Diffusion kurtosis imaging as a biomarker of breast cancer

Diffusion kurtosis imaging (DKI) is a diffusion-weighted imaging method that describes non-Gaussian signal behavior using a relatively simple mathematical model. A parameter, kurtosis K, describes the deviation of the diffusion signal decay from a Gaussian pattern. The deviation reflects the complexity of the tissue microstructure affecting water diffusion. Several studies have investigated the diagnostic performance of DKI in distinguishing malignant from benign breast lesions. DKI has been reported to correlate with subtypes and with several molecular and other factors related to the treatment and prognosis of breast cancer. Some technical considerations remain to be resolved for the clinical application of DKI in the breast

Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer

Purpose: To investigate whether intravoxel incoherent motion (IVIM) and/or non-Gaussian diffusion parameters are associated with distant disease-free survival (DDFS) in patients with invasive breast cancer. Methods: From May 2013 to March 2015, 101 patients (mean age 60.0, range 28-88) with invasive breast cancer were evaluated prospectively. IVIM parameters (flowing blood volume fraction [ɪᴠɪᴍ] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at a b value of 0 s/mm² [ADC₀] and kurtosis [K]) were estimated using a diffusion-weighted imaging series of 16 b values up to 2500 s/mm². Shifted ADC values (sADC₂₀₀-₁₅₀₀) and standard ADC values (ADC₀-₈₀₀) were also calculated. The Kaplan-Meier method was used to generate survival analyses for DDFS, which were compared using the log-rank test. Univariable Cox proportional hazards models were used to assess any associations between each parameter and distant metastasis-free survival. Results: The median observation period was 80 months (range, 35-92 months). Among the 101 patients, 12 (11.9%) developed distant metastasis, with a median time to metastasis of 79 months (range, 10-92 months). Kaplan-Meier analysis showed that DDFS was significantly shorter in patients with K > 0.98 than in those with K ≤ 0.98 ( = 0.04). Cox regression analysis showed a marginal statistical association between K and distant metastasis-free survival ( = 0.05). Conclusion: Non-Gaussian diffusion may be associated with prognosis in invasive breast cancer. A higher K may be a marker to help identify patients at an elevated risk of distant metastasis, which could guide subsequent treatment

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [Td = 2.5/5 ms with 11 b-values (0-600 s/mm2) and Td = 9/27.6 ms with 17 b-values (0-3000 s/mm2), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC0-600 using b = 0 and 600 s/mm2 and shifted ADC [sADC200-1500] using b = 200 and 1500 s/mm2) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC0, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC9-27.6 (between [Formula: see text] and [Formula: see text] and ΔADC2.5_sADC27.6 (between [Formula: see text] and [Formula: see text]) was significantly larger for MCF-7 groups, and ΔADC2.5_sADC27.6 was positively correlated with Ki67max and APT SI. ADC0 decreased significantly in MDA-MB-231 group and K increased significantly with Td in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC2.5_sADC27.6 and API SI with Ki-67 parameters indicate that the Td-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers

The added value of non-contrast 3-Tesla MRI for the pre-operative localization of hyperparathyroidism

OBJECTIVE: We investigated the efficacy of non-contrast 3-Tesla MR imaging added to the combination of sestamibi with99mTc (MIBI) scintigraphy and Ultrasonography (US) for the pre-operative localization of Primary Hyperparathyroidism (PHPT) lesions. METHODS: A total of 34 parathyroid glands, including nine normal glands, were examined with MIBI, US, and non-contrast 3-Tesla MRI. MRI was performed with the acquisition of T1- and T2-weighted images and fat-suppressed T2-weighted images. We calculated the sensitivities of MIBI, US, and the 'additional' MRI, with knowledge of the former two modalities' results. RESULTS: For the diagnosis of PHPT lesions, the sensitivity values of MIBI, US, and additional MRI were 88.0% (22/25), 84.0% (21/25), and 92.0% (23/25), respectively. Normal glands were not visualized with any modality (0/9). One lesion was detected neither with US nor MRI, but only with MIBI, with the limitation that MIBI represented no more than laterality. The two glands not identified in MRI were 4 mm and 6 mm in their size, which are within the range of normal gland's size. Two lesions were not detected with US or MIBI but were visualized with the additional MRI, which indicated that the MRI contributed an 8.0% (2/25) improvement of sensitivity, compared from that of US. Fat-suppressed T2-weighted images were useful in the identification of parathyroid lesions, as these images helped to differentiate between the lesion and the adjacent tissue. CONCLUSION: Additional non-contrast 3-Tesla MRI was a useful adjunctive tool for localization of PHPT, which improved the sensitivity of the pre-operative localization of PHPT lesions. Fat-suppressed T2-weighted images contributed to their identification. LEVEL VI: Evidence from a single descriptive or qualitative study

Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group.

Funder: Radboud University Medical CenterThe European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications. Key Points • The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use. • Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters. • Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed

低リスク非浸潤性乳管癌のMRI上のバイオマーカーとしてのみかけの拡散係数 : パイロット研究

京都大学0048新制・課程博士博士(医学)甲第18128号医博第3848号新制||医||1001(附属図書館)30986京都大学大学院医学研究科医学専攻(主査)教授 福山 秀直, 教授 戸井 雅和, 教授 平岡 眞寛学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDGA

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology

Diffusion magnetic resonance imaging: What water tells us about biological tissues

Since its introduction in the mid-1980s, diffusion magnetic resonance imaging (MRI), which measures the random motion of water molecules in tissues, revealing their microarchitecture, has become a pillar of modern neuroimaging. Its main clinical domain has been the diagnosis of acute brain stroke and neurogical disorders, but it is also used in the body for the detection and management of cancer lesions. It can also produce stunning maps of white matter tracks in the brain, with the potential to aid in the understanding of some psychiatric disorders. However, in order to exploit fully the potential of this method, a deeper understanding of the mechanisms that govern the diffusion of water in tissues is needed

Correction: Diffusion Magnetic Resonance Imaging: What Water Tells Us about Biological Tissues.

[This corrects the article DOI: 10.1371/journal.pbio.1002203.]