Quantitative Radiomic Phenotyping of Cervix Cancer

Abstract

Radiomics aims to extract huge amount of quantifiable features from medical images with data-characterization algorithms [1][2][3][4][5]. Radiologists cannot appreciate all the disease characteristics in generic diagnostic images [6]. Using these radiomic features, it is possible to uncover the physiognomies of disease progression. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy [1,7,8]. Radiomics emerged from oncology [3] is the most cutting-edge innovation. However, the technique can be useful in any medical study involving tomographic imaging. This article analyzes currently available phenotyping algorithms for Lung and Head & Neck malignancies. And It can help researchers to understand the concept to apply the same for cervix cancer.

Exploratory analysis of radiomic as prognostic biomarkers in 18F-FDG PET/CT scan in uterine cervical cancer

“…Radiomic is the extraction of mineable data from medical imaging that has emerged recently ( 12 ). It analyzes the lesion phenotype using mathematical formulas that dissect the image, quantifying and characterizing several tumoral features ( 13 – 15 ). Among the numerous variables of the radiomic analysis of 18 F-FDG PET/CT images, the textural features present a greater correlation with the heterogeneous biological behavior of the tumor.…”

Framework for machine learning of ct and pet radiomics to predict local failure after radiotherapy in locally advanced head and neck cancers

“…However, radiomics is not yet deployed in routine clinical settings in India and is limited to a few research investigations. [ 7 8 9 ] To understand the radiomic features and their correlation to molecular changes in the tumour, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models. These lacunae were the motivation for this study, and an attempt is made to develop a pipeline that can speed the translation of the radiomics research in low and middle income countries (LMIC).…”

Standardization of CT radiomics features for multi-center analysis: impact of software settings and parameters

“…In the last few years, several radiomics studies have been presented, ranging from tumor classification and phenotyping, to modeling of locoregional control, and prognostication of future outcomes (Morin et al 2018). Specific quantitative radiomics modeling studies were performed on a comprehensive list of tumor sites, including head and neck (Leijenaar et al 2015, Wong et al 2016), lung (Lee et al 2017, Thawani et al 2018, breast (Giannini et al 2017, Valdora et al 2018, liver (Saini et al 2019), colorectal (Rosati et al 2018, Giannini et al 2019, cervix (Anbumani 2018), prostate (Vignati et al 2015, Chen et al 2019, soft tissues (Peeken et al 2019), and brain (Grossmann et al 2017). To improve the model robustness, radiomics studies should involve multiple institutions, each one with specific imaging acquisition protocols (Li et al 2016) and radiological devices from diverse vendors and different radiomics tools (Liu et al 2016, Gnep et al 2017, Summers 2017.…”