Cross-Modality Image Registration using a Training-Time Privileged Third Modality

— In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusionweighted scans with high b-value (DWI_{high−b}). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI_{b=0}) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWIb=0, to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI_{high−b} and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI_{high−b} and T2w in this challenging application

Lessons from the first ecancer symposium on angiogenesis in gastric cancer

In March 2015, ecancer hosted a symposium at the European Institute of Oncology in Milan, Italy on the topic of angiogenesis in gastric cancer. During this meeting, leaders in the field focused on the latest research on the topic of angiogenesis in gastric cancer, delivering lectures combined with interactive question and answer (Q & A) sessions and a roundtable discussion with the meeting's chairs. Topics covered included biomarkers, imaging, and the current state of antiangiogenic drugs in gastric cancer. This report will provide an understanding of the relevance of angiogenesis in gastric cancer research, and clinical experiences from diverse perspectives

Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI

This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79–0.98; CV = 1–7%; ICC = 92–98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3

The ReIMAGINE multimodal warehouse: using artificial intelligence for accurate risk stratification of prostate cancer

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community

Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition

Purpose: VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. // Materials and Methods: Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016–October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. // Results: The mean lesion ADC for b0 b1500 had the lowest wSD in most zones (0.18–0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90–10.24x10-4 mm2/s). // Conclusion: ADC maps generated from b0 b1500 have better repeatability in normal TZ, normal PZ, and index lesions

Picture Perfect: The Status of Image Quality in Prostate MRI

Magnetic resonance imaging is the gold standard imaging modality for the diagnosis of prostate cancer (PCa). Image quality is a fundamental prerequisite for the ability to detect clinically significant disease. In this critical review, we separate the issue of image quality into quality improvement and quality assessment. Beginning with the evolution of technical recommendations for scan acquisition, we investigate the role of patient preparation, scanner factors, and more advanced sequences, including those featuring Artificial Intelligence (AI), in determining image quality. As means of quality appraisal, the published literature on scoring systems (including the Prostate Imaging Quality score), is evaluated. Finally, the application of AI and teaching courses as ways to facilitate quality assessment are discussed, encouraging the implementation of future image quality initiatives along the PCa diagnostic and monitoring pathway. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3

Prospective, multisite, international comparison of \u3csup\u3e18\u3c/sup\u3eF-fluoromethylcholine PET/CT, multiparametric MRI, and \u3csup\u3e68\u3c/sup\u3eGa-HBED-CC PSMA-11 PET/CT in men with high-risk features and biochemical failure after radical prostatectomy: Clinical performance and patient outcomes

A significant proportion of men with rising prostate-specific antigen (PSA) levels after radical prostatectomy (RP) fail prostate fossa (PF) salvage radiation treatment (SRT). This study was done to assess the ability of F-fluoromethylcholine ( F-FCH) PET/CT (hereafter referred to as F-FCH), Ga-HBED-CC PSMA-11 PET/CT (hereafter referred to as PSMA), and pelvic multiparametric MRI (hereafter referred to as pelvic MRI) to identify men who will best benefit from SRT. Methods: Prospective, multisite imaging studies were carried out in men who had rising PSA levels after RP, high-risk features, and negative/equivocal conventional imaging results and who were being considered for SRT. F-FCH (91/91), pelvic MRI (88/91), and PSMA (31/91) (Australia) were all performed within 2 wk. Imaging was interpreted by experienced local/central interpreters who were masked with regard to other imaging results, with consensus being reached for discordant interpretations. Expected management was documented before and after imaging, and data about all treatments and PSA levels were collected for 3 y. The treatment response to SRT was defined as a reduction in PSA levels of .50% without androgen deprivation therapy. Results: The median Gleason score, PSA level at imaging, and PSA doubling time were 8, 0.42 (interquartile range, 0.29–0.93) ng/mL, and 5.0 (interquartile range, 3.3–7.6) months. Recurrent prostate cancer was detected in 28% (25/88) by pelvic MRI, 32% (29/91) by F-FCH, and 42% (13/31) by PSMA. This recurrence was found within the PF in 21.5% (19/88), 13% (12/91), and 19% (6/31) and at sites outside the PF (extra-PF) in 8% (7/88), 19% (17/91), and 32% (10/31) by MRI, F-FCH, and PSMA, respectively (P, 0.004). A total of 94% (16/17) of extra-PF sites on F-FCH were within the pelvic MRI field. Intra-pelvic extra-PF disease was detected in 90% (9/10) by PSMA and in 31% (5/16) by MRI. F-FCH changed management in 46% (42/91), and MRI changed management in 24% (21/88). PSMA provided additional management changes over F-FCH in 23% (7/31). The treatment response to SRT was higher in men with negative results or disease confined to the PF than in men with extra-PF disease ( F-FCH 73% [32/44] versus 33% [3/9] [P, 0.02], pelvic MRI 70% [32/46] versus 50% [2/4] [P was not significant], and PSMA 88% [7/ 8] versus 14% [1/7] [P, 0.005]). Men with negative imaging results (MRI, F-FCH, or PSMA) had high (78%) SRT response rates. Conclusion: F-FCH and PSMA had high detection rates for extra-PF disease in men with negative/equivocal conventional imaging results and rising PSA levels after RP. These findings affected management and treatment responses, suggesting an important role for PET in triaging men being considered for curative SRT. 18 18 18 68 18 18 18 18 18 18 18 18 1

Differentiating false positive lesions from clinically significant cancer and normal prostate tissue using VERDICT MRI and other diffusion models

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular–extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases