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

MRI-based Multiregional Radiomics for Pretreatment Prediction of Distant Metastasis After Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram based on intratumoral and peritumoral radiomics signatures for pretreatment prediction of distant metastasis-free survival (DMFS) in patients after neoadjuvant chemoradiotherapy (NCRT) with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

This retrospective study included 230 patients (161 training cohort; 69 validation cohort) with LARC who underwent NCRT and surgery. Radiomics features were extracted on T2-weighted images from gross tumor volume (GTV) and volumes of 4-mm, 6-mm, and 8-mm peritumoral regions (PTV4, PTV6, and PTV8). The least absolute shrinkage and selection operator (LASSO)-Cox analysis were used for features selection and models construction. The performance of each model in predicting DMFS was evaluated by the Concordance index (C-index) and time-independent receiver operating characteristic curve (ROC).

RESULTS

The PTV4 radiomics model demonstrated superior performance compared to the PTV6 and PTV8 radiomics models, with C-indexes of 0.750 and 0.703 in the training and validation cohorts, respectively. The nomogram was constructed by integrating the GTV radiomics signature, PTV4 radiomics signature, and relevant clinical characteristics, including CA19-9 level, clinical T stage, and clinical N stage. The nomogram achieved C-indexes of 0.831 and 0.748, with corresponding AUCs of 0.872 and 0.808 for 5-year DMFS in the training and validation cohorts, respectively. Kaplan-Meier analysis revealed that a cut-off value of 1.653 effectively stratified patients into high- and low-risk groups for DM (P < 0.001).

CONCLUSION

The intra-peritumoral radiomics nomogram is a favorable tool for clinicians to develop personalized systemic treatment and intensive follow-up strategies to improve patient prognosis.

Comparative study of radiomics, tumor morphology, and clinicopathological factors in predicting overall survival of patients with rectal cancer before surgery

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Radiomics analysis of pretreatment MR images could predict overall survival (OS) in patients.

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Clinical, pathological and MRI imaging indexes were included and models were established. Tumor morphological model, clinicopathological model, radiomics model and comprehensive model were used to evaluate the prognosis of patients with rectal cancer. It can explore the influence of the above factors on the prognosis of rectal cancer from multi-level and multi-angle.

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The proposed radiomics nomogram showed better prognostic performance than the clinicopathological and imaging model in risk stratification and can classify patients into high- and low-risk groups with significant differences in OS.

We compared the ability of a radiomics model, morphological imaging model, and clinicopathological risk model to predict 3-year overall survival (OS) in 206 patients with rectal cancer who underwent radical surgery and had magnetic resonance imaging, clinicopathological, and OS data available. The patients were randomized to a training cohort (n = 146) and a verification cohort (n = 60). Radiomics features were extracted from preoperative T2-weighted images, and a radiomics score model was constructed. Factors that were significant in the Cox multivariate analysis were used to construct the final morphological tumor model and clinicopathological model. A comprehensive model in the form of a line chart was established by combining the three models. Ten radiomics features significantly related to OS were selected to construct the radiomics feature model and calculate the radiomics score. In the morphological model, mesorectal extension depth and distance between the lower tumor margin and the anal margin were significant prognostic factors. N stage was the only significant clinicopathological factor. The comprehensive model combined with the above factors had the best prediction performance for OS. The C-index had a predictive performance of 0.872 (95% confidence interval [CI]: 0.832–0.912) in the training cohort and 0.944 (95% CI: 0.890–0.990) in the verification cohort, which was better than for any single model. The comprehensive model was divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that all factors were significantly correlated with poor OS in the high-risk group. A comprehensive nomogram based on multi-model radiomics features can predict 3-year OS after rectal cancer surgery.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00261-021-03137-1.

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

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.

Neoadjuvant chemoradiation improves oncologic outcomes in low and mid clinical T3N0 rectal cancers

BACKGROUND

Controversial data exists in the current literature in regard to the use of neoadjuvant chemoradiation (nCRT) in patients with clinical T3N0 (cT3N0) rectal cancers, specifically based on location and relation to peritoneal reflection. We aimed to analyze the impact of nCRT on oncologic outcomes among cT3N0 rectal cancers, depending on the tumor height from anal verge (AV).

METHODS

A retrospective analysis of patients with cT3N0 rectal cancers was included from a query of a prospectively maintained rectal cancer database from 1980 to 2016. Patients were divided into 3 groups based on the tumor height: low (1-5 cm from AV), mid (6-10 cm from AV), and upper (11-15 cm from AV). Patients were stratified by use of nCRT.

MAIN OUTCOMES

5-year overall survival (OS), disease-free survival (DFS), cancer-specific survival (CSS), and local recurrence (LR) using Kaplan-Meier curves.

RESULTS

Five hundred ninety-two patients were included. Overall, 364 (61.4%) patients received nCRT and 228 (38.6%) patients did not. There were 251 (43%) patients with low, 302 (51%) with mid, and 39 (7%) with upper rectal cancer. Patients with low and mid rectal cancers received nCRT more frequently than those with upper rectal cancers (68.5% and 61.2% vs 43.6%, p = 0.007). The 5-year OS was 78% and 63%, DFS-88% and 73%, LR-1% and 8% in nCRT followed by resection vs. surgery alone (p < 0.001). In regard to cancer location after nCRT compared with surgery alone, low and mid cancers had better OS, DFS, and CSS, compared with upper ones.

CONCLUSION

nCRT prolongs survival among patients with rectal cancer below 10 cm from AV; however, it has no effect on 5-year oncologic survival of patients with upper rectal cancer located below peritoneal reflection.

Nodal staging in the rectal cancer follow-up MRI after chemoradiotherapy: use of morphology, size, and diffusion criteria

AIM

To analyse changes in post-neoadjuvant follow-up magnetic resonance imaging (MRI) staging accuracy for malignant adenopathies in rectal cancer, by comparing size criteria with morphological criteria using high-resolution T2-weighted sequences, as well as variations when adding diffusion-weighted imaging.

METHODS AND MATERIALS

The present study was a cross-sectional study of a database including 46 1.5-T MRI examinations (2011-2016) from patients with biopsy-proven rectal cancer and chemoradiotherapy treatment before surgery. All cases were reviewed by three radiologists individually, who were blinded to any clinical information. The radiologists were experienced in rectal cancer (3-6 years) and evaluated the presence of malignant nodes in each patient. Malignancy was determined using morphological, size (5 mm), and diffusion criteria separately, as well as morphology plus diffusion. Each case was assessed four times: (1) evaluation of morphological criteria; (2) size criteria; (3) evaluation only using diffusion (b-values 50, 400, and 800); and (4) diffusion plus morphological criteria. Histological staging of surgical specimens was the reference standard. Statistical analysis included accuracy (area under the receiver operating characteristic [ROC] curve [AUC]), sensitivity, specificity, and positive/negative predictive values (PPV/NPV) for each radiologist, and group agreement (Fleiss' kappa).

RESULTS

Mean values using morphological criteria were: AUC 0.78, sensitivity 77.7%, specificity 73.8%, PPV 66.1%, NPV 85.2%. Using size criterion: AUC 0.75, sensitivity 62.9%, specificity 83.2%, PPV 74.1%, NPV 80%. Added diffusion yielded no improvement, and yielded worse results by itself.

CONCLUSIONS

Although morphological criteria showed better results in accuracy, sensitivity, and NPV, size criterion yielded the best specificity and PPV. Adding diffusion did not demonstrate a clear advantage over the criteria by themselves. Thus, mixed size-morphology criteria could have the greatest diagnostic value for follow-up N-staging.

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

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.