Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

Coppola, Francesca; Giannini, Valentina; Gabelloni, Michela; Panić, Jovana; Defeudis, Arianna; Monaco, Silvia Lo; Cattabriga, Arrigo; Cocozza, Maria Adriana; Pastore, Luigi Vincenzo; Polici, Michela; Laghi, Andréa; Regge, Daniele; Neri, Emanuele; Golfieri, Rita; Faggioni, Lorenzo

doi:10.3390/diagnostics11050756

Cited by 44 publications

(24 citation statements)

References 81 publications

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“…The recent development of TA and radiomics-based image analysis have introduced new predictive modelling techniques and offered promising insights for quantitative imaging characterization of rectal cancer, when compared to the mere volumetric assessment [ 139 ].…”

Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.

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Section: Resultsmentioning

confidence: 99%

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Nardone

Boldrini

Grassi

et al. 2021

Cancers

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“…These radiomics features extracted from different MRI modalities could reflect tumor heterogeneity caused by variations in tumor intensity, cellularity, and vascularization, and a combination of radiomics features from multi-parametric MRI is likely to improve prognostication in comparison with radiomics features extracted from a single sequence. Although our results do not comprehensively identify the particular MRI sequences that provide the most relevant information for predicting the response to nCRT, radiomics analysis derived from multiparametric MRI clearly has the potential to provide added value to conventional MRI [ 34 , 35 ]. Nie et al found that radiomic features derived from T2WI, DWI, and CE-T1WI enhanced the predictive power of an artificial neural network classifier [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

et al. 2022

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Background The purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Methods Patients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity. Results Eighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation. Conclusions In rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.

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“…Radiomics aims to translate medical images into quantitative data, defined as biomarkers, which may reveal a deeper level of detail than that, which is accessible to the unaided human eye, so as to quantify tumor phenotypes, which could aid in clinical decision-making ( 14 ).Radiomics may provide quantitative and objective support for decisions surrounding cancer detection and treatment ( 15 , 16 ).TA belongs to radiomics. TA by mathematical methods of quantitative imaging image pixel gray level statistics and spatial distribution and structure information, to extract the texture feature which cannot be identified by the naked eye, revealing the heterogeneity of tumor histologic features and certain genes, and by using the quantitative information of the differential diagnosis of the disease, grading, classification and evaluation of curative effect ( 7 , 17 ).…”

Section: Discussionmentioning

confidence: 99%

The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma

et al. 2022

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PurposeThis study aims to evaluate the value of 3.0T MRI Intravoxel Incoherent motion diffusion-weighted magnetic resonance imaging (IVIM-DWI) combined with texture analysis (TA) for evaluating extramural vascular invasion (EMVI) of rectal adenocarcinoma.MethodsNinety-six patients with pathologically confirmed rectal adenocarcinoma after surgical resections were collected. Patients were divided into the EMVI positive group (n=39) and the EMVI negative group (n=57). We measured the IVIM-DWI parameters and TA parameters of rectal adenocarcinoma. We compare the differences of the above parameters between the two groups and establish a prediction model through multivariate logistic regression analysis. the ROC curve was performed for parameters with each individual and in combination.ResultsADC, D, D* value between the two groups were statistically significant (P= 0.015,0.031,0). Six groups of texture parameters were statistically significant between the two groups (P=0.007,0.037,0.011,0.005,0.007,0.002). Logistic regression prediction model shows that GLCM entropy_ALL DIRECTION_offset7_SD and D* are important independent predictors, and the AUC of the regression prediction model was 0.821, the sensitivity was 92.98%, the specificity was 61.54%, and the Yoden index was 0.5452. The AUC was significantly higher than that of other single parameters.Conclusion3.0T MRI IVIM-DWI parameters combined with texture analysis can provide valuable information for EMVI evaluation of rectal adenocarcinoma before the operation.

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Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice

Cited by 44 publications

References 81 publications

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients

The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma

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