Development Of Radiomics Research Articles

A journey from omics to clinicomics in solid cancers: Success stories and challenges.

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Application and progress of X‐ray, computed tomography, and magnetic resonance imaging radiomics in osteosarcoma

AbstractLooking back on the development of radiomics in osteosarcoma over recent years, in addition to distinguishing osteosarcoma from other malignant bone tumors (mainly Ewing's sarcoma), more research directions are using radiomics to evaluate and predict the efficacy and survival of patients undergoing neoadjuvant chemotherapy. Among the three commonly used examination methods of X‐ray, CT, and magnetic resonance imaging (MRI), more and more studies have been conducted on MRI‐based radiomics, which fully reflects the advantages of MRI's high soft tissue contrast, multi‐sequence imaging, and most of the studies used a combination of imaging features and clinical features to make predictions. Some articles also considered relevant laboratory examination results and more and more studies are performing external verification. The field is now gradually developing toward multidimensional data and multicenter cooperation and data sharing.

Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice. .

Certain aspects of radiomics and radiogenomics in glioblastoma: what the images hide?

Radiogenomics is a novel and promising field connecting a variety of imaging possibilities with various genomic events. Advances in genomics provided by the Cancer Genome Atlas and Human Genome projects made it possible to integrate this information with imaging phenotypes of malignant brain tumors for a more detailed understanding of their biology. Radiomics, in turn, lies at the intersection of radiology, computer science and mathematical statistics. Unlike radiogenomics, it does not focus on the specific relationship between the radiophenotype and tumor genotype, but rather identifies the analysis methodology. With its help, quantitative features are extracted from medical images, establishing patient’s genotype-phenotype correlation. This contributes to the risk stratification and patient management. The article discusses some topical aspects of radiomics and radiogenomics of glioblastomas and their application in neurooncology.Previously, several groups of researchers showed the relationship between visualization features of glioblastomas and the prognosis of the course of the disease.One of the modern problems of radiomics is the search for imaging features that can serve as key prognostic markers for risk stratification of patients with glioblastomas using machine learning tools.Thus, the prospects for the development of radiomics and radiogenomics methods include predicting patient survival, differential diagnosis of glioblastomas, determining the degree of malignancy, identifying mutations and amplifications, detecting tumor progression, pseudoprogression, etc.

Radiomics in immuno-oncology.

With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.

Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer.

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.

As computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set. In this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer. Radiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer.

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ©RSNA, 2018.

Advances in imaging research of the digestive system tumors from 2017 annual meeting of the American Society of Clinical Oncology

The 2017 Annual Meeting of the American Society of Clinical Oncology (ASCO) was held on June 2-6, 2017 in Chicago, United States. And the academic event showed the frontier researches in the field of oncology. Survival prediction in patients treated by FOLFIRI regimen and bevacizumab for metastatic colorectal cancer using radiomics approach was made in Abstract 3601. Authors aim to summarize the application and development of radiomics in digestive system tumors. Key words: Digestive system neoplasms; Radiography; Radiomics; American Society of Clinical Oncology

Quantitative Imaging Assessment of Tumor Response to Chemoradiation in Lung Cancer

精准医疗的实施要求及时准确地对治疗疗效进行评估，以便于治疗方案的调整和优化，从而进一步提高疗效，改善预后。以定量评估为基础的影像组学以其无创、直观和可重复的特点在临床疗效评估方面具有不可替代的作用。本文将综述定量影像学在肺癌放化疗疗效评估中的应用现状及其相关进展。

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

ObjectiveThe purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software.Materials and MethodsMR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrast T1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic.ResultsOur study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] ≥ 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR ≥1), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant.ConclusionThe use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics.

Positron emission tomography/computerized tomography for tumor response assessment-a review of clinical practices and radiomics studies.

Even with recent advances in cancer diagnosis and therapy, treatment outcomes for many cancers remain dismal. Patients often show different response to the same therapy regimen, supporting the development of personalized medicine. 18F-FDG PET/CT has been used routinely in the assessment of tumor response, in prediction of outcomes, and in guiding personalized treatment. These assessments are mainly based on physician's subjective or semi-quantitative evaluation. Recent development in Radiomics provides a promising objective way for tumor response assessment, which uses computerized tools to extract a large number of image features that capture additional information not currently used in clinic that has prognostic value. In this review, we summarized the clinical use of PET/CT and the PET/CT Radiomics studies for tumor response assessment. Finally, we discussed some challenges and future perspectives.

TU‐H‐207B‐02: Funding (NCI) for Radiomics

Research and development in radiomics for precision medicine have been advancing at a rapid pace, and can be extended to eventually be mature enough for integration into clinical practice. To facilitate advances in this field, investigators need updated information on research funding, regulatory matters, and clinical translation efforts. In this special radiomics symposium, the attendee will learn of various novel funding mechanisms – both past and present – that have been used to support a number of quantitative imaging and radiomics research and resource infrastructures. In addition, a regulatory presentation will be given to provide the attendee with an understanding of when a radiomics tool might require FDA clearance or approval, and with what supporting performance data. We will end with insights from a clinical radiologist who is greatly involved in quantitative imaging in order to enable the attendee to appreciate the needs and subtleties involved in moving radiomics into the clinical workflow.J. Boone, This is a memorial and not a funded event.

TU‐H‐207B‐00: Moving Radiomics Forward: Funding, Regulatory Issues, and Clinical Translation

Research and development in radiomics for precision medicine have been advancing at a rapid pace, and can be extended to eventually be mature enough for integration into clinical practice. To facilitate advances in this field, investigators need updated information on research funding, regulatory matters, and clinical translation efforts. In this special radiomics symposium, the attendee will learn of various novel funding mechanisms – both past and present – that have been used to support a number of quantitative imaging and radiomics research and resource infrastructures. In addition, a regulatory presentation will be given to provide the attendee with an understanding of when a radiomics tool might require FDA clearance or approval, and with what supporting performance data. We will end with insights from a clinical radiologist who is greatly involved in quantitative imaging in order to enable the attendee to appreciate the needs and subtleties involved in moving radiomics into the clinical workflow.J. Boone, This is a memorial and not a funded event.

TU‐H‐207B‐03: Radiomics &amp; Regulatory Issues

Research and development in radiomics for precision medicine have been advancing at a rapid pace, and can be extended to eventually be mature enough for integration into clinical practice. To facilitate advances in this field, investigators need updated information on research funding, regulatory matters, and clinical translation efforts. In this special radiomics symposium, the attendee will learn of various novel funding mechanisms – both past and present – that have been used to support a number of quantitative imaging and radiomics research and resource infrastructures. In addition, a regulatory presentation will be given to provide the attendee with an understanding of when a radiomics tool might require FDA clearance or approval, and with what supporting performance data. We will end with insights from a clinical radiologist who is greatly involved in quantitative imaging in order to enable the attendee to appreciate the needs and subtleties involved in moving radiomics into the clinical workflow.J. Boone, This is a memorial and not a funded event.

TU‐H‐207B‐01: Memorial Tribute

Research and development in radiomics for precision medicine have been advancing at a rapid pace, and can be extended to eventually be mature enough for integration into clinical practice. To facilitate advances in this field, investigators need updated information on research funding, regulatory matters, and clinical translation efforts. In this special radiomics symposium, the attendee will learn of various novel funding mechanisms – both past and present – that have been used to support a number of quantitative imaging and radiomics research and resource infrastructures. In addition, a regulatory presentation will be given to provide the attendee with an understanding of when a radiomics tool might require FDA clearance or approval, and with what supporting performance data. We will end with insights from a clinical radiologist who is greatly involved in quantitative imaging in order to enable the attendee to appreciate the needs and subtleties involved in moving radiomics into the clinical workflow.J. Boone, This is a memorial and not a funded event.

TU‐H‐207B‐04: Clinical Translation of Radiomics

Research and development in radiomics for precision medicine have been advancing at a rapid pace, and can be extended to eventually be mature enough for integration into clinical practice. To facilitate advances in this field, investigators need updated information on research funding, regulatory matters, and clinical translation efforts. In this special radiomics symposium, the attendee will learn of various novel funding mechanisms – both past and present – that have been used to support a number of quantitative imaging and radiomics research and resource infrastructures. In addition, a regulatory presentation will be given to provide the attendee with an understanding of when a radiomics tool might require FDA clearance or approval, and with what supporting performance data. We will end with insights from a clinical radiologist who is greatly involved in quantitative imaging in order to enable the attendee to appreciate the needs and subtleties involved in moving radiomics into the clinical workflow.J. Boone, This is a memorial and not a funded event.