Diffusion-weighted imaging in rectal cancer: current applications and future perspectives

Abstract

This review summarizes current applications and clinical utility of diffusion-weighted imaging (DWI) for rectal cancer and in addition provides a brief overview of more recent developments (including intravoxel incoherent motion imaging, diffusion kurtosis imaging, and novel postprocessing tools) that are still in more early stages of research. More than 140 papers have been published in the last decade, during which period the use of DWI have slowly moved from mainly qualitative (visual) image interpretation to increasingly advanced methods of quantitative analysis. So far, the largest body of evidence exists for assessment of tumour response to neoadjuvant treatment. In this setting, particularly the benefit of DWI for visual assessment of residual tumour in post-radiation fibrosis has been established and is now increasingly adopted in clinics. Quantitative DWI analysis (mainly the apparent diffusion coefficient) has potential, both for response prediction as well as for tumour prognostication, but protocols require standardization and results need to be prospectively confirmed on larger scale. The role of DWI for further clinical tumour and nodal staging is less well-defined, although there could be a benefit for DWI to help detect lymph nodes. Novel methods of DWI analysis and post-processing are still being developed and optimized; the clinical potential of these tools remains to be established in the upcoming years.

Figure 1.

Overview of the cumulative number of studies published on DWI and rectal cancer in the last decade. The majority focused on response assessment to CRT, initially followed by studies on DWI for staging though now overtaken by studies focusing on new techniques. Over time, the focus of research has shifted from simple qualitative evaluation to increasingly advanced quantitative methods, which is also reflected by the increased proportion of studies focusing on the development of novel DWI models such as IVIM and DKI. Technical/Quality papers indicate papers that focusing on image quality or protocol development. ADC, apparent diffusion coefficient; CRT, chemoradiotherapy; DKI, diffusion kurtosis imaging; DWI, diffusion-weighted imaging; IVIM, intravoxel incoherent motion.

Figure 2.

T ₂weighted MRI (a) and b1000 s mm^{– 2} DWI images (b) of a male patient with a small tumour (white arrowhead) that is hard to detect and initially missed on T ₂ weighted MRI, but is clearly visible on DWI. DWI,diffusion-weighted imaging.

Figure 3.

Pre-treatment, primary staging T ₂ weighted (a) and DWI b1000 s mm^{– 2} (b) images of a female patient with a spiculated tumour in the mid-rectum. Note how the various mesorectal lymph nodes (arrowheads) are very easily detectable on DWI. DWI,diffusion-weighted imaging.

Figure 4.

Pre- (upper row) and post-CRT (bottom row) T ₂ weighted (a, e), b1000 s mm^{– 2} DWI (b, f) and ADC images (c, g) of a patient with a midrectal tumour that responded well to CRT (Histopathology after surgery indicated a very good response with predominant fibrosis and only rare residual tumour cells; Mandard tumour regression grade of 2). The images illustrate the different ways DWI can be used to assess response: on pre-CRT a clear high signal mass can be appreciated on DWI (b), after CRT only a small high signal remnant is visible within the fibrosis on DWI indicating a small residual tumour (f). The tumour volume on DWI decreased from 13.2 to 0.26 cm³, while the ADC value increased from 0.91∙10⁻³ to 1.20∙10⁻³ mm² s^–1. Concordantly, the histograms show that the distribution of ADC values within the tumour has shifted towards more high ADC values, indicating a good response. ADC, apparent diffusion coefficient; CRT, chemo radiotherapy; DWI, diffusion-weighted imaging.

Figure 5.

Traditional DWI models use a monoexponential fit of two or more b-value images between b = 0 and 1000 to calculate the ADC value as the slope of a straight line between these points. At low b-values (b < 200) the signal decay will, however, deviate from this line as it is not only affected by tissue diffusion, but also by microperfusion effects (the IVIM effect). Another phenomena is the deviation of the signal curve when applying very high b-values (b > 1000–1500). This effect is caused by non-Gaussian diffusion as a result of complex structures (such as cell membranes, organells etc) that hinder diffusion. The degree of non-Gaussian behaviour is referred to as the kurtosis effect. Formula’s to calculate the various parameters described in the Figure are as follows: Monoexponential ADC: S/S0 = exp(-b∙ADC); IVIM: S/S0 = f∙exp(-b(D + D*))+(1 f)exp(-b∙D); Kurtosis: S/S0 = exp(-b∙D_app + b²∙D_app ²∙K_app ²/6); where S = signal intensity with (S) and without (S0) diffusion-weighting; b = b-value (s mm^–2) used; ADC = apparent diffusion coefficient (mm² s^–1; observed diffusion); D = diffusion coefficient (mm²s^-1; true diffusion in the tissue; depends on cell density); D*=pseudo diffusion coefficient (mm² s^–1; depends on mean capillary segment length and average blood velocity in a voxel); f = the perfusion fraction (indicates the fractional volume (%) of capilary blood flowing within a voxel); Dapp = apparent Gaussian diffusion coefficient (mm² s^–1; diffusion coefficient under a Gaussian assumption); Kapp = apparent kurtosis (describes how much the measured diffusion departs from the assumed Gaussian distribution; a measure for heterogeneity). ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; IVIM, intravoxel incoherent motion.