Prediction of clinical outcomes Aerts et al. (2014) performed the first large-scale<unk> study that included three lung and two head-and-neck cancer cohorts, consisting of over 1000 patients. They assessed the prognostic values of over 400 textural and shape- and intensity-based features extracted from the computed tomography (CT) images acquired before any treatment. Tumor volumes were defined either by expert radiation oncologists or using semiautomatic segmentation methods. Their results identified a subset of<unk> features that may be useful for predicting patient survival and describing intratumoural heterogeneity. They also confirmed that the prognostic ability of these<unk>  features may be transferred from lung to head-and-neck cancer. However, Parmar et al. (2015) demonstrated that prognostic value of some<unk> features may be cancer type dependent. Particularly, they observed that not every<unk> feature that significantly predicted the survival of lung cancer patients could also predict the survival of head-and-neck cancer patients and vice versa. Nasief et al. (2019) showed that changes of<unk> features over time in longitudinal images (delta-radiomic features, DRFs) can potentially be used as a biomarker to predict treatment response for pancreatic cancer. Their results showed that a Bayesian regularization neural network can be used to identify a subset of DRFs that demonstrated significant changes between good- and bad- responders following 2–4 weeks of treatment with an AUC = 0.94. They also showed (Nasief et al., 2020) that DRFs are independent predictor of survival and if combined with the clinical biomarker CA19-9 can improve treatment response prediction and increase the possibility for response-based treatment adaptation . Several studies have also showed that<unk> features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging. Using this technique an algorithm has been developed, after initial training based on intra tumor lymphocyte density, to predict the probability of tumor response to immunotherapy, providing a demonstration of the clinical potential of<unk>  as a powerful to for personalized therapy in the emerging field of immunooncology. Other studies have also demonstrated the utility of<unk>  for predicting immunotherapy response of NSCLC patients using pre-treatment CT and PET/CT images.<unk>  remains inferior to conventional techniques in some applications, suggesting the necessity of continued improvement and manipulation of<unk>  features to different clinical scenarios. For instance, Ludwig et. al (2020) demonstrated that morphological<unk>  features were inferior to previously established features in the discrimination of intracranial aneurysm rupture status from 3-dimensional rotational angiography. Prognostication<unk> studies have shown that image-based markers have the potential to provide information orthogonal to staging and biomarkers and improve prognostication. Prediction risk of distant metastasis Metastatic potential of tumors may also be predicted by<unk> features. For example, thirty-five CT-based<unk> features were identified to be predictive of distant metastasis of lung cancer in a study by Coroller et al. in 2015. They thus concluded that<unk> features can be useful to identify patients with high risk of developing distant metastasis, guiding physicians to select the effective treatment for individual patients. Assessment of cancer genetics Lung tumor biological mechanisms may demonstrate distinct and complex imaging patterns. In particular, Aerts et al. (2014) showed that<unk> features were associated with biological gene sets, such as cell cycle phase, DNA recombination, regulation of immune system process, etc. Moreover, various mutations of glioblastoma (GBM), such as 1p/19q deletion, MGMT methylation, TP53, EGFR, and NF1, have been shown to be significantly predicted by magnetic resonance imaging (MRI) volumetric measures, including tumor volume, necrosis volume, and contrast enhancing volume. Image guided radiotherapy<unk>  offers the advantage to be non invasive and can therefore be repeated prospectively for a given patient more easily than invasive tumor biopsies. It has been suggested that<unk>  could be a mean to monitor tumor dynamic changes along the course of radiotherapy and to define sub volumes at risk for which dose escalation could be beneficial. Distinguishing true progression from radionecrosis Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression.<unk>  demonstrated significant differences in a set of 82 treated lesions in 66 patients with pathological outcomes. Top-ranked<unk> features feed into an optimized IsoSVM classifier resulted in a sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. These results show that<unk>  holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. Prediction of physiological events<unk>  can also be used to identify challenging physiological events such as brain activity, which is usually studied with imaging techniques such as functional MRI "fMRI". FMRI raw images can undergo<unk> analysis to generate imaging features that can be later correlated with meaningful brain activity. Multiparametric<unk>  Multiparametric radiological imaging is vital for detection, characterization and diagnosis of many different diseases. However, current methods in<unk>  are limited to using single images for the extraction of these textural features and may limit the applicable scope of<unk>  in different clinical settings. Thus, in the current form, they are not capable of capturing the true underlying tissue characteristics in high dimensional multiparametric imaging space. Recently, a Multiparametric imaging<unk> framework termed MPRAD for extraction of<unk> features from high dimensional datasets was developed. The Multiparametric<unk>  was tested on two different organs and diseases; breast cancer and cerebrovascular accidents in brain, commonly referred to as stroke. Breast cancer In breast cancer, The MPRAD framework classified malignant from benign breast lesions with excellent sensitivity and specificity of 87% and 80.5% respectively with an AUC of 0.88. MPRAD provided a 9%-28% increase in AUC over single<unk> parameters. More importantly, in breast, normal glandular tissue MPRAD were similar between each group with no significance differences. Stroke Similarly, the MPRAD features in brain stroke demonstrated increased performance in distinguishing the perfusion-diffusion mismatch compared to single parameter<unk>  and there were no differences within the white and gray matter tissue. The majority of the single<unk> second order features (GLCM) did not show any significant textural difference between infarcted tissue and tissue at risk on the ADC map. Whereas the same second order multiparametric<unk> features (TSPM) were significantly different for the DWI dataset. Similarly, multiparametric<unk> values for the TTP and PWI dataset demonstrated excellent results for the MPRAD. The MPRAD TSPM Entropy exhibited significant difference between infarcted tissue and potential tissue-at-risk: (6.6±0.5 vs 8.4±0.3, p=0.01).
Radiomics
Nuclear medicine
Radiochemistry
Imaging biomarker
Radiology
PET radiotracer
Radiopaedia
ReSPECT process