A comparative study of four diffusion-weighted imaging models in the diagnosis of cervical cancer

MR Diffusion Kurtosis Imaging (DKI) of the Normal Human Uterus in Vivo During the Menstrual Cycle

BACKGROUND

The uterus undergoes dynamic changes throughout the menstrual cycle. Diffusion kurtosis imaging (DKI) is based on the non-Gaussian distribution of water molecules and can perhaps represent the changes of uterine microstructure.

PURPOSE

To investigate the temporal changes in DKI-parameters of the normal uterine corpus and cervix during the menstrual cycle.

STUDY TYPE

Prospective.

POPULATION

21 healthy female volunteers (26.64 ± 4.72 years) with regular menstrual cycles (28 ± 7 days).

FIELD STRENGTH/SEQUENCE

Readout segmentation of long variable echo-trains (RESOLVE)-based DKI and fast spin-echo T2-weighted sequences at 3.0T.

ASSESSMENT

Each volunteer was scanned during the menstrual phase, ovulatory phase, and luteal phase. Regions of interest (ROI) were manually delineated in the endometrium, junctional zone, and myometrium of the uterine body, and in the mucosal layer, fibrous stroma layer, and loose stroma layer of the cervix. The mean Kapp (diffusion kurtosis coefficient), Dapp (diffusion coefficient), and ADC (apparent diffusion coefficient) values were measured in the ROI.

STATISTICAL TESTS

ANOVA with Bonferroni or Tamhane correction. Intraclass correlation coefficient (ICC) for assessing agreement. P < 0.05 was considered statistically significant.

RESULTS

During the menstrual cycle, the highest Kapp (0.848 ± 0.184) and lowest Dapp (1.263 ± 0.283 *10-3 mm2/sec) values were found in the endometrium during the menstrual phase. The Dapp values for the myometrium were significantly higher than those of the endometrium and the junctional zone in every phase. Meanwhile, the Dapp values for the three zonal structures of the cervix during ovulation were significantly higher than those during the luteal phase. However, there was no significant difference in the ADC values of the loose stroma between ovulation and the luteal phase (P = 0.568). The reproducibility of DKI parameters was good (ICC, 0.857-0.944).

DATA CONCLUSION

DKI can show dynamic changes of the normal uterus during the menstrual cycle.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Functional Magnetic Resonance Imaging in Cervical Cancer Diagnosis and Treatment

Cervical Cancer is the fourth most common cancer in women worldwide. Treatment with chemoradiotherapy followed by brachytherapy achieves high local control, but recurrence with metastatic disease impacts survival. This highlights the need for predictive and prognostic biomarkers identifying populations at risk of poorer treatment response and survival. Magnetic resonance imaging (MRI) is routinely used in cervical cancer and is a potential source for biomarkers. Functional MRI (fMRI) can characterise tumour beyond anatomical MRI, which is limited to the assessment of morphology. This review summarises fMRI techniques used in cervical cancer and examines the role of fMRI parameters as predictive or prognostic biomarkers. Different techniques characterise different tumour factors, which helps to explain the variation in patient outcomes. These can impact simultaneously on outcomes, making biomarker identification challenging. Most studies are small, focussing on single MRI techniques, which raises the need to investigate combined fMRI approaches for a more holistic characterisation of tumour.

T1 mapping and multimodel diffusion-weighted imaging in the assessment of cervical cancer: a preliminary study

OBJECTIVE

To evaluate the clinical feasibility of T1 mapping and multimodel diffusion-weighted imaging (DWI) for assessing the histological type, grade, and lymphovascular space invasion (LVSI) of cervical cancer.

METHODS

Eighty patients with cervical cancer and 43 patients with a normal cervix underwent T1 mapping and DWI with 11 b-values (0-2000 s/mm2). Monoexponential, biexponential, and kurtosis models were fitted to calculate the apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion (D*), perfusion fraction (f), mean diffusivity (MD), and mean kurtosis (MK). Native T1 and DWI-derived parameters (ADCmean, ADCmin, Dmean, Dmin, D*, f, MDmean, MDmin, MKmean, and MKmax) were compared based on histological type, grade, and LVSI status.

RESULTS

Native T1 and DWI-derived parameters differed significantly between cervical cancer and normal cervix (all p < 0.05), except D* (p = 0.637). Native T1 and MKmean varied significantly between squamous cell carcinoma (SCC) and adenocarcinoma (both p < 0.05). ADCmin, Dmin, and MDmin were significantly lower while MKmax was significantly higher in the high-grade SCC group than in the low-grade SCC group (all p < 0.05). LVSI-positive SCC had a significantly higher MKmean than LVSI-negative SCC (p < 0.05).

CONCLUSION

Both T1 mapping and multimodel DWI can effectively differentiate cervical cancer from a normal cervix and cervical adenocarcinoma from SCC. Furthermore, multimodel DWI may provide quantitative metrics for non-invasively predicting histological grade and LVSI status in SCC patients.

ADVANCES IN KNOWLEDGE

Combined use of T1 mapping and multimodel DWI may provide more comprehensive information for non-invasive pre-operative evaluation of cervical cancer.

Monoexponential, biexponential, stretched-exponential and kurtosis models of diffusion-weighted imaging in kidney assessment: comparison between patients with primary aldosteronism and healthy controls

PURPOSE

This study used various diffusion-weighted imaging (DWI) models (including monoexponential, biexponential, stretched-exponential and kurtosis models) in renal magnetic resonance imaging (MRI) to compare whether there were differences in each diffusion parameter between patients with primary aldosteronism (PA) and healthy volunteers.

MATERIALS AND METHODS

Twenty-two (female:male, 14:8; age, 48 ± 10 years) patients with PA and 22 age- and sex-matched healthy controls (HCs) underwent MRI examinations of the kidneys. The independent-sample t test or the Mann‒Whitney U test was used to detect differences in the diffusion metrics of the kidneys between the two groups. Univariable and multivariable linear regression were applied to analyze the correlations between diffusion parameters and the clinical indicators.

RESULTS

The mean diffusivity (MD, p < 0.001) and radial diffusivity (Dr, p < 0.001) values in the medulla were lower in the PA group than in the HC group. The medullary fractional anisotropy (FA, p < 0.001) was higher than that of HCs. The FA (p < 0.001) and axial diffusivity (Da, p < 0.001) values in the cortex were lower in the PA group. The cortical α (anomalous exponent term, p = 0.016) was higher in the PA patients than in the HCs. Linear regression analysis showed that log(plasma aldosterone concentration) and the estimated glomerular filtration rate (eGFR) were correlated with medullary FA.

CONCLUSION

The stretched-exponential model (cortical α) and the kurtosis model (FA, MD and Dr in the medulla and FA and Da in the cortex) showed significant differences between PA patients and healthy volunteers and may have potential for noninvasive renal assessment in PA patients.

Post treatment imaging in patients with local advanced cervical carcinoma

Cervical cancer (CC) is the fourth leading cause of death in women worldwide and despite the introduction of screening programs about 30% of patients presents advanced disease at diagnosis and 30-50% of them relapse in the first 5-years after treatment. According to FIGO staging system 2018, stage IB3-IVA are classified as locally advanced cervical cancer (LACC); its correct therapeutic choice remains still controversial and includes neoadjuvant chemo-radiotherapy, external beam radiotherapy, brachytherapy, hysterectomy or a combination of these modalities. In this review we focus on the most appropriated therapeutic options for LACC and imaging protocols used for its correct follow-up. We explore the imaging findings after radiotherapy and surgery and discuss the role of imaging in evaluating the response rate to treatment, selecting patients for salvage surgery and evaluating recurrence of disease. We also introduce and evaluate the advances of the emerging imaging techniques mainly represented by spectroscopy, PET-MRI, and radiomics which have improved diagnostic accuracy and are approaching to future direction.

Intravoxel Incoherent Motion (IVIM) MR Quantification in Locally Advanced Cervical Cancer (LACC): Preliminary Study on Assessment of Tumor Aggressiveness and Response to Neoadjuvant Chemotherapy

The aim of this study was to determine whether quantitative parameters obtained from intravoxel incoherent motion (IVIM) model at baseline magnetic resonance imaging (MRI) correlate with histological parameters and response to neoadjuvant chemotherapy in patients with locally advanced cervical cancer (LACC). Methods: Twenty patients with biopsy-proven cervical cancer, staged as LACC on baseline MRI and addressed for neoadjuvant chemotherapy were enrolled. At treatment completion, tumor response was assessed with a follow-up MRI evaluated using the revised response evaluation criteria in solid tumors (RECIST; version 1.1), and patients were considered good responders (GR) if they had complete response or partial remission, and poor responders/non-responders (PR/NR) if they had stable or progressive disease. MRI protocol included conventional diffusion-weighted imaging (DWI; b = 0 and 1000 s/mm²) and IVIM acquisition using eight b-values (range: 0–1500 s/mm²). MR-images were analyzed using a dedicated software to obtain quantitative parameters: diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp) from the IVIM model; apparent diffusion coefficient (ADC) from conventional DWI. Histologic subtype, grading, and tumor-infiltrating lymphocytes (TILs) were assessed in each LACC. Results: D showed significantly higher values in GR patients (p = 0.001) and in moderate/high TILs (p = 0.018). Fp showed significantly higher values in squamous cell tumors (p = 0.006). Conclusions: D extracted from the IVIM model could represent a promising tool to identify tumor aggressiveness and predict response to therapy.