Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution

Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma

BACKGROUND

 Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric).

METHOD

 This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality.

RESULTS AND CONCLUSION

 Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers.

KEY POINTS

· Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas..

CITATION FORMAT

· Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.

Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions

PURPOSE

This study aimed to assess repeatability after repositioning (inter-scan), intra-rater, inter-rater and inter-sequence variability of mean apparent diffusion coefficient (ADC) measurements in MRI-detected prostate lesions.

METHOD

Forty-three patients with suspicion for prostate cancer were included and received a clinical prostate bi-/multiparametric MRI examination with repeat scans of the T2-weighted and two DWI-weighted sequences (ssEPI and rsEPI). Two raters (R1 and R2) performed single-slice, 2D regions of interest (2D-ROIs) and 3D-segmentation-ROIs (3D-ROIs). Mean bias, corresponding limits of agreement (LoA), mean absolute difference, within-subject coefficient of variation (CoV) and repeatability/reproducibility coefficient (RC/RDC) were calculated. Bradley & Blackwood test was used for variance comparison. Linear mixed models (LMM) were used to account for multiple lesions per patient.

RESULTS

Inter-scan repeatability, intra-rater and inter-sequence reproducibility analysis of ADC showed no significant bias. 3D-ROIs demonstrated significantly less variability than 2D-ROIs (p < 0.01). Inter-rater comparison demonstrated small significant systematic bias of 57 × 10-6 mm2/s for 3D-ROIs (p < 0.001). Intra-rater RC, with the lowest variation, was 145 and 189 × 10-6 mm2/s for 3D- and 2D-ROIs, respectively. For 3D-ROIs of ssEPI, RCs and RDCs were 190-198 × 10-6 mm2/s for inter-scan, inter-rater and inter-sequence variation. No significant differences were found for inter-scan, inter-rater and inter-sequence variability.

CONCLUSIONS

In a single-scanner setting, single-slice ADC measurements showed considerable variation, which may be lowered using 3D-ROIs. For 3D-ROIs, we propose a cut-off of ∼ 200 × 10-6 mm2/s for differences introduced by repositioning, rater or sequence effects. The results suggest that follow-up measurements should be possible by different raters or sequences.

Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

PURPOSE

The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting.

RESULTS

Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings.

CONCLUSION

Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.

Glymphatic system in the thalamus, secondary degeneration area was severely impaired at 2nd week after transient occlusion of the middle cerebral artery in rats

Background and objectives

The glymphatic system is a recently discovered cerebrospinal fluid transport system and little is known about its dynamic changes after stroke. This study aimed to dynamically observe the structural and functional changes of the impaired glymphatic system in the thalamus after ischemic stroke by pathology and MRI.

Materials and methods

Ischemic stroke was induced by the middle cerebral artery occlusion (MCAO) model. A total of 20 Sprague-Dawley rats were randomly assigned into four groups: sham, MCAO 1 week, MCAO 2 week, and MCAO 2 month. All rats successively underwent neurological examination, dynamic contrast-enhanced MRI (DCE-MRI), and immunofluorescence staining. Immunofluorescence staining of glial fibrillary acidic protein (GFAP), aquaporin-4 (AQP4), ionized calcium-binding adaptor molecule 1 (Iba1), and beta-amyloid precursor protein (APP) were done in thalamus ventroposterior nucleus.

Results

The astrocyte and microglial activation and the APP deposition in the MCAO 2 week group were the highest (P < 0.05 for all). The AQP4 polarization rates of the MCAO 2 week and 2 month groups were the lowest (P < 0.05 for all). Although there was no correlation between histological changes and MRI metrics in all four groups (P > 0.05 for all), the tendency of the APP deposition was nearly consistent with the one of the contrast agent retention in DCE-MRI.

Conclusion

The glymphatic system in the thalamus was severely impaired at 2nd week after MCAO, and may be revealed by DCE-MRI. This study may provide a relevant theoretical basis for making a thorough inquiry of the mechanism of brain injury after stroke and clinical treatment of ischemic stroke and help readers appreciate the importance of DCE-MRI.

Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration

OBJECTIVES

The aim of this study was to estimate the prospective utility of a previously retrospectively validated convolutional neural network (CNN) for prostate cancer (PC) detection on prostate magnetic resonance imaging (MRI).

MATERIALS AND METHODS

The biparametric (T2-weighted and diffusion-weighted) portion of clinical multiparametric prostate MRI from consecutive men included between November 2019 and September 2020 was fully automatically and individually analyzed by a CNN briefly after image acquisition (pseudoprospective design). Radiology residents performed 2 research Prostate Imaging Reporting and Data System (PI-RADS) assessments of the multiparametric dataset independent from clinical reporting (paraclinical design) before and after review of the CNN results and completed a survey. Presence of clinically significant PC was determined by the presence of an International Society of Urological Pathology grade 2 or higher PC on combined targeted and extended systematic transperineal MRI/transrectal ultrasound fusion biopsy. Sensitivities and specificities on a patient and prostate sextant basis were compared using the McNemar test and compared with the receiver operating characteristic (ROC) curve of CNN. Survey results were summarized as absolute counts and percentages.

RESULTS

A total of 201 men were included. The CNN achieved an ROC area under the curve of 0.77 on a patient basis. Using PI-RADS ≥3-emulating probability threshold (c3), CNN had a patient-based sensitivity of 81.8% and specificity of 54.8%, not statistically different from the current clinical routine PI-RADS ≥4 assessment at 90.9% and 54.8%, respectively ( P = 0.30/ P = 1.0). In general, residents achieved similar sensitivity and specificity before and after CNN review. On a prostate sextant basis, clinical assessment possessed the highest ROC area under the curve of 0.82, higher than CNN (AUC = 0.76, P = 0.21) and significantly higher than resident performance before and after CNN review (AUC = 0.76 / 0.76, P ≤ 0.03). The resident survey indicated CNN to be helpful and clinically useful.

CONCLUSIONS

Pseudoprospective paraclinical integration of fully automated CNN-based detection of suspicious lesions on prostate multiparametric MRI was demonstrated and showed good acceptance among residents, whereas no significant improvement in resident performance was found. General CNN performance was preserved despite an observed shift in CNN calibration, identifying the requirement for continuous quality control and recalibration.

Estimation of Contrast Agent Concentration in DCE-MRI Using 2 Flip Angles

PURPOSE

The aim of this study was to investigate the feasibility of using 2 flip angles (FAs) with an ultrashort echo time during dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for estimation of plasma gadolinium (Gd) concentration without using a precontrast longitudinal relaxation time T1 (T10) measurement.

METHODS

T1-weighted DCE-MRI experiments were carried out with C57BL/6J mice using the scan protocol with 2 FAs over 3 sequential segments during 1 scan. The data with 2 FAs were used to estimate T10 (T1T) during conversion of a time-intensity curve to the time-concentration curve. Three dosages of gadolinium-based contrast agent were used to achieve a wide range of variability in Gd concentrations when measured at 10 minutes postinjection: 0.05 mmol/kg (n = 6), 0.1 mmol/kg (n = 11), and 0.15 mmol/kg (n = 7). For comparison, the signal-to-concentration conversion was also conducted using the T10 measured from the precontrast scan (T1M) as well as a constant T10 (2.1 seconds) from the literature (T1C). The Gd concentrations ([Gd]) estimated using DCE-MRI data for the time of retro-orbital blood collection ([Gd]T1T, [Gd]T1M, and [Gd]T1C, respectively) were compared against the [Gd] of the blood samples measured by inductively coupled plasma mass spectrometry ([Gd]MS). In addition, contrast kinetic model analysis was conducted on mice with GL261 brain tumors (n = 5) using the 3 different methods for T10.

RESULTS

T1T strongly correlated with T1M (r = 0.81). [Gd]T1M and [Gd]T1T were significantly different from [Gd]T1C. [Gd]T1M and [Gd]T1T were in good agreement with [Gd]MS with strong correlations (mean percentage error ± standard deviation) of r = 0.70 (16% ± 56%) and r = 0.85 (15% ± 44%), respectively. In contrast, [Gd]T1C had a weak correlation of r = 0.52 with larger errors of 33% ± 24%. The contrast kinetic model parameters of GL261 brain tumors using T1T were not significantly different from those using T1M.

CONCLUSIONS

This study substantiates the feasibility of using the 2-FA approach during DCE-MRI scan to estimate [Gd] in the plasma without using an extra scan to perform precontrast T1 measurements.