Role of Magnetic Resonance Imaging in Primary Rectal Cancer—Standard Protocol and Beyond

Gourtsoyianni, Sofia; Papanikolaou, Nikolaos

doi:10.1053/j.sult.2016.02.004

Cited by 8 publications

(3 citation statements)

References 47 publications

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“…T2-weighted imaging has better stability in appearance. High-resolution T2-weighted imaging can provide clear visualization of rectal layers by offering good contrast between the tumor and surrounding tissue, making it one of the most important sequences for staging of rectal cancer [37,38]. Contrast-enhanced T1-weighted images are obtained by scanning after intravenous injection of a contrast agent to determine angiogenesis inside tumors.…”

Section: Discussionmentioning

confidence: 99%

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

et al. 2021

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Purpose To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. Methods A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. Results The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987). Conclusion A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

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Section: Discussionmentioning

confidence: 99%

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

et al. 2021

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“…However, contrary to the above-mentioned investigations, we selected only T2-WI images for our radiomic analysis and model construction. High-resolution T2-weighted sequence is the most important one for the assessment and local staging of rectal cancer since it offers a good visualization of rectal wall layers and it provides good contrast between tumor, surrounding fat and mesorectal fascia [ 65 ]. Therefore, it is an essential sequence included in the standard rectal MR protocol and it is always performed when evaluating rectal tumors [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Petresc

Lebovici

Caraiani

et al. 2020

Cancers

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Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

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“…Magnetic Resonance (MR) is the most accurate imaging modality to stage locally advanced rectal cancer (LARC). MR role in stratification of patient risk and in guiding patient management has been widely investigated [ 1 – 6 ]. The strength of this technique is based on its ability to distinguish normal rectal wall from pathologic tissues on the basis of the differences in signal intensity achievable on T2-weighted sequences [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

Rengo

Picchia

Marzi³

et al. 2017

Oncotarget

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This study aims to evaluate the feasibility of a magnetic resonance (MR) automatic method for quantitative assessment of the percentage of fibrosis developed within locally advanced rectal cancers (LARC) after neoadjuvant radiochemotherapy (RCT). A total of 65 patients were enrolled in the study and MR studies were performed on 3.0 Tesla scanner; patients were followed-up for 30 months. The percentage of fibrosis was quantified on T2-weighted images, using automatic K-Means clustering algorithm. According to the percentage of fibrosis, an optimal cut-off point for separating patients into favorable and unfavorable pathologic response groups was identified by ROC analysis and tumor regression grade (MR-TRG) classes were determined and compared to histopathologic TRG. An optimal cut-off point of 81% of fibrosis was identified to differentiate between favorable and unfavorable pathologic response groups resulting in a sensitivity of 78.26% and a specificity of 97.62% for the identification of complete responders (CRs). Interobserver agreement was good (0.85). The agreement between P-TRG and MR-TRG was excellent (0.923). Significant differences in terms of overall survival (OS) and disease free survival (DFS) were found between favorable and unfavorable pathologic response groups. The automatic quantification of fibrosis determined by MR is feasible and reproducible.

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Role of Magnetic Resonance Imaging in Primary Rectal Cancer—Standard Protocol and Beyond

Cited by 8 publications

References 47 publications

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Magnetic resonance tumor regression grade (MR-TRG) to assess pathological complete response following neoadjuvant radiochemotherapy in locally advanced rectal cancer

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