Radiogenomic and Deep Learning Network Approaches to Predict <i>KRAS</i> Mutation from Radiotherapy Plan CT

A feasibility study was performed to determine if CT-based radiomics could play an augmentative role in predicting neoadjuvant rectal score (NAR), locoregional failure free survival (LRFFS), distant metastasis free survival (DMFS), disease free survival (DFS) and overall survival (OS) in locally advanced rectal cancer (LARC). The NAR score, which takes into account the pathological tumour and nodal stage as well as clinical tumour stage, is a validated surrogate endpoint used for early determination of treatment response whereby a low NAR score (< 8) has been correlated with better outcomes and high NAR score (> 16) has been correlated with poorer outcomes. CT images of 191 patients with LARC were used in this study. Primary tumour (GTV) and mesorectum (CTV) were contoured separately and radiomics features were extracted from both segments. Two NAR models (NAR > 16 and NAR < 8) models were constructed using Least Absolute Shrinkage and Selection Operator (LASSO) and the survival models were constructed using regularized Cox regressions. Area under curve (AUC) and time-dependent AUC were used to quantify the performance of the LASSO and Cox regression respectively, using ten folds cross validations. The NAR > 16 and NAR < 8 models have an average AUCs of 0.68 ± 0.13 and 0.59 ± 0.14 respectively. There are statistically significant differences between the clinical and combined model for LRFFS (from 0.68 ± 0.04 to 0.72 ± 0.04), DMFS (from 0.68 ± 0.05 to 0.70 ± 0.05) and OS (from 0.64 ± 0.06 to 0.66 ± 0.06). CTV radiomics features were also found to be more important than GTV features in the NAR prediction model. The most important clinical features are age and CEA for NAR > 16 and NAR < 8 models respectively, while the most significant clinical features are age, surgical margin and NAR score across all the four survival models.

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Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future

Porto-Álvarez

Barnes

Villanueva

et al. 2023

Applied Sciences

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Computed tomography (CT) introduced medicine to digital imaging. This occurred in the early 1970s and it was the start of the digital medical imaging revolution. The resulting changes and improvements in health care associated with digital imaging have been marked, are occurring now, and are likely to continue into the future. Before CT, medical images were acquired, stored, and displayed in analog form (i.e., on film). Now essentially all medical images are acquired and stored digitally. When they are not viewed by computer, they are converted to an analog image to be seen. The application of computer algorithms and the processing of digital medical images improves the visualization of diagnostically important details and aids diagnosis by extracting significant quantitative information. Examples of this can be seen with CAD and radiomics applications in the diagnosis of lung and colorectal cancer, respectively. The objectives of this article are to point out the key aspects of the digital medical imaging revolution, to review its current status, to discuss its clinical translation in two major areas: lung and colorectal cancer, and to provide future directions and challenges of these techniques.

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Radiogenomic and Deep Learning Network Approaches to Predict KRAS Mutation from Radiotherapy Plan CT

Cited by 3 publications

References 29 publications

The Role of Radiomics in Rectal Cancer

The Role of Radiomics in Rectal Cancer

Predicting outcomes for locally advanced rectal cancer treated with neoadjuvant chemoradiation with CT-based radiomics

Digital Medical X-ray Imaging, CAD in Lung Cancer and Radiomics in Colorectal Cancer: Past, Present and Future

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