What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging

Development and Preclinical Validation of a Cysteine Knottin Peptide Targeting Integrin alpha v beta 6 for Near-infrared Fluorescent-guided Surgery in Pancreatic Cancer

Purpose: Intraoperative near-infrared fluorescence (NIRF) imaging could help stratification for the proper primary treatment for patients with pancreatic ductal adenocarcinoma (PDAC), and achieve complete resection, as it allows visualization of cancer in real time. Integrin avb6, a target specific for PDAC, is present in >90% of patients, and is able to differentiate between pancreatitis and PDAC. A clinically translatable avb6-targeting NIRF agent was developed, based on a previously developed cysteine knottin peptide for PET imaging, R01-MG, and validated in preclinical mouse models. Experimental Design: The applicability of the agent was tested for cell and tissue binding characteristics using cell-based plate assays, subcutaneous, and orthotopic pancreatic models, and a transgenic mouse model of PDAC development (Pdx1-Cretg/þ; KRasLSL G12D/þ;Ink4a/Arf/). IRDye800CW was conjugated to R01-MG in a 1:1 ratio. R01-MG-IRDye800, was compared with a control peptide and IRDye800 alone. Results: In subcutaneous tumor models, a significantly higher tumor-to-background ratio (TBR) was seen in BxPC-3 tumors (2.5 0.1) compared with MiaPaCa-2 (1.2 0.1; P < 0.001), and to the control peptide (1.6 0.4; P < 0.005). In an orthotopic tumor model, tumor-specific uptake of R01-MG-IRDye800 was shown compared with IRDye800 alone (TBR 2.7 vs. 0.86). The fluorescent signal in tumors of transgenic mice was significantly higher, TBR of 3.6 0.94, compared with the normal pancreas of wild-type controls, TBR of 1.0 0.17 (P < 0.001). Conclusions: R01-MG-IRDye800 shows specific targeting to avb6, and holds promise as a diagnostic and therapeutic tool to recognize PDAC for fluorescence-guided surgery. This agent can help improve the stratification of patients for a potentially curative, margin-negative resection

Nondestructive Detection of Targeted Microbubbles Using Dual-Mode Data and Deep Learning for Real-Time Ultrasound Molecular Imaging

Ultrasound molecular imaging (UMI) is enabled by targeted microbubbles (MBs), which are highly reflective ultrasound contrast agents that bind to specific biomarkers. Distinguishing between adherent MBs and background signals can be challenging in vivo. The preferred preclinical technique is differential targeted enhancement (DTE), wherein a strong acoustic pulse is used to destroy MBs to verify their locations. However, DTE intrinsically cannot be used for real-time imaging and may cause undesirable bioeffects. In this work, we propose a simple 4-layer convolutional neural network to nondestructively detect adherent MB signatures. We investigated several types of input data to the network: anatomy-mode (fundamental frequency), contrastmode (pulse-inversion harmonic frequency), or both, i.e., dual-mode , using IQ channel signals, the channel sum, or the channel summagnitude. Training and evaluationwere performed on in vivo mouse tumor data and microvessel phantoms. The dual-mode channel signals yielded optimal performance, achieving a soft Dice coefficient of 0.45 and AUC of 0.91 in two test images. In a volumetric acquisition, the network best detected a breast cancer tumor, resulting in a generalized contrast-to-noise ratio (GCNR) of 0.93 and Kolmogorov-Smirnov statistic (KSS) of 0.86, outperforming both regular contrast mode imaging (GCNR = 0.76, KSS = 0.53) and DTE imaging (GCNR = 0.81, KSS = 0.62). Further development of the methodology is necessary to distinguish free from adherent MBs. These results demonstrate that neural networks can be trained to detect targeted MBs with DTE-like quality using nondestructive dual-mode data, and can be used to facilitate the safe and real-time translation of UMI to clinical applications

Loss of Rnf43 accelerates Kras-mediated neoplasia and remodels the tumor immune microenvironment in pancreatic adenocarcinoma

Background: RNF43 is an E3 ubiquitin ligase that is recurrently mutated in pancreatic ductal adenocarcinoma (PDAC) and precursor cystic neoplasms of the pancreas. The impact of RNF43 mutations on PDAC is poorly understood and autochthonous models have not been sufficiently characterized. In this study we describe a genetically engineered mouse model (GEMM) of PDAC with conditional expression of oncogenic Kras and deletion of the catalytic domain of Rnf43 in exocrine cells. Methods: We generated Ptf1a-Cre;LSL-KrasG12D;Rnf43flox/flox (KRC) and Ptf1a-Cre; LSL-KrasG12D (KC) mice and animal survival was assessed. KRC mice were sacrificed at 2 months, 4 months and at moribund status followed by analysis of pancreata by single cell RNA sequencing (scRNAseq). Comparative analyses between moribund KRC and a moribund Kras/Tp53 driven PDAC GEMM (KPC) was performed. Cell lines were isolated from KRC and KC tumors and interrogated by cytokine array analyses, ATAC-seq and in vitro drug assays. KRC GEMMs were also treated with an anti-CTLA4 neutralizing antibody with treatment response measured by magnetic response imaging. Results: We demonstrate that KRC mice display a marked increase in incidence of high-grade cystic lesions of the pancreas and PDAC compared to KC. Importantly, KRC mice have a significantly decreased survival compared to KC mice. By use of scRNAseq we demonstrated that KRC tumor progression is accompanied by a decrease in macrophages, as well as an increase in T and B lymphocytes with evidence of increased immune checkpoint molecule expression and affinity maturation, respectively. This was in stark contrast to the tumor immune microenvironment observed in the KPC PDAC GEMM. Furthermore, expression of the chemokine, CXCL5, was found to be specifically decreased in KRC cancer cells by means of epigenetic regulation and emerged as a putative candidate for mediating the unique KRC immune landscape. Conclusions: The KRC GEMM establishes RNF43 as a bona fide tumor suppressor gene in PDAC. This GEMM features a markedly different immune microenvironment compared to previously reported PDAC GEMMs and puts forth a rationale for an immunotherapy approach in this subset of PDAC cases