Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

The Continuing Evolution of Molecular Functional Imaging in Clinical Oncology: The Road to Precision Medicine and Radiogenomics (Part II)

The present era of precision medicine sees "cancer" as a consequence of molecular derangements occurring at the commencement of the disease process, with morphological changes happening much later in the process of tumourigenesis. Conventional imaging techniques, such as computed tomography (CT), ultrasound (US) and magnetic resonance imaging (MRI) play an integral role in the detection of disease at the macroscopic level. However, molecular functional imaging (MFI) techniques entail the visualisation and quantification of biochemical and physiological processes occurring during tumourigenesis. MFI has the potential to play a key role in heralding the transition from the concept of "one-size-fits-all" treatment to "precision medicine". Integration of MFI with other fields of tumour biology such as genomics has spawned a novel concept called "radiogenomics", which could serve as an indispensable tool in translational cancer research. With recent advances in medical image processing, such as texture analysis, deep learning and artificial intelligence, the future seems promising; however, their clinical utility remains unproven at present. Despite the emergence of novel imaging biomarkers, the majority of these require validation before clinical translation is possible. In this two part review, we discuss the systematic collaboration across structural, anatomical and molecular imaging techniques that constitute MFI. Part I reviews positron emission tomography, radiogenomics, AI, and optical imaging, while part II reviews MRI, CT and ultrasound, their current status, and recent advances in the field of precision oncology.

Perspectives in Radiomics for Personalized Medicine and Theranostics

Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.

MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity

BACKGROUND

The main purpose of this study was to assess the structural changes in the bladder wall of prostate cancer patients treated with intensity-modulated radiation therapy using magnetic resonance imaging texture features analysis and to correlate image texture changes with radiation dose and urinary toxicity.

METHODS

Ethical clearance was granted to enroll 33 patients into this study who were treated with intensity-modulated radiation therapy for prostate cancer. All patients underwent two magnetic resonance imagings before and after radiation therapy (RT). A total of 274 radiomic features were extracted from MR-T2W-weighted images. Wilcoxon singed rank-test was performed to assess significance of the change in mean radiomic features post-RT relative to pre-RT values. The relationship between radiation dose and feature changes was assessed and depicted. Cystitis was recorded as urinary toxicity. Area under receiver operating characteristic curve of a logistic regression-based classifier was used to find correlation between radiomic features with significant changes and radiation toxicity.

RESULTS

Thirty-three bladder walls were analyzed, with 11 patients developing grade ≥2 urinary toxicity. We showed that radiomic features may predict radiation toxicity and features including S5.0SumVarnc, S2.2SumVarnc, S1.0AngScMom, S0.4SumAverg, and S5. _5InvDfMom with area under receiver operating characteristic curve 0.75, 0.69, 0.65, 0.63, and 0.62 had highest correlation with toxicity, respectively. The results showed that most of the radiomic features were changed with radiation dose.

CONCLUSION

Feature changes have a good correlation with radiation dose and radiation-induced urinary toxicity. These radiomic features can be identified as being potentially important imaging biomarkers and also assessing mechanisms of radiation-induced bladder injuries.

Repeatability of diffusion-weighted MRI of the prostate using whole lesion ADC values, skew and histogram analysis

PURPOSE

To investigate the repeatability of diffusion-weighted imaging parameter including ADC-derived histogram values in prostate cancer.

METHODS

10 patients with prostate cancer were prospectively recruited to a retest cohort. 3 T diffusion-weighted MRI of the prostate was acquired consecutively with patient getting off the scanner between studies. Prostatectomy-histopathology defined tumour regions-of-interest were outlined on ADC maps and diffusion-weighted metrics including histograms were calculated. The coefficient of reproducibility (CoR) and Bland-Altman plots were used to assess repeatability.

RESULTS

10th centile, 90th centile, and median ADC showed good repeatability with mean difference ranging from -0.005 to -0.025 × 103 mm2s-1, and CoR ranging from 0.271-0.294 × 103 mm2s-1 of scan 1 mean). Two measures of heterogeneity and simplified texture, IQR and mean local range, had only moderate repeatability. IQR had a mean difference of -0.032 × 103 mm2s-1 between scans with CoR 0.181 × 103 mm2s-1 (56% of scan 1 mean). Mean local range had a mean difference -0.008 × 103 mm2s-1 between scans (37% of scan 1 mean). Bland-Altman plots showed good repeatability for test and re-test analysis for median, percentile and mean range values. All ADC values had good reliability regardless of whether the tumour border was included in quantitative analysis. ADC histogram skew had poor repeatability, CoR 0.78 × 103 mm2s-1 (373% of scan 1 mean).

CONCLUSION

10th and 90th centile ADC demonstrated sufficient repeatability for clinical use. However, more advanced measures of heterogeneity such as histogram skew, IQR, or mean local range may be limited by their repeatability.

The value of MR textural analysis in prostate cancer

Current diagnosis and treatment stratification of patients with suspected prostate cancer relies on a combination of histological and magnetic resonance imaging (MRI) findings. The aim of this article is to provide a brief overview of prostate pathological grading as well as the relevant aspects of multiparametric (MRI) mpMRI, before indicating the potential that magnetic resonance textural analysis (MRTA) offers within prostate cancer. A review of the evidence base on MRTA in prostate cancer will enable discussion of the utility of this field while also indicating recommendations to future research. Radiomic textural analysis allows the assessment of spatial inter-relationships between pixels within an image by use of mathematical methods. First-order textural analysis is better understood and may have more clinical validity than higher-order textural features. Textural features extracted from apparent diffusion coefficient maps have shown the most potential for clinical utility in MRTA of prostate cancers. Future studies should aim to integrate machine learning techniques to better represent the role of MRTA in prostate cancer clinical practice. Nomenclature should be used to reduce misidentification between first-order and second-order energy and entropy. Automated methods of segmentation should be encouraged in order to reduce problems associated with inclusion of normal tissue within regions of interest. The retrospective and small-scale nature of most published studies, make it difficult to draw meaningful conclusions. Future larger prospective studies are required to validate the textural features indicated to have potential in characterisation and/or diagnosis of prostate cancer before translation into routine clinical practice.

Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection

OBJECTIVES

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection.

METHODS

A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated.

RESULTS

The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice.

CONCLUSIONS

The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.

KEY POINTS

• Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.

Role of texture analysis in breast MRI as a cancer biomarker: A review

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.

Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model

Genetic studies have identified associations between gene mutations and clear cell renal cell carcinoma (ccRCC). Since the complete gene mutational landscape cannot be characterized through biopsy and sequencing assays for each patient, non-invasive tools are needed to determine the mutation status for tumors. Radiogenomics may be an attractive alternative tool to identify disease genomics by analyzing amounts of features extracted from medical images. Most current radiogenomics predictive models are built based on a single classifier and trained through a single objective. However, since many classifiers are available, selecting an optimal model is challenging. On the other hand, a single objective may not be a good measure to guide model training. We proposed a new multi-classifier multi-objective (MCMO) radiogenomics predictive model. To obtain more reliable prediction results, similarity-based sensitivity and specificity were defined and considered as the two objective functions simultaneously during training. To take advantage of different classifiers, the evidential reasoning (ER) approach was used for fusing the output of each classifier. Additionally, a new similarity-based multi-objective optimization algorithm (SMO) was developed for training the MCMO to predict ccRCC related gene mutations (VHL, PBRM1 and BAP1) using quantitative CT features. Using the proposed MCMO model, we achieved a predictive area under the receiver operating characteristic curve (AUC) over 0.85 for VHL, PBRM1 and BAP1 genes with balanced sensitivity and specificity. Furthermore, MCMO outperformed all the individual classifiers, and yielded more reliable results than other optimization algorithms and commonly used fusion strategies.

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.

Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study

BACKGROUND

Accurate classification of gliomas is crucial for prescribing therapy and assessing the prognosis of patients.

PURPOSE

To develop a radiomics nomogram using multiparametric MRI for predicting glioma grading.

STUDY TYPE

Retrospective.

POPULATION

This study involved 85 patients (training cohort: n = 56; validation cohort: n = 29) with pathologically confirmed gliomas.

FIELD STRENGTH/SEQUENCE

1.5T MR, containing contrast-enhanced T₁ -weighted (CET₁ WI), axial T₂ -weighted (T₂ WI), and apparent diffusion coefficient (ADC) sequences.

ASSESSMENT

A region of interest of the tumor was delineated. A total of 652 radiomics features were extracted and were reduced using least absolute shrinkage and selection operator regression.

STATISTICAL TESTING

Radiomic signature, participant's age, and gender were analyzed as potential predictors to perform logistic regression analysis and develop a prediction model of glioma grading, and a radiomics nomogram was used to represent this model. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical value in glioma grading.

RESULTS

The radiomic signature was significantly associated with glioma grade (P < 0.001) in both the training and validation cohorts. The performance of the radiomics nomogram derived from three MRI sequences (with C-index of 0.971 and 0.961 in the training and validation cohorts, respectively) was improved compared to those based on either CET₁ WI, T₂ WI, or ADC alone in glioma grading (with C-index of 0.914, 0.714, 0.842 in the training cohort, and 0.941, 0.500, 0.730 in the validation cohort). The nomogram derived from three sequences showed good calibration: the calibration curve showed good agreement between the estimated and the actual probability. The decision curve demonstrated that combining three sequences had more favorable clinical predictive value than single sequence imaging.

DATA CONCLUSION

We created and assessed a multiparametric MRI-based radiomics nomogram that may help clinicians classify gliomas more accurately.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:825-833.

An MRI-based Radiomics Classifier for Preoperative Prediction of Ki-67 Status in Breast Cancer

RATIONALE AND OBJECTIVES

This study aims to investigate the value of a magnetic resonance imaging-based radiomics classifier for preoperatively predicting the Ki-67 status in patients with breast cancer.

MATERIALS AND METHODS

We chronologically divided 318 patients with clinicopathologically confirmed breast cancer into a training dataset (n = 200) and a validation dataset (n = 118). Radiomics features were extracted from T2-weighted (T2W) and contrast-enhanced T1-weighted (T1+C) images of breast cancer. Radiomics feature selection and radiomics classifiers were generated using the least absolute shrinkage and selection operator regression analysis method. The correlation between the radiomics classifiers and the Ki-67 status in patients with breast cancer was explored. The predictive performances of the radiomics classifiers for the Ki-67 status were evaluated with receiver operating characteristic curves in the training dataset and validated in the validation dataset.

RESULTS

Through the radiomics feature selection, 16 and 14 features based on T2W and T1+C images, respectively, were selected to constitute the radiomics classifiers. The radiomics classifier based on T2W images was significantly correlated with the Ki-67 status in both the training and the validation datasets (both P < .0001). The radiomics classifier based on T1+C images was significantly correlated with the Ki-67 status in the training dataset (P < .0001) but not in the validation dataset (P = .083). The T2W image-based radiomics classifier exhibited good discrimination for Ki-67 status, with areas under the receiver operating characteristic curves of 0.762 (95% confidence interval: 0.685, 0.838) and 0.740 (95% confidence interval: 0.645, 0.836) in the training and validation datasets, respectively.

CONCLUSIONS

The T2W image-based radiomics classifier was a significant predictor of Ki-67 status in patients with breast cancer. Thus, it may serve as a noninvasive approach to facilitate the preoperative prediction of Ki-67 status in clinical practice.

Preoperative tumor texture analysis on MRI predicts high-risk disease and reduced survival in endometrial cancer

BACKGROUND

Improved methods for preoperative risk stratification in endometrial cancer are highly requested by gynecologists. Texture analysis is a method for quantification of heterogeneity in images, increasingly reported as a promising diagnostic tool in various cancer types, but largely unexplored in endometrial cancer.

PURPOSE

To explore whether tumor texture parameters from preoperative MRI are related to known prognostic features (deep myometrial invasion, cervical stroma invasion, lymph node metastases, and high-risk histological subtype) and to outcome in endometrial cancer patients.

STUDY TYPE

Prospective cohort study.

POPULATION/SUBJECTS

In all, 180 patients with endometrial carcinoma were included from April 2009 to November 2013 and studied until January 2017.

FIELD STRENGTH/SEQUENCES

Preoperative pelvic MRI including contrast-enhanced T₁ -weighted (T₁ c), T₂ -weighted, and diffusion-weighted imaging at 1.5T.

ASSESSMENT

Tumor regions of interest (ROIs) were manually drawn on the slice displaying the largest cross-sectional tumor area, using the proprietary research software TexRAD for analysis. With a filtration-histogram technique, the texture parameters standard deviation, entropy, mean of positive pixels (MPP), skewness, and kurtosis were calculated.

STATISTICAL TESTS

Associations between texture parameters and histological features were assessed by uni- and multivariable logistic regression, including models adjusting for preoperative biopsy status and conventional MRI findings. Multivariable Cox regression analysis was used for survival analysis.

RESULTS

High tumor entropy in apparent diffusion coefficient (ADC) maps independently predicted deep myometrial invasion (odds ratio [OR] 3.2, P lt  0.001), and high MPP in T₁ c images independently predicted high-risk histological subtype (OR 1.01, P = 0.004). High kurtosis in T₁ c images predicted reduced recurrence- and progression-free survival (hazard ratio [HR] 1.5, P lt  0.001) after adjusting for MRI-measured tumor volume and histological risk at biopsy.

DATA CONCLUSION

MRI-derived tumor texture parameters independently predicted deep myometrial invasion, high-risk histological subtype, and reduced survival in endometrial carcinomas, and thus, represent promising imaging biomarkers providing a more refined preoperative risk assessment that may ultimately enable better tailored treatment strategies in endometrial cancer.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1637-1647.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Deciphering unclassified tumors of non-small-cell lung cancer through radiomics

BACKGROUND

Tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels. They are categorized in terms of a differentiated appearance under a microscope. Non-small-cell lung cancer tumors are categorized into three main subgroups: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In approximately 20% of pathology reports, they are returned unclassified or classified as not-otherwise-specified (NOS) owing to scant materials or poor tumor differentiation.

METHOD

We present a radiomic interrogation of molecular spatial variations to decode unclassified NOS tumor architecture quantitatively. Twelve spatial descriptors with various displacements and directions were extracted and profiled with respect to the subgroups. The profiled descriptors were used to decipher the NOS tumor morphologic clues from the imaging phenotype perspective. This profiler was built as an extended version of a conventional support-vector-machine classifier, wherein a genetic algorithm and correlation analysis were embedded to define the molecular signatures of poorly differentiated tumors using well-differentiated-tumor information.

RESULTS

Sixteen multi-class classifier models with an 81% average accuracy and descriptor subset size ranging from 12 to 144 were reported. The average area under the curve was 86.3% at a 95% confidence interval and a 0.03-0.08 standard error. Correlation analysis returned an unclassified NOS membership matrix with respect to the cell-architecture similarity score for the subgroups. The best model demonstrated 53% NOS reduction.

CONCLUSION

The membership matrix is expected to assist pathologists and oncologists in cases of unresectable tumors or scant biopsy materials for histological subtyping and cancer therapy.

Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma

OBJECTIVES

To determine the value of radiomics in predicting lymph node (LN) metastasis in resectable esophageal squamous cell carcinoma (ESCC) patients.

METHODS

Data of 230 consecutive patients were retrospectively analyzed (154 in the training set and 76 in the test set). A total of 1576 radiomics features were extracted from arterial-phase CT images of the whole primary tumor. LASSO logistic regression was performed to choose the key features and construct a radiomics signature. A radiomics nomogram incorporating this signature was developed on the basis of multivariable analysis in the training set. Nomogram performance was determined and validated with respect to its discrimination, calibration and reclassification. Clinical usefulness was estimated by decision curve analysis.

RESULTS

The radiomics signature including five features was significantly associated with LN metastasis. The radiomics nomogram, which incorporated the signature and CT-reported LN status (i.e. size criteria), distinguished LN metastasis with an area under curve (AUC) of 0.758 in the training set, and performance was similar in the test set (AUC 0.773). Discrimination of the radiomics nomogram exceeded that of size criteria alone in both the training set (p <0.001) and the test set (p=0.005). Integrated discrimination improvement (IDI) and categorical net reclassification improvement (NRI) showed significant improvement in prognostic value when the radiomics signature was added to size criteria in the test set (IDI 17.3%; p<0.001; categorical NRI 52.3%; p<0.001). Decision curve analysis supported that the radiomics nomogram is superior to size criteria.

CONCLUSIONS

The radiomics nomogram provides individualized risk estimation of LN metastasis in ESCC patients and outperforms size criteria.

KEY POINTS

• A radiomics nomogram was built and validated to predict LN metastasis in resectable ESCC. • The radiomics nomogram outperformed size criteria. • Radiomics helps to unravel intratumor heterogeneity and can serve as a novel biomarker for determination of LN status in resectable ESCC.

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.

Novel Radiomic Features Based on Joint Intensity Matrices for Predicting Glioblastoma Patient Survival Time

This paper presents a novel set of image texture features generalizing standard grey-level co-occurrence matrices (GLCM) to multimodal image data through joint intensity matrices (JIMs). These are used to predict the survival of glioblastoma multiforme (GBM) patients from multimodal MRI data. The scans of 73 GBM patients from the Cancer Imaging Archive are used in our study. Necrosis, active tumor, and edema/invasion subregions of GBM phenotypes are segmented using the coregistration of contrast-enhanced T1-weighted (CE-T1) images and its corresponding fluid-attenuated inversion recovery (FLAIR) images. Texture features are then computed from the JIM of these GBM subregions and a random forest model is employed to classify patients into short or long survival groups. Our survival analysis identified JIM features in necrotic (e.g., entropy and inverse-variance) and edema (e.g., entropy and contrast) subregions that are moderately correlated with survival time (i.e., Spearman rank correlation of 0.35). Moreover, nine features were found to be associated with GBM survival with a Hazard-ratio range of 0.38-2.1 and a significance level of p < 0.05 following Holm-Bonferroni correction. These features also led to the highest accuracy in a univariate analysis for predicting the survival group of patients, with AUC values in the range of 68-70%. Considering multiple features for this task, JIM features led to significantly higher AUC values than those based on standard GLCMs and gene expression. Furthermore, an AUC of 77.56% with p = 0.003 was achieved when combining JIM, GLCM, and gene expression features into a single radiogenomic signature. In summary, our study demonstrated the usefulness of modeling the joint intensity characteristics of CE-T1 and FLAIR images for predicting the prognosis of patients with GBM.

State-of-the-Art Imaging in Human Chordoma of the Skull Base

Purpose of Review

Chordoma are rare tumours of the axial skeleton which occur most often at the base of the skull and in the sacrum. Although chordoma are generally slow-growing lesions, the recurrence rate is high and the location makes it often difficult to treat. Both computed tomography (CT) and magnetic resonance imaging (MRI) are crucial in the initial diagnosis, treatment planning and post-treatment follow-up.

Recent Findings

Basic MRI and CT characteristics of chordoma were described in the late 1980s and early 1990s. Since then, imaging techniques have evolved with increased resolution and new molecular imaging tools are rapidly evolving. New imaging tools have been developed not only to study anatomy, but also physiologic changes and characterization of tissue and assessment of tumour biology. Recent studies show the uptake of multiple PET tracers in chordoma, which may become an important aspect in the diagnosis, follow-up and personalized therapy.

Summary

This review gives an overview of skull base chordoma histopathology, classic imaging characteristics, radiomics and state-of-the-art imaging techniques that are now emerging in diagnosis, treatment planning and disease monitoring of skull base chordoma.

Radiomics and radiogenomics for precision radiotherapy

Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. 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. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Magnetic Resonance Spectroscopy, Positron Emission Tomography and Radiogenomics-Relevance to Glioma

Advances in metabolic imaging techniques have allowed for more precise characterization of gliomas, particularly as it relates to tumor recurrence or pseudoprogression. Furthermore, the emerging field of radiogenomics where radiographic features are systemically correlated with molecular markers has the potential to achieve the holy grail of neuro-oncologic neuro-radiology, namely molecular diagnosis without requiring tissue specimens. In this section, we will review the utility of metabolic imaging and discuss the current state of the art related to the radiogenomics of glioblastoma.

Towards precision medicine: from quantitative imaging to radiomics

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Objective

The standard treatment for patients with locally advanced gastric cancer has relied on perioperative radio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomics for pre-treatment computed tomography (CT) in the prediction of the pathological response of locally advanced gastric cancer with preoperative chemotherapy.

Methods

Thirty consecutive patients with CT-staged II/III gastric cancer receiving neoadjuvant chemotherapy were enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CT during the unenhanced, late arterial phase (AP) and portal venous phase (PP) before the administration of neoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient. Four methods were adopted during feature selection and eight methods were used in the process of building the classifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiver operating characteristic (ROC) curves were used to evaluate the capability of each combination of feature selection and classification method to predict a non-good response (non-GR) based on tumor regression grade (TRG).

Results

The mean area under the curve (AUC) ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722 in the PP, according to different combinations of feature selection and the classification methods. There was only one cross-combination machine-learning method indicating a relatively higher AUC (>0.600) in the AP, while 12 cross-combination machine-learning methods presented relatively higher AUCs (all >0.600) in the PP. The feature selection method adopted by a filter based on linear discriminant analysis + classifier of random forest achieved a significantly prognostic performance in the PP (AUC, 0.722±0.108; accuracy, 0.793; sensitivity, 0.636; specificity, 0.889; Z=2.039; P=0.041).

Conclusions

It is possible to predict non-GR after neoadjuvant chemotherapy in locally advanced gastric cancers based on the radiomics of CT.

Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy

Background

To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma.

Methods

A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis.

Results

The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585–0.7732; model-2: GLNUp < 0.05, AUC 0.6396, C.I.:0.5266–0.7526.

The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232–0.8797)).

Conclusion

A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma

We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice.

Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma

There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, p < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, p = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.

Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics

We aimed to investigate the potential of radiomic features of magnetic resonance imaging (MRI) to predict progression in patients with advanced nasopharyngeal carcinoma (NPC). One hundred and thirteen consecutive patients (01/2007-07/2013) (training cohort: n = 80; validation cohort: n = 33) with advanced NPC were enrolled. A total of 970 initial features were extracted from T2-weighted (T2-w) (n = 485) and contrast-enhanced T1-weighted (CET1-w) MRI (n = 485) for each patient. We used least absolute shrinkage and selection operator (Lasso) method to select features that were most significantly associated with the progression. The selected features were used to construct radiomics-based models and the predictive performance of which were assessed with respect to the area under the curve (AUC). As a result, eight features significantly associated with the progression of advanced NPC were identified. In the training cohort, a radiomic model based on combined CET1-w and T2-w images (AUC: 0.886, 95%CI: 0.815-0.956) demonstrated better prognostic performance than models based on CET1-w (AUC: 0.793, 95%CI: 0.698-0.889) or T2-w images alone (AUC: 0.813, 95%CI: 0.721-0.904). These results were confirmed in the validation cohort. Accordingly, MRI-based radiomic biomarkers present high accuracy in the pre-treatment prediction of progression in advanced NPC.

Gradient-on-a-Chip with Reactive Oxygen Species Reveals Thresholds in the Nucleus Response of Cancer Cells Depending on the Matrix Environment

Oxidative stress-mediated cancer progression depends on exposure to reactive oxygen species (ROS) in the extracellular matrix (ECM). To study the impact of ROS levels on preinvasive breast cancer cells as a function of ECM characteristics, we created a gradient-on-a-chip in which H₂O₂ progressively mixes with the cell culture medium within connected microchannels and diffuses upward into the ECM of the open cell culture window. The device utilizes a paper-based microfluidic bifurcating mixer insert to prevent leakage and favor an even fluid distribution. The gradient was confirmed by measuring H₂O₂ catalyzed into oxygen, and increasing oxidative DNA damage and protective (AOP2) response were recorded in 2D and ECM-based 3D cell cultures. Interestingly, the impact of ROS on nuclear shape and size (annunciating phenotypical changes) was governed by the stiffness of the collagen I matrix, suggesting the existence of thresholds for the phenotypic response to microenvironmental chemical exposure depending on ECM conditions.

Trends in radiology and experimental research

European Radiology Experimental, the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties.

Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy

PURPOSE

To investigate whether imaging features from pretreatment planning CT scans are associated with overall survival (OS), recurrence-free survival (RFS), and loco-regional recurrence-free survival (LR-RFS) after stereotactic body radiotherapy (SBRT) among nonsmall-cell lung cancer (NSCLC) patients.

PATIENTS AND METHODS

A total of 92 patients (median age: 73 yr) with stage I or IIA NSCLC were qualified for this study. A total dose of 50 Gy in five fractions was the standard treatment. Besides clinical characteristics, 24 "semantic" image features were manually scored based on a point scale (up to 5) and 219 computer-derived "radiomic" features were extracted based on whole tumor segmentation. Statistical analysis was performed using Cox proportional hazards model and Harrell's C-index, and the robustness of final prognostic model was assessed using tenfold cross validation by dichotomizing patients according to the survival or recurrence status at 24 months.

RESULTS

Two-year OS, RFS and LR-RFS were 69.95%, 41.3%, and 51.85%, respectively. There was an improvement of Harrell's C-index when adding imaging features to a clinical model. The model for OS contained the Eastern Cooperative Oncology Group (ECOG) performance status [Hazard Ratio (HR) = 2.78, 95% Confidence Interval (CI): 1.37-5.65], pleural retraction (HR = 0.27, 95% CI: 0.08-0.92), F2 (short axis × longest diameter, HR = 1.72, 95% CI: 1.21-2.44) and F186 (Hist-Energy-L1, HR = 1.27, 95% CI: 1.00-1.61); The prognostic model for RFS contained vessel attachment (HR = 2.13, 95% CI: 1.24-3.64) and F2 (HR = 1.69, 95% CI: 1.33-2.15); and the model for LR-RFS contained the ECOG performance status (HR = 2.01, 95% CI: 1.12-3.60) and F2 (HR = 1.67, 95% CI: 1.29-2.18).

CONCLUSIONS

Imaging features derived from planning CT demonstrate prognostic value for recurrence following SBRT treatment, and might be helpful in patient stratification.

Beyond imaging: The promise of radiomics

The domain of investigation of radiomics consists of large-scale radiological image analysis and association with biological or clinical endpoints. The purpose of the present study is to provide a recent update on the status of this rapidly emerging field by performing a systematic review of the literature on radiomics, with a primary focus on oncologic applications. The systematic literature search, performed in Pubmed using the keywords: "radiomics OR radiomic" provided 97 research papers. Based on the results of this search, we describe the methods used for building a model of prognostic value from quantitative analysis of patient images. Then, we provide an up-to-date overview of the results achieved in this field, and discuss the current challenges and future developments of radiomics for oncology.