University of Pisa
Department of Translational Research and New Technologies
in Medicine and Surgery
Residency Program in Diagnostic Radiology
(2012-2017)
Chairman: Prof. Davide Caramella
The Added Value of Diffusion Weighted Imaging (DWI) in Magnetic
Resonance Imaging (MRI) of Uterine Cervical Cancer
Supervisor
Candidate
Prof. Davide Caramella, MD Dr. Giacomo Aringhieri, MD
Academic Year 2015-2016
Abstract
Purpose
The aims of this study are to evaluate the behaviour of the metabolic and functional parameters in cervix uteri cancer, meant as DWI/ADC, to estimate the role of DWI and ADC values in diagnosis and follow-up of the cervix uteri cancer and to assess the possible additional contribution of DWI in this clinical setting.
Materials and Methods
From July 2012 to April 2017, we retrospectively selected 36 patients with clinical suspicion of cervix uteri cancer who underwent pelvic MRI for regional staging on 3.0T-MR scanner. We divided the population into 2 groups: group A composed of 17 patients (median age 48; range 29-81 years) who underwent first staging MRI and then went directly to surgical intervention, group (B) includes 19 patients (median age 50; range 36-70 years) who underwent pelvic MRI before (staging MRI) and after neoadjuvant chemotherapy (NAChT).
All the patients underwent 3.0T-MR scan with conventional pelvic study protocol with additional specific DWI sequences. ADC maps and specific lesions’ ADC (whole lesion and lowest ADC values) were obtained from the elaboration of the DW images.
In both groups, the ADC values were compared with the histological grading; in addition in group B, ADC values before and after NAChT were analysed.
Results
In group A, 12 squamous cell carcinomas, 3 adenocarcinomas, 1 clear cell adenocarcinoma, and 1 negative for neoplasm (according to MRI report) were included. In group B, the following histological diagnosis were present: 9 squamous cell carcinomas, 3 adenocarcinomas and 1 neuroendocrine cancer; 6 patients did not undergo surgical intervention, so the confirm of malignancy was based on biopsy.
First we analysed the performance of MRI, meant as best detection of the lesion, comparing DW and T2w images. Respectively in group A and B, the lesions were better depicted in DWI than in T2w images in 10 and 13 patients, while in 6 and 4 patients the two sequences were highly corresponding; in 1 and 2 patients the lesions were better depicted on T2w images.
No significant correlation between ADC values and histological grading was found neither in group A nor in group B.
In group A, the mean whole ADC and lowest ADC values was respectively 0.00088 s/mm2 and 0.00071 s/mm2. In group B, the mean baseline whole ADC and lowest ADC values was respectively 0.00086 s/mm2 and 0.00068 s/mm2. Regarding the post-ChT ADC value in group B, we obtained 0.00100 s/mm2 and 0.00084 s/mm2 respectively for whole ADC and lowest ADC mean values.
In group B, the results show that there is no statistically significant difference between baseline and post-ChT whole ADC values but the p-value obtained (p=0.0583) is really close to the alpha level of 0.05 indicating a trend. The presence of statistically significant difference was found between baseline and post-ChT lowest ADC values (p=0,0370), but the non-parametric approach used is definitely less robust and more prone to the case for so small samples.
Conclusions
The addition of DWI to T2W sequences considerably improved the diagnostic ability of MRI. Our results support the inclusion of DWI in the MRI protocol for the detection, staging and follow-up of cervix uteri cancer. No correlation between ADC and histological grading was found. A statistically significant difference was found only between baseline and post-ChT lowest ADC values, while for whole ADC values it is suggested only a trend. Probably with a higher sample size a difference would be detected.
Introduction
Cervical cancer is the third most common cancer in women [1]. From the epidemiological data of the EUCAN (European Cancer Observatory [2]), in 2012 in the European Union (27 Countries) its incidence rate was 11.3 new cases/100.000 per year and its 1-year prevalence was 28.008 [3]. Its was estimated that 12.996 women have died from cervical cancer in 2012, with a mortality rate of 3.7 [4]. In addition, a significant percentage of patients will develop recurrent disease (30%) with a 64% 5-year survival rate [5].
Different imaging techniques have a key role in staging and risk assessment of this type of gynaecological cancer. According to the FIGO classification, staging is based on tumour size, vaginal or parametrial involvement, bladder/rectum extension, and distant metastases; histological subtype is also considered in risk assessment. While imaging procedures, such as Computed Tomography (CT) and Positron Emission tomography-CT (PET-CT) might be helpful to identify metastatic lymph nodes, MRI is fundamental in the evaluation of the local extent of the disease. Due to its high contrast resolution, it is considered the best tool to ascertain tumour size and determine the degree of stromal penetration and evaluate the vaginal and corpus extension with high accuracy. Depending on the information given by the imaging studies, primary treatment might consist of surgery, radiotherapy, or a combination of radiotherapy and chemotherapy [1].
Recently, the introduction of the multi-parametric MRI, with its new acquisition techniques (in particular, DWI and Dynamic Contrast Enhancement-MRI, DCE-MRI), led to obtain functional and metabolic dataset that might characterize the single neoplasm. At the same time, introducing these sequences in the protocol allows to increase the conspicuity of the lesion, in a reasonable additional time [6, 7]. In fact, conventional T1 and T2 sequences have some limitations, particularly in detecting recurrences and in differentiating it from post-treatment fibrosis due to the similar morphological appearance. This distinction is essential for clinicians in order to decide which patients need further salvage treatments [6].
DWI evaluates the water motion and neoplasm cellularity and allows exploring the microstructure of the tissues, membrane integrity and cell density, giving back information that, once validated, might be used as imaging biomarkers for response assessment. From the data acquired by the DW sequences is possible to calculate a map of Apparent Diffusion Coefficient (ADC), which permits to obtain quantitative evaluation of the water molecules diffusion in selected areas (Region Of Interest, ROI). Metabolic and proliferative activity of the disease might be estimated also through the DCE-MRI, which assesses the biodistribution of contrast within the tumour. Form the dataset given by the DCE acquisition we can select a ROI and create the related enhancement curves useful to obtain other parameters as peak, mean enhancement, and signal-enhancement ratio, which reflects the effects of the distribution of the contrast agent in the lesion. Both these techniques might give important information about the pathological tissue and its evolution before, after and during the treatment, detecting microscopic changes in tumour microenvironment and cytoarchitecture [8].
The ability to accurately and rapidly predict the response of a tumour to an antineoplastic treatment is today essential to improve patient care and ultimately long-term
survival. This would have a great impact in the clinical practice, leading to a reduction of ineffective therapies. Consequently, also toxic effects, related morbidity, delay in starting new alternative and potentially effective treatments and unnecessary health system expenses would be substantially reduced. Furthermore, this would be a step forward in the direction of the personalized medicine, with treatments customized on the patient and on the disease peculiarities [6, 8].
The aims of this study are to evaluate the behaviour of the metabolic and functional parameters in cervix uteri cancer (such as ADC), to estimate the role of DWI and ADC values in diagnosis and follow-up of the cervix uteri cancer and to assess the additional contribution of DWI in this clinical setting.
Materials and Methods
Patient selection
From July 2012 to April 2017 we retrospectively selected 36 patients with clinical suspicion of cervix uteri cancer who underwent pelvic MRI for regional staging on 3.0T-MR scanner. We divided the population into 2 groups. The first group (A) is composed of 17 patients (median age 48 year old; range 29-81 years old) who underwent first staging 3.0T-MRI (baseline MRI) and then went directly to surgical intervention. The second group (B) includes 19 patients (median age 50 year old; range 36-70 years old) who underwent pelvic staging 3.0T-MRI before (baseline MRI) and after neo-adjuvant chemotherapy (post-ChT MRI).
Inclusion criteria: - Group A:
o Clinically suspected diagnosis of cervical cancer o Staging MRI performed with 3.0T scanner
o Surgical intervention as first therapy - Group B:
o Histological diagnosis of cervical cancer (biopsy included) o Both staging and follow-up MRI performed with 3.0T scanner o NAChT as first therapy
o Surgical intervention or RChT for final treatment Exclusion criteria: common exclusion criteria for MRI.
MR Imaging technique
All the exams were performed with a 3.0T MR scanner (General Electric®, MR 750
Discovery 3.0T) with a eight-channel phased-array receive-only coil (TORSO-PA). All patients underwent routine pelvic MRI and additional axial (b=0, b=1000) and sagittal (b=0, b=800) DWI sequences. The day of the exam, the patient is asked to perform an intestinal preparation with enema. After several optimizations, the final protocol has the following technical parameters:
- Axial Balanced Steady State Free Precession (b-SSFP, FIESTA®) with Parallel Imaging (ARC) [TR/TE, 3.3 ms/1.5 ms; slice thickness/gap, 5 mm/1 mm; Field Of View (FOV), 32 cm; matrix, 144 x 384; Time of Acquisition (TA), 00:36]
- Sagittal T2-weighted Fast Spin-Echo with Radial Multishot K-space Trajectory (RAD-FSE, Propeller®) with Parallel Imaging (ARC) [TR/TE, 5000 ms/95 ms; slice thickness/gap, 3 mm/0,3 mm; FOV, 24 cm; matrix, 320 x 320; number of excitation (NEX), 2.50; TA, 02:30]
- Oblique-Coronal (parallel to the long axis of the cervix uteri) T2-weighted Fast Spin-Echo with Radial Multishot K-space Trajectory (RAD-FSE, Propeller®) with Parallel Imaging (ARC) [TR/TE, 4500 ms/100 ms; slice thickness/gap, 3 mm/0.3 mm; FOV, 24 cm; matrix, 320 x 320; NEX, 2.50; TA, 02:34]
- Oblique-Axial (perpendicular to the long axis of the cervix uteri) T2-weighted Fast Spin-Echo with Radial Multishot K-space Trajectory (RAD-FSE, Propeller®) with
Parallel Imaging (ARC) [TR/TE, 4500 ms/95 ms; slice thickness/gap, 3 mm/0.3 mm; FOV, 24 cm; matrix, 320 x 320; NEX, 2.50; TA, 02:31]
Subsequently, sagittal and axial DW images were obtained using a single-shot spin-echo type echo-planar imaging (EPI) sequences with Parallel Imaging (Asset). Technical parameters for DWI were as follows:
- Sagittal DWI (same plane and slices of sagittal T2w sequence) [TR/TE, 4.615 ms/55 ms; b values, 0 and 800 s/mm2 (all directions), slice thickness/gap, 3 mm/0.3 mm; FOV, 24 cm; matrix, 104-120 x 256; NEX, 6.00; respiratory triggering, active; TA, about 01:50]
- Oblique-Axial DWI (same plane and slices of oblique-axial T2w sequence) [TR/TE, 4615 ms/61 ms; b values, 0 and 1000 s/mm2 (all directions), slice thickness/gap, 3 mm/0.3 mm; FOV, 24 cm; matrix, 104-120 x 256; NEX, 12.00; respiratory triggering, active; TA, about 02:00]
To conclude the conventional protocol, high spatio-temporal resolution Dixon imaging sequence was acquired after injection of Gadolinium-based contrast medium for DCE study. The scan parameters were as follows:
- Oblique-Sag 3D T1-weighted high spatio-temporal resolution Dixon imaging sequence (DISCO®) (same plane and slices of oblique-sag T2w sequence) [TR/TE, 4.6 ms/1.7 ms; slice thickness/gap, 3 mm/0 mm; FOV, 28 cm; matrix, 256x192; NEX, 0.69; TA, 5:00; temporal resolution, 4-5s].
Post-processing and image analysis
All the exams were evaluated comparing the conspicuity of the lesions on T2w and DW images, selecting the best visualization obtained; DCE images were used as a confirm of the extent and localization of the disease. Further and final confirmation of the real extent and localization of the neoplasm was given by the evaluation of the histological data.
All the ADC values were calculated with an Osirix® [9, 10] freeware plug-in called
ADC map calculation (version 2.1) developed by Brian Hargreaves (Stanford University) available online [11]. It allows to calculate the ADC map from images acquired with different diffusion weighting (b value) and to have the specific ADC values for the selected
ROIs. In this study we always use the same values (b=0 and b=1000) to calculate ADC for all patients. T2-weighted and DCE images guided the identification of the lesions on the DW images/ADC maps. First, we put a ROI over the whole lesion in the Axial DW images in order to obtain the whole lesion ADC value (fig. 1a). Then, within the lesion, we selected the area with the highest restriction of diffusivity based on the ADC map (the lowest ADC value) to quantify the lowest ADC value of the lesion (fig. 1b). Finally, we collected the whole and lowest ADC values for group A and the whole and lowest ADC values on baseline and post-ChT MRI for group B.
Statistical analysis
In group A, an analysis of correlation between the baseline whole ADC [s/mm2] and the histological grading (negative values set as 0) have been performed. The Pearson coefficient has been calculated. It is ranged between -1 (indicating a perfect negative correlation) and +1 (indicating a perfect positive correlation). If the result is 0 means a complete absence of correlation. The more the coefficient is close to zero and the more the null hypothesis of correlation is met. Additionally, since the ordinal nature of the histological grading variable a non-parametric approach, meant as the Spearman correlation coefficient, has been also adopted in order to confirm the results obtained from the Pearson analysis. The interpretation of the Spearman coefficient is the same of the Pearson’s one. The statistical evaluation of correlation between the baseline lowest ADC [s/mm2] and the histological grading (negative values set as 0) have been performed with the same tests, Pearson and Spearman tests.
In group B, in order to evaluate the difference of whole ADC [s/mm2] between baseline and post-ChT values a T-Test analysis for paired sample has been implemented. Anyway, the QQ-Plot of the whole ADC difference between baseline and post-ChT values shows the non-normality of the data (points not collocated on the line) and for this reason
Fig. 1. Example of ROI positioning on axial DW images. On the left (a) the ROI is placed immediately inside the border of the cervical
neoplastic lesion in order to obtain the whole ADC value. On the right (b) the ROI placed on the area with the highest restriction of diffusivity within the cervical neoplastic lesion in order to obtain the lowest ADC value.
the T-Test for paired samples is not the optimal test to implement. For this reason a Wilcoxon Signed Rank Test, as a non-parametric approach, has been also adopted to investigate the whole ADC difference between baseline and post-ChT. The same statistical analyses (T-Test for paired sample and Wilcoxon Signed Rank Test) have been performed in order to evaluate the difference of the lowest ADC values between baseline and post-ChT values.
Finally, the statistical evaluation of correlation between the post-ChT ADC [s/mm2],
respectively whole e lowest ADC, and the histological grading (negative values set as 0) have been performed with the same tests used in the analogue analyses in group A, Pearson and Spearman tests. Regarding this latter analyses, 6 patients have been excluded due to the absence of definitive histological reports. In these 6 patients, only biopsy has been performed because they underwent RChT instead of surgery.
All analysis summaries have been generated in SAS v.9.1.3 (or above) by SAS Institute Inc., Cary, NC, USA.
Results
In all the patients included in group A the diagnosis of cervix uteri cancer was confirmed by the histological analysis except in one patient: 12 squamous cell carcinomas, 3 adenocarcinomas, 1 clear cell adenocarcinoma, and 1 negative for neoplasm. This patient underwent staging MRI for positive cervical conisation but the final histological report confirmed the absence of cervical neoplasm, according to the MRI findings. Hysteroadnexectomy was performed for concomitant endometrial polyp excision, so we obtained the histological report.
In group B, 13 patients had the histological confirm of cancer (9 squamous cell carcinomas, 3 adenocarcinomas and 1 neuroendocrine cancer) and 6 patients did not undergo surgical intervention, so the confirm of malignancy was based on biopsy. Among the patients who underwent surgical intervention after ChT, pathology found 8 suboptimal intracervical responses, 1 suboptimal response with positive loco-regional lymph nodes, 1 complete pathological response, 1 optimal pathological response (neoplastic microfoci), and 2 absence of response.
First we analysed the performance of MRI, meant as best detection of the lesion, comparing DW and T2w images. Respectively in group A and B, the lesions were better depicted in DWI than in T2w images in 10 and 13 patients, while in 6 and 4 patients the two sequences were highly corresponding (fig. 2); in 1 and 2 patients the lesions were better depicted on T2w images (table 1).
Tab. 1. Best detection analysis.
DWI>T2wi DWI=T2wi T2wi>DWI
Group A 10 6 1
Group B 13 4 2
Concerning ADC values, in group A, the mean and the median whole ADC values were respectively 0.00088 s/mm2 (Standard Deviation, DS, 0.00043) and 0.00083 s/mm2. The mean and the median lowest ADC values were respectively 0.00071 s/mm2 (DS, 0.00037) and 0.00066 s/mm2.
In group B, the mean and the median baseline whole ADC values were respectively 0.00086 s/mm2 (SD, 0.00014) and 0.00083 s/mm2; the mean and the median baseline lowest ADC values were respectively 0.00068 s/mm2 (SD, 0.00012) and 0.00066 s/mm2. Regarding the post-ChT whole ADC values in group B, we obtained 0.00100 s/mm2 (SD, 0.00014) and 0.00099 s/mm2 respectively for mean and median values; the post-ChT lowest ADC mean and median values in group B were 0.00084 s/mm2 (SD, 0.00017) and 0.00084 s/mm2.
As shown in tables 2a-2d, no significant correlation between ADC (both whole and lowest values) and the histological grading was found neither in group A nor in group B (here only post-ChT whole and lowest ADC values were taken into account; as shown in the materials and methods section).
a
d
b
c
Fig. 2. Comparison between axial and sagittal DW (a, c) and T2w (b, d) images at the same anatomical level. On DW images (a, c) a
focal cervical restriction of diffusivity is well depicted as a hyperintense area. Also on T2w images (b, d) the lesions are well visualized. Here are two examples in which DW and T2w sequences are highly corresponding.
Tab. 2a. Correlation for group A between Baseline whole ADC [s/mm2] and histological grading. Pearson Correlation Coefficients, N = 17
Prob > |r| under H0: Rho=0
Baseline whole ADC [s/mm2] Histological grading (0=negative)
Baseline whole ADC [s/mm2] 100.000 0.11908
Baseline whole ADC [s/mm2] 0.6490
Histological grading (0=negative) 0.11908 100.000
Histological grading (0=negative) 0.6490
Spearman Correlation Coefficients, N = 17 Prob > |r| under H0: Rho=0
Baseline whole ADC [s/mm2] Histological grading (0=negative)
Baseline whole ADC [s/mm2] 100.000 -0.11130
Baseline whole ADC [s/mm2] 0.6706
Histological grading (0=negative) -0.11130 100.000
Histological grading (0=negative) 0.6706
ADC, Apparent Diffusion Coefficient.
Tab. 2b. Correlation for group A between Baseline lowest ADC [s/mm2] and histological grading. Pearson Correlation Coefficients, N = 17
Prob > |r| under H0: Rho=0
Baseline lowest ADC [s/mm2] Histological grading (0=negative)
Baseline lowest ADC [s/mm2] 100.000 0.06883
Baseline lowest ADC [s/mm2] 0.7929
Histological grading (0=negative) 0.06883 100.000
Histological grading (0=negative) 0.7929
Spearman Correlation Coefficients, N = 17 Prob > |r| under H0: Rho=0
Baseline lowest ADC [s/mm2] Histological grading (0=negative)
Baseline lowest ADC [s/mm2] 100.000 -0.16324
Baseline lowest ADC [s/mm2] 0.5313
Histological grading (0=negative) -0.16324 100.000
Histological grading (0=negative) 0.5313
ADC, Apparent Diffusion Coefficient.
Tab. 2c. Correlation for group B between post-ChT whole ADC [s/mm2] and histological grading. Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0 Number of Observations
Post-ChT whole ADC [s/mm2] Histological grading (0=negative)
Post-ChT whole ADC [s/mm2] 100.000 0.27659
Post-ChT whole ADC [s/mm2] 0.3603
19 13
Histological grading (0=negative) 0.27659 100000
Histological grading (0=negative) 0.3603
13 13
Spearman Correlation Coefficients Prob > |r| under H0: Rho=0
Number of Observations
Post-ChT whole ADC [s/mm2] Histological grading (0=negative)
Post-ChT whole ADC [s/mm2] 100.000 -0.02853
Post-ChT whole ADC [s/mm2] 0.9263
19 13
Histological grading (0=negative) -0.02853 100000
Histological grading (0=negative) 0.9263
13 13
Tab. 2d. Correlation for group B between post-ChT lowest ADC [s/mm2] and histological grading. Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0 Number of Observations
Post-ChT lowest ADC [s/mm2] Histological grading (0=negative)
Post-ChT lowest ADC [s/mm2] 100.000 0.21444
Post-ChT lowest ADC [s/mm2] 0.4817
19 13
Histological grading (0=negative) 0.21444 100000
Histological grading (0=negative) 0.4817
13 13
Spearman Correlation Coefficients Prob > |r| under H0: Rho=0
Number of Observations
Post-ChT lowest ADC [s/mm2] Histological grading (0=negative)
Post-ChT lowest ADC [s/mm2] 100.000 0.02853
Post-ChT lowest ADC [s/mm2] 0.9263
19 13
Histological grading (0=negative) 0.02853 100000
Histological grading (0=negative) 0.9263
13 13
ADC, Apparent Diffusion Coefficient.
The statistical analyses displayed in table 3a show a mean difference between baseline and post-ChT of -0.00003 (SD, 0.000332) that indicates how the whole ADC values before and after the ChT are practically the same. The T-Test confirms the absence of difference producing a p-value of 0.6538, higher than the alpha level of 0.05. The Wilcoxon Signed Rank Test results shown in table 3a confirmed that there is no statistically significant difference between baseline and post-ChT for whole ADC but the p-value obtained (p-p-value = 0.0583) is really close to the alpha level of 0.05 indicating a trend. Probably with a higher sample size a difference would be detected.
Tab. 3a. T-Test analysis for paired samples for group B between
baseline whole ADC [s/mm2] e post-ChT whole ADC [s/mm2].
Difference between baseline whole ADC [s/mm2] - post-ChT whole ADC [s/mm2]
N Mean Std Dev Std Err Minimum Maximum
19 -0.00003 0.000332 0.000076 -0.00050 0.000920
Mean 95% CL Mean Std Dev 95% CL Std Dev
-0.00003 -0.00019 0.000125 0.000332 0.000251 0.000491 DF t Value Pr > |t|
18 -0.46 0.6538
Tests for Location: Mu 0=0 (Variable: difference)
Test Statistic p Value
Student's t t 0.45604 Pr > |t| 0.6538
Sign M 5 Pr >= |M| 0.0309
Signed
Rank S 43.5 Pr >= |S| 0.0583
ADC, Apparent Diffusion Coefficient. On the right the Q-Q Plot, part of the statistical analysis.
Concerning lowest ADC values (table 3b), T-Test showed no statistically significant difference between baseline and post-ChT with p-value of 0.3476 (>0,05). Instead, the non-parametric approach on the same sample resulted in a statistically significant
difference between baseline and post-ChT lowest ADC values with a p-value of 0.0370 (<0.05).
Tab. 3b. T-Test analysis for paired samples for group B between
baseline lowest ADC [s/mm2] e post-ChT lowest ADC [s/mm2].
Difference between baseline lowest ADC [s/mm2] - post-ChT lowest ADC [s/mm2]
N Mean Std Dev Std Err Minimum Maximum
19 -0.00007 0.000332 0.000076 -0.00065 0.000850
Mean 95% CL Mean Std Dev 95% CL Std Dev
-0.00007 -0.00023 0.000087 0.000332 0.000251 0.000491 DF t Value Pr > |t|
18 -0.96 0.3476
Tests for Location: Mu 0=0 (Variable: difference)
Test Statistic p Value
Student's
t t 0.964527 Pr > |t| 0.3476
Sign M 4.5 Pr >= |M| 0.0636
Signed
Rank S 51.5 Pr >= |S| 0.0370
ADC, Apparent Diffusion Coefficient. On the right the Q-Q Plot, part of the statistical analysis.
Discussion
MRI has a crucial role in local staging of cervix uteri cancer, both in the first staging exam and in re-staging evaluation after ChT. In particular, Liyanage S. H. et al demonstrated the superior role of MRI in comparison with CT differentiating fibrosis and scarring from active disease; their work highlights also that sometimes MRI might result in indeterminate findings in evaluation of recurrent disease [12]. In conventional pelvic MRI, cervix tumours and recurrences can be distinguished from the cervical stroma due to their higher signal intensity on T2-weighted images. However, necrosis, chronic or acute inflammation, and oedema may also increase signal intensity on T2w images, representing a potential pitfall for the radiologists with increased false positives rate, particularly after ChT or RT [13]. Moreover, treatment can induce changes which might result in areas of fibrosis that are also difficult to differentiate from recurrence [14]. These benign alterations, often observed around the lesions after ChT/RT, may also lead to oversize and overstage the neoplasm. Concerning pelvic conventional MRI based only on T2w images, another limitation is the identification of neoplastic infiltration of the surrounding soft tissues, especially in the early phase of invasion.
DWI is a technique sensitive to the microscopic motion of water molecules, and allows for non-invasive characterisation of biological tissues on the basis of their water-diffusion properties [15]. Tumours frequently show a relatively high signal intensity on DW images due to their high cellular composition [16]. DWI is widely used in the evaluation of cancer in other anatomical districts, such as brain or head and neck. However, the application of DWI to the pelvis has proved more challenging due to artefacts from susceptibility, chemical shift, motion and presence of intestinal air, which might cause image distortion and fat misregistration. Recent improvements in hardware and acquisition parameters led to success in the application of this technique in pelvic studies [17].
In this work we analyse the role of DWI in the detection of cervix uteri cancer in first staging exam and in the follow-up MRI evaluation after NAChT. In accordance with other studies [5, 13, 18], the conspicuity of cervical lesions in this study is higher in DWI in the large part of the exams included, due to the high contrast showed in the DW images (fig. 3a-d). Cervical cancer shows a hyperintense signal in contrast with the normal cervical tissue, particularly with the hypointense cervical stromal ring [5].
Especially, we found that DW images are more precise to define the borders of the lesion and differentiate it from the perilesional inflammatory alterations, when the exam is not invalidated by artefacts caused by the presence of air in the endocervical canal or in the rectum. However, these artefacts are quite frequent and when present they can make the DW images unreadable, as we observed in the cases reported in our results where T2w images lead to a better detection than DW images (fig.4).
Fig. 3. Comparison between axial and sagittal DW (a, c) and T2w (b, d) images at the same anatomical level in two different patients.
On both DW images a focal cervical restriction of diffusivity is well depicted as a hyperintense area. Here are two examples of the better capability of DWI to identify the neoplastic lesions with a better visualization comparing to the same slice on the T2w images.
a b
In our experience, we found that some of these artefacts can be significantly reduced with an intestinal preparation, asking the patients to perform rectal enema right before the exam. While axial DWI is fundamental to characterize the lesions, the additional sagittal DWI sequences we had implemented in our protocol led to a significant better visualization of the lesions and considerably improved the diagnostic ability of MRI. In accordance with Lucas R et al [13], our results support the inclusion of DWI in the MRI protocol for the detection, staging and follow-up of cervix uteri cancer. Further improvement is given by the additional DWI sequence acquired in the sagittal plane. In comparison with the histological data, we found that DWI usually underestimates the dimensions of the lesions, particularly in post-NAChT. It is important to highlight that in 3 patients (2 in group A and 1 in group B) we found a significant difference between both the T2w and DW images in comparison with the histological report. In these cases, the lesions were overestimated in the DW images, according to T2w images, in conflict with the histological reports that described an intense chronic inflammatory infiltration around the lesions. This may lead to MRI false positive and will be object of further studies in order to define whether DWI is able to differentiate between chronic inflammatory alterations and neoplastic proliferation. Conversely, acute inflammatory processes are better defined in DWI than in T2w images due to their lower signal intensity in DWI (higher ADC values) derived from the presence of oedema, which increases the amount of free-moving water. Finally, we found a good correlation between MRI and histological reports about the parametrial infiltration. Also in the parametrial evaluation DWI had a key role to define the borders of the lesions and to
Fig. 4. Comparison between axial and sagittal DW (a, c) and T2w (b, d) images at the same anatomical level in the same patient. Both
DW images are seriously hindered by the artefacts due to air within the endocervical canal and make the DWI quite unreadable and useless. This is an example of better lesion detection of T2w images comparing to DWI.
a b
identify the extra-cervical invasion. Respectively, only in one case of group A and B, MRI was not able to identify the parametrial infiltration found on the specimen analysis. For completeness, the lesion of group B was histologically diagnosed as neuroendocrine cancer, a quite rare histotype that might lead to some variability in MRI.
The diffusion data can also be quantified through the creation of ADC maps and the selection of specific ROIs within the ADC value is measured. This is mostly useful to evaluate the changes within the lesion after anticancer treatment. In this paper, we used ADC data to estimate the changes in the ADC values prior and after NAChT and explore the correlation between ADC values and biological malignancy, meant as histological grading. We found that there is no statistically significant correlation between ADC (both whole and lowest values) and histological grading. This probably could be explained considering the histological grading parameters, such as architectural characteristics, cell differentiation grade, presence of infiltration, nuclear-cytoplasmic ratio, and mitotic index. These parameters do not show a clear influence on the water molecules movements and consequently might lead to a poor or absent correlation with the DWI signal intensity. Intuitively, the cell density increases in accordance with the histological grading, but all different histological grades are included into the range of certain neoplastic diseases. So probably either the intracellular water content (cause of restricted diffusivity signal in DWI) does not linearly vary with the above-mentioned grading parameters or the DWI technique is yet not accurate enough to perceive the difference between the various grades.
Cell density usually decreases after successful treatment due to necrosis and apoptosis, which cause substantial changes in water diffusion and lead to increased ADC values. According to other studies [7, 8], we found a difference between baseline and post-ChT ADC values (fig. 5).
Fig. 5. Baseline (a, b, c) and post-ChT (d, e, f) pelvic MR examination in a patient with good response to ChT (Squamous cell
carcinoma, G3 with suboptimal intracervical response according to the definitive histological report). Here the comparison between T2w (a, d), DW (b, e) and ADC maps (c, f) at the same anatomical level is shown. With the help of DW and T2w images a ROI is placed on the darkest area of the lesion on the ADC map. Then the ROI is copied and pasted on the corresponding DW image in order to obtain the lowest ADC before (baseline lowest ADC value 0.00070 s/mm2) and after ChT (post-ChT lowest ADC value 0.00085 s/mm2).
a b c
In particular, the results regarding whole ADC indicate only a trend, but the comparison about lowest ADC shows a statistically significant difference between baseline and post-ChT values. The non-parametric analysis indicates a statistically significant difference but this approach is definitely less robust and more prone to the case for so small samples. Probably, with a higher sample size a difference could be detected. Hence, even if a statistically significant difference has been shown, further studies are needed to support this hypothesis. Observing the patients in group B who underwent surgery as final treatment, we can deduce that the most of them have positive responses to the ChT, meant as volume reduction on MRI and also as histopathological tumour response ascertained by the pathologists. This correlates with the increase of the ADC values after ChT, showed as significant in the statistical analyses.
This study has some limitations. First the population included is quite small and this influenced negatively the power of the statistical analysis. Then, in group B, the histological analysis is not homogeneous: most of data derives from definitive histological evaluation performed on post-surgery specimens, but 6 of them come from biopsies (patients which underwent RChT as final therapy). These 6 patients were excluded from the post-NAChT ADC comparing to grading analysis. This led to a further reduction of the sample size in evaluating the correlation between ADC and histological grading. Also the different histological types included in the study might lead to some variability: 29 squamous cell carcinomas, 5 adenocarcinomas, and 1 clear cell adenocarcinoma. Finally, the different chemotherapies administered to the patients were not homogeneous and might introduce some errors in the study evaluation.
Conclusions
The addition of DWI to T2W sequences considerably improved the diagnostic ability of MRI. Our results support the inclusion of DWI in the MRI protocol for the detection, staging and follow-up of cervix uteri cancer. No correlation between ADC and histological grading was found. A statistically significant difference was found only between baseline and post-ChT lowest ADC values, while for whole ADC values only a trend is suggested. Probably with a higher sample size a difference could be detected.
References
1. Colombo, N., et al., Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol, 2012. 23 Suppl 7: p. vii27-32.
2. Steliarova-Foucher E, O.C.M., Ferlay J, Masuyer E, Forman D, Comber H, Bray F. European Cancer Observatory: Cancer Incidence, Mortality, Prevalence and Survival in Europe. Version 1.0 (September 2012) European Network of Cancer Registries, International Agency for Research on Cancer. 2012; Available from: http://eco.iarc.fr. 3. Ferlay, J., et al., Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer, 2013. 49(6): p. 1374-403.
4. Bray, F., et al., Global estimates of cancer prevalence for 27 sites in the adult population in 2008. Int J Cancer, 2013. 132(5): p. 1133-45.
5. Chen, J., et al., The utility of diffusion-weighted MR imaging in cervical cancer. Eur J Radiol, 2010. 74(3): p. e101-6.
6. Hameeduddin, A. and A. Sahdev, Diffusion-weighted imaging and dynamic contrast-enhanced MRI in assessing response and recurrent disease in gynaecological malignancies. Cancer Imaging, 2015. 15: p. 3.
7. Georg, P., et al., Changes in Tumor Biology During Chemoradiation of Cervix Cancer Assessed by Multiparametric MRI and Hypoxia PET. Mol Imaging Biol, 2017.
8. Harry, V.N., et al., Use of new imaging techniques to predict tumour response to therapy. Lancet Oncol, 2010. 11(1): p. 92-102.
9. Rosset, A., et al., Informatics in radiology (infoRAD): navigating the fifth dimension: innovative interface for multidimensional multimodality image navigation. Radiographics, 2006. 26(1): p. 299-308.
10. Rosset, A., L. Spadola, and O. Ratib, OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging, 2004. 17(3): p. 205-16.
11. Hargreaves, B. ADC Map Calculation Osirix Plug-in. 2017; Version 2.1:[Stanford University]. Available from: http://web.stanford.edu/~bah/software/ADCmap/.
12. Liyanage, S.H., C.A. Roberts, and A.G. Rockall, MRI and PET scans for primary staging and detection of cervical cancer recurrence. Womens Health (Lond), 2010. 6(2): p. 251-67; quiz 268-9.
13. Lucas, R., J. Lopes Dias, and T.M. Cunha, Added value of diffusion-weighted MRI in detection of cervical cancer recurrence: comparison with morphologic and dynamic contrast-enhanced MRI sequences. Diagn Interv Radiol, 2015. 21(5): p. 368-75.
14. Nougaret, S., et al., Pearls and Pitfalls in MRI of Gynecologic Malignancy With Diffusion-Weighted Technique. http://dx.doi.org/10.2214/AJR.12.9713, 2013.
15. Chenevert, T.L., et al., Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Institute, 2000. 92(24): p. 2029-36.
16. Koh, D.M. and D.J. Collins, Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol, 2007. 188(6): p. 1622-35.
17. Nasu, K., et al., Diffusion-weighted single shot echo planar imaging of colorectal cancer using a sensitivity-encoding technique. Jpn J Clin Oncol, 2004. 34(10): p. 620-6. 18. Nishie, A., et al., Evaluation of locally recurrent pelvic malignancy: performance of T2- and diffusion-weighted MRI with image fusion. J Magn Reson Imaging, 2008. 28(3): p. 705-13.
