Negative multiparametric magnetic resonance imaging for prostate cancer: what's next?

Multiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csPCa). Nevertheless, the clinical utility of negative mpMRI (nMRI) is less clearMultiparametric magnetic resonance imaging (mpMRI) of the prostate has excellent sensitivity in detecting clinically significant prostate cancer (csPCa). Nevertheless, the clinical utility of negative mpMRI (nMRI) is less clear. OBJECTIVE: To assess outcomes of men with nMRI and clinical follow-up after 7 yr of activity at a reference center. DESIGN, SETTING, AND PARTICIPANTS: All mpMRI performed from January 2010 to May 2015 were reviewed. We selected all patients with nMRI and divided them in group A (naïve patients) and group B (previous negative biopsy). All patients without a diagnosis of PCa had a minimum follow-up of 2 yr and at least two consecutive nMRI. Patients with positive mpMRI were also identified to assess their biopsy outcomes. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A Kaplan-Meier analysis was performed to assess both any-grade PCa and csPCa diagnosis-free survival probabilities. Univariable and multivariable Cox regression models were fitted to identify predictors of csPCa diagnosis. RESULTS AND LIMITATIONS: We identified 1545 men with nMRI, and 1255 of them satisfied the inclusion criteria; 659 belonged to group A and 596 to group B. Any-grade PCa and csPCa diagnosis-free survival probabilities after 2 yr of follow-up were 94% and 95%, respectively, in group A; in group B, they were 96%. After 48 mo of follow-up, any-grade PCa diagnosis-free survival probability was 84% in group A and 96% in group B (log rank p<0.001). Diagnosis-free survival probability for csPCa was unchanged after 48 mo of follow-up. On multivariable Cox regression analysis, increasing age (p=0.005) was an independent predictor of lower csPCa diagnosis probability, while increasing prostate-specific antigen (PSA) and PSA density (<0.001) independently predicted higher csPCa diagnosis probability. The prevalence of and positive predictive value for csPCa were 31.6% and 45.5%, respectively. Limitations include limited follow-up and the inability to calculate true csPCa prevalence in the study population. CONCLUSIONS: mpMRI is highly reliable to exclude csPCa. Nevertheless, systematic biopsy should be recommended even after nMRI, especially in younger patients with high or raising PSA levels

Accuracy of multiparametric magnetic resonance imaging to detect significant prostate cancer and index lesion location

Background: Multiparametric magnetic resonance imaging (mpMRI) of the prostate appears to improve prostate cancer detection, but studies comparing mpMRI to histopathology at the time of radical prostatectomy (RP) are lacking. This retrospective study determined the accuracy of mpMRI predicting Gleason score and index lesion location at the time of RP, the current gold standard for diagnosis. Methods: Between April 2013 and April 2016, a database of all men aged more than 40 years who underwent RP after positive transrectal ultrasound biopsy by an experienced urological surgeon was collated at a single regional centre. This was cross‐referenced with a database of all men who had mpMRIs performed at a single centre and reported according to Prostate Imaging Reporting and Data System (PI‐RADS version 1) during this period to generate a sample size of 64 men. A Spearman\u27s rho test was utilized to calculate correlation. Results: Median age of patients was 64 years, the median prostate‐specific antigen at RP was 6.22 ng/mL. mpMRI was positive (≥PI‐RADS 3) in 85.9% of patients who underwent RP. More than 92% of participants had Gleason ≥7 disease. A positive relationship between mpMRI prostate PI‐RADS score and RP cancer volume was demonstrated. An anatomical location correlation calculated in octants was found to be 89.1% accurate. Conclusion: mpMRI accurately detects prostate cancer location and severity when compared with gold standard histopathology at the time of RP. It thus has an important role in planning for future prostate biopsy and cancer treatment

The development of the EULAR–OMERACT rheumatoid arthritis MRI reference image atlas

Based on a previously developed rheumatoid arthritis MRI scoring system (OMERACT 2002 RAMRIS), the development team agreed which joints, MRI features, MRI sequences, and image planes would best illustrate the scoring system in an atlas. After collecting representative examples for all grades for each abnormality (synovitis, bone oedema, and bone erosion), the team met for a three day period to review the images and choose by consensus the most illustrative set for each feature, site, and grade. A predefined subset of images (for example, for erosion—all coronal slices through the bone) was extracted. These images were then re-read by the group at a different time point to confirm the scores originally assigned. Finally, all selected images were photographed and formatted by one centre and distributed to all readers for final approval

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients