Radiogenomics: bridging imaging and genomics

ARISE I Consensus Review on the Management of Intracranial Aneurysms

BACKGROUND

Intracranial aneurysms (IAs) remain a challenging neurological diagnosis associated with significant morbidity and mortality. There is a plethora of microsurgical and endovascular techniques for the treatment of both ruptured and unruptured aneurysms. There is no definitive consensus as to the best treatment option for this cerebrovascular pathology. The Aneurysm, Arteriovenous Malformation, and Chronic Subdural Hematoma Roundtable Discussion With Industry and Stroke Experts discussed best practices and the most promising approaches to improve the management of brain aneurysms.

METHODS

A group of experts from academia, industry, and federal regulators convened to discuss updated clinical trials, scientific research on preclinical system models, management options, screening and monitoring, and promising novel device technologies, aiming to improve the outcomes of patients with IA.

RESULTS

Aneurysm, Arteriovenous Malformation, and Chronic Subdural Hematoma Roundtable Discussion With Industry and Stroke Experts suggested the incorporation of artificial intelligence to capture sequential aneurysm growth, identify predictors of rupture, and predict the risk of rupture to guide treatment options. The consensus strongly recommended nationwide systemic data collection of unruptured IA radiographic images for the analysis and development of machine learning algorithms for rupture risk. The consensus supported centers of excellence for preclinical multicenter trials in areas such as genetics, cellular composition, and radiogenomics. Optical coherence tomography and magnetic resonance imaging contrast-enhanced 3T vessel wall imaging are promising technologies; however, more data are needed to define their role in IA management. Ruptured aneurysms are best managed at large volume centers, which should include comprehensive patient management with expertise in microsurgery, endovascular surgery, neurology, and neurocritical care.

CONCLUSIONS

Clinical and preclinical studies and scientific research on IA should engage high-volume centers and be conducted in multicenter collaborative efforts. The future of IA diagnosis and monitoring could be enhanced by the incorporation of artificial intelligence and national radiographic and biologic registries. A collaborative effort between academic centers, government regulators, and the device industry is paramount for the adequate management of IA and the advancement of the field.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Predicting cytogenetic risk in multiple myeloma using conventional whole-body MRI, spinal dynamic contrast-enhanced MRI, and spinal diffusion-weighted imaging

OBJECTIVES

Cytogenetic abnormalities are predictors of poor prognosis in multiple myeloma (MM). This paper aims to build and validate a multiparametric conventional and functional whole-body MRI-based prediction model for cytogenetic risk classification in newly diagnosed MM.

METHODS

Patients with newly diagnosed MM who underwent multiparametric conventional whole-body MRI, spinal dynamic contrast-enhanced (DCE-)MRI, spinal diffusion-weighted MRI (DWI) and had genetic analysis were retrospectively included (2011-2020/Ghent University Hospital/Belgium). Patients were stratified into standard versus intermediate/high cytogenetic risk groups. After segmentation, 303 MRI features were extracted. Univariate and model-based methods were evaluated for feature and model selection. Testing was performed using receiver operating characteristic (ROC) and precision-recall curves. Models comparing the performance for genetic risk classification of the entire MRI protocol and of all MRI sequences separately were evaluated, including all features. Four final models, including only the top three most predictive features, were evaluated.

RESULTS

Thirty-one patients were enrolled (mean age 66 ± 7 years, 15 men, 13 intermediate-/high-risk genetics). None of the univariate models and none of the models with all features included achieved good performance. The best performing model with only the three most predictive features and including all MRI sequences reached a ROC-area-under-the-curve of 0.80 and precision-recall-area-under-the-curve of 0.79. The highest statistical performance was reached when all three MRI sequences were combined (conventional whole-body MRI + DCE-MRI + DWI). Conventional MRI always outperformed the other sequences. DCE-MRI always outperformed DWI, except for specificity.

CONCLUSIONS

A multiparametric MRI-based model has a better performance in the noninvasive prediction of high-risk cytogenetics in newly diagnosed MM than conventional MRI alone.

CRITICAL RELEVANCE STATEMENT

An elaborate multiparametric MRI-based model performs better than conventional MRI alone for the noninvasive prediction of high-risk cytogenetics in newly diagnosed multiple myeloma; this opens opportunities to assess genetic heterogeneity thus overcoming sampling bias.

KEY POINTS

• Standard genetic techniques in multiple myeloma patients suffer from sampling bias due to tumoral heterogeneity. • Multiparametric MRI noninvasively predicts genetic risk in multiple myeloma. • Combined conventional anatomical MRI, DCE-MRI, and DWI had the highest statistical performance to predict genetic risk. • Conventional MRI alone always outperformed DCE-MRI and DWI separately to predict genetic risk. DCE-MRI alone always outperformed DWI separately, except for the parameter specificity to predict genetic risk. • This multiparametric MRI-based genetic risk prediction model opens opportunities to noninvasively assess genetic heterogeneity thereby overcoming sampling bias in predicting genetic risk in multiple myeloma.

Radiogenomics and Texture Analysis to Detect von Hippel-Lindau (VHL) Mutation in Clear Cell Renal Cell Carcinoma

Radiogenomics, a burgeoning field in biomedical research, explores the correlation between imaging features and genomic data, aiming to link macroscopic manifestations with molecular characteristics. In this review, we examine existing radiogenomics literature in clear cell renal cell carcinoma (ccRCC), the predominant renal cancer, and von Hippel-Lindau (VHL) gene mutation, the most frequent genetic mutation in ccRCC. A thorough examination of the literature was conducted through searches on the PubMed, Medline, Cochrane Library, Google Scholar, and Web of Science databases. Inclusion criteria encompassed articles published in English between 2014 and 2022, resulting in 10 articles meeting the criteria out of 39 initially retrieved articles. Most of these studies applied computed tomography (CT) images obtained from open source and institutional databases. This literature review investigates the role of radiogenomics, with and without texture analysis, in predicting VHL gene mutation in ccRCC patients. Radiogenomics leverages imaging modalities such as CT and magnetic resonance imaging (MRI), to analyze macroscopic features and establish connections with molecular elements, providing insights into tumor heterogeneity and biological behavior. The investigations explored diverse mutations, with a specific focus on VHL mutation, and applied CT imaging features for radiogenomic analysis. Moreover, radiomics and machine learning techniques were employed to predict VHL gene mutations based on CT features, demonstrating promising results. Additional studies delved into the relationship between VHL mutation and body composition, revealing significant associations with adipose tissue distribution. The review concludes by highlighting the potential role of radiogenomics in guiding targeted and selective therapies.

Prediction of malignant esophageal fistula in esophageal cancer using a radiomics-clinical nomogram

BACKGROUND

Malignant esophageal fistula (MEF), which occurs in 5% to 15% of esophageal cancer (EC) patients, has a poor prognosis. Accurate identification of esophageal cancer patients at high risk of MEF is challenging. The goal of this study was to build and validate a model to predict the occurrence of esophageal fistula in EC patients.

METHODS

This study retrospectively enrolled 122 esophageal cancer patients treated by chemotherapy or chemoradiotherapy (53 with fistula, 69 without), and all patients were randomly assigned to a training (n = 86) and a validation (n = 36) cohort. Radiomic features were extracted from pre-treatment CTs, clinically predictors were identified by logistic regression analysis. Lasso regression model was used for feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the clinical nomogram, radiomics-clinical nomogram and radiomics prediction model. The models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.

RESULTS

The radiomic signature consisting of ten selected features, was significantly associated with esophageal fistula (P = 0.001). Radiomics-clinical nomogram was created by two predictors including radiomics signature and stenosis, which was identified by logistic regression analysis. The model showed good discrimination with an AUC = 0.782 (95% CI 0.684-0.8796) in the training set and 0.867 (95% CI 0.7461-0.987) in the validation set, with an AIC = 101.1, and good calibration. When compared to the clinical prediction model, the radiomics-clinical nomogram improved NRI by 0.236 (95% CI 0.153, 0.614) and IDI by 0.125 (95% CI 0.040, 0.210), P = 0.004.

CONCLUSION

We developed and validated the first radiomics-clinical nomogram for malignant esophageal fistula, which could assist clinicians in identifying patients at high risk of MEF.

Methodology for Good Machine Learning with Multi-Omics Data

In 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to approach complex new data modalities and advanced analytics. The scientific question was to find novel radio-genomics-based prognostic and predictive factors for HR+/HER- metastatic breast cancer under a Research Collaboration Agreement. This collaboration has been providing valuable insights to help successfully implement future scientific projects, particularly using artificial intelligence and machine learning. This tutorial aims to provide tangible guidelines for a multi-omics project that includes multidisciplinary expert teams, spanning across different institutions. We cover key ideas, such as "maintaining effective communication" and "following good data science practices," followed by the four steps of exploratory projects, namely (1) plan, (2) design, (3) develop, and (4) disseminate. We break each step into smaller concepts with strategies for implementation and provide illustrations from our collaboration to further give the readers actionable guidance.

MRI-based bone marrow radiomics for predicting cytogenetic abnormalities in multiple myeloma

AIM

To develop a radiomics signature applied to magnetic resonance imaging (MRI)-images to predict cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS

Patients with newly diagnosed MM were enrolled retrospectively from March 2019 to September 2022. They were categorised into the high-risk cytogenetics (HRC) group and standard-risk cytogenetics (SRC) group. The patients were allocated randomly at a ratio of 7:3 into training and validation cohorts. Volumes of interest (VOI) was drawn manually on fat suppression T2-weighted imaging (FS-T2WI) and copied to the same location of the T1-weighted imaging (T1WI) sequence. Radiomics features were extracted from two sequences and selected by reproducibility and redundant analysis. The least absolute shrinkage selection operation (LASSO) algorithm was applied to build the radiomics signatures. The performance of the radiomics signatures to distinguish HRC with SRC was evaluated by ROC curves. The area under the curve (AUC), specificity, and sensitivity were also calculated.

RESULTS

A total of 105 MM patients were enrolled in this study. The four and 11 most significant and relevant features were selected separately from T1WI and FS-T2WI sequences to build the radiomics signatures based on the training cohort. Compared to the T1WI sequence, the radiomics signature based on the FS-T2WI sequence achieved better performance with AUCs of 0.896 and 0.729 in the training and validation cohorts respectively. A sensitivity of 0.833, specificity of 0.667, and Youden index of 0.500 were achieved for the FS-T2WI radiomics signature in the validation cohort.

CONCLUSIONS

The radiomics signature based on MRI provides a non-invasive and convenient tool to predict cytogenetic abnormalities in MM patients.

Recent developments in the diagnosis of pancreatic neuroendocrine neoplasms

INTRODUCTION

Pancreatic Neuroendocrine Neoplasms (PanNENs) are characterized by a highly heterogeneous clinical and biological behavior, making their diagnosis challenging. PanNENs diagnostic work-up mainly relies on biochemical markers, pathological examination, and imaging evaluation. The latter includes radiological imaging (i.e. computed tomography [CT] and magnetic resonance imaging [MRI]), functional imaging (i.e. 68Gallium [68 Ga]Ga-DOTA-peptide PET/CT and Fluorine-18 fluorodeoxyglucose [18F]FDG PET/CT), and endoscopic ultrasound (EUS) with its associated procedures.

AREAS COVERED

This review provides a comprehensive assessment of the recent advancements in the PanNENs diagnostic field. PubMed and Embase databases were used for the research, performed from inception to October 2023.

EXPERT OPINION

A deeper understanding of PanNENs biology, recent technological improvements in imaging modalities, as well as progresses achieved in molecular and cytological assays, are fundamental players for the achievement of early diagnosis and enhanced preoperative characterization of PanNENs. A multimodal diagnostic approach is required for a thorough disease assessment.

Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study

PURPOSE

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients.

METHODS

Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability.

RESULTS

A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70).

CONCLUSIONS

An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

Rim enhancement of pancreatic ductal adenocarcinoma: investigating the relationship with DCE-MRI-based radiomics and next-generation sequencing

Purpose

To identify the clinical and genetic variables associated with rim enhancement of pancreatic ductal adenocarcinoma (PDAC) and to develop a dynamic contrast-enhanced (DCE) MRI-based radiomics model for predicting the genetic status from next-generation sequencing (NGS).

Materials and methods

Patients with PDAC, who underwent pretreatment pancreatic DCE-MRI between November 2019 and July 2021, were eligible in this prospective study. Two radiologists evaluated presence of rim enhancement in PDAC, a known radiological prognostic indicator, on DCE MRI. NGS was conducted for the tissue from the lesion. The Mann-Whitney U and Chi-square tests were employed to identify clinical and genetic variables associated with rim enhancement in PDAC. For continuous variables predicting rim enhancement, the cutoff value was set based on the Youden's index from the receiver operating characteristic (ROC) curve. Radiomics features were extracted from a volume-of-interest of PDAC on four DCE maps (Ktrans, Kep, Ve, and iAUC). A random forest (RF) model was constructed using 10 selected radiomics features from a pool of 392 original features. This model aimed to predict the status of significant NGS variables associated with rim enhancement. The performance of the model was validated using test set.

Results

A total of 55 patients (32 men; median age 71 years) were randomly assigned to the training (n = 41) and test (n = 14) sets. In the training set, KRAS, TP53, CDKN2A, and SMAD4 mutation rates were 92.3%, 61.8%, 14.5%, and 9.1%, respectively. Tumor size and KRAS variant allele frequency (VAF) differed between rim-enhancing (n = 12) and nonrim-enhancing (n = 29) PDACs with a cutoff of 17.22%. The RF model's average AUC from 10-fold cross-validation for predicting KRAS VAF status was 0.698. In the test set comprising 6 tumors with low KRAS VAF and 8 with high KRAS VAF, the RF model's AUC reached 1.000, achieving a sensitivity of 75.0%, specificity of 100% and accuracy of 87.5%.

Conclusion

Rim enhancement of PDAC is associated with KRAS VAF derived from NGS-based genetic information. For predicting the KRAS VAF status in PDAC, a radiomics model based on DCE maps showed promising results.

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

Empowering PET: harnessing deep learning for improved clinical insight

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.

Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology

The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas

The purpose of this study is to predict the mRNA expression of CSF1R in HGG non-invasively using MRI (magnetic resonance imaging) omics technology and to evaluate the correlation between the established radiomics model and prognosis. We investigated the predictive value of CSF1R in the Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) database. The Support vector machine (SVM) and the Logistic regression (LR) algorithms were used to create a radiomics_score (Rad_score), respectively. The effectiveness and performance of the radiomics model was assessed in the training (n = 89) and tenfold cross-validation sets. We further analyzed the correlation between Rad_score and macrophage-related genes using Spearman correlation analysis. A radiomics nomogram combining the clinical factors and Rad_score was constructed to validate the radiomic signatures for individualized survival estimation and risk stratification. The results showed that CSF1R expression was markedly elevated in HGG tissues, which was related to worse prognosis. CSF1R expression was closely related to the abundance of infiltrating immune cells, such as macrophages. We identified nine features for establishing a radiomics model. The radiomics model predicting CSF1R achieved high AUC in training (0.768 in SVM and 0.792 in LR) and tenfold cross-validation sets (0.706 in SVM and 0.717 in LR). Rad_score was highly associated with tumor-related macrophage genes. A radiomics nomogram combining the Rad_score and clinical factors was constructed and revealed satisfactory performance. MRI-based Rad_score is a novel way to predict CSF1R expression and prognosis in high-grade glioma patients. The radiomics nomogram could optimize individualized survival estimation for HGG patients.

Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score

BACKGROUND

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

AIM

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

METHODS

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

RESULTS

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

CONCLUSION

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research

Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.

A cost-effectiveness analysis of an integrated clinical-radiogenomic screening program for the identification of BRCA 1/2 carriers (e-PROBE study)

Current approach to identify BRCA 1/2 carriers in the general population is ineffective as most of the carriers remain undiagnosed. Radiomics is an emerging tool for large scale quantitative analysis of features from standard diagnostic imaging and has been applied also to identify gene mutational status. The objective of this study was to evaluate the clinical and economic impact of integrating a radiogenomics model with clinical and family history data in identifying BRCA mutation carriers in the general population. This cost-effective analysis compares three different approaches to women selection for BRCA testing: established clinical criteria/family history (model 1); established clinical criteria/family history and the currently available radiogenomic model (49% sensitivity and 87% specificity) based on ultrasound images (model 2); same approach used in model 2 but simulating an improvement of the performances of the radiogenomic model (80% sensitivity and 95% specificity) (model 3). All models were trained with literature data. Direct costs were calculated according to the rates currently used in Italy. The analysis was performed simulating different scenarios on the generation of 18-year-old girls in Italy (274,000 people). The main outcome was to identify the most effective model comparing the number of years of BRCA-cancer healthy life expectancy (HLYs). An incremental cost-effectiveness ratio (ICER) was also derived to determine the cost in order to increase BRCA carriers-healthy life span by 1 year. Compared to model 1, model 2 increases the detection rate of BRCA carriers by 41.8%, reduces the rate of BRCA-related cancers by 23.7%, generating over a 62-year observation period a cost increase by 2.51 €/Year/Person. Moreover, model 3 further increases BRCA carriers detection (+ 68.3%) and decrease in BRCA-related cancers (- 38.4%) is observed compared to model 1. Model 3 increases costs by 0.7 €/Year/Person. After one generation, the estimated ICER in the general population amounts to about 3800€ and 653€ in model 2 and model 3 respectively. Model 2 has a massive effect after only one generation in detecting carriers in the general population with only a small cost increment. The clinical impact is limited mainly due to the current low acceptance rate of risk-reducing surgeries. Further multicentric studies are required before implementing the integrated clinical-radiogenomic model in clinical practice.

The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study

PURPOSE

Atypical meningiomas could manifest early recurrence after surgery and even adjuvant radiotherapy. We aimed to construct a clinico-radiomics model to predict post-operative recurrence of atypical meningiomas based on clinicopathological and radiomics features.

MATERIALS AND METHODS

The study cohort was comprised of 224 patients from two neurosurgical centers. 164 patients from center I were divided to the training cohort for model development and the testing cohort for internal validation. 60 patients from center II were used for external validation. Clinicopathological characteristics, radiological semantic, and radiomics features were collected. A radiomic signature was comprised of four radiomics features. A clinico-radiomics model combining the radiomics signature and clinical characteristics was constructed to predict the recurrence of atypical meningiomas.

RESULTS

1920 radiomics features were extracted from the T1 Contrast and T2-FLAIR sequences of patients in center I. The radiomics signature was able to differentiate post-operative patients into low-risk and high-risk groups based on tumor recurrence (P < 0.001). A clinic-radiomics model was established by combining age, extent of resection, Ki-67 index, surgical history and the radiomics signature for recurrence prediction in atypical meningiomas. The model achieved a good prediction performance with the integrated AUC of 0.858 (0.802-0.915), 0.781 (0.649-0.912) and 0.840 (0.747-0.933) in the training, internal validation and external validation cohort, respectively.

CONCLUSIONS

The present study established a radiomics signature and a clinico-radiomics model with a favorable performance in predicting tumor recurrence for atypical meningiomas.

The Role of Radiomics in Rectal Cancer

PURPOSE

Radiomics is a promising method for advancing imaging assessment in rectal cancer. This review aims to describe the emerging role of radiomics in the imaging assessment of rectal cancer, including various applications of radiomics based on CT, MRI, or PET/CT.

METHODS

We conducted a literature review to highlight the progress of radiomic research to date and the challenges that need to be addressed before radiomics can be implemented clinically.

RESULTS

The results suggest that radiomics has the potential to provide valuable information for clinical decision-making in rectal cancer. However, there are still challenges in terms of standardization of imaging protocols, feature extraction, and validation of radiomic models. Despite these challenges, radiomics holds great promise for personalized medicine in rectal cancer, with the potential to improve diagnosis, prognosis, and treatment planning. Further research is needed to validate the clinical utility of radiomics and to establish its role in routine clinical practice.

CONCLUSION

Overall, radiomics has emerged as a powerful tool for improving the imaging assessment of rectal cancer, and its potential benefits should not be underestimated.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer

OBJECTIVES

Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC.

METHODS

The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects.

RESULTS

Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828-0.942) and 0.810 (95%CI: 0.653-0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2.

CONCLUSIONS

The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies.

ADVANCES IN KNOWLEDGE

This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.

MRI-guided Pelvic Radiation Therapy: A Primer for Radiologists

Radiation therapy (RT) is a core pillar of oncologic treatment, and half of all patients with cancer receive this therapy as a curative or palliative treatment. The recent integration of MRI into the RT workflow has led to the advent of MRI-guided RT (MRIgRT). Using MRI rather than CT has clear advantages for guiding RT to pelvic tumors, including superior soft-tissue contrast, improved organ motion visualization, and the potential to image tumor phenotypic characteristics to identify the most aggressive or treatment-resistant areas, which can be targeted with a more focal higher radiation dose. Radiologists should be familiar with the potential uses of MRI in planning pelvic RT; the various RT techniques used, such as brachytherapy and external beam RT; and the impact of MRIgRT on treatment paradigms. Current clinical experience with and the evidence base for MRIgRT in the settings of prostate, cervical, and bladder cancer are discussed, and examples of treated cases are illustrated. In addition, the benefits of MRIgRT, such as real-time online adaptation of RT (during treatment) and interfraction and/or intrafraction adaptation to organ motion, as well as how MRIgRT can decrease toxic effects and improve oncologic outcomes, are highlighted. MRIgRT is particularly beneficial for treating mobile pelvic structures, and real-time adaptive RT for tumors can be achieved by using advanced MRI-guided linear accelerator systems to spare organs at risk. Future opportunities for development of biologically driven adapted RT with use of functional MRI sequences and radiogenomic approaches also are outlined. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study

OBJECTIVE

To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL).

METHODS

A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.

RESULTS

The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits.

CONCLUSIONS

The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.

Exploratory Analysis of the Role of Radiomic Features in the Differentiation of Oncocytoma and Chromophobe RCC in the Nephrographic CT Phase

In diagnostic imaging, distinguishing chromophobe renal cell carcinomas (chRCCs) from renal oncocytomas (ROs) is challenging, since they both present similar radiological characteristics. Radiomics has the potential to help in the differentiation between chRCCs and ROs by extracting quantitative imaging. This is a preliminary study of the role of radiomic features in the differentiation of chRCCs and ROs using machine learning models. In this retrospective work, 38 subjects were involved: 19 diagnosed with chRCCs and 19 with ROs. The CT nephrographic contrast phase was selected in each case. Three-dimensional segmentations of the lesions were performed and the radiomic features were extracted. To assess the reliability of the features, the intraclass correlation coefficient was calculated from the segmentations performed by three radiologists with different degrees of expertise. The selection of features was based on the criteria of excellent intraclass correlation coefficient (ICC), high correlation, and statistical significance. Three machine learning models were elaborated: support vector machine (SVM), random forest (RF), and logistic regression (LR). From 105 extracted features, 41 presented an excellent ICC and 6 were not highly correlated with each other. Only two features showed significant differences according to histological type and machine learning models were developed with them. LR was the better model, in particular, with an 83% precision.

Radiomics could predict surgery at 10 years in Crohn's disease

BACKGROUND

Predicting clinical outcomes represents a major challenge in Crohn's disease (CD). Radiomics provides a method to extract quantitative features from medical images and may successfully predict clinical course.

AIMS

The aim of this pilot study is to evaluate the use of radiomics to predict 10-year surgery for CD patients.

METHODS

We selected a cohort of CD patients with CT scan enterographies and a 10-year follow up. The R library Moddicom was used to extract radiomic features from each lesion of CD, segmented in the CT scans. A logistic regression model based on selected radiomic features was developed to predict 10-year surgery. The model was evaluated by computing the area under the curve (AUC) of the receiver operating characteristic curve, sensitivity, specificity, positive and negative predictive values (PPV, NPV).

RESULTS

We enroled 30 patients, with 44 CT scans and 93 lesions. We extracted 217 radiomic features from each lesion. The developed model was based on two radiomic features and presented an AUC (95% CI) of 0.83 (0.73-0.91) in predicting 10-year surgery. Sensitivity, specificity, PPV, NPV of the radiomic model were equal to 0.72, 0.90, 0.79, 0.86, respectively.

CONCLUSION

Radiomics could be a helpful tool to identify patients with high risk for surgery and needing a stricter monitoring.

Radiomics using computed tomography to predict CD73 expression and prognosis of colorectal cancer liver metastases

BACKGROUND

Finding a noninvasive radiomic surrogate of tumor immune features could help identify patients more likely to respond to novel immune checkpoint inhibitors. Particularly, CD73 is an ectonucleotidase that catalyzes the breakdown of extracellular AMP into immunosuppressive adenosine, which can be blocked by therapeutic antibodies. High CD73 expression in colorectal cancer liver metastasis (CRLM) resected with curative intent is associated with early recurrence and shorter patient survival. The aim of this study was hence to evaluate whether machine learning analysis of preoperative liver CT-scan could estimate high vs low CD73 expression in CRLM and whether such radiomic score would have a prognostic significance.

METHODS

We trained an Attentive Interpretable Tabular Learning (TabNet) model to predict, from preoperative CT images, stratified expression levels of CD73 (CD73^High vs. CD73^Low) assessed by immunofluorescence (IF) on tissue microarrays. Radiomic features were extracted from 160 segmented CRLM of 122 patients with matched IF data, preprocessed and used to train the predictive model. We applied a five-fold cross-validation and validated the performance on a hold-out test set.

RESULTS

TabNet provided areas under the receiver operating characteristic curve of 0.95 (95% CI 0.87 to 1.0) and 0.79 (0.65 to 0.92) on the training and hold-out test sets respectively, and outperformed other machine learning models. The TabNet-derived score, termed rad-CD73, was positively correlated with CD73 histological expression in matched CRLM (Spearman's ρ = 0.6004; P < 0.0001). The median time to recurrence (TTR) and disease-specific survival (DSS) after CRLM resection in rad-CD73^High vs rad-CD73^Low patients was 13.0 vs 23.6 months (P = 0.0098) and 53.4 vs 126.0 months (P = 0.0222), respectively. The prognostic value of rad-CD73 was independent of the standard clinical risk score, for both TTR (HR = 2.11, 95% CI 1.30 to 3.45, P < 0.005) and DSS (HR = 1.88, 95% CI 1.11 to 3.18, P = 0.020).

CONCLUSIONS

Our findings reveal promising results for non-invasive CT-scan-based prediction of CD73 expression in CRLM and warrant further validation as to whether rad-CD73 could assist oncologists as a biomarker of prognosis and response to immunotherapies targeting the adenosine pathway.

Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases

BACKGROUND

Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM).

METHODS

A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia.

RESULTS

Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia.

CONCLUSIONS

T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms.

CRITICAL RELEVANCE STATEMENT

Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).

Current role of machine learning and radiogenomics in precision neuro-oncology

In the past few years, artificial intelligence (AI) has been increasingly used to create tools that can enhance workflow in medicine. In particular, neuro-oncology has benefited from the use of AI and especially machine learning (ML) and radiogenomics, which are subfields of AI. ML can be used to develop algorithms that dynamically learn from available medical data in order to automatically do specific tasks. On the other hand, radiogenomics can identify relationships between tumor genetics and imaging features, thus possibly giving new insights into the pathophysiology of tumors. Therefore, ML and radiogenomics could help treatment tailoring, which is crucial in personalized neuro-oncology. The aim of this review is to illustrate current and possible future applications of ML and radiomics in neuro-oncology.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

Prediction of prostate tumour hypoxia using pre-treatment MRI-derived radiomics: preliminary findings

PURPOSE

To develop a machine learning (ML) model based on radiomic features (RF) extracted from whole prostate gland magnetic resonance imaging (MRI) for prediction of tumour hypoxia pre-radiotherapy.

MATERIAL AND METHODS

Consecutive patients with high-grade prostate cancer and pre-treatment MRI treated with radiotherapy between 01/12/2007 and 1/08/2013 at two cancer centres were included. Cancers were dichotomised as normoxic or hypoxic using a biopsy-based 32-gene hypoxia signature (Ragnum signature). Prostate segmentation was performed on axial T2-weighted (T2w) sequences using RayStation (v9.1). Histogram standardisation was applied prior to RF extraction. PyRadiomics (v3.0.1) was used to extract RFs for analysis. The cohort was split 80:20 into training and test sets. Six different ML classifiers for distinguishing hypoxia were trained and tuned using five different feature selection models and fivefold cross-validation with 20 repeats. The model with the highest mean validation area under the curve (AUC) receiver operating characteristic (ROC) curve was tested on the unseen set, and AUCs were compared via DeLong test with 95% confidence interval (CI).

RESULTS

195 patients were included with 97 (49.7%) having hypoxic tumours. The hypoxia prediction model with best performance was derived using ridge regression and had a test AUC of 0.69 (95% CI: 0.14). The test AUC for the clinical-only model was lower (0.57), but this was not statistically significant (p = 0.35). The five selected RFs included textural and wavelet-transformed features.

CONCLUSION

Whole prostate MRI-radiomics has the potential to non-invasively predict tumour hypoxia prior to radiotherapy which may be helpful for individualised treatment optimisation.

Biomarkers for the Detection and Surveillance of Renal Cancer

Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

Radiological imaging is currently employed as the most effective technique for screening, diagnosis, and follow up of patients with breast cancer (BC), the most common type of tumor in women worldwide. However, the introduction of the omics sciences such as metabolomics, proteomics, and molecular genomics, have optimized the therapeutic path for patients and implementing novel information parallel to the mutational asset targetable by specific clinical treatments. Parallel to the "omics" clusters, radiological imaging has been gradually employed to generate a specific omics cluster termed "radiomics". Radiomics is a novel advanced approach to imaging, extracting quantitative, and ideally, reproducible data from radiological images using sophisticated mathematical analysis, including disease-specific patterns, that could not be detected by the human eye. Along with radiomics, radiogenomics, defined as the integration of "radiology" and "genomics", is an emerging field exploring the relationship between specific features extracted from radiological images and genetic or molecular traits of a particular disease to construct adequate predictive models. Accordingly, radiological characteristics of the tissue are supposed to mimic a defined genotype and phenotype and to better explore the heterogeneity and the dynamic evolution of the tumor over the time. Despite such improvements, we are still far from achieving approved and standardized protocols in clinical practice. Nevertheless, what can we learn by this emerging multidisciplinary clinical approach? This minireview provides a focused overview on the significance of radiomics integrated by RNA sequencing in BC. We will also discuss advances and future challenges of such radiomics-based approach.

CT Image-Based Biopsy to Aid Prediction of HOPX Expression Status and Prognosis for Non-Small Cell Lung Cancer Patients

This study aimed to elucidate a computed tomography (CT) image-based biopsy with a radiogenomic signature to predict homeodomain-only protein homeobox (HOPX) gene expression status and prognosis in patients with non-small cell lung cancer (NSCLC). Patients were labeled as HOPX-negative or positive based on HOPX expression and were separated into training (n = 92) and testing (n = 24) datasets. In correlation analysis between genes and image features extracted by Pyradiomics for 116 patients, eight significant features associated with HOPX expression were selected as radiogenomic signature candidates from the 1218 image features. The final signature was constructed from eight candidates using the least absolute shrinkage and selection operator. An imaging biopsy model with radiogenomic signature was built by a stacking ensemble learning model to predict HOPX expression status and prognosis. The model exhibited predictive power for HOPX expression with an area under the receiver operating characteristic curve of 0.873 and prognostic power in Kaplan-Meier curves (p = 0.0066) in the test dataset. This study's findings implied that the CT image-based biopsy with a radiogenomic signature could aid physicians in predicting HOPX expression status and prognosis in NSCLC.

An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.

Machine-Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant

Background: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. Methods: Using data collected from 2005–2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. Results: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. Conclusions: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

Contrast computed tomography-based radiomics is correlation with COG risk stratification of neuroblastoma

PURPOSE

Although a risk stratification strategy for neuroblastoma (NB) has been proposed, precise and convenient clinical risk estimation remains challenging. This study aimed to investigate the correlation of contrast computed tomography (CT)-based radiomics with NB risk stratification.

METHODS

Patients with NB (n = 289) from two centers (244 and 45 patients in the training/testing and external validation cohorts, respectively) were divided into nonhigh- and high-risk groups. A total of 1648 radiomics features were extracted from the arterial phase, and the radiomics signature was constructed using rad scores, whereas the clinical model was established based on clinical factors. Further, a combined nomogram was developed based on the clinical factors and radiomics signatures. Finally, receiver operating characteristic curve and decision curve analyses (DCA) were used to assess the performance of the established models.

RESULTS

Seventeen radiomics features were used to construct the radiomics signature. A significant difference was observed in the rad score between the two groups in the training (0.540 vs. 0.704, P < 0.001) and testing (0.563 vs. 0.969, P < 0.001) cohorts. The nomogram showed a higher area under the curve (AUC) in the training (AUC = 0.87), testing (AUC = 0.83), and external validation (AUC = 0.84) cohorts than other models. The Hosmer-Lemeshow test and calibration curves indicated that the nomogram fit perfectly. DCA demonstrated that the clinical-radiomics nomogram was more beneficial.

CONCLUSIONS

Contrast CT-based radiomics shows correlation with COG risk stratification of NB. Radiomics features combined with clinical factors showed the best performance, which may improve the management of patients with NB.

Oligodendrocyte Transcription Factor 2 as a Potential Prognostic Biomarker of Glioblastoma: Kaplan-Meier Analysis and the Development of a Binary Predictive Model Based on Visually Accessible Rembrandt Image and Magnetic Resonance Imaging Radiomic Features

OBJECTIVE

Oligodendrocyte transcription factor 2 (OLIG2) is universally expressed in human glioblastoma (GB). Our study explores whether OLIG2 expression impacts GB patients' overall survival and establishes a machine learning model for OLIG2 level prediction in patients with GB based on clinical, semantic, and magnetic resonance imaging radiomic features.

METHODS

Kaplan-Meier analysis was used to determine the optimal cutoff value of the OLIG2 in 168 GB patients. Three hundred thirteen patients enrolled in the OLIG2 prediction model were randomly divided into training and testing sets in a ratio of 7:3. The radiomic, semantic, and clinical features were collected for each patient. Recursive feature elimination (RFE) was used for feature selection. The random forest (RF) model was built and fine-tuned, and the area under the curve was calculated to evaluate the performance. Finally, a new testing set excluding IDH-mutant patients was built and tested in a predictive model using the fifth edition of the central nervous system tumor classification criteria.

RESULTS

One hundred nineteen patients were included in the survival analysis. Oligodendrocyte transcription factor 2 was positively associated with GB survival, with an optimal cutoff of 10% (P = 0.00093). One hundred thirty-four patients were eligible for the OLIG2 prediction model. An RFE-RF model based on 2 semantic and 21 radiomic signatures achieved areas under the curve of 0.854 in the training set, 0.819 in the testing set, and 0.825 in the new testing set.

CONCLUSIONS

Glioblastoma patients with ≤10% OLIG2 expression tended to have worse overall survival. An RFE-RF model integrating 23 features can predict the OLIG2 level of GB patients preoperatively, irrespective of the central nervous system classification criteria, further guiding individualized treatment.

Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning

PURPOSE

Classical prognostic indicators of head and neck squamous cell carcinoma (HNSCC) can no longer meet the clinical needs of precision medicine. This study aimed to establish a radiomics model to predict Granzyme A (GZMA) expression in patients with HNSCC.

METHODS

We downloaded transcriptomic data of HNSCC patients from The Cancer Genome Atlas for prognosis analysis and then used corresponding enhanced computed tomography (CT) images from The Cancer Imaging Archive for feature extraction and model construction. We explored the influence of differences in GZMA expression on signaling pathways and analyzed the potential molecular mechanism and its relationship with immune cell infiltration. Subsequently, non-invasive CT radiomics models were established to predict the expression of GZMA mRNA and evaluate the correlation with the radiomics-score (Rad-score), related genes, and prognosis.

RESULTS

We found that GZMA was highly expressed in tumor tissues, and high GZMA expression was a protective factor for overall survival. The degree of B and T lymphocyte and natural killer cell infiltration was significantly correlated with GZMA expression. The receiver operating characteristic curve showed that the Relief GBM and RFE_GBM radiomics models had good predictive ability, and there were significant differences in the Rad-score distribution between the high- and low-GZMA-expression groups.

CONCLUSIONS

GZMA expression can significantly affect the prognosis of patients with HNSCC. Enhanced CT radiomics models can effectively predict the expression of GZMA mRNA.

Radiomics and Dosiomics Signature From Whole Lung Predicts Radiation Pneumonitis: A Model Development Study With Prospective External Validation and Decision-curve Analysis

PURPOSE

Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction.

METHODS AND MATERIALS

Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models.

RESULTS

A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram.

CONCLUSIONS

Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.

Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects

Renal cancer management is challenging from diagnosis to treatment and follow-up. In cases of small renal masses and cystic lesions the differential diagnosis of benign or malignant tissues has potential pitfalls when imaging or even renal biopsy is applied. The recent artificial intelligence, imaging techniques, and genomics advancements have the ability to help clinicians set the stratification risk, treatment selection, follow-up strategy, and prognosis of the disease. The combination of radiomics features and genomics data has achieved good results but is currently limited by the retrospective design and the small number of patients included in clinical trials. The road ahead for radiogenomics is open to new, well-designed prospective studies, with large cohorts of patients required to validate previously obtained results and enter clinical practice.

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management.

Synergies of Radiomics and Transcriptomics in Lung Cancer Diagnosis: A Pilot Study

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.

Imaging genomics: data fusion in uncovering disease heritability

Sequencing of the human genome in the early 2000s enabled probing of the genetic basis of disease on a scale previously unimaginable. Now, two decades later, after interrogating millions of markers in thousands of individuals, a significant portion of disease heritability still remains hidden. Recent efforts to unravel this 'missing heritability' have focused on garnering new insight from merging different data types, including medical imaging. Imaging offers promising intermediate phenotypes to bridge the gap between genetic variation and disease pathology. In this review we outline this fusion and provide examples of imaging genomics in a range of diseases, from oncology to cardiovascular and neurodegenerative disease. Finally, we discuss how ongoing revolutions in data science and sharing are primed to advance the field.

Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma

Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images. Three classifiers, logistic regression (LR), support vector machine, and Adaboost were adopted for modeling. The areas under the curve of the three models were 0.70, 0.67, and 0.61, respectively, in the external test cohort. Alpha-fetoprotein (AFP) was the only significant clinicopathological variable associated with HCC grading (odds ratio: 2.75). When combining AFP, the LR+AFP model showed the best performance, with an AUC of 0.71 (95%CI: 0.59-0.82) in the external test cohort. A radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades. Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels.

Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T₂-weighted imaging, T₁-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T₁-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.

A Review of Fusion Methods for Omics and Imaging Data

The development of omics data and biomedical images has greatly advanced the progress of precision medicine in diagnosis, treatment, and prognosis. The fusion of omics and imaging data, i.e., omics-imaging fusion, offers a new strategy for understanding complex diseases. However, due to a variety of issues such as the limited number of samples, high dimensionality of features, and heterogeneity of different data types, efficiently learning complementary or associated discriminative fusion information from omics and imaging data remains a challenge. Recently, numerous machine learning methods have been proposed to alleviate these problems. In this review, from the perspective of fusion levels and fusion methods, we first provide an overview of preprocessing and feature extraction methods for omics and imaging data, and comprehensively analyze and summarize the basic forms and variations of commonly used and newly emerging fusion methods, along with their advantages, disadvantages and the applicable scope. We then describe public datasets and compare experimental results of various fusion methods on the ADNI and TCGA datasets. Finally, we discuss future prospects and highlight remaining challenges in the field.