Role of multiparametric MRI in detection of prostatic lesions; of evaluation contrast enhanced MRI, diffusion weighted imaging and MR spectroscopy in malignant and benign prostatic lesions

Background: Prostate cancer is the most commonly diagnosed cancer in males and one of the leading causes of cancer-related death in men. Pretreatment assessment of prostate cancer is divided into detection, localization, and staging; accurate assessment is a prerequisite for optimal clinical management and therapy selection. The purpose of the study is to determine the diagnostic accuracy of multiparametric MRI for prostatic cancer detection using T2 weighted MR imaging, diffusion weighted imaging (DWI) and contrast enhanced MRI. To determine the use of MR spectroscopy in prostatic lesions.Methods: It is a prospective single institutional study done on 29 patients with prostate lesions and elevated PSA level. Axial, coronal and sagittal images were obtained using T1WI, T2WI and STIR sequences. Advanced sequences like Diffusion weighted images, Spectroscopy and post gadolinium T1WI were taken after the basic MRI images.Results: Study was done in 29 patients, age was ranging between 51years to 90 years, mean age is 70.7 years. On multiparametric MRI findings 45% were detected malignant lesions and 55% patients detected benign lesions. On biopsy correlation 42% of these cases turned out to be malignant and 58% as benign lesions. Detection of malignancy by T2WI imaging alone given sensitivity of 80.1% and specificity of 85.4%.By DWI alone sensitivity was 85.7% and specificity was 89.4%,on MRS sensitivity is 90.6% and specificity was 91.1%. Combined (MRI+DWI+MRS) gave sensitivity of 92.3% and specificity of 94.4% for detection of malignant prostatic lesion. Positive predictive value is 90% and negative predictive value was 88%.Conclusions: The best characterization of prostatic cancer in individual patients will most likely result from a multiparametric exam. Recent advances include additional functional and physiologic MR imaging techniques (diffusion weighted imaging, MR spectroscopy, and perfusion imaging), which allow extension of the obtainable information beyond anatomic assessment. Multiparametric MR imaging provides the highest accuracy in diagnosis and staging of prostate cancer

Prostate cancer assessment using MR elastography of fresh prostatectomy specimens at 9.4 T

Purpose: Despite its success in the assessment of prostate cancer (PCa), in vivo multiparametric MRI has limitations such as interobserver variability and low specificity. Several MRI methods, among them MR elastography, are currently being discussed as candidates for supplementing conventional multiparametric MRI. This study aims to investigate the detection of PCa in fresh ex vivo human prostatectomy specimens using MR elastography. Methods: Fourteen fresh prostate specimens from men with clinically significant PCa without formalin fixation or prior radiation therapy were examined by MR elastography at 500 Hz immediately after radical prostatectomy in a 9.4T preclinical scanner. Specimens were divided into 12 segments for both calculation of storage modulus (G ' in kilopascals) and pathology (Gleason score) as reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve were calculated to assess PCa detection. Results: The mean G ' and SD were as follows: all segments, 8.74 ± 5.26 kPa; healthy segments, 5.44 ± 4.40 kPa; and cancerous segments, 10.84 ± 4.65 kPa. The difference between healthy and cancerous segments was significant with P ≤ .001. Diagnostic performance assessed with the Youden index was as follows: sensitivity, 69%; specificity, 79%; area under the curve, 0.81; and cutoff, 10.67 kPa. Conclusion: Our results suggest that prostate MR elastography has the potential to improve diagnostic performance of multiparametric MRI, especially regarding its 2 major limitations: interobserver variability and low specificity. Particularly the high value for specificity in PCa detection is a stimulating result and encourages further investigation of this method

Artificial intelligence in cancer imaging: Clinical challenges and applications

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered data with nuanced decision making. Cancer offers a unique context for medical decisions given not only its variegated forms with evolution of disease but also the need to take into account the individual condition of patients, their ability to receive treatment, and their responses to treatment. Challenges remain in the accurate detection, characterization, and monitoring of cancers despite improved technologies. Radiographic assessment of disease most commonly relies upon visual evaluations, the interpretations of which may be augmented by advanced computational analyses. In particular, artificial intelligence (AI) promises to make great strides in the qualitative interpretation of cancer imaging by expert clinicians, including volumetric delineation of tumors over time, extrapolation of the tumor genotype and biological course from its radiographic phenotype, prediction of clinical outcome, and assessment of the impact of disease and treatment on adjacent organs. AI may automate processes in the initial interpretation of images and shift the clinical workflow of radiographic detection, management decisions on whether or not to administer an intervention, and subsequent observation to a yet to be envisioned paradigm. Here, the authors review the current state of AI as applied to medical imaging of cancer and describe advances in 4 tumor types (lung, brain, breast, and prostate) to illustrate how common clinical problems are being addressed. Although most studies evaluating AI applications in oncology to date have not been vigorously validated for reproducibility and generalizability, the results do highlight increasingly concerted efforts in pushing AI technology to clinical use and to impact future directions in cancer care

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature

Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naive patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images

The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging

Purpose: This study has focused on investigating the relationship between the exponential apparent diffusion coefficient (exp-ADC), selective apparent diffusion coefficient (sel-ADC) values, the ADC ratio (ADCr), and prostate cancer aggressiveness with transrectal ultrasound-guided prostate biopsy in patients with prostate cancer. Material and methods: All patients underwent a multiparametric magnetic resonance imaging (mpMRI) including tri-planar T2-weighted (T2W), dynamic contrast-enhanced (DCE), diffusion-weighted sequences using a 3.0-Tesla MR scanner (Skyra, Siemens Medical Systems, Germany) with a dedicated 18-channel body coil and a spine coil underneath the pelvis, with the patient in the supine position. Exp-ADC, sel-ADC, and ADCr of defined lesions were evaluated using region-of-interest-based measurements. Exp-ADC, sel-ADC, and ADCr were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy. Results: Patients were divided into 2 groups. Group I is Gleason score ≥ 3 + 4, group II is Gleason score = 6. Sel-ADC and exp-ADC were statistically significant between 2 groups (0.014 and 0.012, respectively). However, the ADCr difference between nonclinical significant prostate cancer from clinically significant prostate cancer was not significant (p = 0.09). Conclusions: This study is the first to evaluate exp-ADC and sel-ADC values of prostate carcinoma with ADCr. One limitation of this study might be the limited number of patients. Exp-ADC and sel-ADC values in prostate MRI imaging improved the specificity, accuracy, and area under the curve (AUC) for detecting clinically relevant prostate carcinoma. Adding exp-ADC and sel-ADC values to ADCr can be used to increase the diagnostic accuracy of DWI

What Is the Negative Predictive Value of Multiparametric Magnetic Resonance Imaging in Excluding Prostate Cancer at Biopsy? A Systematic Review and Meta-analysis from the European Association of Urology Prostate Cancer Guidelines Panel

Context: It remains unclear whether patients with a suspicion of prostate cancer (PCa) and negative multiparametric magnetic resonance imaging (mpMRI) can safely obviate prostate biopsy. Objective: To systematically review the literature assessing the negative predictive value (NPV) of mpMRI in patients with a suspicion of PCa. Evidence acquisition: The Embase, Medline, and Cochrane databases were searched up to February 2016. Studies reporting prebiopsy mpMRI results using transrectal or transperineal biopsy as a reference standard were included. We further selected for meta-analysis studies with at least 10-core biopsies as the reference standard, mpMRI comprising at least T2-weighted and diffusion-weighted imaging, positive mpMRI defined as a Prostate Imaging Reporting Data System/Likert score of ≥3/5 or ≥4/5, and results reported at patient level for the detection of overall PCa or clinically significant PCa (csPCa) defined as Gleason ≥7 cancer. Evidence synthesis: A total of 48 studies (9613 patients) were eligible for inclusion. At patient level, the median prevalence was 50.4% (interquartile range [IQR], 36.4–57.7%) for overall cancer and 32.9% (IQR, 28.1–37.2%) for csPCa. The median mpMRI NPV was 82.4% (IQR, 69.0–92.4%) for overall cancer and 88.1% (IQR, 85.7–92.3) for csPCa. NPV significantly decreased when cancer prevalence increased, for overall cancer (r = –0.64, p < 0.0001) and csPCa (r = –0.75, p = 0.032). Eight studies fulfilled the inclusion criteria for meta-analysis. Seven reported results for overall PCa. When the overall PCa prevalence increased from 30% to 60%, the combined NPV estimates decreased from 88% (95% confidence interval [95% CI], 77–99%) to 67% (95% CI, 56–79%) for a cut-off score of 3/5. Only one study selected for meta-analysis reported results for Gleason ≥7 cancers, with a positive biopsy rate of 29.3%. The corresponding NPV for a cut-off score of ≥3/5 was 87.9%. Conclusions: The NPV of mpMRI varied greatly depending on study design, cancer prevalence, and definitions of positive mpMRI and csPCa. As cancer prevalence was highly variable among series, risk stratification of patients should be the initial step before considering prebiopsy mpMRI and defining those in whom biopsy may be omitted when the mpMRI is negative. Patient summary This systematic review examined if multiparametric magnetic resonance imaging (MRI) scan can be used to reliably predict the absence of prostate cancer in patients suspected of having prostate cancer, thereby avoiding a prostate biopsy. The results suggest that whilst it is a promising tool, it is not accurate enough to replace prostate biopsy in such patients, mainly because its accuracy is variable and influenced by the prostate cancer risk. However, its performance can be enhanced if there were more accurate ways of determining the risk of having prostate cancer. When such tools are available, it should be possible to use an MRI scan to avoid biopsy in patients at a low risk of prostate cancer

The future direction of imaging in prostate cancer: MRI with or without contrast injection

Background: Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with prostate cancer (PCa) suspicion. The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. Objective: To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. Materials and methods: Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. Results: Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies which compared the non-contrast and contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low quality mpMRI for the general population, might represent the main shortage to overcome. Discussion: Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic and bpMRI, and has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. Conclusion: Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader and paired validation studies are needed to provided evidence supporting its role in the clinical practice

Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

Radiomics and prostate MRI: Current role and future applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer