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Primary and metastatic ovarian cancer: Characterization by 3.0T diffusion-weighted MRI.

ABSTRACT: We aimed to investigate whether apparent diffusion coefficients (ADCs) measured by 3.0T diffusion-weighted magnetic resonance imaging (DWI) associate with histological aggressiveness of ovarian cancer (OC) or predict the clinical outcome. This prospective study enrolled 40 patients with primary OC, treated 2011-2014.DWI was performed prior to surgery. Two observers used whole lesion single plane region of interest (WLsp-ROI) and five small ROIs (S-ROI) to analyze ADCs. Samples from tumours and metastases were collected during surgery. Immunohistochemistry and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to measure the expression of vascular endothelial growth factor (VEGF) and its receptors.The interobserver reliability of ADC measurements was excellent for primary tumours ICC 0.912 (WLsp-ROI). Low ADCs significantly associated with poorly differentiated OC (WLsp-ROI P = 0.035). In primary tumours, lower ADCs significantly associated with high Ki-67 (P = 0.001) and low VEGF (P = 0.001) expression. In metastases, lower ADCs (WLsp-ROI) significantly correlated with low VEGF receptors mRNA levels. ADCs had predictive value; 3-year overall survival was poorer in patients with lower ADCs (WLsp-ROI P = 0.023, S-ROI P = 0.038).Reduced ADCs are associated with histological severity and worse outcome in OC. ADCs measured with WLsp-ROI may serve as a prognostic biomarker of OC.• Reduced ADCs correlate with prognostic markers: poor differentiation and high Ki-67 expression • ADCs also significantly correlated with VEGF protein expression in primary tumours • Lower ADC values are associated with poorer survival in ovarian cancer • Whole lesion single plane-ROI ADCs may be used as a prognostic biomarker in OC.

SUBMITTER: Lindgren A

PROVIDER: S-EPMC5544807 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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Project description:Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Project description:Diffusion-weighted magnetic resonance imaging (DWI) enables non-invasive, quantitative staging of prostate cancer via measurement of the apparent diffusion coefficient (ADC) of water within tissues. In cancer, more advanced disease is often characterized by higher cellular density (cellularity), which is generally accepted to correspond to a lower measured ADC. A quantitative relationship between tissue structure and in vivo measurements of ADC has yet to be determined for prostate cancer. In this study, we establish a theoretical framework for relating ADC measurements with tissue cellularity and the proportion of space occupied by prostate lumina, both of which are estimated through automatic image processing of whole-slide digital histology samples taken from a cohort of six healthy mice and nine transgenic adenocarcinoma of the mouse prostate (TRAMP) mice. We demonstrate that a significant inverse relationship exists between ADC and tissue cellularity that is well characterized by our model, and that a decrease of the luminal space within the prostate is associated with a decrease in ADC and more aggressive tumor subtype. The parameters estimated from our model in this mouse cohort predict the diffusion coefficient of water within the prostate-tissue to be 2.18?×?10-3?mm2/s (95% CI: 1.90, 2.55). This value is significantly lower than the diffusion coefficient of free water at body temperature suggesting that the presence of organelles and macromolecules within tissues can drastically hinder the random motion of water molecules within prostate tissue. We validate the assumptions made by our model using novel in silico analysis of whole-slide histology to provide the simulated ADC (sADC); this is demonstrated to have a significant positive correlation with in vivo measured ADC (r2?=?0.55) in our mouse population. The estimation of the structural properties of prostate tissue is vital for predicting and staging cancer aggressiveness, but prostate tissue biopsies are painful, invasive, and are prone to complications such as sepsis. The developments made in this study provide the possibility of estimating the structural properties of prostate tissue via non-invasive virtual biopsies from MRI, minimizing the need for multiple tissue biopsies and allowing sequential measurements to be made for prostate cancer monitoring.

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Primary and metastatic ovarian cancer: Characterization by 3.0T diffusion-weighted MRI.

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