With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Purpose To identify associations between magnetic resonance (MR) imaging features and gene expression in retinoblastoma. Materials and Methods A retinoblastoma MR imaging atlas was validated by using anonymized MR images from referral centers in Essen, Germany, and Paris, France. Images were from 39 patients with retinoblastoma (16 male and 18 female patients [the sex in five patients was unknown]; age range, 5-90 months; inclusion criterion: pretreatment MR imaging). This atlas was used to compare MR imaging features with genome-wide messenger RNA (mRNA) expression data from 60 consecutive patients obtained from 1995 to 2012 (35 male patients [58%]; age range, 2-69 months; inclusion criteria: pretreatment MR imaging, genome-wide mRNA expression data available). Imaging pathway associations were analyzed by means of gene enrichment. In addition, imaging features were compared with a predefined gene expression signature of photoreceptorness. Statistical analysis was performed with generalized linear modeling of radiology traits on normalized log2-transformed expression values. P values were corrected for multiple hypothesis testing. Results Radiogenomic analysis revealed 1336 differentially expressed genes for qualitative imaging features (threshold P = .05 after multiple hypothesis correction). Loss of photoreceptorness gene expression correlated with advanced stage imaging features, including multiple lesions (P = .03) and greater eye size (P < .001). The number of lesions on MR images was associated with expression of MYCN (P = .04). A newly defined radiophenotype of diffuse-growing, plaque-shaped, multifocal tumors displayed overexpression of SERTAD3 (P = .003, P = .049, and P = .06, respectively), a protein that stimulates cell growth by activating the E2F network. Conclusion Radiogenomic biomarkers can potentially help predict molecular features, such as photoreceptorness loss, that indicate tumor progression. Results imply a possible role for radiogenomics in future staging and treatment decision making in retinoblastoma.
Retinoblastoma mimickers, or pseudoretinoblastoma, are conditions that show similarities with the pediatric cancer retinoblastoma. However, false-positive retinoblastoma diagnosis can cause mistreatment, while false-negative diagnosis can cause life-threatening treatment delay. The purpose of this study is to identify the MR imaging features that best differentiate between retinoblastoma and the most common pseudoretinoblastoma diagnoses: Coats’ disease and persistent fetal vasculature (PFV). Here, six expert radiologists performed retrospective assessments (blinded for diagnosis) of MR images of patients with a final diagnosis based on histopathology or clinical follow-up. Associations between 20 predefined imaging features and diagnosis were assessed with exact tests corrected for multiple hypothesis testing. Sixty-six patients were included, of which 33 (50%) were retinoblastoma and 33 (50%) pseudoretinoblastoma patients. A larger eye size, vitreous seeding, and sharp-V-shaped retinal detachment were almost exclusively found in retinoblastoma (p < 0.001–0.022, specificity 93–97%). Features that were almost exclusively found in pseudoretinoblastoma included smaller eye size, ciliary/lens deformations, optic nerve atrophy, a central stalk between optic disc and lens, Y-shaped retinal detachment, and absence of calcifications (p < 0.001–0.022, specificity 91–100%). Additionally, three newly identified imaging features were exclusively present in pseudoretinoblastoma: intraretinal macrocysts (p < 0.001, 38% [9/24] in Coats’ disease and 20% [2/10] in PFV), contrast enhancement outside the solid lesion (p < 0.001, 30% [7/23] in Coats’ disease and 57% [4/7] in PFV), and enhancing subfoveal nodules (38% [9/24] in Coats’ disease). An assessment strategy was proposed for MR imaging differentiation between retinoblastoma and pseudoretinoblastoma, including three newly identified differentiating MR imaging features.
In retinoblastoma, accurate segmentation of ocular structure and tumor tissue is important when working towards personalized treatment. This retrospective study serves to evaluate the performance of multi-view convolutional neural networks (MV-CNNs) for automated eye and tumor segmentation on MRI in retinoblastoma patients. Forty retinoblastoma and 20 healthy-eyes from 30 patients were included in a train/test (N = 29 retinoblastoma-, 17 healthy-eyes) and independent validation (N = 11 retinoblastoma-, 3 healthy-eyes) set. Imaging was done using 3.0 T Fast Imaging Employing Steady-state Acquisition (FIESTA), T2-weighted and contrast-enhanced T1-weighted sequences. Sclera, vitreous humour, lens, retinal detachment and tumor were manually delineated on FIESTA images to serve as a reference standard. Volumetric and spatial performance were assessed by calculating intra-class correlation (ICC) and dice similarity coefficient (DSC). Additionally, the effects of multi-scale, sequences and data augmentation were explored. Optimal performance was obtained by using a three-level pyramid MV-CNN with FIESTA, T2 and T1c sequences and data augmentation. Eye and tumor volumetric ICC were 0.997 and 0.996, respectively. Median [Interquartile range] DSC for eye, sclera, vitreous, lens, retinal detachment and tumor were 0.965 [0.950–0.975], 0.847 [0.782–0.893], 0.975 [0.930–0.986], 0.909 [0.847–0.951], 0.828 [0.458–0.962] and 0.914 [0.852–0.958], respectively. MV-CNN can be used to obtain accurate ocular structure and tumor segmentations in retinoblastoma.
de Graaf (2020) Full-width postlaminar optic nerve tumor invasion of retinoblastoma as risk-factor for leptomeningeal spread of retinoblastoma. A case report and review of the literature,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.