There are two major types of radiogenomic association studies. There are multiple on-going efforts for standardization and for a full list of the organizations and initiatives, please refer to Gillies et al. In practice, due to the relatively large number of features compared with the small number of samples, feature selection is an essential step in mitigating the risk overfitting [28]. On the commercial software side, we mention that companies such as Huiyihuiying, a Beijing-based company focusing on the use of radiomics and artificial intelligence for solving various clinical problems, afford a practically useful cloud-based platform for radiomics research (for more details or to set up a free research account, please visit the company’s website: www.huiyihuiying.com). 0000002673 00000 n adding value. Harrell's C-index was used to demonstrate the incremental value of the radiomics signature to the traditional clinical risk factors for the individualized prediction performance. combined gene expression and CT radiomic signatures to enhance the accuracy of survival prediction in lung cancer. Cottereau et al. Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. In current oncology practice, various imaging modalities such as CT, MRI and FDG-PET are used to provide direct visualization and evaluation of the underlying anatomical or physiological properties of each tumor in individual patients [18]. 0000044106 00000 n Ashraf AB, Daye D, Gavenonis S et al. Based on image features characterizing tumor morphology and intratumoral metabolic heterogeneity, a radiomic signature was built that significantly improved the prognostic value compared with conventional imaging metrics. There are several approaches to achieving this. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. 0000005426 00000 n Overview of attention for article published in Journal of radiation research, January 2018. Grossmann et al. Fehr D, Veeraraghavan H, Wibmer A et al. Radiogenomics can also be used create association maps between molecular features and a specific imaging phenotype so as to reveal its biological underpinnings. Radiomics refers to automated extraction of mathematically defined, numerical descriptors (“radiomics features”) from 2-dimensional – or more commonly – 3-dimensional medical images and subsequent application of data mining and analysis techniques. Radiomics has been rapidly developed toward clinical application [ 7 - 9] in the hope that it will advance precision diagnostics and cancer treatment. 0000077078 00000 n . After the images are acquired, the next step for radiomics is segmentation of the region of interest—in most cases, the gross tumor. More details about each step are presented below. Larue RT, Defraene G, De Ruysscher D et al. These preexisting contours can greatly facilitate retrospective radiomic analysis. He also serves as the Chief Scientific Advisor of Huiyihuiying Medical Technology (Beijing) Co., Ltd. O’Connor JP, Aboagye EO, Adams JE et al. However, the current literature is limited by its retrospective nature, as well as significant heterogeneity between studies. Cross validation is needed to minimize the potential selection bias. . While validation in a prospective clinical trial remains the gold standard and provides the highest level of evidence, there are several other more practical ways to demonstrate a model’s validity and allow a quicker assessment of multiple competing models. Radiomics typically involves multiple serial steps, including image acquisition, tumor segmentation, feature extraction, predictive modeling, and model validation. In this context, radiomics is defined as the discovery of imaging biomarkers with potential diagnostic, prognostic, or predictive value; and radiogenomics is the identification of molecular biology behind these imaging … 0000002944 00000 n To account for intra- and inter-rater variations, it is important to evaluate the robustness of image features and their effect on downstream analysis by perturbing the tumor contours or using multiple delineations. 0000048763 00000 n Indeed, radiomics features have already been associated with improved diagnosis accuracy in cancer, 7 specific gene mutations, 8 and treatment responses to chemotherapy and/or radiation therapy in the brain, 9,10 head and neck, 11,12 lung, 13-17 breast, 18,19 and abdomen. “Radiomics,” as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). . Published by Oxford University Press on behalf of The Japan Radiation Research Society and Japanese Society for Radiation Oncology. For radiomics, there can be many causes that render the radiomic analysis and results invalid, including poor experimental design, model overfitting, and unadjusted biases or confounding factors, among others. . 0000018505 00000 n radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In an ongoing study, they are investigating whether adding diffusion-weighted MRI radiomic features could improve potential predictive power. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. For radiation oncology it offers the potential to significantly influence clinical decision-making and thus therapy planning and … Using deep-learning techniques for substantially improved segmentation of normal and malignant structures is an active area of research [21–23]. Weissleder R, Schwaiger MC, Gambhir SS et al. showed that early change in texture features for the intratumoral subregion (associated with fast contrast-agent washout at DCE MRI) predicted pathological complete response to neoadjuvant chemotherapy in breast cancer. Roelofs E, Dekker A, Meldolesi E et al. [47]. <]/Prev 277434>> Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. Given the growing interest in the field, it is important to highlight some technical and practical challenges associated with radiomics and its ultimate clinical translation. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. 0000077117 00000 n In the near future, deep-learning–based auto-segmentation tools that are robust enough for routine radiomics applications should be available. Abstract. . Below we highlight a few studies that may be potentially relevant for improving patient management in radiotherapy. 0000005807 00000 n Two types of radiomic features, semantic and agnostic, can be extracted from images to comprehensively characterize the tumor phenotypes. Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. . Rios Velazquez E, Parmar C, Liu Y et al. The proposed radiomic signature showed significant association with survival after independent validation and, importantly, remained an independent predictor of survival after adjusting for known clinicopathological risk factors. Another practical strategy is to gauge the imaging values with the value of the selected normal tissue region of interest as a baseline. One approach that most radiogenomic studies so far have adopted is to find imaging correlates or surrogates of a specific genotype or molecular phenotype of the tumor. Moreover, these findings were independently validated in a multicenter clinical trial cohort. Gatenby and colleagues proposed cascading T1 post-gadolinium MRI with T2-weighted fluid-attenuated inversion recovery sequences in order to divide the whole tumor into multiple regional habitats with distinct contrast enhancement and edema/cellularity [45]. Cui et al. In addition, the phantom study can be adopted to investigate the interscan and inter-vendor variability of the imaging-derived features [67, 68], which can provide useful insights into the uncertainties of quantitative imaging analysis. discovered and independently validated three breast cancer imaging subtypes, which were characterized as having homogeneous intratumoral enhancement, minimal parenchymal enhancement, or prominent parenchymal enhancement. For patients treated with radiotherapy, their tumors have already been manually delineated by radiation oncologists, and are available from the treatment planning system. 0000019039 00000 n 0000014345 00000 n Radiomics can be applied to any type of standard-of-care clinical images such as CT, MRI or PET, and used in a variety of clinical settings, including diagnosis, prediction of prognosis, and evaluation of treatment response. In addition, this is particularly relevant for radiotherapy treatment planning and adaptation, because high-risk tumor subregions associated with the aggressive disease can then be targeted with a radiation boost to potentially improve local control and patient survival. Radiation therapy is an integral part of cancer treatment, and it has been estimated that over 60% of cancer patients require radiation therapy as part of their management protocol (1). A cloud-based platform such as the one provided by Huiyihuiying Inc. may prove to be useful in facilitating data sharing and multi-institutional collaborative research. Radiomics and radiogenomics for precision radiotherapy Abstract. . cancer, radiotherapy can affect radiomic features, which can be used as a predictor of tumor clinical response at the end of radiotherapy, known as delta-radiomics (Δradiomics). 0000023037 00000 n 0000092273 00000 n 257 67 The prognostic value of constructed prediction models was confirmed in an external cohort. 0000018671 00000 n An emerging field that is closely related to radiomics is radiogenomics, which integrates imaging and genomic data with the goal of gaining biological interpretation or improving patient stratification for precision medicine [10–15, 50–54]. [46] developed a robust tumor-partitioning method by a two-stage clustering procedure, and identified three spatially distinct and phenotypically consistent subregions in lung tumors. Radiogenomics in head and neck cancer: correlation of radiomic heterogeneity and somatic mutations in TP53, FAT1 and KMT2D Strahlenther Onkol . Many commonly used radiomic features have been integrated into open source software or commercial software platforms. Verma V, Simone CB, Krishnan S et al. Sanming Project of Medicine - The 2nd International Symposium on Specialist Education and Advances in Radiation Oncology-dc.title: Medical imaging perspectives of radiomics/radiogenomics in the era of precision oncology-dc.type: Conference_Paper-dc.identifier.email: Vardhanabhuti, V: varv@hku.hk-dc.identifier.authority: Vardhanabhuti, V=rp01900- Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomics has recently emerged as a promising tool for discovering new imaging biomarkers, by high-throughput extraction of quantitative image features such as shape, histogram and texture that captures tumor heterogeneity [5–9]. In current radiology practice, the interpretation of clinical images mainly relies on visual assessment of relatively few qualitative imaging metrics. Aerts and colleagues proposed a radiomics signature for predicting overall survival in lung cancer patients treated with radiotherapy [37]. For instance, CT semantic and radiomic image features have been found to be associated with EGFR mutations in lung cancer [55, 56]; MRI radiomic features have been correlated with intrinsic molecular subtypes or existing genomic assays in breast cancer [57–59]. The RQS contains sixteen key components that intend to minimize bias and enhance the usefulness of radiomics models. For radiomic analysis, it is essential to standardize or harmonize the imaging data in multicenter validation studies. Yankeelov TE, Mankoff DA, Schwartz LH et al. To overcome this issue, there have been several efforts to standardize the imaging protocol by the quantitative imaging biomarkers alliance (QIBA) [64] and the quantitative imaging network (QIN) [65], among others. Buckler AJ, Bresolin L, Dunnick NR et al. . Recently, Wu et al. ML is commonly used in radiomics model development, which can be hypothetically defined as a branch of artificial intelligence (AI) , which is actually an algorithm trained by inferences from data sets and then helps establish prediction models with high precision and efficiency on the basis of radiomic analysis. Those radiomic signatures that provide independent prediction power are more likely to add clinical value for patient management. Kalpathy-Cramer J, Freymann JB, Kirby JS et al. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide, This PDF is available to Subscribers Only. Radiomics has the potential to personalize patient treatment by using medical images that are already being acquired in clinical practice. Cottereau AS, Lanic H, Mareschal S et al. In another study, Cui et al. h�b```e``����� � Ȁ �@v��� E&�2V1�,� j(�_y.� ���m�A������YtYqY�ci���pg9'%g�>�������(1U*�+qU�Ƭ�O8zTLf. Finally, one important direction that is particularly relevant for precision medicine is to leverage the complementary power of imaging and molecular data, and integrate them into a unifying model to further improve the prediction accuracy of clinical outcomes. First, it is essential to assure the predictive accuracy during radiomic signature construction. [63] showed that integrating MGMT methylation status and volume of the high-risk subregion at multiparametric MRI improved survival stratification in glioblastoma. Taken together, these studies support the need for tumor partitioning to identify aggressive intratumoral subregions, and this is applicable to many types of solid tumors that demonstrate intratumor heterogeneity at imaging. 59, No. There are various types of agnostic image features that describe tumor shape, intensity, and texture to capture intratumoral heterogeneity. Stoyanova R, Pollack A, Takhar M et al. Ibragimov B, Korez R, Likar B et al. 0000006528 00000 n Based on these features, they constructed a radiomic signature that captured intratumor heterogeneity, which was shown to be prognostic in several independent validation cohorts, including one head-and-neck cohort. 0000019524 00000 n Any radiomic signature should be validated on independent, preferably multiple external cohorts. In another study by the same group, radiomics analysis was used to investigate the association of MRI features with survival and progression in glioblastoma [38]. 0000002372 00000 n One subregion, associated with the most metabolically active, metabolically heterogeneous, and solid component of the tumor, was defined as the ‘high-risk’ subregion. Second, each radiomic analysis step should be well documented, and original codes and data should be easily accessible, allowing other investigators to replicate the results. . For instance, image features that show minimal changes to tumor contour variations and minimal redundancy or overlap with other features may be preferentially selected. Grossmann P, Narayan V, Chang K et al. What should physicians look for in evaluating prognostic gene-expression signatures? 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