Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 2009;70:232-241. [PMID: 19303233 DOI: 10.1016/j.ejrad.2009.01.050]

Investigating glioma genetics through perfusion MRI: rCBV and rCBF as predictive biomarkers

BACKGROUND

Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes.

PURPOSE

To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in IDH mutations.

STUDY TYPE

Retrospective cohort study.

POPULATION

67 patients with brain tumors (including glioblastoma, astrocytoma, oligodendroglioma) undergoing surgical resection.

FIELD STRENGTH

1.5 Tesla MRI, including T1 pre- and post-contrast, FLAIR, DWI, and DSC sequences.

ASSESSMENT

Semiquantitative perfusion metrics (rCBV, rCBF) were evaluated against genetic markers (IDH1, EGFR, CDKN2A, PDGFRA, MGMT, TERT, 1p19q, PTEN, TP53, H3F3A) through advanced MRI techniques. Contrast enhancement was assessed, and genetic alterations were confirmed via histopathological and molecular analyses.

STATISTICAL TESTS

Chi-square test, sensitivity, specificity, and ROC analysis for predictive modeling; significance level set at p < 0.05.

RESULTS

Statistically significant differences in perfusion metrics were observed among tumors with distinct genetic profiles, with primary tumors and those harboring specific mutations (IDH1 wildtype, EGFR amplification, CDKN2A homozygous deletion, PDGFRA amplification) showing higher perfusion values. A cut-off value of <4 for rCBV in predicting IDH1 mutation yielded a sensitivity of 61.5 % and specificity of 82.1 %. For CDKN2A deletion, a cut-off of >5 resulted in a sensitivity of 75 % and specificity of 74.6 %, with an ROC value of 0.78.

DATA CONCLUSION

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. Additionally, findings like the T2/FLAIR mismatch sign highlight the potential for preoperative molecular predictions when biopsy is not feasible. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.

DATA CONCLUSION

Integrating perfusion MRI with genetic analysis offers a promising approach to improving the diagnostic and therapeutic landscape for brain tumors, indicating a substantial step toward personalized neuro-oncology. These findings support further validation in larger, multi-institutional studies to solidify their role in clinical practice.

Ultrasound-based radiogenomics: status, applications, and future direction

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Multi-parametric MRI Habitat Radiomics Based on Interpretable Machine Learning for Preoperative Assessment of Microsatellite Instability in Rectal Cancer

RATIONALE AND OBJECTIVES

This study constructed an interpretable machine learning model based on multi-parameter MRI sub-region habitat radiomics and clinicopathological features, aiming to preoperatively evaluate the microsatellite instability (MSI) status of rectal cancer (RC) patients.

MATERIALS AND METHODS

This retrospective study recruited 291 rectal cancer patients with pathologically confirmed MSI status and randomly divided them into a training cohort and a testing cohort at a ratio of 8:2. First, the K-means method was used for cluster analysis of tumor voxels, and sub-region radiomics features and classical radiomics features were respectively extracted from multi-parameter MRI sequences. Then, the synthetic minority over-sampling technique method was used to balance the sample size, and finally, the features were screened. Prediction models were established using logistic regression based on clinicopathological variables, classical radiomics features, and MSI-related sub-region radiomics features, and the contribution of each feature to the model decision was quantified by the Shapley-Additive-Explanations (SHAP) algorithm.

RESULTS

The area under the curve (AUC) of the sub-region radiomics model in the training and testing groups was 0.848 and 0.8, respectively, both better than that of the classical radiomics and clinical models. The combined model performed the best, with AUCs of 0.908 and 0.863 in the training and testing groups, respectively.

CONCLUSION

We developed and validated a robust combined model that integrates clinical variables, classical radiomics features, and sub-region radiomics features to accurately determine the MSI status of RC patients. We visualized the prediction process using SHAP, enabling more effective personalized treatment plans and ultimately improving RC patient survival rates.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.

The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review

This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods. A comprehensive bias assessment was conducted, including risk assessment and model performance indicators. Ten studies between 2016 and 2023 were analyzed. Studies were mostly retrospective. Patient cohorts varied in size and characteristics, with the number of patients ranging from 79 to 315. The construction of the model involves various radiomic and genotic datasets, and most models show promising prediction performance with the area under the receiver operating characteristic curve (AUC) values ranging from 0.64 to 0.94 and the concordance index (C-index) values from 0.28 to 0.80. The combination model typically outperforms the single method model, indicating higher prediction accuracy and the highest AUC was 0.99. Combining radiomics and genomics in the prognostic model of lung cancer may improve the predictive performance. However, further research on standardized data and larger cohorts is needed to validate and integrate these findings into clinical practice. CRITICAL RELEVANCE STATEMENT: The combination of radiomics and genomics in the prognostic model of lung cancer improved prediction accuracy in most included studies. KEY POINTS: The combination of radiomics and genomics can improve model performance in most studies. The results of establishing prognosis models by different methods are discussed. The combination of radiomics and genomics may be helpful to provide better treatment for patients.

Development trends and knowledge framework of artificial intelligence (AI) applications in oncology by years: a bibliometric analysis from 1992 to 2022

PURPOSE

Oncology is the primary field in medicine with a high rate of artificial intelligence (AI) use. Thus, this study aimed to investigate the trends of AI in oncology, evaluating the bibliographic characteristics of articles. We evaluated the related research on the knowledge framework of Artificial Intelligence (AI) applications in Oncology through bibliometrics analysis and explored the research hotspots and current status from 1992 to 2022.

METHODS

The research employed a scientometric methodology and leveraged scientific visualization tools such as Bibliometrix R Package Software, VOSviewer, and Litmaps for comprehensive data analysis. Scientific AI-related publications in oncology were retrieved from the Web of Science (WoS) and InCites from 1992 to 2022.

RESULTS

A total of 7,815 articles authored by 35,098 authors and published in 1,492 journals were included in the final analysis. The most prolific authors were Esteva A (citaition = 5,821) and Gillies RJ (citaition = 4288). The most active institutions were the Chinese Academy of Science and Harward University. The leading journals were Frontiers ın Oncology and Scientific Reports. The most Frequent Author Keywords are "machine learning", "deep learning," "radiomics", "breast cancer", "melanoma" and "artificial intelligence," which are the research hotspots in this field. A total of 10,866 Authors' keywords were investigated. The average number of citations per document is 23. After 2015, the number of publications proliferated.

CONCLUSION

The investigation of Artificial Intelligence (AI) applications in the field of Oncology is still in its early phases especially for genomics, proteomics, and clinicomics, with extensive studies focused on biology, diagnosis, treatment, and cancer risk assessment. This bibliometric analysis offered valuable perspectives into AI's role in Oncology research, shedding light on emerging research paths. Notably, a significant portion of these publications originated from developed nations. These findings could prove beneficial for both researchers and policymakers seeking to navigate this field.

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance.

METHODS

In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test.

RESULTS

Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment.

CONCLUSION

Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials.

CLINICAL RELEVANCE STATEMENT

Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.

Ultrasomics differentiation of malignant and benign focal liver lesions based on contrast-enhanced ultrasound

OBJECTIVES

To establish a nomogram for differentiating malignant and benign focal liver lesions (FLLs) using ultrasomics features derived from contrast-enhanced ultrasound (CEUS).

METHODS

527 patients were retrospectively enrolled. On the training cohort, ultrasomics features were extracted from CEUS and b-mode ultrasound (BUS). Automatic feature selection and model development were performed using the Ultrasomics-Platform software, outputting the corresponding ultrasomics scores. A nomogram based on the ultrasomics scores from artery phase (AP), portal venous phase (PVP) and delayed phase (DP) of CEUS, and clinical factors were established. On the validation cohort, the diagnostic performance of the nomogram was assessed and compared with seniorexpert and resident radiologists.

RESULTS

In the training cohort, the AP, PVP and DP scores exhibited better differential performance than BUS score, with area under the curve (AUC) of 84.1-85.1% compared with the BUS (74.6%, P < 0.05). In the validation cohort, the AUC of combined nomogram and expert was significantly higher than that of the resident (91.4% vs. 89.5% vs. 79.3%, P < 0.05). The combined nomogram had a comparable sensitivity with the expert and resident (95.2% vs. 98.4% vs. 97.6%), while the expert had a higher specificity than the nomogram and the resident (80.6% vs. 72.2% vs. 61.1%, P = 0.205).

CONCLUSIONS

A CEUS ultrasomics based nomogram had an expert level performance in FLL characterization.

Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma

OBJECTIVES

To develop and validate a radiomics-based model for predicting SOX9-positive hepatocellular carcinoma (HCC) using preoperative contrast-enhanced computed tomography (CT) images.

METHODS

From January 2013 to April 2017, patients with histologically proven HCC who received systemic sorafenib treatment after curative resection were retrospectively enrolled. Radiomic features were extracted from portal venous phase CT images and selected to build a radiomics score using logistic regression analysis. The factors associated with SOX9 expression were selected and combined by univariate and multivariate analyses to establish clinico-liver imaging (CL) model and clinico-liver imaging-radiomics (CLR) model. Diagnostic performance was measured by area under curve (AUC). Overall survival (OS) and recurrence-free survival (RFS) rates were compared using Kaplan-Meier method.

RESULTS

A total of 108 patients (training cohort: n = 80; validation cohort: n = 28) were enrolled. Multivariate analyses revealed that the albumin-bilirubin grade and tumor size were significant independent factors for predicting SOX9-positive HCCs and were included in the CL model. The CLR model integrating the radiomics score with albumin-bilirubin grade and tumor size showed better discriminative performance than the CL model with AUCs of 0.912 and 0.790 in the training and validation cohorts. Survival curves for RFS and OS showed that SOX9 expression was closely related to the prognosis of HCC patients. RFS and OS rates were significantly lower in patients with SOX9-positive than SOX9-negative (51.02% vs. 75.00% at 1-year RFS rates; 76.92% vs. 94.94% at 2-year OS rates).

CONCLUSION

Radiomics signatures may serve as noninvasive predictors for SOX9 status evaluation in patients with HCC and may aid in constructing individualized treatment strategies.

Is ctDNA ready to outpace imaging in monitoring early and advanced breast cancer?

INTRODUCTION

Circulating tumor DNA (ctDNA) and radiological imaging are increasingly recognized as crucial elements in breast cancer management. While radiology remains the cornerstone for screening and monitoring, ctDNA holds distinctive advantages in anticipating diagnosis, recurrence, or progression, providing concurrent biological insights complementary to imaging results.

AREAS COVERED

This review delves into the current evidence on the synergistic relationship between ctDNA and imaging in breast cancer. It presents data on the clinical validity and utility of ctDNA in both early and advanced settings, providing insights into emerging liquid biopsy techniques like epigenetics and fragmentomics. Simultaneously, it explores the present and future landscape of imaging methodologies, particularly focusing on radiomics.

EXPERT OPINION

Numerous are the current technical, strategic, and economic challenges preventing the clinical integration of ctDNA analysis in the breast cancer monitoring. Understanding these complexities and devising targeted strategies is pivotal to effectively embedding this methodology into personalized patient care.

Identification of Calculous Pyonephrosis by CT-Based Radiomics and Deep Learning

Urgent detection of calculous pyonephrosis is crucial for surgical planning and preventing severe outcomes. This study aims to evaluate the performance of computed tomography (CT)-based radiomics and a three-dimensional convolutional neural network (3D-CNN) model, integrated with independent clinical factors, to identify patients with calculous pyonephrosis. We recruited 182 patients receiving either percutaneous nephrostomy tube placement or percutaneous nephrolithotomy for calculous hydronephrosis or pyonephrosis. The regions of interest were manually delineated on plain CT images and the CT attenuation value (HU) was measured. Radiomics analysis was performed using least absolute shrinkage and selection operator (LASSO). A 3D-CNN model was also developed. The better-performing machine-learning model was combined with independent clinical factors to build a comprehensive clinical machine-learning model. The performance of these models was assessed using receiver operating characteristic analysis and decision curve analysis. Fever, blood neutrophils, and urine leukocytes were independent risk factors for pyonephrosis. The radiomics model showed higher area under the curve (AUC) than the 3D-CNN model and HU (0.876 vs. 0.599, 0.578; p = 0.003, 0.002) in the testing cohort. The clinical machine-learning model surpassed the clinical model in both the training (0.975 vs. 0.904, p = 0.019) and testing (0.967 vs. 0.889, p = 0.045) cohorts.

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.

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.

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.

Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation

Radiogenomics has shown potential to predict genomic phenotypes from medical images. The development of models using standard-of-care pre-operative MRI images, as opposed to advanced MRI images, enables a broader reach of such models. In this work, a radiogenomics model for IDH mutation status prediction from standard-of-care MRIs in patients with glioma was developed and validated using multicentric data. A cohort of 142 (wild-type: 32.4%) patients with glioma retrieved from the TCIA/TCGA was used to train a logistic regression model to predict the IDH mutation status. The model was evaluated using retrospective data collected in two distinct hospitals, comprising 36 (wild-type: 63.9%) and 53 (wild-type: 75.5%) patients. Model development utilized ROC analysis. Model discrimination and calibration were used for validation. The model yielded an AUC of 0.741 vs. 0.716 vs. 0.938, a sensitivity of 0.784 vs. 0.739 vs. 0.875, and a specificity of 0.657 vs. 0.692 vs. 1.000 on the training, test cohort 1, and test cohort 2, respectively. The assessment of model fairness suggested an unbiased model for age and sex, and calibration tests showed a p < 0.05. These results indicate that the developed model allows the prediction of the IDH mutation status in gliomas using standard-of-care MRI images and does not appear to hold sex and age biases.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

OBJECTIVE

To establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.

METHODS

We retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model's performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan-Meier survival curve to analyze the prognosis of BCa patients.

RESULTS

The combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659-1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival.

CONCLUSION

The proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively.

CRITICAL RELEVANCE STATEMENT

Bladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients.

KEY POINTS

• Conventional imaging is not sufficiently accurate to determine lymph node status. • Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis. • The proposed method showed satisfactory patient risk stratification for progression-free survival.

Accurate non-invasive grading of nonfunctional pancreatic neuroendocrine tumors with a CT derived radiomics signature

PURPOSE

The purpose of this study was to develop a radiomics-signature using computed tomography (CT) data for the preoperative prediction of grade of nonfunctional pancreatic neuroendocrine tumors (NF-PNETs).

MATERIALS AND METHODS

A retrospective study was performed on patients undergoing resection for NF-PNETs between 2010 and 2019. A total of 2436 radiomic features were extracted from arterial and venous phases of pancreas-protocol CT examinations. Radiomic features that were associated with final pathologic grade observed in the surgical specimens were subjected to joint mutual information maximization for hierarchical feature selection and the development of the radiomic-signature. Youden-index was used to identify optimal cutoff for determining tumor grade. A random forest prediction model was trained and validated internally. The performance of this tool in predicting tumor grade was compared to that of EUS-FNA sampling that was used as the standard of reference.

RESULTS

A total of 270 patients were included and a fusion radiomic-signature based on 10 selected features was developed using the development cohort (n = 201). There were 149 men and 121 women with a mean age of 59.4 ± 12.3 (standard deviation) years (range: 23.3-85.0 years). Upon internal validation in a new set of 69 patients, a strong discrimination was observed with an area under the curve (AUC) of 0.80 (95% confidence interval [CI]: 0.71-0.90) with corresponding sensitivity and specificity of 87.5% (95% CI: 79.7-95.3) and 73.3% (95% CI: 62.9-83.8) respectively. Of the study population, 143 patients (52.9%) underwent EUS-FNA. Biopsies were non-diagnostic in 26 patients (18.2%) and could not be graded due to insufficient sample in 42 patients (29.4%). In the cohort of 75 patients (52.4%) in whom biopsies were graded the radiomic-signature demonstrated not different AUC as compared to EUS-FNA (AUC: 0.69 vs. 0.67; P = 0.723), however greater sensitivity (i.e., ability to accurately identify G2/3 lesion was observed (80.8% vs. 42.3%; P < 0.001).

CONCLUSION

Non-invasive assessment of tumor grade in patients with PNETs using the proposed radiomic-signature demonstrated high accuracy. Prospective validation and optimization could overcome the commonly experienced diagnostic uncertainty in the assessment of tumor grade in patients with PNETs and could facilitate clinical decision-making.

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.

A Cox Nomogram for Assessing Recurrence Free Survival in Hepatocellular Carcinoma Following Surgical Resection Using Dynamic Contrast-Enhanced MRI Radiomics

BACKGROUND

The prognosis of hepatocellular carcinoma (HCC) is difficult to predict and carries high mortality. This study utilized radiomic techniques with clinical examinations to assess recurrence in HCC.

PURPOSE

To develop a Cox nomogram to assess the risk of postoperative recurrence in HCC using radiomic features of three volumes of interest (VOIs) in preoperative dynamic contrast-enhanced MRI (DCE-MRI), along with clinical findings.

STUDY TYPE

Retrospective.

SUBJECTS

249 patients with pathologically proven HCCs undergoing surgical resection at three institutions were selected.

FIELD STRENGTH/SEQUENCE

Fat saturated T2-weighted, Fat saturated T1-weighted, and DCE-MRI performed at 1.5 T and 3.0 T.

ASSESSMENT

Three VOIs were generated; the tumor VOI corresponds to the area from the tumor core to the outer perimeter of the tumor, the tumor +10 mm VOI represents the area from the tumor perimeter to 10 mm distal to the tumor in all directions, finally, the background liver parenchyma VOI represents the hepatic tissue outside the tumor. Three models were generated. The total radiomic model combined information from the three listed VOI's above. The clinical-radiological model combines physical examination findings with imaging characteristics such as tumor size, margin features, and metastasis. The combined radiomic model includes features from both models listed above and showed the highest reliability for assessing 24-month survival for HCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) Cox regression, univariable, and multivariable Cox regression, Kmeans clustering, and Kaplan-Meier analysis. The discrimination performance of each model was quantified by the C-index. A P value <0.05 was considered statistically significant.

RESULTS

The combined radiomic model, which included features from the radiomic VOI's and clinical imaging provided the highest performance (C-index: training cohort = 0.893, test cohort = 0.851, external cohort = 0.797) in assessing the survival of HCC.

CONCLUSION

The combined radiomic model provides superior ability to discern the possibility of recurrence-free survival in HCC over the total radiomic and the clinical-radiological models.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Posterior fossa tumors in children: current insights

While in adults most intracranial tumors develop around the cerebral hemispheres, 45 to 60% of pediatric lesions are found in the posterior fossa, although this anatomical region represents only 10% of the intracranial volume. The latest edition of the WHO classification for CNS tumors presented some fundamental paradigm shifts that particularly affected the classification of pediatric tumors, also influencing those that affect posterior fossa. Molecular biomarkers play an important role in the diagnosis, prognosis, and treatment of childhood posterior fossa tumors and can be used to predict patient outcomes and response to treatment and monitor its effectiveness. Although genetic studies have identified several posterior fossa tumor types, differing in terms of their location, cell of origin, genetic mechanisms, and clinical behavior, recent management strategies still depend on uniform approaches, mainly based on the extent of resection. However, significant progress has been made in guiding therapy decisions with biological or molecular stratification criteria and utilizing molecularly targeted treatments that address specific tumor biological characteristics. The primary focus of this review is on the latest advances in the diagnosis and treatment of common subtypes of posterior fossa tumors in children, as well as potential therapeutic approaches in the future.   Conclusion: Molecular biomarkers play a central role, not only in the diagnosis and prognosis of posterior fossa tumors in children but also in customizing treatment plans. They anticipate patient outcomes, measure treatment responses, and assess therapeutic effectiveness. Advances in neuroimaging and treatment have significantly enhanced outcomes for children with these tumors. What is Known: • Central nervous system tumors are the most common solid neoplasms in children and adolescents, with approximately 45 to 60% of them located in the posterior fossa. • Multimodal approaches that include neurosurgery, radiation therapy, and chemotherapy are typically used to manage childhood posterior fossa tumors What is New: • Notable progress has been achieved in the diagnosis, categorization and management of posterior fossa tumors in children, leading to improvement in survival and quality of life.

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based radiomics.

METHODS

The data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models.

RESULTS

Among the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit.

CONCLUSION

The DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance.

Predicting cervical lymph node metastasis in OSCC based on computed tomography imaging genomics

BACKGROUND

To investigate the correlation between computed tomography (CT) radiomic characteristics and key genes for cervical lymph node metastasis (LNM) in oral squamous cell carcinoma (OSCC).

METHODS

The region of interest was annotated at the edge of the primary tumor on enhanced CT images from 140 patients with OSCC and obtained radiomic features. Ribonucleic acid (RNA) sequencing was performed on pathological sections from 20 patients. the DESeq software package was used to compare differential gene expression between groups. Weighted gene co-expression network analysis was used to construct co-expressed gene modules, and the KEGG and GO databases were used for pathway enrichment analysis of key gene modules. Finally, Pearson correlation coefficients were calculated between key genes of enriched pathways and radiomic features.

RESULTS

Four hundred and eighty radiomic features were extracted from enhanced CT images of 140 patients; seven of these correlated significantly with cervical LNM in OSCC (p < 0.01). A total of 3527 differentially expressed RNAs were screened from RNA sequencing data of 20 cases. original_glrlm_RunVariance showed significant positive correlation with most long noncoding RNAs.

CONCLUSIONS

OSCC cervical LNM is related to the salivary hair bump signaling pathway and biological process. Original_glrlm_RunVariance correlated with LNM and most differentially expressed long noncoding RNAs.

Prediction of Parkinson's disease pathogenic variants using hybrid Machine learning systems and radiomic features

PURPOSE

In Parkinson's disease (PD), 5-10% of cases are of genetic origin with mutations identified in several genes such as leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA). We aim to predict these two gene mutations using hybrid machine learning systems (HMLS), via imaging and non-imaging data, with the long-term goal to predict conversion to active disease.

METHODS

We studied 264 and 129 patients with known LRRK2 and GBA mutations status from PPMI database. Each dataset includes 513 features such as clinical features (CFs), conventional imaging features (CIFs) and radiomic features (RFs) extracted from DAT-SPECT images. Features, normalized by Z-score, were univariately analyzed for statistical significance by the t-test and chi-square test, adjusted by Benjamini-Hochberg correction. Multiple HMLSs, including 11 features extraction (FEA) or 10 features selection algorithms (FSA) linked with 21 classifiers were utilized. We also employed Ensemble Voting (EV) to classify the genes.

RESULTS

For prediction of LRRK2 mutation status, a number of HMLSs resulted in accuracies of 0.98 ± 0.02 and 1.00 in 5-fold cross-validation (80% out of total data points) and external testing (remaining 20%), respectively. For predicting GBA mutation status, multiple HMLSs resulted in high accuracies of 0.90 ± 0.08 and 0.96 in 5-fold cross-validation and external testing, respectively. We additionally showed that SPECT-based RFs added value to the specific prediction of of GBA mutation status.

CONCLUSION

We demonstrated that combining medical information with SPECT-based imaging features, and optimal utilization of HMLS can produce excellent prediction of the mutations status in PD patients.

Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation

Background

Recurrence of atrial fibrillation (AF) after catheter ablation (CA) remains a challenge today. Although it is believed that evaluating the structural and functional remodeling of the left atrium (LA) may be helpful in predicting AF recurrence, there is a lack of consensus on prediction accuracy. Ultrasound-based radiomics is currently receiving increasing attention because it might aid in the diagnosis and prognosis prediction of AF recurrence. However, research on LA ultrasound radiomics is limited.

Objective

We aim to investigate the incremental predictive utility of LA radiomics and construct a radiomics nomogram to preoperatively predict AF recurrence following CA.

Methods

A training cohort of 232 AF patients was designed for nomogram construction, while a validation cohort (n = 100) served as the model performance test. AF recurrence during a follow-up period of 3-12 months was defined as the endpoint. The radiomics features related to AF recurrence were extracted and selected to create the radiomics score (rad score). These rad scores, along with other morphological and functional indicators for AF recurrence, were included in the multivariate Cox analysis to establish a nomogram for the prediction of the likelihood of AF recurrence within 1 year following CA.

Results

In the training and validation cohorts, AF recurrence rates accounted for 32.3% (75/232) and 25.0% (25/100), respectively. We extracted seven types of radiomics features associated with AF recurrence from apical four-chamber view echocardiography images and established a rad score for each patient. The radiomics nomogram was built with the rad score, AF type, left atrial appendage emptying flow velocity, and peak atrial longitudinal strain. It outperformed the nomogram building without the rad score in terms of the predictive efficacy of CA outcome and showed favorable performance in both cohorts.

Conclusion

We revealed the incremental utility of a radiomics signature in the prediction of AF recurrence and preliminarily developed and validated a radiomics nomogram for identifying patients who were at high risk of post-CA recurrence, which contributed to an appropriate management strategy for AF.

Radiomics optimizing the evaluation of endometrial receptivity for women with unexplained recurrent pregnancy loss

Background

The optimization of endometrial receptivity (ER) through individualized therapies has been shown to enhance the likelihood of successful gestation. However, current practice lacks comprehensive methods for evaluating the ER of patients with recurrent pregnancy loss (RPL). Radiomics, an emerging AI-based technique that enables the extraction of mineable information from medical images, holds potential to offer a more objective and quantitative approach to ER assessment. This innovative tool may facilitate a deeper understanding of the endometrial environment and enable clinicians to optimize ER evaluation in RPL patients.

Objective

This study aimed to identify ultrasound radiomics features associated with ER, with the purpose of predicting successful ongoing pregnancies in RPL patients, and to assess the predictive accuracy of these features against regular ER parameters.

Methods

This retrospective, controlled study involved 262 patients with unexplained RPL and 273 controls with a history of uncomplicated full-term pregnancies. Radiomics features were extracted from ultrasound endometrial segmentation images to derive a radiomics score (rad-score) for each participant. Associations between rad-scores, baseline clinical variables, and sonographic data were evaluated using univariate and multivariate logistic regression analyses to identify potential indicators of RPL. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive accuracy of the rad-score and other identified indicators in discriminating RPL cases. Furthermore, the relationships between age and these identified indicators were assessed via Pearson correlation analysis.

Results

From the 1312 extracted radiomics features, five non-zero coefficient radiomics signatures were identified as significantly associated with RPL, forming the basis of the rad-score. Following multivariate logistic regression analysis, age, spiral artery pulsatility index (SA-PI), vascularisation index (VI), and rad-score emerged as independent correlates of RPL (all P<0.05). ROC curve analyses revealed the superior discriminative capability of the rad-score (AUC=0.882) over age (AUC=0.778), SA-PI (AUC=0.771), and VI (AUC=0.595). There were notable correlations between age and rad-score (r=0.275), VI (r=-0.224), and SA-PI (r=0.211), indicating age-related variations in RPL predictors.

Conclusion

This study revealed a significant association between unexplained RPL and elevated endometrial rad-scores during the WOI. Furthermore, it demonstrated the potential of rad-scores as a promising predictive tool for successful ongoing pregnancies in RPL patients.

Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm

Purpose

The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN).

Methods and Materials

A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated.

Results

The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice.

Conclusions

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

Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis

OBJECTIVE

This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation.

METHODS

Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters.

RESULTS

 Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters.

CONCLUSION

 This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation

BACKGROUND

Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics.

PURPOSE

The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors.

METHODS AND MATERIALS

DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings.

RESULTS

The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively.

CONCLUSIONS

The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

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.

Imaging Recommendations for Diagnosis, Staging, and Management of Hereditary Malignancies

Hereditary cancer syndromes, characterized by genetically distinct neoplasms developing in specific organs in more than one family members, predispose an individual to early onset of distinct site-specific tumors. Early age of onset, multiorgan involvement, multiple and bilateral tumors, advanced disease at presentation, and aggressive tumor histology are few characteristic features of hereditary cancer syndromes. A multidisciplinary approach to hereditary cancers has led to a paradigm shift in the field of preventive oncology and precision medicine. Imaging plays a pivotal role in the screening, testing, and follow-up of individuals and their first- and second-degree relatives with hereditary cancers. In fact, a radiologist is often the first to apprise the clinician about the possibility of an underlying hereditary cancer syndrome based on pathognomonic imaging findings. This article focuses on the imaging spectrum of few common hereditary cancer syndromes with specific mention of the imaging features of associated common and uncommon tumors in each syndrome. The screening and surveillance recommendations for each condition with specific management approaches, in contrast to sporadic cases, have also been described.

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.

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives

MRI is undoubtedly the cornerstone of brain tumor imaging, playing a key role in all phases of patient management, starting from diagnosis, through therapy planning, to treatment response and/or recurrence assessment. Currently, neuroimaging can describe morphologic and non-morphologic (functional, hemodynamic, metabolic, cellular, microstructural, and sometimes even genetic) characteristics of brain tumors, greatly contributing to diagnosis and follow-up. Knowing the technical aspects, strength and limits of each MR technique is crucial to correctly interpret MR brain studies and to address clinicians to the best treatment strategy. This article aimed to provide an overview of neuroimaging in the assessment of adult primary brain tumors. We started from the basilar role of conventional/morphological MR sequences, then analyzed, one by one, the non-morphological techniques, and finally highlighted future perspectives, such as radiomics and artificial intelligence.

Artificial Intelligence in Hepatology- Ready for the Primetime

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PURPOSE

To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.

METHODS

This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.

RESULTS

A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.

CONCLUSIONS

The CR model could predict the OS of the HCC patients after hepatectomy.

Radiomics nomogram based on multi-parametric magnetic resonance imaging for predicting early recurrence in small hepatocellular carcinoma after radiofrequency ablation

BACKGROUND

There are few studies on the application of radiomics in the risk prediction of early recurrence (ER) after radiofrequency ablation (RFA). This study evaluated the value of a multi-parametric magnetic resonance imaging (MRI, mpMRI)-based radiomics nomogram in predicting ER of small hepatocellular carcinoma (HCC) after RFA.

MATERIALS AND METHODS

A retrospective analysis was performed on 90 patients with small HCC who were treated with RFA. Patients were divided into two groups according to recurrence within 2 years: the ER group (n=38) and the non-ER group (n=52). Preoperative T1WI, T2WI, and contrast-enhanced MRI (CE-MRI) were used for radiomic analysis. Tumor segmentation was performed on the images and applied to extract 1316 radiomics features. The most predictive features were selected using analysis of variance + Mann-Whitney, Spearman's rank correlation test, random forest (importance), and least absolute shrinkage and selection operator analysis. Radiomics models based on each sequence or combined sequences were established using logistic regression analysis. A predictive nomogram was constructed based on the radiomics score (rad-score) and clinical predictors. The predictive efficiency of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to evaluate the clinical efficacy of the nomogram.

RESULTS

The radiomics model mpMRI, which is based on T1WI, T2WI, and CE-MRI sequences, showed the best predictive performance, with an AUC of 0.812 for the validation cohort. Combined with the clinical risk factors of albumin level, number of tumors, and rad-score of mpMRI, the AUC of the preoperative predictive nomogram in the training and validation cohorts were 0.869 and 0.812, respectively. DCA demonstrated that the combined nomogram is clinically useful.

CONCLUSIONS

The multi-parametric MRI-based radiomics nomogram has a high predictive value for ER of small HCC after RFA, which could be helpful for personalized risk stratification and further treatment decision-making for patients with small HCC.

Integration of artificial intelligence and precision oncology in Latin America

Next-generation medicine encompasses different concepts related to healthcare models and technological developments. In Latin America and the Caribbean, healthcare systems are quite different between countries, and cancer control is known to be insufficient and inefficient considering socioeconomically discrepancies. Despite advancements in knowledge about the biology of different oncological diseases, the disease remains a challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers. With the development of molecular biology, better diagnosis methods, and therapeutic tools in the last years, artificial intelligence (AI) has become important, because it could improve different clinical scenarios: predicting clinically relevant parameters, cancer diagnosis, cancer research, and accelerating the growth of personalized medicine. The incorporation of AI represents an important challenge in terms of diagnosis, treatment, and prognosis for clinicians and researchers in cancer care. Therefore, some studies about AI in Latin America and the Caribbean are being conducted with the aim to improve the performance of AI in those countries. This review introduces AI in cancer care in Latin America and the Caribbean, and the advantages and promising results that it has shown in this socio-demographic context.

Non-invasive measurement of tumor immune microenvironment and prediction of survival and chemotherapeutic benefits from 18F fluorodeoxyglucose PET/CT images in gastric cancer

BACKGROUND

The tumor immune microenvironment could provide prognostic and predictive information. It is necessary to develop a noninvasive radiomics-based biomarker of a previously validated tumor immune microenvironment signature of gastric cancer (GC) with immunohistochemistry staining.

METHODS

A total of 230 patients (training (n = 153) or validation (n = 77) cohort) with gastric cancer were subjected to (Positron Emission Tomography-Computed Tomography) radiomics feature extraction (80 features). A radiomics tumor immune microenvironment score (RTIMS) was developed to predict the tumor immune microenvironment signature with LASSO logistic regression. Furthermore, we evaluated its relation with prognosis and chemotherapy benefits.

RESULTS

A 8-feature radiomics signature was established and validated (area under the curve=0.692 and 0.713). The RTIMS signature was significantly associated with disease-free survival and overall survival both in the training and validation cohort (all P<0.001). RTIMS was an independent prognostic factor in the Multivariate analysis. Further analysis revealed that high RTIMS patients benefitted from adjuvant chemotherapy (for DFS, stage II: HR 0.208(95% CI 0.061-0.711), p=0.012; stage III: HR 0.321(0.180-0.570), p<0.001, respectively); while there were no benefits from chemotherapy in a low RTIMS patients.

CONCLUSION

This PET/CT radiomics model provided a promising way to assess the tumor immune microenvironment and to predict clinical outcomes and chemotherapy response. The RTIMS signature could be useful in estimating tumor immune microenvironment and predicting survival and chemotherapy benefit for patients with gastric cancer, when validated by further prospective randomized trials.

Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features

Purpose

To develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features.

Methods

Between January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically proven breast lesions in our hospital. The data set was divided into model training group and validation testing group with a 75/25 split. Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram was developed for BI-RADS US category 4 or 5 lesions according to the results of multivariate regression analysis from the training group.

Result

The AUCs of radiomics score consisting of 31 US features was 0.886. The AUC of the model constructed with radiomics score, patient age, lesion diameter identified by US and BI-RADS category involved was 0.956 (95% CI, 0.910–0.972) for the training group and 0.937 (95% CI, 0.893–0.965) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.

Conclusions

Both nomogram and radiomics score can be used as methods to assist radiologists and clinicians in predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

[Artificial intelligence in orthopaedic and trauma surgery imaging]

Artificial intelligence (AI) is playing an increasing role in radiological imaging in orthopaedics and trauma surgery. The algorithms available to date are predominantly used in the detection of (occult) fractures and in length and angle measurements in conventional X‑ray images. However, current AI solutions also enable the analysis and pattern recognition of CT datasets, e.g. in the detection of rib or vertebral body fractures. A special application is EOS™ (ATEC Spine Group, Paris, France), which enables a 3‑D simulation of the axial skeleton and semi-automatic length and angle calculations based on a digital 2‑D X‑ray image. In this paper, the current spectrum of AI applications for orthopaedics and trauma surgery is presented and discussed.

Radiomics quality score in renal masses: a systematic assessment on current literature

OBJECTIVE

To perform a systematic assessment and analyze the quality of radiomics methodology in current literature in the evaluation of renal masses using the Radiomics Quality Score (RQS) approach.

METHODS

We systematically reviewed recent radiomics literature in renal masses published in PubMed, EMBASE, Elsevier, and Web of Science. Two reviewers blinded by each other's scores evaluated the quality of radiomics methodology in studies published from 2015 to August 2021 using the RQS approach. Owing to the diversity in the imaging modalities and radiomics applications, a meta-analysis could not be performed.

RESULTS

Based on our inclusion/exclusion criteria, a total of 87 published studies were included in our study. The highest RQS was noted in three categories: reporting of clinical utility, gold standard, and feature reduction. The average RQS of the two reviewers ranged from 5 ≤ RQS≤19, with the maximum attainable RQS being 36. Very few (7/87 i.e., 8%) studies received an average RQS that ranged from 17 < RQS≤19, which represents studies with the highest RQS in our study. Many (39/87 i.e., 45%) studies received an average RQS that ranged from 13 < RQS≤15. No significant interreviewer scoring differences were observed.

CONCLUSIONS

We report that the overall scientific quality and reporting of radiomics studies in renal masses is suboptimal, and subsequent studies should bolster current deficiencies to improve reporting of radiomics methodologies.

ADVANCES IN KNOWLEDGE

The RQS approach is a meaningful quantitative scoring system to assess radiomics methodology quality and supports a comprehensive evaluation of the radiomics approach before its incorporation into clinical practice.

Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis

Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). Materials and Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction. Results: Six radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built. Conclusion: The radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Simple Summary

Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices.

Abstract

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

A novel integrative computational framework for breast cancer radiogenomic biomarker discovery

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Bayesian tensor factorization is used to integrate multiomics data for radiogenomics analysis.

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A regression framework is proposed to handle the unmatched data issue in radiogenomics analysis.

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Deep learning is used to identify prognostic meaningful radiogenomic biomarkers for cancer.

In precise medicine, it is with great value to develop computational frameworks for identifying prognostic biomarkers which can capture both multi-genomic and phenotypic heterogeneity of breast cancer (BC). Radiogenomics is a field where medical images and genomic measurements are integrated and mined to solve challenging clinical problems. Previous radiogenomic studies suffered from data incompleteness, feature subjectivity and low interpretability. For example, the majority of the radiogenomic studies miss one or two of medical imaging data, genomic data, and clinical outcome data, which results in the data incomplete issue. Feature subjectivity issue comes from the extraction of imaging features with significant human involvement. Thus, there is an urgent need to address above-mentioned limitations so that fully automatic and transparent radiogenomic prognostic biomarkers could be identified for BC.

We proposed a novel framework for BC prognostic radiogenomic biomarker identification. This framework involves an explainable DL model for image feature extraction, a Bayesian tensor factorization (BTF) processing for multi-genomic feature extraction, a leverage strategy to utilize unpaired imaging, genomic, and survival outcome data, and a mediation analysis to provide further interpretation for identified biomarkers. This work provided a new perspective for conducting a comprehensive radiogenomic study when only limited resources are given. Compared with baseline traditional radiogenomic biomarkers, the 23 biomarkers identified by the proposed framework performed better in indicating patients’ survival outcome. And their interpretability is guaranteed by different levels of build-in and follow-up analyses.