Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, ...which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
Purpose:
To evaluate the ability of a multiscale radiobiological model of tumor response to predict mid-treatment hypoxia images, based on pretreatment imaging of perfusion and hypoxia with 18-FFMISO ...dynamic PET and glucose metabolism with 18-FFDG PET.
Methods:
A mechanistic tumor control probability (TCP) radiobiological model describing the interplay between tumor cell proliferation and hypoxia (Jeong et al., PMB 2013) was extended to account for intra-tumor nutrient heterogeneity, dynamic cell migration due to nutrient gradients, and stromal cells. This extended model was tested on 10 head and neck cancer patients treated with chemoradiotherapy, randomly drawn from a larger MSKCC protocol involving baseline and mid-therapy dynamic PET scans. For each voxel, initial fractions of proliferative and hypoxic tumor cells were obtained by finding an approximate solution to a system of linear equations relating cell fractions to voxel-level FDG uptake, perfusion (FMISO K1) and hypoxia (FMISO k3). The TCP model then predicted their evolution over time up until the mid treatment scan. Finally, the linear model was reapplied to predict each lesion’s median hypoxia level (k3med,sim) which in turn was compared to the FMISO k3med measured at mid-therapy.
Results:
The average k3med of the tumors in pre-treatment scans was 0.0035 min−1, with an inter-tumor standard deviation of σpre=0.0034 min−1. The initial simulated k3med,sim of each tumor agreed with the corresponding measurements within 0.1σpre. In 7 out of 10 lesions, the mid-treatment k3med,sim prediction agreed with the data within 0.3σpre. The remaining cases corresponded to the most extreme relative changes in k3med.
Conclusion:
This work presents a method to personalize the prediction of a TCP model using pre-treatment kinetic imaging data, and validates the modeling of radiotherapy response by predicting changes in median hypoxia values during treatment. Variations from predicted response may be a useful biomarker, which should be further explored.
Partially supported by NIH grant #1 R01 CA157770-01A1 and a grant from Varian Corporation.
Complete physical processes contributing to the shape of the depth-dose distribution and the so-called Bragg peak of proton beams passing through liquid water are given. Height, width and depth of ...the Bragg peak through investigation of collisions of the projectile, nuclear processes and stochastic phenomena are discussed. Different analytical models for stopping cross sections which lead to Bragg peak are contrasted. Results are also compared with GEANT4 package simulation.
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment
. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic ...therapy
. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery
were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to ...predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.
•A CT ± FDG-PET radiomics signature accurately discerned normoxic from hypoxic tumors.•A significant survival split was found between CTAgnostic,-classified hypoxia strata.•There were 117 significant ...yet low hypoxia gene-CTAgnostic feature associations.•By identifying hypoxic patients we can potentially “enrich” hypoxia targeting trials.•The disease-specific radiomics signatures perform better than disease-agnostic ones.•The performance of the CT signature was lower than the CT-FDG signatures.
Tumor hypoxia increases resistance to radiotherapy and systemic therapy. Our aim was to develop and validate a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature.
A total of 808 patients with imaging data were included: N = 100 training/N = 183 external validation cases for a disease-agnostic CT hypoxia classification signature, N = 76 training/N = 39 validation cases for the H&N CT signature and N = 62 training/N = 36 validation cases for the Lung CT signature. The primary gross tumor volumes (GTV) were manually defined by experts on CT. In order to dichotomize between hypoxic/well-oxygenated tumors a threshold of 20% was used for the 18F-HX4-derived hypoxic fractions (HF). A random forest (RF)-based machine-learning classifier/regressor was trained to classify patients as hypoxia-positive/ negative based on radiomic features.
A 11 feature “disease-agnostic CT model” reached AUC’s of respectively 0.78 (95% confidence interval CI, 0.62–0.94), 0.82 (95% CI, 0.67–0.96) and 0.78 (95% CI, 0.67–0.89) in three external validation datasets. A “disease-agnostic FDG-PET model” reached an AUC of 0.73 (0.95% CI, 0.49–0.97) in validation by combining 5 features. The highest “lung-specific CT model” reached an AUC of 0.80 (0.95% CI, 0.65–0.95) in validation with 4 CT features, while the “H&N-specific CT model” reached an AUC of 0.84 (0.95% CI, 0.64–1.00) in validation with 15 CT features. A tumor volume-alone model was unable to significantly classify patients as hypoxia-positive/ negative. A significant survival split (P = 0.037) was found between CT-classified hypoxia strata in an external H&N cohort (n = 517), while 117 significant hypoxia gene-CT signature feature associations were found in an external lung cohort (n = 80).
The disease-specific radiomics signatures perform better than the disease agnostic ones. By identifying hypoxic patients our signatures have the potential to enrich interventional hypoxia-targeting trials.
Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of ...renal tumors using carbon-13 MRI following injection of hyperpolarized 1-
Cpyruvate (HP-
C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP-
C-MRI and conventional proton (
H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant (
) between
C-pyruvate and
C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing
in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92,
= 0.009) and MCT1 expression (r = 0.89,
= 0.016), with similar results acquired from the multi-regional analysis. Conventional
H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset (
< 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP-
C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP-
C-MRI may non-invasively characterize metabolic phenotypes within renal cancer.
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