<p>Application of Radiomics for Personalized Treatment of Cancer Patients</p>

Abstract

Radiomics is a novel concept that relies on obtaining image data from examinations such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). With the appropriate algorithm, the extracted results have broad applicability and potential for a massive positive impact in radiology. For example, clinicians can verify treatment efficiency, predict the location of tumor metastasis, correlate results with a histopathological examination, or more accurately define the type of cancer. Combining radiomics with other testing techniques allows every patient to have a personalized treatment plan that is essential for advanced examination and treatment. This article explains the process of radiomics, including data collection mechanisms, combined use with genomics, and artificial intelligence and immunology techniques, which may solve many of the challenges faced by doctors in diagnosing and treating their patients.

A Non-invasive Method to Diagnose Lung Adenocarcinoma

“…It is an effective way to use radiomics to support therapy decision-making, which will advance personalized medicine. Radiomics has been applied to a variety of organs and systems such as brain, breast, lung, heart, liver, kidney, adrenal gland, cervix, limbs, and prostate (6,10,11). For example, Chaddad et al (6,12) proposed a multiscale texture features to predict progression free and overall survival in patients newly diagnosed with glioblastoma, they also reviewed the clinical implementation of radiomic in the current management of glioblastoma, which is important for advancing the personalized treatment of glioblastoma patients.…”

Ki-67 as a Prognostic Biomarker in Invasive Breast Cancer

“…Tagliafico et al provided similar results using digital breast tomosynthesis imaging in their series of 70 women diagnosed with invasive breast carcinoma; tumour sphericity, autocorrelation (grey level co-occurrence matrix), interquartile range, robust mean absolute deviation, and short-run high grey-level emphasis all show an association with Ki-67 expression [156]. Ma et al yielded similar results from their analysis using DCE-MRI, with previously described parameters, such as tumour area, skewness, kurtosis, and homogeneity, all correlating with Ki-67 indices [157], while Cui et al have recently illustrated the clinical utility of ultrasound sonography in determining Ki-67 status [158]. These analyses highlight the opportunities presented through machine and deep learning radiomic techniques to further personalise medical treatment while promoting minimally invasive techniques where feasible.…”

Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

“…AI-driven analysis of radiomics data can overcome limitations of classical pathology. Radiomic features are promising tools in defining cancer subtypes ( 28 , 111 ) and may appear as an alternative or complimenting data to primary omics data in the context of tumor classification for precision medicine ( 112 , 113 ). Several other factors like lifestyle and environmental effects can be integrated to add a new dimension to the analysis.…”