• Diagnostic performance
• Diffusion kurtosis imaging
• Diffusion weighted imaging
• Intravoxel incoherent motion
• Meta-analysis
• Prostate cancer

• Humans
• Prostatic Neoplasms/diagnostic imaging
• Male
• Sensitivity and Specificity
• Diffusion Magnetic Resonance Imaging/methods
• Motion

• Arterial spin labeled MRI
• Dynamic contrast-enhanced (DCE) MRI
• Intravoxel incoherent motion
• Multiparametric MRI
• Prostate cancer

• Male
• Humans
• Magnetic Resonance Imaging/methods
• Prostate/diagnostic imaging
• Diffusion Magnetic Resonance Imaging/methods
• Contrast Media
• Perfusion
• Perfusion Imaging
• Magnetic Resonance Spectroscopy

• PI-RADS
• diffusion kurtosis imaging
• intravoxel incoherent motion
• prostate cancer
• stretched exponential model

• artificial intelligence
• breast cancer
• medicinal imaging
• personalized medicine
• quantitative biomarkers
• radiomics

• Amide proton transfer-weighted
• Bone metastasis
• Intravoxel incoherent motion
• Predicting
• Prostate cancer

• abdomen
• diffusion weighted imaging
• intravoxel incoherent motion
• liver
• quantitative magnetic resonance imaging

• Diffusion Magnetic Resonance Imaging/methods
• Humans
• Liver/diagnostic imaging
• Motion
• Perfusion
• Reproducibility of Results

• R01 EB030497 NIBIB NIH HHS

The value of conventional magnetic resonance imaging in the differential diagnosis of thyroid nodules is limited; however, the value of multi-parameter diffusion-weighted imaging (DWI) in the quantitative evaluation of thyroid nodules has not been well determined.

To determine the utility of multi-parametric DWI including mono-exponential, bi-exponential, stretched exponential, and kurtosis models for the differentiation of thyroid lesions.

Seventy-nine patients (62 with benign and 17 with malignant nodules) underwent multi-b value diffusion-weighted imaging of the thyroid. Multiple DWI parameters were obtained for statistical analysis.

Good agreement was found for diffusion parameters of thyroid nodules. Malignant lesions displayed lower diffusion parameters including apparent diffusion coefficient (ADC), the true diffusion coefficient (D), the perfusion fraction (f), the distributed diffusion coefficient (DDC), the intravoxel water diffusion heterogeneity (α) and kurtosis model-derived ADC (Dapp), and higher apparent diffusional kurtosis (Kapp) than benign entities (all P < 0.01), except for the pseudodiffusion coefficient (D*) (P > 0.05). The area under the ROC curve (AUC) of the ADC_{(0 and 1000)} was not significantly different from that of the ADC_{(0 and 2000)}, ADC_{(0 to 2000)}, ADC_{(0 to 1000)}, D, DDC, Dapp and Kapp (all P > 0.05), but was significantly higher than the AUC of D*, f and α (all P < 0.05) for differentiating benign from malignant lesions.

Multiple DWI parameters including ADC, D, f, DDC, α, Dapp and Kapp could discriminate benign and malignant thyroid nodules. The metrics including D, DDC, Dapp and Kapp provide additional information with similar diagnostic performance of ADC, combination of these metrics may contribute to differentiate benign and malignant thyroid nodules. The ADC calculated with higher b values may not lead to improved diagnostic performance.

• Thyroid nodule
• Magnetic resonance imaging
• Diffusion-weighted imaging
• Quantitative study
• Sensitivity
• Specificity

The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination revealed that 137 patients had PCa and 13 patients had BPH. The pathological results were the gold standard and were compared with the MRI results of different sequences. Therefore, the accuracy of the MRI results was evaluated. The results showed that with the rise of Gaussian noise, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of all three algorithms gradually decreased, but the PSNR and SSIM of the RLRE algorithm were always higher than those of the RL and BM3D algorithms (P < 0.05). The sensitivity (97.08%), specificity (92.31%), accuracy (96.67%), and consistency (0.678) of the dynamic contrast enhancement (DCE) sequence were higher than those of the plain scan (86.13%, 69.23%, 84.67%, and 0.469, respectively). In conclusion, the RLRE algorithm could promote the resolution of MRI images and improve the display effect. DCE could better differentiate PCa from BPH, had great clinical application value, and was worthy of clinical promotion.

• DWI
• abdomen
• intravoxel incoherent motion
• liver
• quantitative biomarkers

• Abdomen
• Diffusion Magnetic Resonance Imaging
• Humans
• Liver/diagnostic imaging
• Motion
• Perfusion

• R01 EB030497 NIBIB NIH HHS

• Apparent diffusion coefficient
• Computer-assisted diagnosis
• Intra-voxel incoherent motion
• Machine learning
• Magnetic resonance imaging
• Prostate cancer

• Diffusion Magnetic Resonance Imaging/methods
• Humans
• Magnetic Resonance Imaging
• Male
• Motion
• Prostatic Neoplasms/diagnostic imaging
• ROC Curve

• aljalila foundation

• histogram analysis
• intravoxel incoherent motion
• kurtosis
• monoexponential
• prostate cancer

To explore the value of combining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters with apparent diffusion coefficient (ADC) values in the diagnosis of prostate cancer.

The clinical data of 146 patients with prostate lesions, including 87 patients with prostate cancer (PCa) and 59 with benign prostatic hyperplasia (BPH), were collected. After DCE-MRI and diffusion-weighted imaging (DWI) prostate scans, the magnitude of the DCE-MRI transfer constant (K^trans), rate constant (k_ep), the volume of the extravascular extracellular space (v_e), and the ADC between the groups were compared, and the correlations between the DCE-MRI parameters and Gleason scores were analyzed. The diagnostic efficacy of these quantitative parameters was assessed by the area under the receiver operating characteristic (ROC) curve.

The DCE-MRI parameters K^trans and k_ep were significantly greater in the PCa group than in the BPH group (p < 0.05). The ROC curve showed the area under the K^trans, k_ep, and ADC curves to be 0.665, 0.658, and 0.782, respectively. When all three quantitative indicators were combined, the area under the ROC curve was 0.904, with sensitivity and specificity rates of 83.6% and 93.7%, respectively. The Gleason scores were positively correlated with the K^trans, k_ep, and v_e (r = 0.39, 0.572, 0.30, respectively; p < 0.05) and negatively correlated with the ADC (r = –0.525; p < 0.05).

The DCE-MRI quantitative parameters K^trans and k_ep, as well as the ADC value, provided effective references for the differential diagnosis of PCa and BPH, as well as more precise and reliable quantitative parameters for grading the aggressiveness of PCa.

• ADC value
• Ktrans
• benign prostatic hyperplasia
• dynamic contrast-enhanced MRI
• kep
• prostate cancer
• quantitative DWI

Radiomics is an emerging translational field of medicine based on the extraction of high-dimensional data from radiological images, with the purpose to reach reliable models to be applied into clinical practice for the purposes of diagnosis, prognosis and evaluation of disease response to treatment. We aim to provide the basic information on radiomics to radiologists and clinicians who are focused on breast cancer care, encouraging cooperation with scientists to mine data for a better application in clinical practice. We investigate the workflow and clinical application of radiomics in breast cancer care, as well as the outlook and challenges based on recent studies. Currently, radiomics has the potential ability to distinguish between benign and malignant breast lesions, to predict breast cancer’s molecular subtypes, the response to neoadjuvant chemotherapy and the lymph node metastases. Even though radiomics has been used in tumor diagnosis and prognosis, it is still in the research phase and some challenges need to be faced to obtain a clinical translation. In this review, we discuss the current limitations and promises of radiomics for improvement in further research.

• artificial intelligence
• breast cancer
• medical physics
• oncology
• radiology
• radiomics
• radiotherapy

• Diffusion-weighted imaging
• Fractional-order calculus
• Prostate cancer
• differentiation diagnosis

• Calculi
• Diffusion Magnetic Resonance Imaging
• Humans
• Image Interpretation, Computer-Assisted
• Male
• Neoplasm Grading
• Prostate/diagnostic imaging
• Prostatic Neoplasms/diagnostic imaging

• agreement
• breast cancer
• diffusion-weighted imaging
• oncology
• radiology
• reproducibility

• Diffusion magnetic resonance imaging
• inflammation
• intravoxel incoherent motion
• ulcerative colitis

• prostate cancer
• multiparametric magnetic resonance imaging (mpmri)
• mri-targeted biopsy
• in-bore biopsy

• Biopsy
• Humans
• Image-Guided Biopsy
• Magnetic Resonance Imaging
• Male
• Multiparametric Magnetic Resonance Imaging
• Prostatic Neoplasms/diagnostic imaging

Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa.

Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities.

Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D^* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D^*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D^* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76).

Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.

• diagnostic performance
• gleason grade
• intravoxel incoherent motion diffusion-weighted imaging
• meta-analysis
• post-test probability
• prostate cancer

Objective: This study aimed to compare the potential of monoexponential model (MEM), intravoxel incoherent motion (IVIM) model, kurtosis model, and IVIM–kurtosis model in the diagnosis and aggressiveness assessment of prostate cancer (PCa).

Materials and Methods: Thirty-six patients were recruited. Diffusion-weighted images were acquired on a 3.0-T magnetic resonance imaging (MRI) system using 0 b values up to 2,000 s/mm² and analyzed using four models: MEM (ADC_MEM), IVIM (D_IVIM, D*_IVIM, f_IVIM), kurtosis (D_kurtosis, K_kurtosis), and IVIM–kurtosis (D_{IVIM−kurtosis}, D^*_{IVIM−kurtosis}, f_{IVIM−kurtosis}, D_{IVIM−kurtosis}) models. The values of these parameters were calculated and compared between PCa, benign prostatic hyperplasia (BPH), and prostatitis. Correlations between these parameters and the Gleason score (GS) of PCa were evaluated using the Pearson test.

Results: Forty-five lesions were studied, including 18 PCa, 12 prostatitis, and 15 BPH lesions. The ADC_MEM, D_IVIM, f_IVIM, D_kurtosis, and D_{IVIM−kurtosis} values were significantly lower and K_kurtosis and K_{IVIM−kurtosis} values were significantly higher in PCa compared with prostatitis and BPH. The area under the curve (AUC) of ADC_MEM showed significantly higher values than that of f_IVIM and K_{IVIM−kurtosis}, but no statistical differences were found between the other parameters. The D^*_{IVIM−kurtosis} value correlated negatively and f_{IVIM−kurtosis} and K_{IVIM−kurtosis} values correlated positively with the GS.

Conclusion: The MEM, IVIM, kurtosis, and IVIM–kurtosis models were all useful for the diagnosis of PCa, and the diagnostic efficacy seemed to be similar. The IVIM–kurtosis model may be superior to the MEM, IVIM, and kurtosis models in the grading of PCa.

• IVIM–kurtosis model
• benign prostatic hyperplasia
• intravoxel incoherent motion model
• kurtosis model
• monoexponential model
• prostate cancer
• prostatitis

• ct
• mri
• hepatocellular tumors
• hepatocellular adenoma
• focal nodular hyperplasia
• hepatocellular carcinoma

• Carcinoma, Hepatocellular/diagnostic imaging
• Diagnosis, Differential
• Focal Nodular Hyperplasia/diagnostic imaging
• Humans
• Liver Neoplasms/diagnostic imaging
• Magnetic Resonance Imaging

• Artificial intelligence
• Breast MRI
• Breast cancer
• Breast imaging
• Diffusion-weighted imaging
• Oncology
• Radiology
• Vacuum-assisted breast biopsy

• Breast Neoplasms/diagnosis
• Breast Neoplasms/diagnostic imaging
• Breast Neoplasms/genetics
• Breast Neoplasms/pathology
• Circulating Tumor DNA/analysis
• Female
• Genomics
• Humans
• Image-Guided Biopsy
• Liquid Biopsy
• Mammography
• Precision Medicine

• Diffusion
• Hypoxia
• Multiparametric magnetic resonance imaging
• Prostate cancer
• Support vector machines

• Gleason score
• Prostate cancer
• magnetic resonance imaging
• texture analysis

• Aged
• Humans
• Image Interpretation, Computer-Assisted/methods
• Magnetic Resonance Imaging/methods
• Male
• Middle Aged
• Neoplasm Grading
• Prostate/diagnostic imaging
• Prostatic Neoplasms/diagnostic imaging
• ROC Curve

• MRI
• diffusion imaging
• prostate
• prostate cancer
• relative enhanced diffusivity

• cancer imaging
• prostate

• Algorithms
• Diffusion Magnetic Resonance Imaging/methods
• Humans
• Image Interpretation, Computer-Assisted/methods
• Male
• Models, Theoretical
• Neoplasm Grading
• Prostatic Neoplasms/diagnostic imaging
• Prostatic Neoplasms/pathology

• Gleason score
• Prostate cancer
• diffusion-weighted imaging
• radical prostatectomy
• whole-tumor histogram

• Diffusion kurtosis imaging
• Diffusion-weighted imaging
• Gleason score
• Intravoxel incoherent motion
• Magnetic resonance imaging
• Prostate cancer

Our purpose was to combine intravoxel incoherent motion diffusion-weighted MR imaging (IVIM-DWI) and magnetic resonance spectroscopy (MRS) to differentiate osteoporotic fractures from osteolytic metastatic vertebral compression fractures (VCFs).

A total of 70 patients with VCFs were included and divided into two groups, according to their causes of fractures based on pathological findings or clinical follow-up. All patients underwent conventional sagittal T1WI, T2WI, STIR, IVIM-DWI, and single-voxel MRS. The diffusion coefficient (D), pseudo diffusion (D*), and perfusion fraction (f) parameters from IVIM-DWI and the lipid water ratio (LWR) and fat fraction (FF) parameters from MRS were obtained and compared among groups. Furthermore, the diagnostic performance of MRS, IVIM-DWI, and IVIM-DWI combined with MRS for differentiation between osteoporotic and osteolytic metastatic VCFs was assessed by using receiver operating characteristic (ROC) curve analysis.

Compared with the osteoporotic group, the metastatic group had significantly lower values for f, D, and FF, but higher D* (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of MRS, IVIM-DWI, and IVIM-DWI combined with MRS were 0.73, 0.88, and 0.94, respectively. Among these, the IVIM-DWI combined with MRS showed the highest sensitivity, specificity, and accuracy, which are 90.63% (29/32), 97.37 % (37/38), and 94.29% (66/70), respectively.

IVIM-DWI combined with MRS can be more accurate and efficient for differentiation between osteoporotic and osteolytic metastatic VCFs than single MRS or IVIM-DWI.

• Fractures
• IVIM-DWI
• MRS
• Osteoporotic
• Vertebra

• Diffusion kurtosis
• Diffusion-weighted imaging
• Gleason score
• Magnetic resonance imaging
• Prostate cancer

• Aged
• Diffusion Magnetic Resonance Imaging/methods
• Diffusion Tensor Imaging/methods
• Feasibility Studies
• Humans
• Image Interpretation, Computer-Assisted/methods
• Image-Guided Biopsy
• Male
• Middle Aged
• Neoplasm Grading
• Observer Variation
• Prostatic Hyperplasia/pathology
• Prostatic Neoplasms/pathology
• ROC Curve

• Breast cancer
• DCE-MRI
• Diffusion-weighted imaging
• Molecular subtype
• Prognostic factor

One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

• Artificial intelligence
• Deep learning
• Machine learning
• Neural networks (computer)
• Radiology

• Cirrhotic livers
• Hepatocellular carcinoma (HCC)
• LI-RADS
• Radiology

• Prostate gland
• embolization
• interventional radiology
• review

• Prostate cancer (PCa)
• electroporation
• focal therapy (FT)
• interventional radiology
• partial ablation

• Diffusion weighted imaging
• Dynamic contrast enhanced MRI
• Magnetic resonance spectroscopy
• Multiparametric MRI
• Prostate cancer

• Digital Rectal Examination/methods
• Humans
• Image-Guided Biopsy/methods
• Magnetic Resonance Imaging/methods
• Male
• Neoplasm Grading
• Prostate/diagnostic imaging
• Prostate/pathology
• Prostatic Neoplasms/diagnostic imaging
• Reproducibility of Results

• DCE-MRI
• DW-MRI
• Warthin tumors
• extended Tofts–Kermode model
• intravoxel incoherent motionparotid tumors
• pleomorphic adenoma

• Diagnosis, Differential
• Diffusion Magnetic Resonance Imaging/methods
• Humans
• Liver/diagnostic imaging
• Liver Diseases/diagnostic imaging
• Liver Neoplasms/diagnostic imaging
• Motion
• Water/chemistry

• bladder cancer
• diffusion-weighted imaging
• intravoxel incoherent motion
• invasiveness