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Hao Li^1,2, Nikita Sushentsev², Dimitri Kessler², Shaohang Li³, Kang-Lung Lee², Andrew Nicholas Priest^2,4, Ferdia A Gallagher², and Tristan Barrett ^2
1The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China, ²Department of Radiology, University of Cambridge, Cambridge, United Kingdom, ³Shanghai Jiao Tong University University, Shanghai, China, ⁴Department of Radiology, CUH NHS Foundation Trust, Cambridge, United Kingdom

Keywords: Prostate, Cancer

The clinical application of luminal water imaging (LWI) has been restricted by its long acquisition time. We present a high acceleration method based on T₂ mapping using echo merging plus k-t undersampling with reduced flip angles (TEMPURA), which enables a fast acquisition in 2.8 min as well as a high-resolution acquisition in 5.4 min. Their performance was evaluated on 13 patients with biopsy-proven prostate cancer. Compared with the standard acquisition in 8.3 min, both the fast and high-resolution methods showed a high correlation in LWI measurements and consistent diagnostic performance in detecting malignant lesions.

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Peter Qiao¹, Donald Brennan¹, Sree Harsha Tirumani^2,3, Yong Chen^2,3, Mark Griswold^2,3, and Leonardo Kayat Bittencourt^2,3
1School of Medicine, Case Western Reserve University, Cleveland, OH, United States, ²Department of Radiology, Case Western Reserve University, Cleveland, OH, United States, ³Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States

Keywords: Prostate, Cancer

In this pilot study, we assessed the background signal change (BSC) of the prostate peripheral zone based on quantitative T1 and T2 measurement obtained using Magnetic Resonance Fingerprinting (MRF) and correlated with a previously-developed qualitative BSC score. Our results demonstrate the importance of prostate BSC toward reducing uncertainty in prostate MRI. Compared to the qualitative BSC score, the MRF-derived measurements provide a more reproducible and objective assessment of background heterogeneity of the prostate. BSC scoring has great potential in risk-stratifying patients with incidentally-detected clinically significant prostate cancer and yielding better biopsy decision-making for patients with negative MRI.

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Keywords: Prostate, Quantitative Imaging, Multiparametric MRI

1.        In a retrospective review of 61 men by four readers, two inexperienced-readers had higher accuracy using multiparametric MRI+Hybrid-Multidimensional MRI (mpMRI+HM-MRI)=82%,81%  versus Multiparametric MRI=77%, 71% (p=.006,<.001) and higher specificity=89%,88% versus 84%,75% (p=.009, p<.001) on a per-sextant analysis.

2.        Multiparametric MRI+Hybrid-Multidimensional MRI increased trainee specificity=46% versus Multiparametric MRI=7% (p<.001) on a per-patient analysis.

3.        Multiparametric MRI+Hybrid-Multidimensional MRI increased interreader Kappa agreement=0.36 versus Multiparametric MRI (mpMRI) Kappa=0.17 (p=0.009) in diagnosing clinically significant prostate cancer (CS PCa).

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Guanyong He¹, Liying Han¹, Ronghua Yan¹, Hao Li¹, Zhigang Wu², Qingping Gu², and Guanxun Cheng^1
1Peking University Shenzhen Hospital, Shenzhen, China, ²Clinical & Technical Support, Philips Healthcare (Shenzhen) Ltd, Shenzhen, China

Keywords: Prostate, CEST & MT, APT

Diagnosis of prostate cancer (PCa) remains a challenge since PCa often occurs simultaneously with benign prostatic hyperplasia and has similar nodule appearance on T2-weighted imaging and diffusion weighted imaging. Amide proton transfer weighted (APTw) imaging is an endogenous biomarker to detect proteins and peptides in tissues non-invasively. Results of this study indicate the APTw ratio (rAPTw) values of PCa were significantly higher than those of prostate benign nodules. Therefore, rAPTw value is potentially a promising and valuable non-invasive biomarker in differentiating PCa from benign nodules.

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Alonso Garcia-Ruiz¹, Francesco Grussu¹, Snigdha Sen², Chen Jin², Alex Freeman³, Aiman Haider³, Shonit Punwani⁴, Daniel C. Alexander², Raquel Perez-Lopez¹, and Eleftheria Panagiotaki^2
1Radiomics Group, Vall d'Hebron Institute of Oncology, Barcelona, Spain, ²Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom, ³Department of Pathology, University College London Hospitals NHS Foundation Trust, London, United Kingdom, ⁴Centre for Medical Imaging, University College London, London, United Kingdom

Keywords: Prostate, Cancer, Histology

Diffusion MRI has shown promising results for characterizing prostate cancer. However, diagnostic clinical apparent diffusion coefficient (ADC) has limited specificity and interpretability. Towards addressing these limitations, we aim to unravel clinical ADC into microstructural components using histology.

Histology from two prostatectomies with prostate cancer (Gleason 3+4, 4+3) were analysed in benign, inflammation and cancer regions. Cell and tissue properties were used to decouple clinical ADC into intracellular, extracellular-extravascular ADCs.

Results revealed significantly low intracellular ADC in cancer. Low ADC was also found for inflammation, which could explain ADC’s low specificity, demonstrating the added value of histology data for clinical ADC.

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Keywords: Prostate, Prostate, Diffusion

We showcase, for the first time, a direct/model-free measurement of luminal diffusivity in prostate, where ADC contrast no longer contains the cellular contribution, and only describes features of the glandular lumen, which prominently shrink with increasing Gleason score. We show that with a 4m30s protocol, the luminal volume fraction, cellular and luminal diffusivities can be obtained with high accuracy/precision at 1.5x1.5x3.0 mm³, suggesting that this approach can feasibly replace the current state-of-the-art, ~6 minute, prostate diffusion acquisition.

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Ante Zhu¹, Matthew Tarasek¹, Yihe Hua¹, Eric Fiveland¹, Desmond T.B. Yeo¹, Stephan E. Maier², Yousef Mazaheri³, Filip Szczepankiewicz⁴, Carl-Fredrik Westin², Clare Tempany², Oguz Akin³, and Thomas K.F. Foo^1
1GE Research, Niskayuna, NY, United States, ²Brigham and Women's Hospital, Boston, MA, United States, ³Memorial Sloan Kettering Cancer Center, New York, NY, United States, ⁴Lund University, Lund, Sweden

Keywords: Prostate, Diffusion/other diffusion imaging techniques, Tumor, Microstructure

Diffusion MRI has been shown to yield imaging biomarkers for prostate cancer identification and grading. However, the image quality remains poor due to the limited gradient performance of clinical whole-body MRI systems. We assessed the technical feasibility of human prostate MRI in a 42-cm inner-diameter gradient coil operating at maximum gradient amplitude of 200 mT/m and slew rate of 500 T/m/s. No sensation of peripheral nerve stimulation was reported. Significantly reduced image distortion and higher image signal-to-noise ratios were achieved in diffusion MRI due to shorter echo times and shorter echo spacings, compared to diffusion MRI at whole-body MRI systems.

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Kaibao Sun¹, Guangyu Dan^1,2, Muge Karaman^1,2, Qingfei Luo¹, and Xiaohong Joe Zhou^1,2,3
1Center for MR Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States, ³Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Keywords: Prostate, Quantitative Imaging

Prostate cancer remains one of the leading causes of cancer-related deaths in men. Reduced luminal water fraction has been observed in prostate cancer using diffusion MRI, but the image acquisition takes a long time. We herein introduce a multi-echo DWI sequence that is capable of quantifying luminal water fraction with a substantially improved time efficiency. The sequence incorporated multiple readout echo-trains into diffusion-weighted EPI, together with a 2D RF excitation pulse to reduce the FOV, thereby enabling multiple TEs in one shot. With this sequence, we have evaluated the volume fractions of lumen and non-lumen tissues in the human prostate.

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Barbara Daria Wichtmann¹, Niklas Westhoff², Cleo-Aron Weis³, Ralph Strecker⁴, Thorsten Feiweier⁵, Steffen Albert^6,7, Moritz Wolter⁸, Frank Zöllner^6,7, Bettina Baeßler⁹, Aapo Nummenmaa¹⁰, Qiuyun Fan^10,11, Susie Huang^10,12, and Ulrike Attenberger^1
1Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany, ²Department of Urology and Urosurgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ³Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany, Heidelberg, Germany, ⁴EMEA Scientific Partnerships, Siemens Healthcare GmbH, Erlangen, Germany, ⁵MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany, ⁶Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ⁷Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ⁸High Performance Computing & Analytics Lab, University Bonn, Bonn, Germany, ⁹Institute of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany, ¹⁰A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ¹¹Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China, ¹²Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Keywords: Prostate, Cancer

Linear Multi-scale Modeling (LMM) is an advanced diffusion-weighted imaging(DWI) technique that uses multi-shell, multi-diffusion-time DWI data to estimate tissue microstructure parameters, including volume fractions of restricted and hindered water compartments over a range of length scales and orientation distribution information. Here,we apply the LMM framework to characterize prostate cancer(PCa) lesions and correlate our results with histology. Within the histopathologically proven cancerous lesions we observed a significantly increased fraction of restricted diffusion, particularly within the 2μm and 7μm sized water compartments. LMM may enable the development of distinct diffusion microstructural signatures of PCa to facilitate diagnosis of clinically significant PCa lesions.  

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Christopher C Conlin¹, Roshan Karunamuni², Troy S Hussain², Allison Y Zhong³, Karoline Kallis², Deondre D Do^2,4, Asona J Lui², Garnier Mani³, Courtney Ollison⁵, Mariluz Rojo Domingo⁴, Ahmed Shabaik⁶, Christopher J Kane⁷, Aditya Bagrodia⁷, Rana R McKay^7,8, Joshua M Kuperman¹, Rebecca Rakow-Penner¹, Michael E Hahn¹, Anders M Dale^1,9,10, and Tyler M Seibert^1,2,4
1Department of Radiology, University of California San Diego, La Jolla, CA, United States, ²Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, United States, ³School of Medicine, University of California San Diego, La Jolla, CA, United States, ⁴Department of Bioengineering, University of California San Diego, La Jolla, CA, United States, ⁵Department of Biology, San Diego State University, San Diego, CA, United States, ⁶Department of Pathology, University of California San Diego, La Jolla, CA, United States, ⁷Department of Urology, University of California San Diego, La Jolla, CA, United States, ⁸Department of Medicine, University of California San Diego, La Jolla, CA, United States, ⁹Department of Neurosciences, University of California San Diego, La Jolla, CA, United States, ¹⁰Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, United States

Keywords: Prostate, Cancer, T2-weighted MRI; Background prostate; Benign-appearing prostate

In this study, we examined whether patients with clinically significant prostate cancer (csPCa) have abnormal T₂-weighted signal in prostate tissue outside of index lesions identified on MRI—i.e., in the background prostate (BP). In two independent patient cohorts, normalized T₂-weighted signal was systematically lower in the BP of subjects with csPCa compared to those without. Reduced T₂-weighted BP signal indicated the presence of csPCa with accuracy comparable to lesion-based measurements. Consideration of T₂-weighted signal in the whole prostate improved patient-level detection of csPCa over DWI alone, suggesting that it provides complementary diagnostic value.

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Angeliki Stamatelatou¹, ‪Carlo Giuseppe Bertinetto‬², Jeroen J Jansen², Geert J Postma², Kirsten Margrete Selnæs³, Tone Frost Bathen³, Arend Heerschap¹, and Tom WJ Scheenen^1
1Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands, ²Department of Analytical Chemistry & Chemometrics, Radboud University, Nijmegen, Netherlands, ³Department of Circulation and Medical Imaging, Norwegian University of Technology and Science, Trondheim, Norway

Keywords: Prostate, Spectroscopy, multi-center, cancer, aggressiveness

Location and aggressiveness of cancer lesions in the prostate were assessed with a Multivariate Curve Resolution (MCR) approach. This data-driven method was trained on 3D ¹H MRSI data from 63 patients from 6 institutions to extract common spectroscopic components without needing prior knowledge. MCR identified 5 components related to known prostate metabolites, residual lipid and water signals. The relative amounts of these components robustly separated cancer from benign tissue in a test set of 43 other subjects from 8 centers, and showed a strong correlation with cancer aggressiveness. This approach facilitates automatic analysis of prostate MRSI, avoiding subjective human intervention.

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Bendik Skarre Abrahamsen¹, Trond Velde Bogsrud^2,3, Eivor Hernes⁴, Torgrim Tandstad^5,6, Sverre Langørgen⁷, Miguel Castillejo², Kirsten Margrete Selnæs⁷, Ingerid Skjei Knudtsen¹, Thomas Morten Ingmar Keil⁷, Håkon Johansen⁷, Tone Frost Bathen^1,7, and Mattijs Elschot^1,7
1Derpartment of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway, ²PET Imaging Centre, University Hospital of North Norway, Tromsø, Norway, ³PET-Centre, Aarhus University Hospital, Aarhus, Denmark, ⁴Department of nuclear medicine, Oslo University Hospital, Oslo, Norway, ⁵The Cancer Clinic, St. Olavs Hospital, Trondheim, Norway, ⁶Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway, ⁷Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim, Norway

Keywords: Prostate, PET/MR

For patients with suspected prostate cancer recurrence, we examined the added value of ¹⁸F-PSMA-1007 PET to MR and CT imaging, and whether choice of PET modality, i.e. PET/MR or PET/CT, had an impact on clinical management. An intention-to-treat analysis was performed using findings from MR and CT alone, MR and CT with PET from PET/CT, and MR and CT with PET from PET/MR. Addition of ¹⁸F-PSMA PET changed clinical management in over 40% of patients. Differences in clinical management between PET/CT and PET/MR were smaller than adding any PET modality to MR and CT.

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Keywords: Prostate, Cancer

The first-in-human hyperpolarized [1-¹³C]pyruvate+[¹³C,¹⁵N₂]urea dual-agent MRI demonstrated the safety and feasibility of simultaneous characterization of prostate cancer metabolism and blood flow as a two-minute addition to the standard ¹H-multiparametric MRI. Metabolism-perfusion mismatch (i.e. elevated pyruvate-lactate conversion and decreased urea perfusion) in subregions of the high-grade prostate tumor was in agreement with the histopathological and immunochemical markers that reflected lethal phenotypes and their associated hypoxic tumor microenvironment. Correlative analysis of urea and Gadolinium-derived pharmacokinetic parameters found low correlation between the two, which indicates that HP ¹³C urea’s unique contrast mechanism offers independent information inaccessible through conventional ¹H DCE-MRI.

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Haoxin Zheng^1,2, Miao Qi^2,3, Alex Ling Yu Hung^1,2, Kai Zhao², Steven Raman², and Kyunghyun Sung^2
1Computer Science, University of California, Los Angeles, Los Angeles, CA, United States, ²Radiological Science, University of California, Los Angeles, Los Angeles, CA, United States, ³Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China

Keywords: Prostate, Cancer, Machine Learning

The study aimed to build a deep-learning-based prostate cancer (PCa) detection model integrating the anatomical priors related to PCa’s zonal appearance difference and asymmetric patterns of PCa. A total of 220 patients with 246 whole-mount histopathology (WMHP) confirmed clinically significant prostate cancer (csPCa), and 432 patients with no indication of lesions on multi-parametric MRI (mpMRI) were included in the study. A proposed 3D Siamese nnUNet with self-designed Zonal Loss was implemented, and results were evaluated using 5-fold cross-validation. The proposed model that is aware of PCa-related anatomical information performed the best on both lesion-level detection and patient-level classification experiments.

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Wenting Jiang¹, Yingying Lin¹, Varut Vardhanabhuti¹, and Peng Cao^1
1Department of Diagnostic Radiology, the University of Hong Kong, Hong Kong, Hong Kong

Keywords: Multimodal, Cancer

Prostate Imaging Reporting and Data System (PI-RADS) on multiparametric MRI (mpMRI) provides fundamental MRI interpretation guidelines but suffers from inter-reader variability. Deep learning networks show great promise in automatic lesion segmentation and classification, which help to ease the burden on radiologists and reduce inter-reader variability. In this study, we proposed a novel multi-branch network, MiniSegCaps, for prostate cancer segmentation and PI-RADS classification on mpMRI, and a graphical user interface (GUI) integrated into the clinical workflow for diagnosis reports generation. Our model achieved the best performance in prostate cancer segmentation and PIRADS classification compared with state-of-the-art methods.