDMM which generate diffeomorphic mappings of dense human MRI atlases at tissue scales onto sparse measurements at micron scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating new technologies for crossing modalities that use non-linear scattering transforms to represent multiple radiomic-like textures at micron scales and incorporating a Gaussian mixture-model frame-work for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, as relevant to the development of biomarkers for Alzheimer’s disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.

show abstract

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

Cited by 32 publications

References 43 publications

Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy

Value of CT-Textural Features and Volume-Based PET Parameters in Comparison to Serologic Markers for Response Prediction in Patients with Diffuse Large B-Cell Lymphoma Undergoing CD19-CAR-T Cell Therapy

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I−III colorectal cancer

Projective LDDMM: Mapping Molecular Digital Pathology with Tissue MRI

Contact Info

Product

Resources

About