Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations

Horvat, Natally; Veeraraghavan, Harini; Pelossof, Raphael; Fernandes, Maria Clara; Arora, Arshi; Khan, Monika; Marco, Michael R.; Cheng, Chung-Chih; Gönen, Mithat; Pernicka, Jennifer S. Golia; Gollub, Marc J.; Garcia-Aguillar, Julio; Petkovska, Iva

doi:10.1016/j.ejrad.2019.02.022

Cited by 39 publications

(23 citation statements)

References 28 publications

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“…Understanding the radio-genomic correlations are important for furthering their application as biomarkers of treatment response [ 11 ]. Multiple studies have reported the association of image-based qualitative [ 31 ] and quantitative radiomics features with genomic measures [ 32 , 33 , 34 , 35 , 36 ]. We measured the correlation of cluDiss and two radiomics measures known as DCN and GLN with the Hallmark gene sets and TME cell types extracted from ssgsea analysis of the RNA expressions.…”

Section: Discussionmentioning

confidence: 99%

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Veeraraghavan

Vargas

Sánchez

et al. 2020

Cancers

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Purpose: Develop an integrated intra-site and inter-site radiomics-clinical-genomic marker of high grade serous ovarian cancer (HGSOC) outcomes and explore the biological basis of radiomics with respect to molecular signaling pathways and the tumor microenvironment (TME). Method: Seventy-five stage III-IV HGSOC patients from internal (N = 40) and external factors via the Cancer Imaging Archive (TCGA) (N = 35) with pre-operative contrast enhanced CT, attempted primary cytoreduction, at least two disease sites, and molecular analysis performed within TCGA were retrospectively analyzed. An intra-site and inter-site radiomics (cluDiss) measure was combined with clinical-genomic variables (iRCG) and compared against conventional (volume and number of sites) and average radiomics (N = 75) for prognosticating progression-free survival (PFS) and platinum resistance. Correlation with molecular signaling and TME derived using a single sample gene set enrichment that was measured. Results: The iRCG model had the best platinum resistance classification accuracy (AUROC of 0.78 [95% CI 0.77 to 0.80]). CluDiss was associated with PFS (HR 1.03 [95% CI: 1.01 to 1.05], p = 0.002), negatively correlated with Wnt signaling, and positively to immune TME. Conclusions: CluDiss and the iRCG prognosticated HGSOC outcomes better than conventional and average radiomic measures and could better stratify patient outcomes if validated on larger multi-center trials.

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Section: Discussionmentioning

confidence: 99%

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Veeraraghavan

Vargas

Sánchez

et al. 2020

Cancers

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“…A preliminary study ( 124 ) that sought to identify other frequent gene mutations in CRC found a significant correlation of tumor location with APC and RASA1 mutation, a significant association of absence of lymph node metastasis with BRCA2 mutation, and a correlation of tumor size with FLT4 mutation, as well as a higher frequency of ATM mutation in patients with a positive circumferential resection margin. However, the results of multiple comparisons were not significant.…”

Section: Development Of Radiomics Prediction Modelsmentioning

confidence: 99%

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

et al. 2021

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With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

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“…Hong et al found no significant correlation between parameters of dynamic contrast-enhanced MRI and molecular prognostic factors (mutation of the KRAS oncogene status and microsatellite instability) [ 79 ]. Horvat et al in a retrospective study of 65 patients found no correlation between radiomics and genetic mutations only significant correlation between qualitative MRI imaging features and genetic mutations [ 80 ]. Huang et al model, built with clinical and radiomic features extracted from T2WI images, predicted the microsatellite instability status in rectal cancer [ 81 ].…”

Section: Radiogenomics In Colorectal Cancermentioning

confidence: 99%

Radiogenomics in Colorectal Cancer

Badic

Tixier

Rest

et al. 2021

Cancers

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The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features—radiomics and genetic profile modifications—genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

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Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations

Cited by 39 publications

References 28 publications

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

Radiogenomics in Colorectal Cancer

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