The role of readout-segmented echo-planar imaging-based diffusion-weighted imaging in evaluating tumor response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy

The optimized rectal cancer MRI protocol: choosing the right sequences, sequence parameters, and preparatory strategies

Pelvic MRI plays a critical role in rectal cancer staging and treatment response assessment. Despite a consensus regarding the essential protocol components of a rectal cancer MRI, substantial differences in image quality persist across institutions and vendor software/hardware platforms. In this review, we present image optimization strategies for rectal cancer MRI examinations, including but not limited to preparation strategies, high-resolution T2-weighted imaging, and diffusion-weighted imaging. Our specific recommendations are supported by case studies from multiple institutions. Finally, we describe an ongoing initiative by the Society of Abdominal Radiology's Disease-Focused Panel (DFP) on Rectal and Anal Cancer to create standardized rectal cancer MRI protocols across scanner platforms.

Radiomics based on readout-segmented echo-planar imaging (RS-EPI) diffusion-weighted imaging (DWI) for prognostic risk stratification of patients with rectal cancer: a two-centre, machine learning study using the framework of predictive, preventive, and personalized medicine

Background

Currently, the rate of recurrence or metastasis (ROM) remains high in rectal cancer (RC) patients treated with the standard regimen. The potential of diffusion-weighted imaging (DWI) in predicting ROM risk has been reported, but the efficacy is insufficient.

Aims

This study investigated the potential of a new sequence called readout-segmented echo-planar imaging (RS-EPI) DWI in predicting the ROM risk of patients with RC using machine learning methods to achieve the principle of predictive, preventive, and personalized medicine (PPPM) application in RC treatment.

Methods

A total of 195 RC patients from two centres who directly received total mesorectal excision were retrospectively enrolled in our study. Machine learning methods, including recursive feature elimination (RFE), the synthetic minority oversampling technique (SMOTE), and the support vector machine (SVM) classifier, were used to construct models based on clinical-pathological factors (clinical model), radiomic features from RS-EPI DWI (radiomics model), and their combination (merged model). The Harrell concordance index (C-index) and the area under the time-dependent receiver operating characteristic curve (AUC) were calculated to evaluate the predictive performance at 1 year, 3 years, and 5 years. Kaplan‒Meier analysis was performed to evaluate the ability to stratify patients according to the risk of ROM.

Findings

The merged model performed well in predicting tumour ROM in patients with RC at 1 year, 3 years, and 5 years in both cohorts (AUC = 0.887/0.813/0.794; 0.819/0.795/0.783) and was significantly superior to the clinical model (AUC = 0.87 [95% CI: 0.80-0.93] vs. 0.71 [95% CI: 0.59-0.81], p = 0.009; C-index = 0.83 [95% CI: 0.76-0.90] vs. 0.68 [95% CI: 0.56-0.79], p = 0.002). It also had a significant ability to differentiate patients with a high and low risk of ROM (HR = 12.189 [95% CI: 4.976-29.853], p < 0.001; HR = 6.427 [95% CI: 2.265-13.036], p = 0.002).

Conclusion

Our developed merged model based on RS-EPI DWI accurately predicted and effectively stratified patients with RC according to the ROM risk at an early stage with an individualized profile, which may be able to assist physicians in individualizing the treatment protocols and promote a meaningful paradigm shift in RC treatment from traditional reactive medicine to PPPM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00303-3.

High-Resolution Diffusion-Weighted Imaging of C6 Glioma on a 7T BioSpec MRI Scanner: Correlation of Tumor Cellularity and Nuclear-to-Cytoplasmic Ratio with Apparent Diffusion Coefficient

RATIONALE AND OBJECTIVES

To determine the association of the apparent diffusion coefficient (ADC) with quantitative cellularity and the nuclear-to-cytoplasmic ratio in C6 glioma.

MATERIALS AND METHODS

Animal models bearing C6 gliomas underwent MR scans with T1 rapid acquisition with relaxation enhancement (RARE), T2 RARE, and high-resolution diffusion-weighted imaging sequences. For each model, three consecutive sections were used to draw regions of interest (ROIs) and measure ADC values; the middle section was localized in the plane with the maximal solid tumor area. The minimal, mean, and maximal ADC values were recorded for each ROI. GFAP-immunostained sections coregistered with ADC measurements were used to calculate tumor cellularity and the nuclear-to-cytoplasmic (N/C) ratio. Spearman's correlation was used to assess the relationship between ADC values and quantitative tumor cellularity as well as N/C ratios with a significance level of p < 0.05.

RESULTS

Thirty-three sections from 11 glioma-bearing rats were analyzed. The median values of the minimal, mean, and maximal ADC were 0.443 × 10^-3, 0.744 × 10^-3, and 1.140 × 10^-3 mm²/s, respectively. The median cellularity and N/C ratio were 2151.234 per 0.025 mm² and 0.857, respectively. The minimal, mean, and maximal ADCs were all significantly associated with cellularity, with correlation coefficients of -0.712 (p < 0.001), -0.631 (p < 0.001), and -0.460 (p = 0.007), respectively. The minimal and mean ADC had significant negative relationships with the N/C ratio, with correlation coefficients of -0.565 (p =  0.001) and -0.426 (p = 0.013), respectively.

CONCLUSION

The minimal ADC correlated well with cellularity and N/C ratios in C6 glioma and may be used as a biomarker of these two pathological features.

MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends

Complete tumor response can be achieved in a certain proportion of patients with locally advanced rectal cancer, who achieve maximal response to neoadjuvant therapy (NAT). For these patients, a watch-and-wait (WW) or nonsurgical strategy has been proposed and is becoming widely practiced in order to avoid unnecessary surgical complications. Therefore, a non-invasive, reliable diagnostic tool for accurately evaluating complete tumor response is needed. Magnetic resonance imaging (MRI) plays a crucial role in both primary staging and restaging tumor response to NAT in rectal cancer without relying on resected specimen. In recent years, numerous efforts have been made to research the value of MRI in predicting and evaluating complete response in rectal cancer. Current MRI evaluation is mainly based on morphological and functional images. Morphologic MRI yields high soft tissue resolution, multiplanar images, and provides detailed depictions of rectal cancer and its surrounding structures. Functional MRI may help to distinguish residual tumor from fibrosis, therefore improving the diagnostic performance of morphologic MRI in identifying complete tumor response. Both morphologic and functional MRI have several promising parameters that may help accurately evaluate and/or predict complete response of rectal cancer. However, these parameters still have limitations and the results remain inconsistent. Recent development of new techniques, such as textural analysis, radiomics analysis and deep learning, demonstrate great potential based on MRI-derived parameters. This article aimed to review and help better understand the strengths, limitations, and future trends of these MRI-derived methods in evaluating complete response in rectal cancer.

Comparison of percentage changes in quantitative diffusion parameters for assessing pathological complete response to neoadjuvant therapy in locally advanced rectal cancer: a meta-analysis

PURPOSE

To evaluate and compare the diagnostic performance of percentage changes in apparent diffusion coefficient (∆ADC%) and slow diffusion coefficient (∆D%) for assessing pathological complete response (pCR) to neoadjuvant therapy in patients with locally advanced rectal cancer (LARC).

METHODS

A systematic search in PubMed, EMBASE, the Web of Science, and the Cochrane Library was performed to retrieve related original studies. For each parameter (∆ADC% and ∆D%), we pooled the sensitivity, specificity and calculated the area under summary receiver operating characteristic curve (AUROC) values. Meta-regression and subgroup analyses were performed to explore heterogeneity among the studies on ∆ADC%.

RESULTS

15 original studies (804 patients with 805 lesions, 15 studies on ∆ADC%, 4 of the studies both on ∆ADC% and ∆D%) were included. pCR was observed in 213 lesions (26.46%). For the assessment of pCR, the pooled sensitivity, specificity and AUROC of ∆ADC% were 0.83 (95% confidence intervals [CI] 0.76, 0.89), 0.74 (95% CI 0.66, 0.81), 0.87 (95% CI 0.83, 0.89), and ∆D% were 0.70 (95% CI 0.52, 0.84), 0.81 (95% CI 0.65, 0.90), 0.81 (95% CI 0.77, 0.84), respectively. In the four studies on the both metrics, ∆ADC% yielded an equivalent diagnostic performance (AUROC 0.80 [95% CI 0.76, 0.83]) to ∆D%, but lower than in the studies (n = 11) only on ∆ADC% (AUROC 0.88 [95% CI 0.85, 0.91]). Meta-regression and subgroup analyses showed no significant factors affecting heterogeneity.

CONCLUSIONS

Our meta-analysis confirms that ∆ADC% could reliably evaluate pCR in patients with LARC after neoadjuvant therapy. ∆D% may not be superior to ∆ADC%, which deserves further investigation.