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      Radiomics of dynamic contrast-enhanced magnetic resonance imaging parametric maps and apparent diffusion coefficient maps to predict Ki-67 status in breast cancer


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          This study was aimed at evaluating whether a radiomics model based on the entire tumor region from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps and apparent diffusion coefficient (ADC) maps could indicate the Ki-67 status of patients with breast cancer.

          Materials and methods

          This retrospective study enrolled 205 women with breast cancer who underwent clinicopathological examination. Among them, 93 (45%) had a low Ki-67 amplification index (Ki-67 positivity< 14%), and 112 (55%) had a high Ki-67 amplification index (Ki-67 positivity ≥ 14%). Radiomics features were extracted from three DCE-MRI parametric maps and ADC maps calculated from two different b values of diffusion-weighted imaging sequences. The patients were randomly divided into a training set (70% of patients) and a validation set (30% of patients). After feature selection, we trained six support vector machine classifiers by combining different parameter maps and used 10-fold cross-validation to predict the expression level of Ki-67. The performance of six classifiers was evaluated with receiver operating characteristic (ROC) analysis, sensitivity, and specificity in both cohorts.


          Among the six classifiers constructed, a radiomics feature set combining three DCE-MRI parametric maps and ADC maps yielded an area under the ROC curve (AUC) of 0.839 (95% confidence interval [CI], 0.768−0.895) within the training set and 0.795 (95% CI, 0.674−0.887) within the independent validation set. Additionally, the AUC value, compared with that for a single parameter map, was moderately increased by combining features from the three parametric maps.


          Radiomics features derived from the DCE-MRI parametric maps and ADC maps have the potential to serve as imaging biomarkers to determine Ki-67 status in patients with breast cancer.

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          Most cited references43

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Radiomics: Images Are More than Pictures, They Are Data

            This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
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              Computational Radiomics System to Decode the Radiographic Phenotype

              Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

                Author and article information

                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                25 November 2022
                : 12
                : 847880
                [1] 1 Department of Radiology, Shengjing Hospital of China Medical University , Shenyang, Liaoning, China
                [2] 2 School of Intelligent Medicine, China Medical University , Shenyang, Liaoning, China
                Author notes

                Edited by: Siuly Siuly, Victoria University, Australia

                Reviewed by: HyungJoon Cho, Ulsan National Institute of Science and Technology, South Korea; Ming Fan, Hangzhou Dianzi University, China

                *Correspondence: Jiandong Yin, jiandongyin@ 123456sina.com

                This article was submitted to Breast Cancer, a section of the journal Frontiers in Oncology

                Copyright © 2022 Feng and Yin

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                : 03 January 2022
                : 27 October 2022
                Page count
                Figures: 4, Tables: 4, Equations: 4, References: 43, Pages: 11, Words: 4696
                Original Research

                Oncology & Radiotherapy
                breast cancer,radiomics,dynamic contrast-enhanced magnetic resonance imaging,apparent diffusion coefficient,ki-67


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