Harmonic Motion Imaging of Pancreatic Tumor Stiffness Indicates Disease State and Treatment Response

Mechanisms of breast cancer metastasis: the role of extracellular matrix

The components of the extracellular matrix (ECM) are dynamic, and they mediate mechanical signals that modulate cellular behaviors. Disruption of the ECM can induce the migration and invasion of cancer cells via specific signaling pathways and cytokines. Metastasis is a leading cause of high mortality in malignancies, and early intervention can improve survival rates. However, breast cancer is frequently diagnosed subsequent to metastasis, resulting in poor prognosis and distant metastasis poses substantial hurdles in therapy. In breast cancer, there is notable tissue remodeling of ECM proteins, with several identified as essential components for metastasis. Moreover, specific ECM molecules, receptors, enzymes, and various signaling pathways play crucial roles in breast cancer metastasis, drug treatment, and resistance. The in-depth consideration of these elements could provide potential therapeutic targets to enhance the survival rates and quality of life for breast cancer patients. This review explores the mechanisms by which alterations in the ECM contribute to breast cancer metastasis and discusses current clinical applications targeting ECM in breast cancer treatment, offering valuable perspectives for future ECM-based therapies.

Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis

To address the need for a simple model to predict ≥ F2 fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, a study utilized data from 791 biopsy-proven MASLD patients from the NASH Clinical Research Network and Jinan University First Affiliated Hospital. The data were divided into training and internal testing sets through randomized stratified sampling. A multivariable logistic regression model using key categorical variables was developed to identify ≥ F2 fibrosis. External validation was performed using data from the FLINT trial and multiple centers in China. The DA-GAG score, incorporating diabetes, age, GGT, aspartate aminotransferase/ platelet ratio, and globulin/ total protein ratio, demonstrated superior performance in distinguishing ≥ F2 fibrosis with an area under the receiver operating characteristic curve of 0.79 in training and over 0.80 in testing datasets. The DA-GAG score efficiently identifies MASLD patients with ≥ F2 fibrosis, significantly reducing the medical burden.

A novel DNA repair protein, N-Myc downstream regulated gene 1 (NDRG1), links stromal tumour microenvironment to chemoresistance

In pancreatic ductal adenocarcinoma cancer (PDAC) drug resistance is a severe clinical problem and patients relapse within a few months after receiving the standard-of-care chemotherapy. One contributing factor to treatment resistance is the desmoplastic nature of PDAC; the tumours are surrounded by thick layers of stroma composing up to 90% of the tumour mass. This stroma, which is mostly comprised of extracellular matrix (ECM) proteins, is secreted by cancer-associated fibroblasts (CAFs) residing in the tumour microenvironment. However, the mechanistic basis by which the tumour stroma directly contributes to chemoresistance remains unclear. Here, we show that CAF-secreted ECM proteins induce chemoresistance by blunting chemotherapy-induced DNA damage. Mechanistically, we identify N-myc downstream regulated gene 1 (NDRG1) as a key protein required for stroma-induced chemoresistance that responds to signals from the ECM and adhesion receptors. We further show that NDRG1 is a novel DNA repair protein that physically interacts with replication forks, maintains DNA replication and functions to resolve stalled forks caused by chemotherapy. More specifically, NDRG1 reduces R-loops, RNA-DNA hybrids that are known to cause genomic instability. R-loops occur during replication-transcription conflicts in S-phase and after chemotherapy treatments, thus posing a major threat to normal replication fork homeostasis. We identify NDRG1 as highly expressed in PDAC tumours, and its high expression correlates with chemoresistance and poor disease-specific survival. Importantly, knock-out of NDRG1 or inhibition of its phosphorylation restores chemotherapy-induced DNA damage and resensitizes tumour cells to treatment. In conclusion, our data reveal an unexpected role for CAF-secreted ECM proteins in enhancing DNA repair via NDRG1, a novel DNA repair protein, directly linking tumour stroma to replication fork homeostasis and R-loop biology, with important therapeutic implications for restoring DNA damage response pathways in pancreatic cancer.

Summary paragraph

Drug resistance is a severe clinical problem in stroma-rich tumours, such as pancreatic ductal adenocarcinoma (PDAC), and patients often relapse within a few months on chemotherapy 1-9 . The stroma, comprised of extracellular matrix (ECM) proteins, is secreted by cancer-associated fibroblasts (CAFs) residing in the tumour microenvironment 10-13 . Prior work show that ECM proteins provide survival benefits to cancer cells 14,15 . However, the precise role of CAF-secreted ECM in resistance to DNA damaging chemotherapies remains poorly understood. Here, we link ECM proteins to chemoresistance by enhanced DNA damage repair (DDR). Mechanistically, we identify N-myc downstream-regulated gene 1 (NDRG1) as a key effector downstream of ECM and the integrin-Src-SGK1-signalling axis that mediates enhanced DDR. We show that NDRG1 loss, mutation of conserved His194, or inhibition of NDRG1 phosphorylation by SGK1 lead to replication fork stalling, increased R-loops, and higher transcription-replication conflicts, resulting in genomic instability and sensitivity to chemotherapies. Our analysis of PDAC patient cohorts 16 found that high NDRG1 expression correlates with chemoresistance and poor patient survival. In conclusion, we uncover an unexpected role for CAF-secreted ECM proteins in promoting therapeutic resistance by enhancing DDR and establish NDRG1 as a novel DNA repair protein directly linking tumour stroma to DDR.

Amplitude-Modulation Frequency Optimization for Enhancing Harmonic Motion Imaging Performance of Breast Tumors in the Clinic

OBJECTIVE

Elastography images tissue mechanical responses and infers the underlying properties to aid diagnosis and treatment response monitoring. The estimation of absolute or relative tumor properties may vary with dimensions even when the mechanical properties remain constant. Harmonic motion imaging (HMI) uses amplitude-modulated (AM) focused ultrasound to interrogate the targeted tissue's viscoelastic properties. In this study, effects of AM frequencies on HMI were investigated in terms of inclusion relative stiffness and size estimation.

METHODS

AM frequencies from 200 to 600 Hz in steps of 100 Hz were considered using a 5.3-kPa phantom with cylindrical inclusions (Young's modulus: 22, 31, 44, 56 kPa, and diameter: 4.8, 8.1, 13.6, 19.8 mm) to optimize the performance of HMI in characterizing tumors with the same mechanical properties and of different dimensions.

RESULTS

Consistent displacement ratios (DRs) (17.5% variation) of the inclusion to background were obtained with 200-Hz AM for breast-tumor-mimicking inclusions albeit a suboptimal inclusion size estimation obtained. 400-Hz was otherwise used for small and low-contrast inclusions (4.8 mm, 22 or 31 kPa). A linear relationship (R2 = 0.9043) was found between the inverse DR at these frequencies and the Young's modulus ratio. 400 Hz obtained the most accurate inclusion size estimation with an overall estimation error on the lateral dimension of 0.5 mm. In vivo imaging of breast cancer patients (n = 5) was performed at 200 or 400 Hz.

CONCLUSION

The results presented herein indicate that the HMI AM frequency could be optimized adaptively in cases of different applications, i.e., at 200 or 400 Hz, depending on whether aimed for consistent DR measurement for tumor response assessment or tumor margin delineation for surgical planning. HMI may thus be capable of predicting the pathologic endpoint of tumors in response to neoadjuvant chemotherapy (NACT) as early as 3 weeks into treatment.

Targeting Pancreatic Cancer Cell Stemness by Blocking Fibronectin-Binding Integrins on Cancer-Associated Fibroblasts

SIGNIFICANCE

Simultaneous targeting of two integrins that function as receptors for FN, a protumor ECM protein, can prevent fibroblasts from supporting the malignant behavior of pancreatic cancer cells.

Addressing Biological Questions with Preclinical Cancer Imaging

The broad application of noninvasive imaging has transformed preclinical cancer research, providing a powerful means to measure dynamic processes in living animals. While imaging technologies are routinely used to monitor tumor growth in model systems, their greatest potential lies in their ability to answer fundamental biological questions. Here we present the broad range of potential imaging applications according to the needs of a cancer biologist with a focus on some of the common biological processes that can be used to visualize and measure. Topics include imaging metastasis; biophysical properties such as perfusion, diffusion, oxygenation, and stiffness; imaging the immune system and tumor microenvironment; and imaging tumor metabolism. We also discuss the general ability of each approach and the level of training needed to both acquire and analyze images. The overall goal is to provide a practical guide for cancer biologists interested in answering biological questions with preclinical imaging technologies.

A mathematical model for pancreatic cancer during intraepithelial neoplasia

Cancer is the result of complex interactions of intrinsic and extrinsic cell processes, which promote sustained proliferation, resistance to apoptosis, reprogramming and reorganization. The evolution of any type of cancer emerges from the role of the microenvironmental conditions and their impact of some molecular complexes on certain signalling pathways. The understanding of the early onset of cancer requires a multiscale analysis of the cellular microenvironment. In this paper, we analyse a qualitative multiscale model of pancreatic adenocarcinoma by modelling the cellular microenvironment through elastic cell interactions and their intercellular communication mechanisms, such as growth factors and cytokines. We focus on the low-grade dysplasia (PanIN 1) and moderate dysplasia (PanIN 2) stages of pancreatic adenocarcinoma. To this end, we propose a gene-regulatory network associated with the processes of proliferation and apoptosis of pancreatic cells and its kinetics in terms of delayed differential equations to mimic cell development. Likewise, we couple the cell cycle with the spatial distribution of cells and the transport of growth factors to show that the adenocarcinoma evolution is triggered by inflammatory processes. We show that the oncogene RAS may be an important target for developing anti-inflammatory strategies that limit the emergence of more aggressive adenocarcinomas.

Pancreatic Schwann cell reprogramming supports cancer-associated neuronal remodeling

The peripheral nervous system is a key regulator of cancer progression. In pancreatic ductal adenocarcinoma (PDAC), the sympathetic branch of the autonomic nervous system inhibits cancer development. This inhibition is associated with extensive sympathetic nerve sprouting in early pancreatic cancer precursor lesions. However, the underlying mechanisms behind this process remain unclear. This study aimed to investigate the roles of pancreatic Schwann cells in the structural plasticity of sympathetic neurons. We examined the changes in the number and distribution of Schwann cells in a transgenic mouse model of PDAC and in a model of metaplastic pancreatic lesions induced by chronic inflammation. Schwann cells proliferated and expanded simultaneously with new sympathetic nerve sprouts in metaplastic/neoplastic pancreatic lesions. Sparse genetic labeling showed that individual Schwann cells in these lesions had a more elongated and branched structure than those under physiological conditions. Schwann cells overexpressed neurotrophic factors, including glial cell-derived neurotrophic factor (GDNF). Sympathetic neurons upregulated the GDNF receptors and exhibited enhanced neurite growth in response to GDNF in vitro. Selective genetic deletion of Gdnf in Schwann cells completely blocked sympathetic nerve sprouting in metaplastic pancreatic lesions in vivo. This study demonstrated that pancreatic Schwann cells underwent adaptive reprogramming during early cancer development, supporting a protective antitumor neuronal response. These finding could help to develop new strategies to modulate cancer associated neural plasticity.

A CLIC1 network coordinates matrix stiffness and the Warburg effect to promote tumor growth in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) features substantial matrix stiffening and reprogrammed glucose metabolism, particularly the Warburg effect. However, the complex interplay between these traits and their impact on tumor advancement remains inadequately explored. Here, we integrated clinical, cellular, and bioinformatics approaches to explore the connection between matrix stiffness and the Warburg effect in PDAC, identifying CLIC1 as a key mediator. Elevated CLIC1 expression, induced by matrix stiffness through Wnt/β-catenin/TCF4 signaling, signifies poorer prognostic outcomes in PDAC. Functionally, CLIC1 serves as a catalyst for glycolytic metabolism, propelling tumor proliferation. Mechanistically, CLIC1 fortifies HIF1α stability by curbing hydroxylation via reactive oxygen species (ROS). Collectively, PDAC cells elevate CLIC1 levels in a matrix-stiffness-responsive manner, bolstering the Warburg effect to drive tumor growth via ROS/HIF1α signaling. Our insights highlight opportunities for targeted therapies that concurrently address matrix properties and metabolic rewiring, with CLIC1 emerging as a promising intervention point.

Boosting antitumor efficacy of nanoparticles by modulating tumor mechanical microenvironment

Nanoparticles have shown great potential for tumor targeting delivery via enhanced permeability and retention effect. However, the tumor mechanical microenvironment, characterized by dense extracellular matrix (ECM), high tumor stiffness and solid stress, leads to only 0.7% of administered dose accumulating in solid tumors and even fewer (∼0.0014%) reaching tumor cells, limiting the therapeutic efficacy of nanoparticles. Furthermore, the tumor mechanical microenvironment can regulate tumor cell stemness, promote tumor invasion, metastasis and reduce treatment efficacy. In this review, methods detecting the mechanical are introduced. Strategies for modulating the mechanical microenvironment including elimination of dense ECM by physical, chemical and biological methods, disruption of ECM formation, depletion or inhibition of cancer-associated fibroblasts, are then summarized. Finally, prospects and challenges for further clinical applications of mechano-modulating strategies to enhance the therapeutic efficacy of nanomedicines are discussed. This review may provide guidance for the rational design and application of nanoparticles in clinical settings.

Biophysical perspectives to understanding cancer-associated fibroblasts

The understanding of cancer has evolved significantly, with the tumor microenvironment (TME) now recognized as a critical factor influencing the onset and progression of the disease. This broader perspective challenges the traditional view that cancer is primarily caused by mutations, instead emphasizing the dynamic interaction between different cell types and physicochemical factors within the TME. Among these factors, cancer-associated fibroblasts (CAFs) command attention for their profound influence on tumor behavior and patient prognoses. Despite their recognized importance, the biophysical and mechanical interactions of CAFs within the TME remain elusive. This review examines the distinctive physical characteristics of CAFs, their morphological attributes, and mechanical interactions within the TME. We discuss the impact of mechanotransduction on CAF function and highlight how these cells communicate mechanically with neighboring cancer cells, thereby shaping the path of tumor development and progression. By concentrating on the biomechanical regulation of CAFs, this review aims to deepen our understanding of their role in the TME and to illuminate new biomechanical-based therapeutic strategies.

Dual-crosslinking gelatin-hyaluronic acid methacrylate based biomimetic PDAC desmoplastic niche enhances tumor-associated macrophages recruitment and M2-like polarization

The tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) is characterized by deposition of desmoplastic matrix (including collagen and hyaluronic acid). And the interactions between tumor-associated macrophages (TAMs) and tumor cells play a crucial role in progression of PDAC. Hence, the appropriate model of tumor cell-macrophage interaction within the unique PDAC TME is of significantly important. To this end, a 3D tumor niche based on dual-crosslinking gelatin methacrylate and hyaluronic acid methacrylate hydrogels was constructed to simulate the desmoplastic tumor matrix with matching compressive modulus and composition. The bionic 3D tumor niche creates an immunosuppressive microenvironment characterized by the downregulation of M1 markers and upregulation of M2 markers in TAMs. Mechanistically, RNA-seq analysis revealed that the PI3K-AKT signaling pathway might modulate the phenotypic balance and recruitment of macrophages through regulating SELE and VCAM-1. Furthermore, GO and GSEA revealed the biological process of leukocyte migration and the activation of cytokine-associated signaling were involved. Finally, the 3D tumor-macrophage niches with three different ratios were fabricated which displayed increased M2-like polarization and stemness. The utilization of the 3D tumor niche has the potential to provide a more accurate investigation of the interplay between PDAC tumor cells and macrophages within an in vivo setting.

Development and validation of a nomogram model based on pretreatment ultrasound and contrast-enhanced ultrasound to predict the efficacy of neoadjuvant chemotherapy in patients with borderline resectable or locally advanced pancreatic cancer

OBJECTIVES

To develop a nomogram using pretreatment ultrasound (US) and contrast-enhanced ultrasound (CEUS) to predict the clinical response of neoadjuvant chemotherapy (NAC) in patients with borderline resectable pancreatic cancer (BRPC) or locally advanced pancreatic cancer (LAPC).

METHODS

A total of 111 patients with pancreatic ductal adenocarcinoma (PDAC) treated with NAC between October 2017 and February 2022 were retrospectively enrolled. The patients were randomly divided (7:3) into training and validation cohorts. The pretreatment US and CEUS features were reviewed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of clinical response in the training cohort. Then a prediction nomogram model based on the independent predictors was constructed. The area under the curve (AUC), calibration plot, C-index and decision curve analysis (DCA) were used to assess the nomogram's performance, calibration, discrimination and clinical benefit.

RESULTS

The multivariate logistic regression analysis showed that the taller-than-wide shape in the longitudinal plane (odds ratio [OR]:0.20, p = 0.01), time from injection of contrast agent to peak enhancement (OR:3.64; p = 0.05) and Peaktumor/ Peaknormal (OR:1.51; p = 0.03) were independent predictors of clinical response to NAC. The predictive nomogram developed based on the above imaging features showed AUCs were 0.852 and 0.854 in the primary and validation cohorts, respectively. Good calibration was achieved in the training datasets, with C-index of 0.852. DCA verified the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram based on pretreatment US and CEUS can effectively predict the clinical response of NAC in patients with BRPC and LAPC; it may help guide personalized treatment.

Extracellular Matrix Cues Regulate Mechanosensing and Mechanotransduction of Cancer Cells

Extracellular biophysical properties have particular implications for a wide spectrum of cellular behaviors and functions, including growth, motility, differentiation, apoptosis, gene expression, cell-matrix and cell-cell adhesion, and signal transduction including mechanotransduction. Cells not only react to unambiguously mechanical cues from the extracellular matrix (ECM), but can occasionally manipulate the mechanical features of the matrix in parallel with biological characteristics, thus interfering with downstream matrix-based cues in both physiological and pathological processes. Bidirectional interactions between cells and (bio)materials in vitro can alter cell phenotype and mechanotransduction, as well as ECM structure, intentionally or unintentionally. Interactions between cell and matrix mechanics in vivo are of particular importance in a variety of diseases, including primarily cancer. Stiffness values between normal and cancerous tissue can range between 500 Pa (soft) and 48 kPa (stiff), respectively. Even the shear flow can increase from 0.1-1 dyn/cm2 (normal tissue) to 1-10 dyn/cm2 (cancerous tissue). There are currently many new areas of activity in tumor research on various biological length scales, which are highlighted in this review. Moreover, the complexity of interactions between ECM and cancer cells is reduced to common features of different tumors and the characteristics are highlighted to identify the main pathways of interaction. This all contributes to the standardization of mechanotransduction models and approaches, which, ultimately, increases the understanding of the complex interaction. Finally, both the in vitro and in vivo effects of this mechanics-biology pairing have key insights and implications for clinical practice in tumor treatment and, consequently, clinical translation.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Optimization of the Tracking Beam Sequence in Harmonic Motion Imaging

Harmonic motion imaging (HMI) is an ultrasound elastography technique that estimates the viscoelastic properties of tissues by inducing localized oscillatory motion using focused ultrasound (FUS). The resulting displacement, assumed to be inversely proportional to the tissue local stiffness, is estimated using an imaging array based on RF speckle tracking. In conventional HMI, this is accomplished with plane-wave (PW) imaging, which inherently suffers from low lateral resolution. Coherent PW compounding (PWC) leverages spatial and temporal resolution using synthetic focusing in transmit. In this study, we introduced focused imaging with parallel tracking in HMI and compared parallel tracking of various transmit F-numbers (F/2.6, 3, 4, and 5) qualitatively and quantitatively with PW and PWC imaging at various compounded angle ranges (6°, 12°, and 18°). An in silico model of a 56-kPa spherical inclusion (diameter: 3.6 mm) embedded in a 5.3-kPa background and a 5.3-kPa elastic phantom with cylindrical inclusions (Young's moduli: 22-56 kPa, diameters: 2.0-8.6 mm) were imaged to assess different tracking beam sequences. Speckle biasing in displacement estimation associated with parallel tracking was also investigated and concluded to be negligible in HMI. Parallel tracking in receive (Rx) resulted in 2%-7% and 8%-12% increase compared to PW imaging ( ) in HMI contrast and contrast-to-noise ratio in silico and phantoms. Focused imaging with parallel tracking in Rx was concluded to be most robust among PW and PWC imaging for displacement estimation, and its preclinical feasibility was demonstrated in postsurgical human cancerous breast tissue specimens and in vivo murine models of breast cancer.

Innovative Experimental Ultrasound and US-Related Techniques Using the Murine Model in Pancreatic Ductal Adenocarcinoma: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is a cancer with one of the highest mortality rates in the world. Several studies have been conductedusing preclinical experiments in mice to find new therapeutic strategies. Experimental ultrasound, in expert hands, is a safe, multifaceted, and relatively not-expensive device that helps researchers in several ways. In this systematic review, we propose a summary of the applications of ultrasonography in a preclinical mouse model of PDAC. Eighty-eight studies met our inclusion criteria. The included studies could be divided into seven main topics: ultrasound in pancreatic cancer diagnosis and progression (n: 21); dynamic contrast-enhanced ultrasound (DCE-US) (n: 5); microbubble ultra-sound-mediated drug delivery; focused ultrasound (n: 23); sonodynamic therapy (SDT) (n: 7); harmonic motion elastography (HME) and shear wave elastography (SWE) (n: 6); ultrasound-guided procedures (n: 9). In six cases, the articles fit into two or more sections. In conclusion, ultrasound can be a really useful, eclectic, and ductile tool in different diagnostic areas, not only regarding diagnosis but also in therapy, pharmacological and interventional treatment, and follow-up. All these multiple possibilities of use certainly represent a good starting point for the effective and wide use of murine ultrasonography in the study and comprehensive evaluation of pancreatic cancer.

Elastic modulus-reflected liver lesion stiffness relates to worse prognosis in pancreatic cancer patients with liver metastasis

BACKGROUND

Liver stiffness relates to more advanced tumor status and poor outcomes in primary liver cancer, while its prognostic role in pancreatic cancer with liver metastasis is unclear. Therefore, the current study aimed to explore the correlation of elastic modulus (EM)-reflected liver lesion stiffness with clinical characteristics, tumor markers, and survival among pancreatic cancer patients with liver metastasis.

METHODS

Fifty-four pancreatic cancer patients with liver metastasis were enrolled, and the EM of liver metastasis and peripheral liver tissue was measured by two-dimensional shear wave elastography. Relative EM was calculated as the ratio of EM in liver metastasis to that in peripheral liver tissue, which reflected the relative liver lesion stiffness.

RESULTS

The median relative EM of liver metastasis was 7.8 (interquartile range: 4.8-10.7) folds. Relative EM of liver metastasis was correlated with primary pancreatic cancer location (P = 0.048), the presence of extra lung metastasis (P = 0.040), liver metastasis ≥ 3 cm (P = 0.007), and the absence of extraskeletal metastasis (P = 0.036); but it was not correlated with tumor markers such as CA199, CA125, or CEA (all P > 0.05). Encouragingly, high relative EM of liver metastasis (cut off by median value) was correlated with poor progression-free survival (PFS) (P = 0.032) but not overall survival (OS) (P = 0.285). Multivariable Cox analysis showed that high relative EM of liver metastasis (hazard ratio (HR) = 1.768, P = 0.048) and multiple metastases (HR = 2.262, P = 0.036) independently predicted decreased PFS, but only abnormal CEA independently forecasted decreased OS (HR = 2.390, P = 0.027).

CONCLUSION

Elastic modulus reflected liver lesion stiffness may predict a worse prognosis in pancreatic cancer patients with liver metastasis.

The impact of amplitude modulation frequency in harmonic motion imaging on inclusion characterization

OBJECTIVE

Ultrasound elasticity imaging techniques aim to provide a non-invasive characterization of tissue mechanical properties to detect pathological changes and monitor disease progression. Harmonic motion imaging (HMI) is an ultrasound-based elasticity imaging technique that utilizes an oscillatory acoustic radiation force to induce localized displacements and estimate relative tissue stiffness. Previous studies have applied a low amplitude modulation (AM) frequency of 25 or 50 Hz in HMI to assess the mechanical properties of different tissue types. In this study, we investigate the dependence of AM frequency in HMI and whether the frequency can be adjusted based on the size and mechanical properties of the underlying medium for enhanced image contrast and inclusion detection.

METHODS

A tissue-mimicking phantom with embedded inclusions at different sizes and stiffnesses was imaged within a range of AM frequencies from 25 to 250 Hz at 25-Hz step size.

DISCUSSION

The AM frequency at which the maximum contrast and CNR are achieved depends on the size and stiffness of the inclusions. A general trend shows that contrast and CNR peak at higher frequencies for smaller inclusions. In addition, for some inclusions with the same size but different stiffnesses, the optimized AM frequency increases with the stiffness of the inclusion. Nevertheless, there is a shift between the frequencies at which the contrast peaks and those with maximum CNR. Finally, in agreement with the phantom findings, imaging an ex-vivo human specimen with a 2.7-cm breast tumor at a range of AM frequencies showed that the highest contrast and CNR are achieved at the AM frequency of 50 Hz.

CONCLUSION

These findings indicate that the AM frequency can be optimized in different applications of HMI, especially in the clinic, for improved detection and characterization of tumors with different geometries and mechanical properties.

Adjustable extracellular matrix rigidity tumor model for studying stiffness dependent pancreatic ductal adenocarcinomas progression and tumor immunosuppression

Pancreatic ductal adenocarcinomas (PDAC) is one of the stiffest malignancies with strong solid stresses. Increasing stiffness could alter cellular behavior and trigger internal signaling pathways and is strongly associated with a poor prognosis in PDAC. So far, there has been no report on of an experimental model that can rapidly construct and stably maintain a stiffness gradient dimension in both vitro and in vivo. In this study, a gelatin methacryloyl (GelMA)-based hydrogel was designed for in vitro and in vivo PDAC experiments. The GelMA-based hydrogel has porous, adjustable mechanical properties and excellent in vitro and in vivo biocompatibility. The GelMA-based in vitro 3D culture method can effectively form a gradient and stable extracellular matrix stiffness, affecting cell morphology, cytoskeleton remodeling, and malignant biological behaviors such as proliferation and metastasis. This model is suitable for in vivo studies with long-term maintenance of matrix stiffness and no significant toxicity. High matrix stiffness can significantly promote PDAC progression and tumor immunosuppression. This novel adaptive extracellular matrix rigidity tumor model is an excellent candidate for further development as an in vitro and in vivo biomechanical study model of PDAC or other tumors with strong solid stresses.

An Efficient and Multi-Focal Focused Ultrasound Technique for Harmonic Motion Imaging

Harmonic motion imaging (HMI) is an ultrasound-based elasticity imaging technique that utilizes oscillatory acoustic radiation force to estimate the mechanical properties of tissues, as well as monitor high-intensity focused ultrasound (HIFU) treatment. Conventionally, in HMI, a focused ultrasound (FUS) transducer generates oscillatory tissue displacements, and an imaging transducer acquires channel data for displacement estimation, with each transducer being driven with a separate system. The fixed position of the FUS focal spot requires mechanical translation of the transducers, which can be a time-consuming and challenging procedure. In this study, we developed and characterized a new HMI system with a multi-element FUS transducer with the capability of electronic focal steering of ±5 mm and ±2 mm from the geometric focus in the axial and lateral directions, respectively. A pulse sequence was developed to drive both the FUS and imaging transducers using a single ultrasound data acquisition (DAQ) system. The setup was validated on a tissue-mimicking phantom with embedded inclusions. Integrating beam steering with the mechanical translation of the transducers resulted in a consistent high contrast-to-noise ratio (CNR) for the inclusions with Young's moduli of 22 and 44 kPa within a 5-kPa background while the data acquisition speed is increased by 4.5-5.2-fold compared to the case when only mechanical movements were applied. The feasibility of simultaneous generation of multiple foci and tracking the induced displacements is demonstrated in phantoms for applications where imaging or treatment of a larger region is needed. Moreover, preliminary feasibility is shown in a human subject with a breast tumor, where the mean HMI displacement within the tumor was about 4 times lower than that within perilesional tissues. The proposed HMI system facilitates data acquisition in terms of flexibility and speed and can be potentially used in the clinic for breast cancer imaging and treatment.

Extracellular matrix physical properties govern the diffusion of nanoparticles in tumor microenvironment

Nanoparticles (NPs) are confronted with limited and disappointing delivery efficiency in tumors clinically. The tumor extracellular matrix (ECM), whose physical traits have recently been recognized as new hallmarks of cancer, forms a main steric obstacle for NP diffusion, yet the role of tumor ECM physical traits in NP diffusion remains largely unexplored. Here, we characterized the physical properties of clinical gastric tumor samples and observed limited distribution of NPs in decellularized tumor tissues. We also performed molecular dynamics simulations and in vitro hydrogel experiments through single-particle tracking to investigate the diffusion mechanism of NPs and understand the influence of tumor ECM physical properties on NP diffusion both individually and collectively. Furthermore, we developed an estimation matrix model with evaluation scores of NP diffusion efficiency through comprehensive analyses of the data. Thus, beyond finding that loose and soft ECM with aligned structure contribute to efficient diffusion, we now have a systemic model to predict NP diffusion efficiency based on ECM physical traits and provide critical guidance for personalized tumor diagnosis and treatment.

Biological role of matrix stiffness in tumor growth and treatment

In recent years, the biological role of changes in physical factors in carcinogenesis and progression has attracted increasing attention. Matrix stiffness, also known as ECM stress, is a critical physical factor of tumor microenvironment and remains alternating during carcinogenesis as a result of ECM remodeling through activation of cancer-associated fibroblasts and extracellular collagen accumulation, crosslinking and fibrosis. Different content and density of extracellular collagen in ECM endows matrix with varying stiffness. Physical signals induced by matrix stiffness are transmitted to tumor cells primarily by the integrins receptor family and trigger a series of mechanotransduction that result in changes in tumor cell morphology, proliferative capacity, and invasive ability. Importantly, accumulating evidence revealed that changes in matrix stiffness in tumor tissues greatly control the sensitivity of tumor cells in response to chemotherapy, radiotherapy, and immunotherapy through integrin signaling, YAP signaling, and related signaling pathways. Here, the present review analyzes the current research advances on matrix stiffness and tumor cell behavior with a view to contributing to tumor cell growth and treatment, with the hope of improving the understanding of the biological role of matrix stiffness in tumors.

Imaging of Single Transducer-Harmonic Motion Imaging-Derived Displacements at Several Oscillation Frequencies Simultaneously

Mapping of mechanical properties, dependent on the frequency of motion, is relevant in diagnosis, monitoring treatment response, or intra-operative surgical resection planning. While shear wave speeds at different frequencies have been described elsewhere, the effect of frequency on the "on-axis" acoustic radiation force (ARF)-induced displacement has not been previously investigated. Instead of generating single transducer-harmonic motion imaging (ST-HMI)-derived peak-to-peak displacement (P2PD) image at a particular frequency, a novel multi-frequency excitation pulse is proposed to generate P2PD images at 100-1000 Hz simultaneously. The performance of the proposed excitation pulse is compared with the ARFI by imaging 16 different inclusions (Young's moduli of 6, 9, 36, 70 kPa and diameters of 1.6, 2.5, 6.5, and 10.4 mm) embedded in an 18 kPa background. Depending on inclusion size and stiffness, the maximum CNR and contrast were achieved at different frequencies and were always higher than ARFI. The frequency, at which maximum CNR and contrast were achieved, increased with stiffness for fixed inclusion's size and decreased with size for fixed stiffness. In vivo feasibility is tested by imaging a 4T1 breast cancer mouse tumor on Day 6, 12, and 19 post-injection of tumor cells. Similar to phantoms, the CNR of ST-HMI images was higher than ARFI and increased with frequency for the tumor on Day 6. Besides, P2PD at 100-1000 Hz indicated that the tumor became stiffer with respect to the neighboring non-cancerous tissue over time. These results indicate the importance of using a multi-frequency excitation pulse to simultaneously generate displacement at multiple frequencies to better delineate inclusions or tumors.

FUS-Net: U-Net-Based FUS Interference Filtering

Imaging applications tailored towards ultrasound-based treatment, such as high intensity focused ultrasound (FUS), where higher power ultrasound generates a radiation force for ultrasound elasticity imaging or therapeutics/theranostics, are affected by interference from FUS. The artifact becomes more pronounced with intensity and power. To overcome this limitation, we propose FUS-net, a method that incorporates a CNN-based U-net autoencoder trained end-to-end on 'clean' and 'corrupted' RF data in Tensorflow 2.3 for FUS artifact removal. The network learns the representation of RF data and FUS artifacts in latent space so that the output of corrupted RF input is clean RF data. We find that FUS-net perform 15% better than stacked autoencoders (SAE) on evaluated test datasets. B-mode images beamformed from FUS-net RF shows superior speckle quality and better contrast-to-noise (CNR) than both notch-filtered and adaptive least means squares filtered RF data. Furthermore, FUS-net filtered images had lower errors and higher similarity to clean images collected from unseen scans at all pressure levels. Lastly, FUS-net RF can be used with existing cross-correlation speckle-tracking algorithms to generate displacement maps. FUS-net currently outperforms conventional filtering and SAEs for removing high pressure FUS interference from RF data, and hence may be applicable to all FUS-based imaging and therapeutic methods.

Modeling the mechanical stiffness of pancreatic ductal adenocarcinoma

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The PDAC stroma stiffness underlines its malignant behavior and drug resistance.

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3D in vitro cultures must model the PDAC stroma to effectively drug efficacy.

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PSCs are responsible for the stroma, and its activity is increased with TGF-β.

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Develop a 3D culture model of PDAC, which includes PSCs and TGF-β.

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Assess the mechanical stiffness, stain for collagen, and investigate gemcitabine efficacy.

Despite improvements in the understanding of disease biology, pancreatic ductal adenocarcinoma (PDAC) remains the most malignant cancer of the pancreas. PDAC constitutes ∼95% of all pancreatic cancers, and it is highly resistant to therapeutics. The increased tissue rigidity, which stems from the rich fibrotic stroma in the tumor microenvironment, is central to disease development, physiology, and resistance to drug perfusion. Pancreatic stellate cells (PSCs) are responsible for overproduction of extracellular matrix in the fibrotic stroma, and this is exacerbated by the overexpression of transforming growth factor-β (TGF-β). However, there are few in vitro PDAC models, which include both PSCs and TGF-β or mimic in vivo-like tumor stiffness. In this study, we present a three-dimensional in vitro PDAC model, which includes PSCs and TGF-β, and recapitulates PDAC tissue mechanical stiffness. Using oscillatory shear rheology, we show the mechanical stiffness of the model is within range of the PDAC tissue stiffness by day 21 of culture and highlight that the matrix environment is essential to adequately capture PDAC disease. PDAC is a complex, aggressive disease with poor prognosis, and biophysically relevant in vitro PDAC models, which take into account tissue mechanics, will provide improved tumor models for effective therapeutic assessment.

3D Collagen-Nanocellulose Matrices Model the Tumour Microenvironment of Pancreatic Cancer

Three-dimensional (3D) cancer models are invaluable tools designed to study tumour biology and new treatments. Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest types of cancer, has been progressively explored with bioengineered 3D approaches by deconstructing elements of its tumour microenvironment. Here, we investigated the suitability of collagen-nanocellulose hydrogels to mimic the extracellular matrix of PDAC and to promote the formation of tumour spheroids and multicellular 3D cultures with stromal cells. Blending of type I collagen fibrils and cellulose nanofibres formed a matrix of controllable stiffness, which resembled the lower profile of pancreatic tumour tissues. Collagen-nanocellulose hydrogels supported the growth of tumour spheroids and multicellular 3D cultures, with increased metabolic activity and matrix stiffness. To validate our 3D cancer model, we tested the individual and combined effects of the anti-cancer compound triptolide and the chemotherapeutics gemcitabine and paclitaxel, resulting in differential cell responses. Our blended 3D matrices with tuneable mechanical properties consistently maintain the growth of PDAC cells and its cellular microenvironment and allow the screening of anti-cancer treatments.

Pancreatic Ductal Adenocarcinoma: Relating Biomechanics and Prognosis

Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer and carries a dismal prognosis. Resectable patients are treated predominantly with surgery while borderline resectable patients may receive neoadjuvant treatment (NAT) to downstage their disease prior to possible resection. PDAC tissue is stiffer than healthy pancreas, and tissue stiffness is associated with cancer progression. Another feature of PDAC is increased tissue heterogeneity. We postulate that tumour stiffness and heterogeneity may be used alongside currently employed diagnostics to better predict prognosis and response to treatment. In this review we summarise the biomechanical changes observed in PDAC, explore the factors behind these changes and describe the clinical consequences. We identify methods available for assessing PDAC biomechanics ex vivo and in vivo, outlining the relative merits of each. Finally, we discuss the potential use of radiological imaging for prognostic use.

Feasibility of Harmonic Motion Imaging Using a Single Transducer: In Vivo Imaging of Breast Cancer in a Mouse Model and Human Subjects

Harmonic motion imaging (HMI) interrogates the mechanical properties of tissues by simultaneously generating and tracking harmonic oscillation using focused ultrasound and imaging transducers, respectively. Instead of using two transducers, the objective of this work is to develop a single transducer HMI (ST-HMI) to both generate and track harmonic motion at "on-axis" to the force for facilitating data acquisition. In ST-HMI, the amplitude-modulated force was generated by modulating excitation pulse duration and tracking of motion was performed by transmitting tracking pulses interleaved between excitation pulses. The feasibility of ST-HMI was performed by imaging two elastic phantoms with three inclusions (N = 6) and comparing it with acoustic radiation force impulse (ARFI) imaging, in vivo longitudinal monitoring of 4T1, orthotropic breast cancer mice (N = 4), and patients (N = 3) with breast masses in vivo. Six inclusions with Young's moduli of 8, 10, 15, 20, 40, and 60 kPa were embedded in a 5 kPa background. The ST-HMI-derived peak-to-peak displacement (P2PD) successfully detected all inclusions with [Formula: see text] of the linear regression between the P2PD ratio of background to inclusion versus Young's moduli ratio of inclusion to background. The contrasts of 10 and 15 kPa inclusions were higher in ST-HMI than ARFI-derived images. In the mouse study, the median P2PD ratio of tumor to non-cancerous tissues was 3.0, 5.1, 6.1, and 7.7 at 1, 2, 3, and 4 weeks post-injection of the tumor cells, respectively. In the clinical study, ST-HMI detected breast masses including fibroadenoma, pseudo angiomatous stromal hyperplasia, and invasive ductal carcinoma with a P2PD ratio of 1.37, 1.61, and 1.78, respectively. These results indicate that ST-HMI can assess the mechanical properties of tissues via generation and tracking of harmonic motion "on-axis" to the ARF. This study is the first step towards translating ST-HMI in clinics.

3D Cell Culture: Recent Development in Materials with Tunable Stiffness

It is widely accepted that three-dimensional cell culture systems simulate physiological conditions better than traditional 2D systems. Although extracellular matrix components strongly modulate cell behavior, several studies underlined the importance of mechanosensing in the control of different cell functions such as growth, proliferation, differentiation, and migration. Human tissues are characterized by different degrees of stiffness, and various pathologies (e.g., tumor or fibrosis) cause changes in the mechanical properties through the alteration of the extracellular matrix structure. Additionally, these modifications have an impact on disease progression and on therapy response. Hence, the development of platforms whose stiffness could be modulated may improve our knowledge of cell behavior under different mechanical stress stimuli. In this review, we have analyzed the mechanical diversity of healthy and diseased tissues, and we have summarized recently developed materials with a wide range of stiffness.

Synchronous temperature variation monitoring during ultrasound imaging and/or treatment pulse application: a phantom study

Ultrasound attenuation through soft tissues can produce an acoustic radiation force (ARF) and heating. The ARF-induced displacements and temperature evaluations can reveal tissue properties and provide insights into focused ultrasound (FUS) bio-effects. In this study, we describe an interleaving pulse sequence tested in a tissue-mimicking phantom that alternates FUS and plane-wave imaging pulses at a 1 kHz frame rate. The FUS is amplitude modulated, enabling the simultaneous evaluation of tissue-mimicking phantom displacement using harmonic motion imaging (HMI) and temperature rise using thermal strain imaging (TSI). The parameters were varied with a spatial peak temporal average acoustic intensity (I _spta ) ranging from 1.5 to 311 W.cm^-2, mechanical index (MI) from 0.43 to 4.0, and total energy (E) from 0.24 to 83 J.cm^-2. The HMI and TSI processing could estimate displacement and temperature independently for temperatures below 1.80°C and displacements up to ~117 μm (I _spta <311 W.cm^-2, MI<4.0, and E<83 J.cm^-2) indicated by a steady-state tissue-mimicking phantom displacement throughout the sonication and a comparable temperature estimation with simulations in the absence of tissue-mimicking phantom motion. The TSI estimations presented a mean error of ±0.03°C versus thermocouple estimations with a mean error of ±0.24°C. The results presented herein indicate that HMI can operate at diagnostic-temperature levels (i.e., <1°C) even when exceeding diagnostic acoustic intensity levels (720 mW.cm^-2 < I _spta < 207 W.cm^-2). In addition, the combined HMI and TSI can potentially be used for simultaneous evaluation of safety during tissue elasticity imaging as well as FUS mechanism involved in novel ultrasound applications such as ultrasound neuromodulation and tumor ablation.

Toward the Clinical Development and Validation of a Thy1-Targeted Ultrasound Contrast Agent for the Early Detection of Pancreatic Ductal Adenocarcinoma

Early detection of pancreatic ductal adenocarcinoma (PDAC) represents the most significant step toward the treatment of this aggressive lethal disease. Previously, we engineered a preclinical Thy1-targeted microbubble (MBThy1) contrast agent that specifically recognizes Thy1 antigen overexpressed in the vasculature of murine PDAC tissues by ultrasound (US) imaging. In this study, we adopted a single-chain variable fragment (scFv) site-specific bioconjugation approach to construct clinically translatable MBThy1-scFv and test for its efficacy in vivo in murine PDAC imaging, and functionally evaluated the binding specificity of scFv ligand to human Thy1 in patient PDAC tissues ex vivo.

MATERIALS AND METHODS

We recombinantly expressed the Thy1-scFv with a carboxy-terminus cysteine residue to facilitate its thioether conjugation to the PEGylated MBs presenting with maleimide functional groups. After the scFv-MB conjugations, we tested binding activity of the MBThy1-scFv to MS1 cells overexpressing human Thy1 (MS1Thy1) under liquid shear stress conditions in vitro using a flow chamber setup at 0.6 mL/min flow rate, corresponding to a wall shear stress rate of 100 seconds, similar to that in tumor capillaries. For in vivo Thy1 US molecular imaging, MBThy1-scFv was tested in the transgenic mouse model (C57BL/6J - Pdx1-Cre; KRas; Ink4a/Arf) of PDAC and in control mice (C57BL/6J) with L-arginine-induced pancreatitis or normal pancreas. To facilitate its clinical feasibility, we further produced Thy1-scFv without the bacterial fusion tags and confirmed its recognition of human Thy1 in cell lines by flow cytometry and in patient PDAC frozen tissue sections of different clinical grades by immunofluorescence staining.

RESULTS

Under shear stress flow conditions in vitro, MBThy1-scFv bound to MS1Thy1 cells at significantly higher numbers (3.0 ± 0.8 MB/cell; P < 0.01) compared with MBNontargeted (0.5 ± 0.5 MB/cell). In vivo, MBThy1-scFv (5.3 ± 1.9 arbitrary units [a.u.]) but not the MBNontargeted (1.2 ± 1.0 a.u.) produced high US molecular imaging signal (4.4-fold vs MBNontargeted; n = 8; P < 0.01) in the transgenic mice with spontaneous PDAC tumors (2-6 mm). Imaging signal from mice with L-arginine-induced pancreatitis (n = 8) or normal pancreas (n = 3) were not significantly different between the two MB constructs and were significantly lower than PDAC Thy1 molecular signal. Clinical-grade scFv conjugated to Alexa Fluor 647 dye recognized MS1Thy1 cells but not the parental wild-type cells as evaluated by flow cytometry. More importantly, scFv showed highly specific binding to VEGFR2-positive vasculature and fibroblast-like stromal components surrounding the ducts of human PDAC tissues as evaluated by confocal microscopy.

CONCLUSIONS

Our findings summarize the development and validation of a clinically relevant Thy1-targeted US contrast agent for the early detection of human PDAC by US molecular imaging.

Harmonic motion imaging of human breast masses: an in vivo clinical feasibility

Non-invasive diagnosis of breast cancer is still challenging due to the low specificity of the imaging modalities that calls for unnecessary biopsies. The diagnostic accuracy can be improved by assessing the breast tissue mechanical properties associated with pathological changes. Harmonic motion imaging (HMI) is an elasticity imaging technique that uses acoustic radiation force to evaluate the localized mechanical properties of the underlying tissue. Herein, we studied the in vivo feasibility of a clinical HMI system to differentiate breast tumors based on their relative HMI displacements, in human subjects. We performed HMI scans in 10 female subjects with breast masses: five benign and five malignant masses. Results revealed that both benign and malignant masses were stiffer than the surrounding tissues. However, malignant tumors underwent lower mean HMI displacement (1.1 ± 0.5 µm) compared to benign tumors (3.6 ± 1.5 µm) and the adjacent non-cancerous tissue (6.4 ± 2.5 µm), which allowed to differentiate between tumor types. Additionally, the excised breast specimens of the same patients (n = 5) were imaged post-surgically, where there was an excellent agreement between the in vivo and ex vivo findings, confirmed with histology. Higher displacement contrast between cancerous and non-cancerous tissue was found ex vivo, potentially due to the lower nonlinearity in the elastic properties of ex vivo tissue. This preliminary study lays the foundation for the potential complementary application of HMI in clinical practice in conjunction with the B-mode to classify suspicious breast masses.

Noninvasive Young's modulus visualization of fibrosis progression and delineation of pancreatic ductal adenocarcinoma (PDAC) tumors using Harmonic Motion Elastography (HME) in vivo

Background and aims: Poor specificity and predictive values of current cross-sectional radiological imaging methods in evaluation of pancreatic adenocarcinoma (PDAC) limit the clinical capability to accurately stage the tumor pre-operatively and provide optimal surgical treatment and improve patient outcomes.

Methods: In this study, we applied Harmonic Motion Elastography (HME), a quantitative ultrasound-based imaging method to calculate Young's modulus (YM) in PDAC mouse models (n = 30) and human pancreatic resection specimens of PDAC (n=32). We compared the YM to the collagen assessment by Picrosirius red (PSR) stain on corresponding histologic sections.

Results: HME is capable of differentiating between different levels of fibrosis in transgenic mice. In mice without pancreatic fibrosis, the measured YM was 4.2 ± 1.3 kPa, in fibrotic murine pancreata, YM was 5.5 ± 2.0 kPa and in murine PDAC tumors, YM was 11.3 ± 1.7 kPa. The corresponding PSR values were 2.0 ± 0.8 %, 9.8 ± 3.4 %, and 13.2 ± 1.2%, respectively. In addition, three regions within each human surgical PDAC specimen were assessed: tumor, which had both the highest Young's modulus (YM > 40 kPa) and collagen density (PSR > 40 %); non-neoplastic adjacent pancreas, which had the lowest Young's modulus (YM < 15 kPa) and collagen density (PSR < 10%) and a transitional peri-lesional region between the tumor and non-neoplastic pancreas with an intermediate value of measured Young's modulus (15 kPa < YM < 40 kPa) and collagen density (15% < PSR < 35 %).

Conclusion: In conclusion, a non-invasive, quantitative imaging tool for detecting, staging and delineating PDAC tumor margins based on the change in collagen density was developed.