Multi-parametric magnetic resonance imaging assessment of whole tumour heterogeneity for chemoradiotherapy response prediction in rectal cancer

Predictive role of diffusion-weighted MRI in the assessment of response to total neoadjuvant therapy in locally advanced rectal cancer

OBJECTIVE

To investigate the predictive role of diffusion-weighted magnetic resonance imaging (DW-MRI) in the assessment of response to total neoadjuvant therapy (TNT) in patients with locally advanced rectal cancer (LARC).

METHODS

In this single-center retrospective study, patients with LARC who underwent staging MRI and TNT were enrolled. MRI-based staging, tumor volume, and DWI-ADC values were analyzed. Patients were classified as complete responders (pCR) and non-complete responders (non-pCR), according to post-surgical outcome. Pre-treatment ADC values were compared to pathological outcome, post-treatment downstaging, and reduction of tumor volume. The diagnostic accuracy of DWI-ADC in differentiating between pCR and non-pCR groups was calculated with receiver operating characteristic (ROC) analysis.

RESULTS

A total of 36 patients were evaluated (pCR, n = 20; non-pCR, n = 16). Pre-treatment ADC values were significantly different between the two groups (p = 0.034), while no association was found between pre-TNT tumor volume and pathological response. ADC values showed significant correlations with loco-regional downstaging after therapy (r = -0.537, p = 0.022), and with the reduction of tumor volume (r = -0.480, p = 0.044). ADC values were able to differentiate pCR from non-pCR patients with a sensitivity of 75% and specificity of 70%.

CONCLUSIONS

ADC values on pre-treatment MRI were strongly associated with the outcome in patients with LARC, both in terms of pathological response and in loco-regional downstaging after TNT, suggesting the use of DW-MRI as a potential predictive tool of response to therapy.

KEY POINTS

• ADC values of pre-TNT MRI examinations of patients with LARC were significantly associated with a pathological complete response (pCR) and with post-treatment regression of TNM staging. • An ADC value of 1.042 ×10^-3 mm²/s was found to be the optimal cutoff value for discriminating between pCR and non-pCR patients, with a sensitivity of 75% and specificity of 70%. • DW-MRI proved to have a potential predictive role in the assessment of response to therapy in patients with LARC, throughout the analysis of ADC map values.

Correlations of tumour permeability parameters with apparent diffusion coefficient in nasopharyngeal carcinoma

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Vascular permeability is associated with diffusability in nasopharyngeal tumour.

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Both influx and reflux rates have inverse linear correlations with ADC.

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Reflux rate has the strongest inverse linear correlation with ADC.

Background and Purpose

Functional imaging has an established role in therapeutic monitoring of cancer treatments. This study evaluated the correlations of tumour permeability parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and tumour cellularity derived from apparent diffusion coefficient (ADC) in nasopharyngeal carcinoma (NPC).

Material and Methods

Twenty NPC patients were examined with DCE-MRI and RESOLVE diffusion-weighted MRI (DW-MRI). Tumour permeability parameters were quantitatively measured with Tofts compartment model. Volume transfer constant (K^trans), volume of extravascular extracellular space (EES) per unit volume of tissue (V_e), and the flux rate constant between EES and plasma (K_ep) from DCE-MRI scan were measured. The time-intensity curve was plotted from the 60 dynamic phases of DCE-MRI. The initial area under the curve for the first 60 s of the contrast agent arrival (iAUC60) was also calculated. They were compared with the ADC value derived from DW-MRI with Pearson correlation analyses.

Results

Among the DCE-MRI permeability parameters, K_ep had higher linearity in inverse correlation with ADC value (r = −0.69, p = <0.05). K^trans (r = −0.60, p=<0.05) and iAUC60 (r = −0.64, p = <0.05) also had significant inverse correlations with ADC. V_e showed a significant positive correlation with ADC (r = 0.63, p = <0.05).

Conclusions

Nasopharyngeal tumour vascular permeability parameters derived from DCE-MRI scan were correlated linearly with tumour cellularity measured by free water diffusability with ADC. The clinical implementations of these linear correlations in the quantitative assessments of therapeutic response for NPC patients may be worth to further explore.

Predicting perineural invasion using histogram analysis of zoomed EPI diffusion-weighted imaging in rectal cancer

PURPOSE

To investigate the utility of histogram analysis of zoomed EPI diffusion-weighted imaging (DWI) for predicting the perineural invasion (PNI) status of rectal cancer (RC).

METHODS

This prospective study evaluated 94 patients diagnosed with histopathologically confirmed RC between July 2020 and July 2021. Patients underwent preoperative rectal magnetic resonance imaging (MRI) examinations, including the zoomed EPI DWI sequence. Ten whole-tumor histogram parameters of each patient were derived from zoomed EPI DWI. Reproducibility was evaluated according to the intra-class correlation coefficient (ICC). The association of the clinico-radiological and histogram features with PNI status was assessed using univariable analysis for trend and multivariable logistic regression analysis with β value calculation. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance.

RESULTS

Forty-two patients exhibited positive PNI. The inter- and intraobserver agreements were excellent for the histogram parameters (all ICCs > 0.80). The maximum (p = 0.001), energy (p = 0.021), entropy (p = 0.021), kurtosis (p < 0.001), and skewness (p < 0.001) were significantly higher in the positive PNI group than in the negative PNI group. Multivariable analysis showed that higher MRI T stage [β = 2.154, 95% confidence interval (CI) 0.932-3.688; p = 0.002] and skewness (β = 0.779, 95% CI 0.255-1.382; p = 0.006) were associated with positive PNI. The model combining skewness and MRI T stage had an area under the ROC curve of 0.811 (95% CI 0.724-0.899) for predicting PNI status.

CONCLUSION

Histogram parameters in zoomed EPI DWI can help predict the PNI status in RC.

Predicting neoadjuvant chemoradiotherapy response with functional imaging and liquid biomarkers in locally advanced rectal cancer

INTRODUCTION

Non-invasive predictive quantitative biomarkers are required to guide treatment individualization in patients with locally advanced rectal cancer (LARC) in order to maximise therapeutic outcomes and minimise treatment toxicity. Magnetic resonance imaging (MRI), positron emission tomography (PET) and blood biomarkers have the potential to predict chemoradiotherapy (CRT) response in LARC.

AREAS COVERED

This review examines the value of functional imaging (MRI and PET) and liquid biomarkers (circulating tumor cells (CTCs) and circulating tumor nucleic acid (ctNA)) in the prediction of CRT response in LARC. Selected imaging and liquid biomarker studies are presented and the current status of the most promising imaging (apparent diffusion co-efficient (ADC), K^trans, SUV_max, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and liquid biomarkers (circulating tumor cells (CTCs), circulating tumor nucleic acid (ctNA)) is discussed. The potential applications of imaging and liquid biomarkers for treatment stratification and a pathway to clinical translation are presented.

EXPERT OPINION

Functional imaging and liquid biomarkers provide novel ways of predicting CRT response. The clinical and technical validation of the most promising imaging and liquid biopsy biomarkers in multi-centre studies with harmonised acquisition techniques is required. This will enable clinical trials to investigate treatment escalation or de-escalation pathways in rectal cancer.

Artificial intelligence in multi-parametric magnetic resonance imaging: A review

Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.