Dan Wu¹, Kewen Jiang², Yi-Cheng Hsu³, Yi Sun³, Yi Zhang¹, and Yudong Zhang^2
1Biomedical Engineering, Zhejiang University, Hangzhou, China, ²Radiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China, ³MR Collaboration, Siemens Healthcare Ltd., Shanghai, China

Diffusion-time-dependent diffusion MRI (dMRI) has shown potentials in characterizing tumor microstructure. This study investigated the diagnostic value of time-dependent dMRI to differentiate prostate cancer pathological grades at 3T. Oscillating and pulsed gradient dMRI was performed in 55 patients, and the data were fitted with the IMPULSED model to estimate cell diameter, intracellular fraction, cellularity, and diffusivities. We found f_in and cellularity increased as Gleason score increased, while diameter and D_ex decreased. Cellularity achieved the highest diagnostic accuracy with an accuracy of 0.87 and area-under-the curve of 0.96, and the combination of cellularity and ADC further improved the accuracy to 0.91.

Christopher C Conlin¹, Christine H Feng², Leonardino A Digma², Ana E Rodriguez-Soto¹, Joshua M Kuperman¹, Dominic Holland³, Rebecca Rakow-Penner¹, Tyler M Seibert^1,2,4, Anders M Dale^1,3,5, and Michael E Hahn^1
1Department of Radiology, UC San Diego School of Medicine, La Jolla, CA, United States, ²Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, CA, United States, ³Department of Neurosciences, UC San Diego School of Medicine, La Jolla, CA, United States, ⁴Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, CA, United States, ⁵Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, CA, United States

Multicompartmental diffusion modeling shows promise for overcoming the limitations of conventional DWI methods and may help to improve the clinical evaluation of prostate-cancer bone involvement. In this study, we applied multicompartmental modeling to develop an empirical tissue classifier for identifying bone lesions in whole-body DWI. The proposed classifier relates signal contributions from model compartments with lower diffusion coefficients to the likelihood that such contributions are from cancerous tissue. This approach proved effective for detecting metastatic lesions in whole-body DWI data, considerably outperforming a classifier based on conventional ADC values.

Scott D. Swanson¹, Thomas L. Chenevert¹, Prasad R. Shankar¹, Ted Lynch², and Dariya I. Malyarenko^1
1Department of Radiology, University of Michigan, Ann Arbor, MI, United States, ²CIRS, Norfolk, VA, United States

PI-RADS guidelines use quantitative mono-exponential apparent diffusion coefficient (ADC) for risk stratification of small potentially aggressive prostate cancer (PCa). Inherently, multi-exponential diffusion in complex tumor microenvironment, limits accuracy of ADC-based thresholds and clinical utility of DWI acquisition protocols. Biomimetic phantoms that provide ground-truth diffusion parameters help establish accuracy of advanced multi-b DWI protocols and parametric diffusion models in prostate tumors. This work demonstrates development of stable phantoms based on lipid nanoparticles that provide diffusion parameter ranges typical of PCa.

Xiuzhong Yao¹, Yunfei Zhang², Mengsu Zeng¹, and Yongming Dai^2
1Zhongshan Hospital affiliated to Fudan University, Shanghai, China, ²Central Research Institute, United Imaging Healthcare, Shanghai, China

The determination of micro-vascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is significant for prognostic prediction. However, histologic assessment is the gold standard for evaluating the MVI, which is accompanied with many disadvantages including invasiveness, potential sampling bias and so forth. DWI MRI has shown clinical potential for assessing the MVI. However, some recently-developed non-gaussian DWI techniques have hardly been applied for predicting the MVI. This research, hence, sought to evaluate the MVI with a Fractional Order Calculus DWI Model (FROC-DWI). The results indicated that FROC-derived parameters were valuable for accurately identifying the MVI in patients with HCC.

Francesco Grussu¹, Ignasi Barba², Kinga Bernatowicz¹, Irene Casanova-Salas³, Alba Escriche Villarroya⁴, Natalia Castro³, Emanuela Greco⁴, Juan Francisco Corral^5,6, Marta Vidorreta⁷, Manuel Escobar Amores^5,6, Núria Roson^5,6, Xavier Merino^5,6, Richard Mast^5,6, Nahúm Calvo‐Malvar^5,8, Joaquin Mateo³, Paolo Nuciforo⁹, María Abad⁴, Josep R. Garcia-Bennett⁸, and Raquel Perez-Lopez^1,6
1Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ²NMR Lab, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ³Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ⁴Cellular Plasticity and Cancer Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain, ⁵IDI (Institut de Diagnòstic per la Imatge), Catalonia, Spain, ⁶Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain, ⁷Siemens Healthineers, Madrid, Spain, ⁸Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain, ⁹Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain

Liver cancer is a leading cause of cancer-related death, and new quantitative MRI (qMRI) techniques are needed to inform treatment selection and monitor disease progression. We propose a new technique, Diffusion-Relaxation Hepatic Imaging via Generalisable Assessment of DiffusiOn Simulations (DR-HIGADOS), with the aim of improving sensitivity and biological specificity of liver qMRI. DR-HIGADOS is a diffusion-relaxation method that uses information from Monte Carlo simulations to map parameters of an extended intra-voxel incoherent motion model to microstructural indices (e.g. cell size, cellularity). DR-HIGADOS is demonstrated on multi-vendor clinical data, and its histological correlates are investigated on preclinical high-field scans.

Carlos Bilreiro^1,2, Francisca F Fernandes¹, Rui V Simões¹, Mireia Castillo-Martin^1,3, Andrada Ianus¹, Cristina Chavarrias¹, Celso Matos^1,2, and Noam Shemesh^1
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ²Department of Radiology, Champalimaud Clinical Centre, Lisbon, Portugal, ³Department of Pathology, Champalimaud Clinical Centre, Lisbon, Portugal

As current therapeutic options for pancreatic cancer are mostly ineffective, developing diagnostic tools for early detection of precursor lesions (mainly pancreatic intraepithelial neoplasia – PanIN) could change the course of this disease. Here, we investigated Diffusion-MRI (dMRI) contrasts for this purpose, using transgenic mouse models. First, we performed ex vivo dMRI Microscopy at ultrahigh field with histological validation, defining the most sensitive contrasts. Then, we performed in vivo abdominal imaging, demonstrating their applicability in the non-invasive study of PanIN and pancreatic cancer. These findings provide new tools for researching PanIN and hold promise for a future translation to clinical practice.

Muge Karaman^1,2, Guangyu Dan^1,2, Lingdao Sha³, Tingqi Shi¹, Weiguo Li^4,5, Dan Schonfeld^2,3,6, Tibor Valyi-Nagy⁷, and X. Joe Zhou^1,2,8
1Center for Magnetic Resonance Research, University of Illinois at Chicago, Chicago, IL, United States, ²Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States, ³Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, IL, United States, ⁴Research Resources Center, University of Illinois at Chicago, Chicago, IL, United States, ⁵Department of Radiology, Northwestern University, Chicago, IL, United States, ⁶Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States, ⁷Department of Pathology, University of Illinois at Chicago, Chicago, IL, United States, ⁸Departments of Radiology and Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States

Studies on tissue structural heterogeneity have been the focus of a growing number of non-Gaussian diffusion models, such as the continuous-time random-walk (CTRW) model. Establishing a correlation between the voxel-level CTRW parameters and the microscopic tissue heterogeneity from gold-standard histology, however, has been challenging due to the lack of quantitative measure of histopathological heterogeneity and different spatial scales. We establish a one-to-one correspondence between imaging-based tissue heterogeneity revealed by CTRW parameters and histology-based tissue structural heterogeneity predicted by a machine-learning classifier to address an overarching question: “Can we look inside a voxel noninvasively through the lenses of the CTRW model?”.

Sungheon Gene Kim¹, Mehran Baboli¹, Justin Fogarty², Steven H. Baete², Joseph Kim³, Paulina Galavis³, Moses Tam³, Kenneth Hu³, and Elcin Zan^2
1Radiology, Weill Cornell Medical College, New York, NY, United States, ²Radiology, New York University School of Medicine, New York, NY, United States, ³Radiation Oncology, New York University School of Medicine, New York, NY, United States

In this study, we evaluated diffusion and kurtosis time-dependence for HPV-positive oropharyngeal squamous cell carcinoma before and during chemo-radiation treatment over a wide range for longer diffusion times (200-700 ms). The patients with less than 40% nodal volume shrinkage had significantly higher diffusivity at pretreatment and lower kurtosis at week4 than the patients with more than 40% nodal volume shrinkage. The water exchange times were 68-80 ms without a significant difference between the groups.  This study demonstrates the feasibility of using diffusion MRI at relatively long diffusion times to predict and evaluate the response to chemo-radiation therapy.

Jessica M Winfield^1,2, Jennifer C Wakefield^1,2, James D Brenton^3,4,5, Khalid AbdulJabbar^6,7, Antonella Savio⁸, Susan Freeman⁹, Erika Pace^1,2, Kerryn Lutchman-Singh¹⁰, Katherine M Vroobel⁸, Yinyin Yuan^6,7, Susana Banerjee¹¹, Nuria Porta¹², Shan E Ahmed Raza^6,7,13, and Nandita M deSouza^1,2
1MRI Unit, Royal Marsden NHS Foundation Trust, London, United Kingdom, ²Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom, ³Cancer Research UK Cambridge Institute, Cambridge, United Kingdom, ⁴Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, ⁵Department of Oncology, University of Cambridge, Cambridge, United Kingdom, ⁶Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom, ⁷Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom, ⁸Department of Pathology, Royal Marsden NHS Foundation Trust, London, United Kingdom, ⁹Department of Radiology, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom, ¹⁰Swansea Gynaecological Oncology Centre, Swansea Bay University Health Board, Singleton Hospital, Swansea, United Kingdom, ¹¹Gynaecology Unit, Royal Marsden NHS Foundation Trust, Sutton, United Kingdom, ¹²Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, United Kingdom, ¹³Department of Computer Science, University of Warwick, Warwick, United Kingdom

In Epithelial Ovarian Cancer, ADC_median demonstrates good repeatability at both primary and metastatic sites. After neoadjuvant chemotherapy, a differential increase in ADC_median at disease sites is seen despite similar tumor shrinkage. The negative correlation between ADC_median and tumor cell fraction after neoadjuvant chemotherapy, and positive correlation between change in ADC_median and percentage necrosis, are driven primarily by changes in the peritoneal lesions.

Nan Meng¹, Zhun Huang², Ting Fang¹, Pengyang Feng², Xuejia Wang³, Dongming Han³, Kaiyu Wang⁴, and Meiyun Wang*^1
1Department of Radiology, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China, ²Department of Radiology, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China, ³Department of MRI, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China, ⁴GE Healthcare, MR Research China, BeiJing, China

Our results showed that both Amide proton transfer-weighted imaging (APTWI) and Diffusion kurtosis imaging (DKI) were effective in the assessment of endometrial carcinoma in terms of clinical type, histological grade, subtype, and Ki-67 index.