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BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (

Radiomics offers the potential to predict oncological outcomes from pre-operative imaging in order to identify 'high risk' patients at increased risk of recurrence. The application of radiomics in predicting disease recurrence provides tailoring of therapeutic strategies. We aim to comprehensively assess the existing literature regarding the current role of radiomics as a predictor of disease recurrence in gastric cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Inclusion criteria encompassed retrospective and prospective studies investigating the use of radiomics to predict post-operative recurrence in ovarian cancer. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Nine studies met the inclusion criteria, involving a total of 6,662 participants. Radiomic-based nomograms demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.72 - 1). The pooled AUCs calculated using the inverse-variance method for both the training and validation datasets were 0.819 and 0.789 respectively Our review provides good evidence supporting the role of radiomics as a predictor of post-operative disease recurrence in gastric cancer. Included studies noted good performance in predicting their primary outcome. Radiomics may enhance personalised medicine by tailoring treatment decision based on predicted prognosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Lymphoma is a type of cancer that affects the immune system and can be difficult to diagnose and treat effectively, especially in the early stages. Current imaging methods, such as PET/CT scans, are valuable tools for diagnosing and monitoring the disease, but they have limitations in providing precise information for personalized treatment plans. Recently, radiomics and artificial intelligence (AI) have emerged as promising technologies that can analyze detailed patterns in medical images, helping to uncover information that might not be visible to the human eye. This study explores the potential of these technologies in improving the diagnosis, staging, and treatment selection for lymphoma patients. However, further research is needed to confirm their reliability and ensure they can be effectively used in clinical practice. Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients. [ABSTRACT FROM AUTHOR]

Ovarian cancer is associated with high cancer-related mortality rate attributed to late-stage diagnosis, limited treatment options, and frequent disease recurrence. As a result, careful patient selection is important especially in setting of radical surgery. Radiomics is an emerging field in medical imaging, which may help provide vital prognostic evaluation and help patient selection for radical treatment strategies. This systematic review aims to assess the role of radiomics as a predictor of disease recurrence in ovarian cancer. A systematic search was conducted in Medline, EMBASE, and Web of Science databases. Studies meeting inclusion criteria investigating the use of radiomics to predict post-operative recurrence in ovarian cancer were included in our qualitative analysis. Study quality was assessed using the QUADAS-2 and Radiomics Quality Score tools. Six retrospective studies met the inclusion criteria, involving a total of 952 participants. Radiomic-based signatures demonstrated consistent performance in predicting disease recurrence, as evidenced by satisfactory area under the receiver operating characteristic curve values (AUC range 0.77–0.89). Radiomic-based signatures appear to good prognosticators of disease recurrence in ovarian cancer as estimated by AUC. The reviewed studies consistently reported the potential of radiomic features to enhance risk stratification and personalise treatment decisions in this complex cohort of patients. Further research is warranted to address limitations related to feature reliability, workflow heterogeneity, and the need for prospective validation studies. [ABSTRACT FROM AUTHOR]

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. • Modern MRI techniques are key in diagnosing and guiding glioma treatment. • Modern MRI techniques are key in diagnosing and guiding glioma treatment. • Standardizing MRI protocols remains challenging, needing more research and collaboration. • Differentiating tumor recurrence from pseudoprogression is crucial; advanced MRI often needs biopsy validation. [ABSTRACT FROM AUTHOR]

Simple Summary: This research aims to explore how specific imaging features on computed tomography (CT) scans, related to lipid metabolism, can be linked to a protein called adipose differentiation-related protein (ADFP) in clear cell renal cell carcinoma. This study suggests that tumors with higher lipid content, which can be seen as lower values on a CT scan, are associated with lower-grade, less aggressive cancers that express ADFP. This finding is significant because it could lead to non-invasive methods for evaluating tumor characteristics, potentially helping better understand the nature of the cancer and tailor treatment plans without needing invasive procedures. This study aimed to investigate the association between metabolic lipid computed tomography (CT) features and adipose differentiation-related protein (ADFP) expression in clear cell renal cell carcinoma (ccRCC), providing insights into non-invasive methods for assessing ADFP expression and tumor characteristics. This study utilized data from The Cancer Genome Atlas and the Cancer Imaging Archive to analyze genetic alterations and imaging characteristics in ccRCC patients. Tumoral Hounsfield units (HU) analysis and quantification of abdominal adipose tissue compartments were performed using CT images. Statistical analyses were conducted to compare tumoral HU values according to ADFP gene expression and World Health Organization/International Society of Urological Pathology (WHO/ISUP) tumor grade, as well as to explore correlations between tumoral HU values and adipose tissue quantification. Among the 174 identified patients, those with ADFP gene expression showed significantly lower minimum tumoral HU values in low-grade cancers compared to high-grade cancers. Similarly, patients with low-grade cancers expressing ADFP exhibited lower minimum tumoral HU values compared to those without ADFP expression. Negative correlations were observed between minimum tumoral HU values and visceral adipose tissue, subcutaneous adipose tissue, and total adipose tissue in both ccRCC patients with and without ADFP expression. This study reveals a significant association between metabolic lipid CT features and ADFP expression in ccRCC patients. Lower minimum tumoral HU values, suggestive of higher intracellular lipid accumulation, were observed in tumors with low WHO/ISUP grade and ADFP expression. [ABSTRACT FROM AUTHOR]

Background: CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach. Method: We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database (n = 371) and validated it in a validation cohort from GEO database (n = 357). High- and low-risk groups were divided by hub gene (EGFLAM and GNG11) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis. Results: The overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225–2.974/1.308–2.844). The low-risk group exhibited significantly higher TMB levels (P = 1.6e− 07) and greater sensitivity to ICIs. Significant higher expression of hub-genes was identified on multiple GC cell lines and original samples. Hub-genes knockdown in gastric cancer cell lines inhibited their proliferation, metastatic and invasive capacity to varying degrees. In vivo experiments indicate that EGFLAM, as one of the hub genes, its high expression can serve as a biomarker for low response to immunotherapy. Conclusion: Our study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages. [ABSTRACT FROM AUTHOR]

Background: The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. Methods: We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). Results: The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. Conclusions: Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features. [ABSTRACT FROM AUTHOR]

In the battle against the COVID-19 pneumonia outbreak, which is brought on by the coronavirus strain SARS-Cov-2, radiological chest exams, such as chest X-rays, are crucial. In order to understand the unique radiographic characteristics of COVID-19, this research looks into classification models to distinguish chest X-ray images based on Radiomics features. This study is performed with datasets composed of 136 segmented chest X-rays, which were used to train and test the categorization algorithms. First and second-order statistical texture characteristics were extracted from the right (R), left (L), superior, middle, and bottom lung zones for each lung side using the Pyradiomics collection. Data was divided into training (80%) and test (20%) groups for feature selection. After assessing the respective feature significance and confirmation accuracy, the most pertinent Radiomics features were chosen. A model of lung segmentation based grey level pixels was used to evaluate support vector machines (SVM) as possible classifiers (AUC = 83.7%). Our research reveals a preference for the upper lung zone and a preponderance of Radiomics feature selection in the right lung. Our future research will concentrate on COVID-19 categorization and segmentation for more precise forecast using a hybrid method based on SVM and Radiogenomics features. [ABSTRACT FROM AUTHOR]

Radiogenomics of clear cell renal cell carcinoma (ccRCC) has been developed thanks to the availability of genomic data, both gene expressions and gene mutations, obtained through the sequencing of ccRCC genome. These data are collected in the Cancer Genome Atlas (TCGA) Research Network-work. Disintegrin and metalloproteinase domain-containing protein 12 (ADAM12) gene belongs to the family of genes coding for multidomain and multifunctional type I transmembrane proteins ADAMs. These proteins are fundamental for regulating cell adhesion and mediating proteolysis of a series of cell surface receptors and signal molecules extracellular domains. Recently, a correlation was detected between ADAM12 expression in ccRCC and tumor aggressiveness in terms of cell proliferation, migration, invasion, tumor progression, metastases, and poor prognosis, suggesting ADAM12 as a prognostic marker and therapeutic target in ccRCC. The computed tomography (CT) imaging phenotype of ADAM12 expression in ccRCC has never been studied. The aim of this study is to investigate the CT imaging phenotype of ADAM12 expression in ccRCC patients. In this retrospective study, we enrolled 202 ccRCC patients divided into two groups: ccRCC patients with ADAM12 expression (n = 35) and ccRCC patients without ADAM12 expression (n = 167). Different imaging features were evaluated on CT scan at first diagnosis. The statistical significance threshold was set at p < 0.05. A statistically significant correlation was found with larger primary tumor size (p = 0.020), ill-defined tumor margins (p = 0.044), tumor necrosis (p = 0.011), and collecting system invasion (p = 0.014). This study demonstrates CT imaging features associated to ADAM12 expression in ccRCC. These results could help delve into ADAM12 gene status through CT approach and to further investigate towards the development of targeted therapies in ccRCC. [ABSTRACT FROM AUTHOR]

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker‐guided clinical decision‐making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high‐throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross‐scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology. [ABSTRACT FROM AUTHOR]

Background: Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. Aims and Methods: This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. Results: We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Conclusions: Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies. [ABSTRACT FROM AUTHOR]

Abstract Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates and variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/−10 chromosome copy number changes, and others into the grading and classification of adult and pediatric Gliomas. The inclusion of these markers and the corresponding introduction of new Glioma subtypes has allowed for more specific tailoring of clinical interventions and has inspired a new wave of Radiogenomic studies seeking to leverage medical imaging information to explore the diagnostic and prognostic implications of these new biomarkers. Radiomics, deep learning, and combined approaches have enabled the development of powerful computational tools for MRI analysis correlating imaging characteristics with various molecular biomarkers integrated into the updated WHO CNS-5 guidelines. Recent studies have leveraged these methods to accurately classify Gliomas in accordance with these updated molecular-based criteria based solely on non-invasive MRI, demonstrating the great promise of Radiogenomic tools. In this review, we explore the relative benefits and drawbacks of these computational frameworks and highlight the technical and clinical innovations presented by recent studies in the landscape of fast evolving molecular-based Glioma subtyping. Furthermore, the potential benefits and challenges of incorporating these tools into routine radiological workflows, aiming to enhance patient care and optimize clinical outcomes in the evolving field of CNS tumor management, have been highlighted.

Purpose of the study. To study the CT semiotics of intrahepatic cholangiocarcinoma (ICC) to determine the prognostic markers of recurrence. To analyze the association between CT characteristics of ICC and mutations in IDH1/2, MET, KRAS, BRAF, ERBB2, EGFR, FGFR genes.Materials and methods. We analyzed databases and diagnostic images of Vishnevsky National Medical Research Center of Surgery and Loginov Moscow Clinical Research Center for the period from April 2016 to January 2022 using the key queries «intrahepatic cholangiocarcinoma», «liver», «hepatocellular carcinoma», «metastases», «radio genomics». 142 patients with liver neoplasms were identified, including 90 cases of ICC, 31 cases of hepatocellular carcinoma and 21 cases of metastatic liver lesions, all morphologically verified (histologic and immunohistochemical analysis of biopsy material).Results. Associations between CT features and mutations of MET and IDH1/2 genes were determined. According to the results of statistical analysis all four CT-signs, such as bile duct dilatation, capsule retraction, presence of dropout foci and tissue volume changes, are correlated with the probability of recurrence (death) in patients with ICC.Conclusion. In a retrospective study, our results emphasize the potential prognostic significance of CT signs of ICC. We identified CT signs that allow differential diagnosis of ICC with hepatocellular carcinoma and colorectal cancer metastases. We also identified associations between CT signs of ICC and mutations of IDH1/2 and MET genes, which may allow us to non-invasively obtain data on clinically significant molecular markers of tumors to apply a personalized approach to patient treatment.

Abstract Background CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach. Method We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P

Abstract Background The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. Methods We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). Results The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. Conclusions Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Abstract Background High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor. Methods Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups. Results By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses. Conclusions This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Abstract Background Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis. Materials and methods In this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression. Results Among the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively). Conclusion Multiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.

Background: High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor. Methods: Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups. Results: By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses. Conclusions: This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine. [ABSTRACT FROM AUTHOR]

This comprehensive review explores the pivotal role of radiotherapy in cancer treatment, emphasizing the diverse applications of genetic profiling. The review highlights genetic markers for predicting radiation toxicity, enabling personalized treatment planning. It delves into the impact of genetic profiling on radiotherapy strategies across various cancer types, discussing research findings related to treatment response, prognosis, and therapeutic resistance. The integration of genetic profiling is shown to transform cancer treatment paradigms, offering insights into personalized radiotherapy regimens and guiding decisions in cases where standard protocols may fall short. Ultimately, the review underscores the potential of genetic profiling to enhance patient outcomes and advance precision medicine in oncology. [ABSTRACT FROM AUTHOR]

This study aims to explore the relationship between radiological imaging and genomic characteristics in clear cell renal cell carcinoma (ccRCC), focusing on the expression of adipose differentiation-related protein (ADFP) detected through computed tomography (CT). The goal is to establish a radiogenomic lipid profile and understand its association with tumor characteristics. Data from The Cancer Genome Atlas (TCGA) and the Cancer Imaging Archive (TCIA) were utilized to correlate imaging features with adipose differentiation-related protein (ADFP) expression in ccRCC. CT scans assessed various tumor features, including size, composition, margin, necrosis, and growth pattern, alongside measurements of tumoral Hounsfield units (HU) and abdominal adipose tissue compartments. Statistical analyses compared demographics, clinical–pathological features, adipose tissue quantification, and tumoral HU between groups. Among 197 patients, 22.8% exhibited ADFP expression significantly associated with hydronephrosis. Low-grade ccRCC patients expressing ADFP had higher quantities of visceral and subcutaneous adipose tissue and lower tumoral HU values compared to their high-grade counterparts. Similar trends were observed in low-grade ccRCC patients without ADFP expression. ADFP expression in ccRCC correlates with specific imaging features such as hydronephrosis and altered adipose tissue distribution. Low-grade ccRCC patients with ADFP expression display a distinct lipid metabolic profile, emphasizing the relationship between radiological features, genomic expression, and tumor metabolism. These findings suggest potential for personalized diagnostic and therapeutic strategies targeting tumor lipid metabolism. [ABSTRACT FROM AUTHOR]

The T2-Fluid-Attenuated Inversion Recovery (T2-FLAIR) mismatch sign is a radiogenomic marker that is easily discernible on preoperative conventional MR imaging. Application of strict criteria (adult population, cerebral hemisphere location, and classic imaging morphology) permits the noninvasive preoperative diagnosis of isocitrate dehydrogenase (IDH)-mutant 1p/19q-non-codeleted diffuse astrocytoma with near-perfect specificity, albeit with variably low sensitivity. This leads to improved preoperative planning and patient counseling. More recent research has shown that the application of less strict criteria compromises the near-perfect specificity of the sign but remains adequate for ruling out IDH-wildtype (glioblastoma) phenotype, which bears a far grimmer prognosis compared to IDH-mutant diffuse astrocytic disease. In this review, we elaborate on the various definitions of the T2-FLAIR mismatch sign present in the literature, illustrate these with images obtained at a comprehensive cancer center, discuss the potential of the mismatch sign for application to certain pediatric-type brain tumors, namely dysembryoplastic neuroepithelial tumor and diffuse midline glioma, and elaborate upon the clinical, histologic, and molecular associations of the T2-FLAIR mismatch sign as recognized to date. Finally, the sign's correlates in diffusion- and perfusion-weighted imaging are presented, and opportunities to further maximize the diagnostic and prognostic applications of the sign in the context of the 2021 revision of the WHO Classification of Central Nervous System Tumors are discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to develop and validate a radiogenomics nomogram for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) on the basis of MRI and microRNAs (miRNAs). Materials and Methods: This cohort study included 168 patients (training cohort: n =116; validationcohort:n=52)withpathologically confirmed HCC, who underwent preoperative MRI and plasma miRNA examination. Univariate and multivariate logistic regressions were used to identify independent risk factors associated with MVI. These risk factors were used to produce a nomogram. The performance of the nomogram was evaluated by receiver operating characteristic curve (ROC) analysis, sensitivity, specificity, accuracy, and F1-score. Decision curve analysis was performed to determine whether the nomogram was clinically useful. Results: The independent risk factors for MVI were maximum tumor length, radscore, and miRNA-21 (all P < 0.001). The sensitivity, specificity, accuracy, and F1-score of the nomogram in the validation cohort were 0.970, 0.722, 0.884, and 0.916, respectively. The AUC of the nomogram was 0.900 (95% CI: 0.808-0.992) in the validation cohort, higher than that of anyother single factormodel (maximumtumor length, rad-score, and miRNA-21). Conclusion: The radiogenomics nomogram shows satisfactory predictive performance in predicting MVI in HCC and provides a feasible and practical reference for tumor treatment decisions. [ABSTRACT FROM AUTHOR]

Purpose: Gain-of-function mutations in CTNNB1, gene encoding for β-catenin, are observed in 25–30% of hepatocellular carcinomas (HCCs). Recent studies have shown β-catenin activation to have distinct roles in HCC susceptibility to mTOR inhibitors and resistance to immunotherapy. Our goal was to develop and test a computational imaging-based model to non-invasively assess β-catenin activation in HCC, since liver biopsies are often not done due to risk of complications. Methods: This IRB-approved retrospective study included 134 subjects with pathologically proven HCC and available β-catenin activation status, who also had either CT or MR imaging of the liver performed within 1 year of histological assessment. For qualitative descriptors, experienced radiologists assessed the presence of imaging features listed in LI-RADS v2018. For quantitative analysis, a single biopsy proven tumor underwent a 3D segmentation and radiomics features were extracted. We developed prediction models to assess the β-catenin activation in HCC using both qualitative and quantitative descriptors. Results: There were 41 cases (31%) with β-catenin mutation and 93 cases (69%) without. The model's AUC was 0.70 (95% CI 0.60, 0.79) using radiomics features and 0.64 (0.52, 0.74; p = 0.468) using qualitative descriptors. However, when combined, the AUC increased to 0.88 (0.80, 0.92; p = 0.009). Among the LI-RADS descriptors, the presence of a nodule-in-nodule showed a significant association with β-catenin mutations (p = 0.015). Additionally, 88 radiomics features exhibited a significant association (p < 0.05) with β-catenin mutations. Conclusion: Combination of LI-RADS descriptors and CT/MRI-derived radiomics determine β-catenin activation status in HCC with high confidence, making precision medicine a possibility. [ABSTRACT FROM AUTHOR]

Background: Arteriovenous malformations (AVMs) are rare vascular anomalies involving a disorganization of arteries and veins with no intervening capillaries. In the past 10 years, radiomics and machine learning (ML) models became increasingly popular for analyzing diagnostic medical images. The goal of this review was to provide a comprehensive summary of current radiomic models being employed for the diagnostic, therapeutic, prognostic, and predictive outcomes in AVM management. Methods: A systematic literature review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, in which the PubMed and Embase databases were searched using the following terms: (cerebral OR brain OR intracranial OR central nervous system OR spine OR spinal) AND (AVM OR arteriovenous malformation OR arteriovenous malformations) AND (radiomics OR radiogenomics OR machine learning OR artificial intelligence OR deep learning OR computeraided detection OR computer-aided prediction OR computer-aided treatment decision). A radiomics quality score (RQS) was calculated for all included studies. Results: Thirteen studies were included, which were all retrospective in nature. Three studies (23%) dealt with AVM diagnosis and grading, 1 study (8%) gauged treatment response, 8 (62%) predicted outcomes, and the last one (8%) addressed prognosis. No radiomics model had undergone external validation. The mean RQS was 15.92 (range: 10-18). Conclusion: We demonstrated that radiomics is currently being studied in different facets of AVM management. While not ready for clinical use, radiomics is a rapidly emerging field expected to play a significant future role in medical imaging. More prospective studies are warranted to determine the role of radiomics in the diagnosis, prediction of comorbidities, and treatment selection in AVM management. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM)is the most common and aggressive primary brain tumor. Although temozolomide (TMZ)-based radiochemotherapy improves overall GBM patients' survival, it also increases the frequency of false positive post-treatment magnetic resonance imaging (MRI) assessments for tumor progression. Pseudo-progression (PsP) is a treatment-related reaction with an increased contrast-enhancing lesion size at the tumor site or resection margins miming tumor recurrence on MRI. The accurate and reliable prognostication of GBM progression is urgently needed in the clinical management of GBM patients. Clinical data analysis indicates that the patients with PsP had superior overall and progression-free survival rates. In this study, we aimed to develop a prognostic model to evaluate the tumor progression potential of GBM patients following standard therapies. We applied a dictionary learning scheme to obtain imaging features of GBM patients with PsP or true tumor progression (TTP) from the Wake dataset. Based on these radiographic features, we conducted a radiogenomics analysis to identify the significantly associated genes. These significantly associated genes were used as features to construct a 2YS (2-year survival rate) logistic regression model. GBM patients were classified into low- and high-survival risk groups based on the individual 2YS scores derived from this model. We tested our model using an independent The Cancer Genome Atlas Program (TCGA) dataset and found that 2YS scores were significantly associated with the patient's overall survival. We used two cohorts of the TCGA data to train and test our model. Our results show that the 2YS scores-based classification results from the training and testing TCGA datasets were significantly associated with the overall survival of patients. We also analyzed the survival prediction ability of other clinical factors (gender, age, KPS (Karnofsky performance status), normal cell ratio) and found that these factors were unrelated or weakly correlated with patients' survival. Overall, our studies have demonstrated the effectiveness and robustness of the 2YS model in predicting the clinical outcomes of GBM patients after standard therapies. [ABSTRACT FROM AUTHOR]

To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients with head and neck squamous cell carcinoma (HNSCC). Radiomics features derived from T2-weighted, pre- and post-contrast-enhanced T1-weighted MRI sequences, clinical data, and RNA sequencing data of 150 patients with HNSCC were included in the study. Analysis of variance or recursive feature elimination was used to reduce radiomics features. Three RBML classifiers were developed to distinguish non-responders from responders. Weighted correlation network analysis (WGCNA) was performed to identify the correlation between clinical data or radiomics features and molecular features; subsequently, protein interaction and functional enrichment analyses were performed. The predictive performance of the radiogenomics model integrating significant clinical variables, RBML classifiers, and molecular features was evaluated using receiver operating characteristic curve analysis. Five radiomics features and two conventional MRI findings significantly stratified HNSCC patients into responders and non-responders. On WGCNA analysis, 809 genes showed a significant correlation with two radiomics features. Functional enrichment analysis suggested that our proposed radiomics features could reflect the T cell-mediated immune response and immune infiltration of HNSCC. The radiogenomics model showed the highest area under the curve (0.88[95%CI 0.75–0.96]) for predicting IC response, which was better than MRI findings(p = 0.0407) or molecular features(p = 0.004) alone, but showed no significant difference with that of RBML model (p = 0.2254) in test cohort. Merging imaging phenotypes with transcriptomic data improved the prediction of IC response in HNSCC. [ABSTRACT FROM AUTHOR]

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Abstract Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker‐guided clinical decision‐making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high‐throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross‐scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Introduction. Radiomics is a rapidly developing field in oncology visualization aimed at searching for prognostically effective imaging features associated with specific genetic events that determine various characteristics of the disease course. According to numerous studies, the presence of IDH mutations in glial tumors determines a longer overall survival. Despite the fact that biopsy is considered to be the «gold standard» for brain tumors differential diagnosis, it is though quite difficult to perform due to the complexity of surgical access, common cases of the repeat procedure impossibility, serious complications and mortality.Aim: a search for imaging features providing prognostic data on the presence of certain mutations and gene expression in gliomas, obtained using traditional pulse sequences and characterized by the absence of restrictions on applicability depending on the tumors visible morphological features.Material and methods: retrospective analysis of 49 eligible patients' primary brain MRI data between 2021 and 2023 from Almazov National Medical Research Centre (n = 31) and Napalkov Oncological Centre (n = 18) with glial tumors and subsequently identified status of the target variable; preprocessing of MR images using the histogram matching; regions of interest determination and semi-automated slice-by-slice segmentation with subsequent extraction of radiomics features; search for predictive radiomics features regarding the status of target variable using statistical analysis tools.Results. Dependence Entropy was found to be highly effective as a predictor of IDH mutations (area under the ROC-curve – 0.766 [0.627–0.880]).Conclusions. We determined a target variable for the development of a predictive model (IDH status), a pulse sequence (T2-Tirm), a tool for initial imaging data preprocessing (histogram matching), regions of interest (tumor-associated T2-Tirm-hyperintensity including cystic and/or necrotic lesions). As a result, a statistically significant relationship between the Dependence Entropy feature and IDH status of glial tumors was found. In the course of further work it is planned to increase the size of a database, improve the accuracy of the existing statistical model, search for relevant radiomic features extracted using other traditional pulse sequences, create a comprehensive predictive radiogenomics model and develop a software.

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.

Abstract Head and neck cancer is a significant threat to human health and is characterized by high 5‐year morbidity and mortality rates. Addressing this challenge requires the application of precision medicine, but the inherent heterogeneity of head and neck cancer complicates its treatment. Radiogenomics, an interdisciplinary field at the intersection of genomics and radiology, may represent a solution. Radiogenomics offers the potential to revolutionize the diagnosis and treatment of this complex and diverse disease. By comprehensively analyzing the genetic information and radiological features of tumors, clinicians can gain a profound understanding of patients' conditions. Gaining such in‐depth insight facilitates early detection and implementation of personalized treatment strategies, both of which are integral components of precision medicine. Tailored treatments, including surgical interventions and targeted therapies, provide improved outcomes and reduced side effects. Radiogenomics represents a groundbreaking advancement that has the potential to significantly enhance the quality of care and outcomes of patients with head and neck cancer. To shed light on this transformative approach, we performed a comprehensive overview of radiomics and radiogenomics‐based diagnostic methods tailored to the unique characteristics of head and neck cancer.

Purpose of the study. Analytical review and analysis of available literature on texture analysis of computed tomgraphy (CT) and magnetic resonance imaging (MRI) in noninvasive diagnosis of ICC and correlation with molecular genetic features and tumor immunophenotype.Materials and methods. The scientific publications and clinical guidelines in the information- analytical systems PubMed, Scopus for 2012–2022 were carried out using the keywords: "mri", "radiomics", "texture analysis", "radiogenomics", "intrahepatic cholangiocarcinoma", "molecular". 49 articles were selected for analysis after excluding studies dealing with technical aspects of radiomics and describing individual clinical observations.Results. The presented review demonstrated the broad possibilities and prospects of application of CT and MRI texture analysis in the study of cholangiocellular cancer, including the first results in the study of molecular features (signatures) of this tumor. Correlation of texture features with the expression of immunotherapy target genes KRAS/NRAS/BRAF as well as IDH1/2 mutation was shown. Texture scores were the predominant independent predictor of microvascular invasion, which was a major independent risk factor for postoperative recurrence.Conclusion. The use of texture analysis undoubtedly demonstrates promising possibilities both in noninvasive assessment of the HCC histological differentiation grade, as well as in differential diagnosis with hepatocellular carcinoma, metastases, and requires further study for systematization and standardization of the obtained data.

Abstract The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots mainly around the preliminary radiological findings and the subsequent definitive surgical diagnosis (via surgical sampling). Radiomics and radiogenomics present a potential to precisely diagnose and predict survival and treatment responses, via morphological, textural, and functional features derived from MRI data, as well as genomic data. In spite of their advantages, it is still lacking standardized processes of feature extraction and analysis methodology among different research groups, which have made external validations infeasible. Radiomics and radiogenomics can be used to better understand the genomic basis of gliomas, such as tumor spatial heterogeneity, treatment response, molecular classifications and tumor microenvironment immune infiltration. These novel techniques have also been used to predict histological features, grade or even overall survival in gliomas. In this review, workflows of radiomics and radiogenomics are elucidated, with recent research on machine learning or artificial intelligence in glioma.

Abstract Background Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes. Methods We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification. Results The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC_RadiogenomicGAN . Conclusion Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies.

Hexanucleotide repeat expansion (HRE) within C9orf72 is the most common genetic cause of frontotemporal dementia (FTD). Thalamic atrophy occurs in both sporadic and familial FTD but is thought to distinctly affect HRE carriers. Separately, emerging evidence suggests widespread derepression of transposable elements (TEs) in the brain in several neurodegenerative diseases, including C9orf72 HRE-mediated FTD (C9-FTD). Whether TE activation can be measured in peripheral blood and how the reduction in peripheral C9orf72 expression observed in HRE carriers relates to atrophy and clinical impairment remain unknown. We used FreeSurfer software to assess the effects of C9orf72 HRE and clinical diagnosis (n = 78 individuals, male and female) on atrophy of thalamic nuclei. We also generated a novel, human, whole-blood RNA-sequencing dataset to determine the relationships among peripheral C9orf72 expression, TE activation, thalamic atrophy, and clinical severity (n = 114 individuals, male and female). We confirmed global thalamic atrophy and reduced C9orf72 expression in HRE carriers. Moreover, we identified disproportionate atrophy of the right mediodorsal lateral nucleus in HRE carriers and showed that C9orf72 expression associated with clinical severity, independent of thalamic atrophy. Strikingly, we found global peripheral activation of TEs, including the human endogenous LINE-1 element L1HS L1HS levels were associated with atrophy of multiple pulvinar nuclei, a thalamic region implicated in C9-FTD. Integration of peripheral transcriptomic and neuroimaging data from human HRE carriers revealed atrophy of specific thalamic nuclei, demonstrated that C9orf72 levels relate to clinical severity, and identified marked derepression of TEs, including L1HS, which predicted atrophy of FTD-relevant thalamic nuclei.SIGNIFICANCE STATEMENT Pathogenic repeat expansion in C9orf72 is the most frequent genetic cause of FTD and amyotrophic lateral sclerosis (ALS; C9-FTD/ALS). The clinical, neuroimaging, and pathologic features of C9-FTD/ALS are well characterized, whereas the intersections of transcriptomic dysregulation and brain structure remain largely unexplored. Herein, we used a novel radiogenomic approach to examine the relationship between peripheral blood transcriptomics and thalamic atrophy, a neuroimaging feature disproportionately impacted in C9-FTD/ALS. We confirmed reduction of C9orf72 in blood and found broad dysregulation of transposable elements-genetic elements typically repressed in the human genome-in symptomatic C9orf72 expansion carriers, which associated with atrophy of thalamic nuclei relevant to FTD. C9orf72 expression was also associated with clinical severity, suggesting that peripheral C9orf72 levels capture disease-relevant information.

BackgroundPancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC.MethodsIn this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses.ResultsSurvival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer. [ABSTRACT FROM AUTHOR]

Radiotherapy is focused on the tumor but also reaches healthy tissues, causing toxicities that are possibly related to genomic factors. In this context, radiogenomics can help reduce the toxicity, increase the effectiveness of radiotherapy, and personalize treatment. It is important to consider the genomic profiles of populations not yet studied in radiogenomics, such as the indigenous Amazonian population. Thus, our objective was to analyze important genes for radiogenomics, such as ATM, TGFB1, RAD51, AREG, XRCC4, CDK1, MEG3, PRKCE, TANC1, and KDR, in indigenous people and draw a radiogenomic profile of this population. The NextSeq 500® platform was used for sequencing reactions; for differences in the allelic frequency between populations, Fisher's Exact Test was used. We identified 39 variants, 2 of which were high impact: 1 in KDR (rs41452948) and another in XRCC4 (rs1805377). We found four modifying variants not yet described in the literature in PRKCE. We did not find any variants in TANC1—an important gene for personalized medicine in radiotherapy—that were associated with toxicities in previous cohorts, configuring a protective factor for indigenous people. We identified four SNVs (rs664143, rs1801516, rs1870377, rs1800470) that were associated with toxicity in previous studies. Knowing the radiogenomic profile of indigenous people can help personalize their radiotherapy. [ABSTRACT FROM AUTHOR]

The integration of multiple data sources is becoming increasingly important in the field of medical imaging for the purpose of improving predictions. In this study, we investigated the use of a unique combination of imaging and genomic data to predict the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT). Utilizing a stacking generalization approach, our model achieved promising results. The classification report from the stacking ensemble shows a precision of 0.82, recall of 0.81, and an F1-score of 0.80. These findings demonstrate the potential of integrating diverse data sources in medical imaging to enhance predictive capabilities, specifically in the context of MGMT methylation status. Our study highlights the importance of ongoing research to refine and optimize the integration of multiple data sources, ultimately leading to more accurate predictions and tailored treatment strategies. This research has the potential to significantly impact patient outcomes by facilitating personalized interventions based on precise predictions of MGMT methylation status. [ABSTRACT FROM AUTHOR]

Background: Breast Cancer (BC) is a highly heterogeneous and complex disease. Personalized treatment options require the integration of multi-omic data and consideration of phenotypic variability. Radiogenomics aims to merge medical images with genomic measurements but encounter challenges due to unpaired data consisting of imaging, genomic, or clinical outcome data. In this study, we propose the utilization of a well-trained conditional generative adversarial network (cGAN) to address the unpaired data issue in radiogenomic analysis of BC. The generated images will then be used to predict the mutations status of key driver genes and BC subtypes. Methods: We integrated the paired MRI and multi-omic (mRNA gene expression, DNA methylation, and copy number variation) profiles of 61 BC patients from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To facilitate this integration, we employed a Bayesian Tensor Factorization approach to factorize the multi-omic data into 17 latent features. Subsequently, a cGAN model was trained based on the matched side-view patient MRIs and their corresponding latent features to predict MRIs for BC patients who lack MRIs. Model performance was evaluated by calculating the distance between real and generated images using the Fréchet Inception Distance (FID) metric. BC subtype and mutation status of driver genes were obtained from the cBioPortal platform, where 3 genes were selected based on the number of mutated patients. A convolutional neural network (CNN) was constructed and trained using the generated MRIs for mutation status prediction. Receiver operating characteristic area under curve (ROC-AUC) and precision-recall area under curve (PR-AUC) were used to evaluate the performance of the CNN models for mutation status prediction. Precision, recall and F1 score were used to evaluate the performance of the CNN model in subtype classification. Results: The FID of the images from the well-trained cGAN model based on the test set is 1.31. The CNN for TP53, PIK3CA, and CDH1 mutation prediction yielded ROC-AUC values 0.9508, 0.7515, and 0.8136 and PR-AUC are 0.9009, 0.7184, and 0.5007, respectively for the three genes. Multi-class subtype prediction achieved precision, recall and F1 scores of 0.8444, 0.8435 and 0.8336 respectively. The source code and related data implemented the algorithms can be found in the project GitHub at https://github.com/mattthuang/BC%5fRadiogenomicGAN. Conclusion: Our study establishes cGAN as a viable tool for generating synthetic BC MRIs for mutation status prediction and subtype classification to better characterize the heterogeneity of BC in patients. The synthetic images also have the potential to significantly augment existing MRI data and circumvent issues surrounding data sharing and patient privacy for future BC machine learning studies. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is one of the deadliest tumours. This study aimed to construct radiogenomic prognostic models of glioblastoma overall survival (OS) based on magnetic resonance imaging (MRI) Gd-T1WI images and deoxyribonucleic acid (DNA) methylation-seq and to understand the related biological pathways. The ResNet3D-18 model was used to extract radiomic features, and Lasso-Cox regression analysis was utilized to establish the prognostic models. A nomogram was constructed by combining the radiogenomic features and clinicopathological variables. The DeLong test was performed to compare the area under the curve (AUC) of the models. We screened differentially expressed genes (DEGs) with original ribonucleic acid (RNA)-seq in risk stratification and used Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotations for functional enrichment analysis. For the 1-year OS models, the AUCs of the radiogenomic set, methylation set and deep learning set in the training cohort were 0.864, 0.804 and 0.787, and those in the validation cohort were 0.835, 0.768 and 0.651, respectively. The AUCs of the 0.5-, 1- and 2-year nomograms in the training cohort were 0.943, 0.861 and 0.871, and those in the validation cohort were 0.864, 0.885 and 0.805, respectively. A total of 245 DEGs were screened; functional enrichment analysis showed that these DEGs were associated with cell immunity. The survival risk-stratifying radiogenomic models for glioblastoma OS had high predictability and were associated with biological pathways related to cell immunity. [ABSTRACT FROM AUTHOR]