Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer

BACKGROUND

Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response.

PURPOSE

To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020.

FIELD STRENGTH/SEQUENCE

A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing.

STATISTICAL TESTS

Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test.

RESULTS

About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05).

DATA CONCLUSION

FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: 4.

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC)

PURPOSE

To determine if tumor necrosis by pretreatment breast MRI and its quantitative imaging characteristics are associated with response to NAST in TNBC.

METHODS

This retrospective study included 85 TNBC patients (mean age 51.8 ± 13 years) with MRI before NAST and definitive surgery during 2010-2018. Each MRI included T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. For each index carcinoma, total tumor volume including necrosis (TTV), excluding necrosis (TV), and the necrosis-only volume (NV) were segmented on early-phase DCE subtractions and DWI images. NV and %NV were calculated. Percent enhancement on early and late phases of DCE and apparent diffusion coefficient were extracted from TTV, TV, and NV. Association between necrosis with pathological complete response (pCR) was assessed using odds ratio (OR). Multivariable analysis was used to evaluate the prognostic value of necrosis with T stage and nodal status at staging. Mann-Whitney U tests and area under the curve (AUC) were used to assess performance of imaging metrics for discriminating pCR vs non-pCR.

RESULTS

Of 39 patients (46%) with necrosis, 17 had pCR and 22 did not. Necrosis was not associated with pCR (OR, 0.995; 95% confidence interval [CI] 0.4-2.3) and was not an independent prognostic factor when combined with T stage and nodal status at staging (P = 0.46). None of the imaging metrics differed significantly between pCR and non-pCR in patients with necrosis (AUC < 0.6 and P > 0.40).

CONCLUSION

No significant association was found between necrosis by pretreatment MRI or the quantitative imaging characteristics of tumor necrosis and response to NAST in TNBC.

Differentiation of Myositis-Induced Models of Bacterial Infection and Inflammation with T2-Weighted, CEST, and DCE-MRI

We used T₂ relaxation, chemical exchange saturation transfer (CEST), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to assess whether bacterial infection can be differentiated from inflammation in a myositis-induced mouse model. We measured the T₂ relaxation time constants, %CEST at 5 saturation frequencies, and area under the curve (AUC) from DCE-MRI after maltose injection from infected, inflamed, and normal muscle tissue models. We applied principal component analysis (PCA) to reduce dimensionality of entire CEST spectra and DCE signal evolutions, which were analyzed using standard classification methods. We extracted features from dimensional reduction as predictors for machine learning classifier algorithms. Normal, inflamed, and infected tissues were evaluated with H&E and gram-staining histological studies, and bacterial-burden studies. The T₂ relaxation time constants and AUC of DCE-MRI after injection of maltose differentiated infected, inflamed, and normal tissues. %CEST amplitudes at −1.6 and −3.5 ppm differentiated infected tissues from other tissues, but these did not differentiate inflamed tissue from normal tissue. %CEST amplitudes at 3.5, 3.0, and 2.5 ppm, AUC of DCE-MRI for shorter time periods, and relative K^trans and k_ep values from DCE-MRI could not differentiate tissues. PCA and machine learning of CEST-MRI and DCE-MRI did not improve tissue classifications relative to traditional analysis methods. Similarly, PCA and machine learning did not further improve tissue classifications relative to T₂ MRI. Therefore, future MRI studies of infection models should focus on T₂-weighted MRI and analysis of T₂ relaxation times.

Piwnica-Worms D, Schellingerhout D. Axonal Transport as an In Vivo Biomarker for Retinal Neuropathy

We illuminate a possible explanatory pathophysiologic mechanism for retinal cellular neuropathy by means of a novel diagnostic method using ophthalmoscopic imaging and a molecular imaging agent targeted to fast axonal transport. The retinal neuropathies are a group of diseases with damage to retinal neural elements. Retinopathies lead to blindness but are typically diagnosed late, when substantial neuronal loss and vision loss have already occurred. We devised a fluorescent imaging agent based on the non-toxic C fragment of tetanus toxin (TTc), which is taken up and transported in neurons using the highly conserved fast axonal transport mechanism. TTc serves as an imaging biomarker for normal axonal transport and demonstrates impairment of axonal transport early in the course of an N-methyl-D-aspartic acid (NMDA)-induced excitotoxic retinopathy model in rats. Transport-related imaging findings were dramatically different between normal and retinopathic eyes prior to presumed neuronal cell death. This proof-of-concept study provides justification for future clinical translation.

Technical Note: A deuterated 13 C-urea reference for clinical multiparametric MRI prostate cancer studies including hyperpolarized pyruvate

PURPOSE

Metabolic magnetic resonance imaging (MRI) using hyperpolarized [1-¹³ C]-pyruvate offers unprecedented new insight into disease and response to therapy. ¹³ C-enriched reference standards are required to enable fast and accurate calibration for ¹³ C studies, but care must be taken to ensure that the reference is compatible with both ¹³ C and ¹ H acquisitions. The goal of this study was to optimize the composition of a ¹³ C-urea reference for a dual-tuned ¹³ C/¹ H endorectal coil and minimize imaging artifacts in metabolic and multiparametric MRI studies involving hyperpolarized [1-¹³ C]-pyruvate.

METHODS

Due to a high amount of Gd doping for the purpose of reducing the spin-lattice relaxation time (T₁ ) of urea, the ¹ H signal produced by a reference of ¹³ C-urea in normal water was rapidly relaxed, resulting in severe artifacts in heavily T₁ -weighted images. Hyperintense ringing artifacts in ¹ H images were mitigated by reducing the ¹ H concentration in a ¹³ C-urea reference via deuteration and lyophilization. Several references were fabricated and their SNR was compared using ¹ H and ¹³ C imaging sequences on a 3T MRI scanner. Finally, ¹ H prostate phantom imaging was conducted to compare image quality and ¹ H signal intensity of normal and deuterated urea references.

RESULTS

The deuterated ¹³ C-urea reference provides strong ¹³ C signal for calibration and an attenuated ¹ H signal that does not interfere with heavily T₁ -weighted scans. Deuteration and lyophilization were fundamental to the reduction in ¹ H signal and hyperintense ringing artifacts. There was a 25-fold reduction in signal intensity when comparing the nondeuterated reference to the deuterated reference, while the ¹³ C signal was unaffected.

CONCLUSION

A deuterated reference reduced hyperintense ringing artifacts in ¹ H images by reducing the ¹ H signal produced from the ¹³ C-urea in the reference. The deuterated reference can be used to improve anatomical image quality in future clinical ¹ H and hyperpolarized [1-¹³ C]-pyruvate MRI prostate imaging studies.

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors

Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially challenging. Purpose To develop and investigate deep learning (DL) methods for detecting brain metastasis with MRI to aid in treatment planning for SRS. Materials and Methods In this retrospective study, contrast material-enhanced three-dimensional T1-weighted gradient-echo MRI scans from patients who underwent gamma knife SRS from January 2011 to August 2018 were analyzed. Brain metastases were manually identified and contoured by neuroradiologists and treating radiation oncologists. DL single-shot detector (SSD) algorithms were constructed and trained to map axial MRI slices to a set of bounding box predictions encompassing metastases and associated detection confidences. Performances of different DL SSDs were compared for per-lesion metastasis-based detection sensitivity and positive predictive value (PPV) at a 50% confidence threshold. For the highest-performing model, detection performance was analyzed by using free-response receiver operating characteristic analysis. Results Two hundred sixty-six patients (mean age, 60 years ± 14 [standard deviation]; 148 women) were randomly split into 80% training and 20% testing groups (212 and 54 patients, respectively). For the testing group, sensitivity of the highest-performing (baseline) SSD was 81% (95% confidence interval [CI]: 80%, 82%; 190 of 234) and PPV was 36% (95% CI: 35%, 37%; 190 of 530). For metastases measuring at least 6 mm, sensitivity was 98% (95% CI: 97%, 99%; 130 of 132) and PPV was 36% (95% CI: 35%, 37%; 130 of 366). Other models (SSD with a ResNet50 backbone, SSD with focal loss, and RetinaNet) yielded lower sensitivities of 73% (95% CI: 72%, 74%; 171 of 234), 77% (95% CI: 76%, 78%; 180 of 234), and 79% (95% CI: 77%, 81%; 184 of 234), respectively, and lower PPVs of 29% (95% CI: 28%, 30%; 171 of 581), 26% (95% CI: 26%, 26%; 180 of 681), and 13% (95% CI: 12%, 14%; 184 of 1412). Conclusion Deep-learning single-shot detector models detected nearly all brain metastases that were 6 mm or larger with limited false-positive findings using postcontrast T1-weighted MRI. © RSNA, 2020 See also the editorial by Kikinis and Wells in this issue.

Abstract P6-02-03: Quantitative apparent diffusion coefficient (ADC) radiomics of tumor and peritumoral regions as potential predictors of treatment response to neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients

Background and Purpose: TNBC is comprised of biologically aggressive tumors with diverse clinical behavior and response to chemotherapy. Prediction of disease response to NACT is critical to the development of personalized medicine in TNBC. We evaluated first-order radiomic features from quantitative ADC maps of the tumor and peritumoral region as discriminators of response to NACT in TNBC patients.

Materials and Methods: This IRB-approved prospective study (ARTEMIS trial, NCT02276443) included 34 patients with biopsy proven stage I-III TNBC who underwent evaluation of treatment response by multi-parametric MRI. Patients had a baseline MRI (BL) and a second MRI after 4 cycles (C4) of their treatment. After completion of NACT, all patients underwent surgery and were classified as pathologic complete response (pCR) or non-pCR.

Both MRI exams included T2W series, a dynamic contrast enhanced series (DCE), a conventional diffusion weighted imaging (DWI) series, and a reduced field of view (rFOV) DWI series. Tumor volumes were contoured by an experienced breast radiologist on ADC maps with reference to b1000 DWI images. Regions with necrosis or clip artifacts were excluded from the contour. Peritumoral regions were defined as a 5 mm rim of tissue surrounding the tumor based on DCE series, T2-weighted images with fat suppression and ADC maps. Thirteen first-order radiomic features, including mean, minimum, maximum, percentiles, kurtosis and skewness at a single measurement and the difference between BL and C4 were compared between pCR and non-pCR using Receiver Operating Characteristic (ROC) curve and Wilcoxon rank sum test.

Results: The kurtosis of tumor at C4 by conventional DWI was significantly higher in non-pCR than in pCR patients (AUC=0.785, p=0.0097). The change in kurtosis from BL to C4 by conventional DWI was also significantly higher in non-pCR than in pCR patients (AUC=0.73, p=0.043). The skewness of tumor at C4 by rFOV DWI scan was significantly lower in pCR than non-pCR patients (AUC=0.73, p=0.023).

The 10th percentile of the peritumoral region’s ADC was significantly different between pCR and non-pCR (mean=1.19, SD is ± 0.27 10-3 mm2/s vs mean=1.34, SD ± 0.27 10-3 mm2/s respectively, AUC=0.70, p=0.048). The kurtosis and 25th percentile of the ADC of peritumoral region were borderline significantly different between pCR and non-pCR (AUC=0.69, p=0.067; AUC=0.69, p= 0.073 respectively).

Conclusion: ADC first-order radiomic features from tumor and peritumoral region in TNBC may be useful for predicting treatment response to NACT. Larger study is necessary and is currently in progress to validate these findings.

Citation Format: Beatriz E. Adrada, Abeer H. Abdelhafez, Benjamin C. Musall, Kenneth R. Hess, Jong Bum Son, Mark D. Pagel, Ken-Pin Hwang, Rosalind P. Candelaria, Lumarie Santiago, Gary J. Whitman, Huong Le-Petross, Tanya W. Moseley, Elsa Arribas, Deanna L. Lane, Marion E. Scoggins, David A. Spak, Jessica W.T. Leung, Senthil Damodaran, Bora Lim, Vicente Valeo, Jason B White, Alastair M. Thompson, Jennifer K. Litton, Stacy L. Moulder, Jingfei Ma, Wei T. Yang, Gaiane M Rauch. Quantitative apparent diffusion coefficient (ADC) radiomics of tumor and peritumoral regions as potential predictors of treatment response to neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-03.

Evaluation of the Biodistribution of Serinolamide-Derivatized C60 Fullerene

Carbon nanoparticles have consistently been of great interest in medicine. However, there are currently no clinical materials based on carbon nanoparticles, due to inconsistent biodistribution and excretion data. In this work, we have synthesized a novel C60 derivative with a metal chelating agent (1,4,7-Triazacyclononane-1,4,7-triacetic acid; NOTA) covalently bound to the C60 cage and radiolabeled with copper-64 (t1/2 = 12.7 h). Biodistribution of the material was assessed in vivo using positron emission tomography (PET). Bingel-Hirsch chemistry was employed to functionalize the fullerene cage with highly water-soluble serinolamide groups allowing this new C60 conjugate to clear quickly from mice almost exclusively through the kidneys. Comparing the present results to the larger context of reports of biocompatible fullerene derivatives, this work offers an important evaluation of the in vivo biodistribution, using experimental evidence to establish functionalization guidelines for future C60-based biomedical platforms.

Assessments of tumor metabolism with CEST MRI

Chemical exchange saturation transfer (CEST) is a relatively new contrast mechanism for MRI. CEST MRI exploits a specific MR frequency (chemical shift) of a molecule while generating an image with good spatial resolution using standard MRI techniques, combining the specificity of MRS with the spatial resolution of MRI. Many CEST MRI acquisition methods have been developed to improve analyses of tumor metabolism. GluCEST, CrCEST, and LATEST can map glutamate, creatine, and lactate, which are important metabolites involved in tumor metabolism. GlucoCEST MRI tracks the pharmacokinetics of glucose transport and cell internalization within tumors. CatalyCEST MRI detects enzyme catalysis that changes a substrate CEST agent. AcidoCEST MRI measures extracellular pH of the tumor microenvironment by exploiting a ratio of two pH-dependent CEST signals. This review describes each technique, the technical issues involved with CEST MRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.

Differentiating lung cancer and infection based on measurements of extracellular pH with acidoCEST MRI

Lung cancer diagnosis via imaging may be confounded by the presence of indolent infectious nodules in imaging studies. This issue is pervasive in the southwestern US where coccidioidomycosis (Valley Fever) is endemic. AcidoCEST MRI is a noninvasive imaging method that quantifies the extracellular pH (pHe) of tissues in vivo, allowing tumor acidosis to be used as a diagnostic biomarker. Using murine models of lung adenocarcinoma and coccidoidomycosis, we found that average lesion pHe differed significantly between tumors and granulomas. Our study shows that acidoCEST MRI is a promising tool for improving the specificity of lung cancer diagnosis.

Thermosensitive Biodegradable Copper Sulfide Nanoparticles for Real-Time Multispectral Optoacoustic Tomography

Although multifunctional inorganic nanoparticles have been extensively explored for effective cancer diagnosis and therapy, their clinical translation has been greatly impeded because of significant uptake in the reticuloendothelial system and concerns about potential toxicity. In this study, we uncovered the thermosensitive biodegradability of CuS nanoparticles, which have classically been considered as stable in bulk state. Polyethylene glycol (PEG)-coated CuS nanoparticles (CuS-PEG) were well preserved at 4 ºC but were rapidly degraded at 37 ºC within 1 week in both in vitro and in vivo tests. Furthermore, real-time multispectral optoacoustic tomography, which is more convenient and accurate than traditional ex vivo analysis, was successfully employed to noninvasively demonstrate the biodegradability of CuS-PEG nanoparticles and dynamically monitor their tumor imaging capacity. The temperature-dependent controllable degradation profile and excellent tumor retention of CuS-PEG nanoparticles endows them with great potential for clinical applications since it ensures that the nanoparticles remain intact during production, transportation, and storage but degrade and clear from the body at physiological temperature after accomplishing sufficient diagnosis and therapeutic operations.

Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging

Herein we introduce a deep learning (DL) application engine (DLAE) system concept, present potential uses of it, and describe pathways for its integration in clinical workflows. An open-source software application was developed to provide a code-free approach to DL for medical imaging applications. DLAE supports several DL techniques used in medical imaging, including convolutional neural networks, fully convolutional networks, generative adversarial networks, and bounding box detectors. Several example applications using clinical images were developed and tested to demonstrate the capabilities of DLAE. Additionally, a model deployment example was demonstrated in which DLAE was used to integrate two trained models into a commercial clinical software package.

Summary of Imaging in 2020: Visualizing the Future of Healthcare with MR Imaging

The Imaging in 2020 meeting convenes biannually to discuss innovations in medical imaging. The 2018 meeting, titled "Visualizing the Future of Healthcare with MR Imaging," sought to encourage discussions of the future goals of MRI research, feature important discoveries, and foster scientific discourse between scientists from a variety of fields of expertise. Here, we highlight presented research and resulting discussions of the meeting.

Detection of Enzyme Activity and Inhibition during Studies in Solution, In Vitro and In Vivo with CatalyCEST MRI

PURPOSE

The detection of enzyme activities and evaluation of enzyme inhibitors have been challenging with magnetic resonance imaging (MRI). To address this need, we have developed a diamagnetic, nonmetallic contrast agent and a protocol known as catalyCEST MRI that uses chemical exchange saturation transfer (CEST) to detect enzyme activity as well as enzyme inhibition.

PROCEDURES

We synthesized a diamagnetic MRI contrast agent that has enzyme responsive and enzyme unresponsive CEST signals. We tested the ability of this agent to detect the activity of kallikrein 6 (KLK6) in biochemical solutions, in vitro and in vivo, with and without a KLK6 inhibitor.

RESULTS

The agent detected KLK6 activity in solution and also detected KLK6 inhibition by antithrombin III. KLK6 activity was detected during in vitro studies with HCT116 colon cancer cells, relative to the detection of almost no activity in a KLK6-knockdown HCT116 cell line and HCT116 cells treated with antithrombin III inhibitor. Finally, strong enzyme activity was detected within an in vivo HCT116 tumor model, while lower enzyme activity was detected in a KLK6 knockdown tumor model and in the HCT116 tumor model treated with antithrombin III inhibitor. In all cases, comparisons of the enzyme responsive and enzyme unresponsive CEST signals were critical for the detection of enzyme activity.

CONCLUSIONS

This study has established that catalyCEST MRI with an exogenous diaCEST agent can evaluate enzyme activity and inhibition in solution, in vitro and in vivo.

Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI

PURPOSE

We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T1 relaxation, CEST, and DCE MRI.

METHODS

The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms. We also used principal component analysis to reduce the dimensionality of entire CEST spectra and DCE signal evolutions, which were then analyzed using classification methods.

RESULTS

The T1 relaxation time constants, % CEST amplitudes at specific saturation frequencies, and the relative Ktrans and kep values from DCE MRI could not classify all three tumor types. However, the area under the curve from DCE signal evolutions could classify each tumor type. Principal component analysis was used to analyze the entire CEST spectrum and DCE signal evolutions, which predicted the correct tumor model with 87.5% and 85.1% accuracy, respectively.

CONCLUSIONS

Machine learning applied to the entire CEST spectrum improved the classification of the three tumor models, relative to classifications that used % CEST values at single saturation frequencies. A similar improvement was not attained with machine learning applied to T1 relaxation times or DCE signal evolutions, relative to more simplistic analysis methods.

Calcium carbonate nanoparticles stimulate tumor metabolic reprogramming and modulate tumor metastasis

AIM

CaCO₃ nanoparticles (nano-CaCO₃) can neutralize the acidic pHe of solid tumors, but the lack of intrinsic imaging signal precludes noninvasive monitoring of pH-perturbation in tumor microenvironment. We aim to develop a theranostic version of nano-CaCO₃ to noninvasively monitor pH modulation and subsequent tumor response.

MATERIALS & METHODS

We synthesized ferromagnetic core coated with CaCO₃ (magnetite CaCO₃). Magnetic resonance imaging (MRI) was used to determine the biodistribution and pH modulation using murine fibrosarcoma and breast cancer models.

RESULTS

Magnetite CaCO₃-MRI imaging showed that nano-CaCO₃ rapidly raised tumor pHe, followed by excessive tumor-associated acid production after its clearance. Continuous nano-CaCO₃ infusion could inhibit metastasis.

CONCLUSION

Nano-CaCO₃ exposure induces tumor metabolic reprogramming that could account for the failure of previous intermittent pH-modulation strategies to achieve sustainable therapeutic effect.

A Comparison of Iron Oxide Particles and Silica Particles for Tracking Organ Recellularization

Reseeding of decellularized organ scaffolds with a patient’s own cells has promise for eliminating graft versus host disease. This study investigated whether ultrasound imaging or magnetic resonance imaging (MRI) can track the reseeding of murine liver scaffolds with silica-labeled or iron-labeled liver hepatocytes. Mesoporous silica particles were created using the Stöber method, loaded with Alexa Flour 647 fluorophore, and conjugated with protamine sulfate, glutamine, and glycine. Fluorescent iron oxide particles were obtained from a commercial source. Liver cells from donor mice were loaded with the silica particles or iron oxide particles. Donor livers were decellularized and reperfused with silica-labeled or iron-labeled cells. The reseeded livers were longitudinally analyzed with ultrasound imaging and MRI. Liver biopsies were imaged with confocal microscopy and scanning electron microscopy. Ultrasound imaging had a detection limit of 0.28 mg/mL, while MRI had a lower detection limit of 0.08 mg/mL based on particle weight. The silica-loaded cells proliferated at a slower rate compared to iron-loaded cells. Ultrasound imaging, MRI, and confocal microscopy underestimated cell numbers relative to scanning electron microscopy. Ultrasound imaging had the greatest underestimation due to coarse resolution compared to the other imaging modalities. Despite this underestimation, both ultrasound imaging and MRI successfully tracked the longitudinal recellularization of liver scaffolds.

Characterization of D-maltose as a T2 -exchange contrast agent for dynamic contrast-enhanced MRI

Purpose

We sought to investigate the potential of D-maltose, D-sorbitol, and D-mannitol as T2 exchange magnetic resonance imaging (MRI) contrast agents. We also sought to compare the in vivo pharmacokinetics of D-maltose with D-glucose with dynamic contrast enhancement (DCE) MRI.

Methods

T1 and T2 relaxation time constants of the saccharides were measured using eight pH values and nine concentrations. The effect of echo spacing in a multiecho acquisition sequence used for the T2 measurement was evaluated for all samples. Finally, performances of D-maltose and D-glucose during T2-weighted DCE-MRI were compared in vivo.

Results

Estimated T2 relaxivities (r2) of D-glucose and D-maltose were highly and nonlinearly dependent on pH and echo spacing, reaching their maximum at pH=7.0 (~0.08mM−1 s−1). The r2 values of D-sorbitol and D-mannitol were estimated to be ~0.02mM−1 s−1 and were invariant to pH and echo spacing for pH ≤7.0. The change in T2 in tumor and muscle tissues remained constant after administration of D-maltose, whereas the change in T2 decreased in tumor and muscle after administration of D-glucose. Therefore, D-maltose has a longer time window for T2-weighted DCE-MRI in tumors.

Conclusion

We have demonstrated that D-maltose can be used as a T2 exchange MRI contrast agent. The larger, sustained T2-weighted contrast from D-maltose relative to D-glucose has practical advantages for tumor diagnoses during T2-weighted DCE-MRI.

Parallel Accumulation of Tumor Hyaluronan, Collagen, and Other Drivers of Tumor Progression

Purpose: The tumor microenvironment (TME) evolves to support tumor progression. One marker of more aggressive malignancy is hyaluronan (HA) accumulation. Here, we characterize biological and physical changes associated with HA-accumulating (HA-high) tumors.Experimental Design: We used immunohistochemistry, in vivo imaging of tumor pH, and microdialysis to characterize the TME of HA-high tumors, including tumor vascular structure, hypoxia, tumor perfusion by doxorubicin, pH, content of collagen. and smooth muscle actin (α-SMA). A novel method was developed to measure real-time tumor-associated soluble cytokines and growth factors. We also evaluated biopsies of murine and pancreatic cancer patients to investigate HA and collagen content, important contributors to drug resistance.Results: In immunodeficient and immunocompetent mice, increasing tumor HA content is accompanied by increasing collagen content, vascular collapse, hypoxia, and increased metastatic potential, as reflected by increased α-SMA. In vivo treatment of HA-high tumors with PEGylated recombinant human hyaluronidase (PEGPH20) dramatically reversed these changes and depleted stores of VEGF-A165, suggesting that PEGPH20 may also diminish the angiogenic potential of the TME. Finally, we observed in xenografts and in pancreatic cancer patients a coordinated increase in HA and collagen tumor content.Conclusions: The accumulation of HA in tumors is associated with high tIP, vascular collapse, hypoxia, and drug resistance. These findings may partially explain why more aggressive malignancy is observed in the HA-high phenotype. We have shown that degradation of HA by PEGPH20 partially reverses this phenotype and leads to depletion of tumor-associated VEGF-A165. These results encourage further clinical investigation of PEGPH20. Clin Cancer Res; 24(19); 4798-807. ©2018 AACR.

Abstract 3050: Imaging tissue extracellular pH for the differential diagnosis of coccidioidomycosis and lung cancer

Introduction: Our objective was to determine whether acidoCEST MRI—a novel, non-invasive MRI method that measures extracellular pH (pHe)—can differentiate between lung tumors and coccidioidomycosis (valley fever) granulomas within in vivo mouse models of lung cancer and valley fever.

Methods: To develop a spontaneous murine lung tumor model, A/J mice received orthotopic injections of urethane to induce formation of lung adenocarcinomas. The Valley Fever Center for Excellence at the University of Arizona infected SW mice with a BSL 2-compatible mutant Coccidioides strain, Δcps1, to develop our preclinical valley fever model. All scans were performed with a Bruker BioSpin 7T MRI system. For all MRI scans, mice were anesthetized with 2.0% isofluorane, respiration and body temperature were monitoreduring scans, and body temperature was maintained at 37 °C. Respiration-triggering (gating) was used in all imaging sequences to compensate for motion artifacts in the lung. For optimal gating, the mouse's respiration rate was maintained at &lt; 50 breaths per minute. Each mouse was scanned with acidoCEST MRI using 370 mg/mL Iopamidol (200 μL IV bolus, 400 μL/hr IV infusion). AcidoCEST MRI (3.5 μT, 300 ms imaging time, 6000 ms presaturation pulse) was performed according to previously published methods, updated with improved respiration gating. For acidoCEST MRI, the saturation pulse continued until terminated by the gating trigger. Spatial pHe maps of tumor and granuloma ROI were produced using Bloch fitting in Matlab 2014a. Average lesion pHe and iopamidol concentration we also recorded.

Results: AcidoCEST MRI was successfully applied to the in vivo imaging of murine lung tumors and coccidioidomycosis granulomas. Lung tumors demonstrated successful uptake of the iopamidol contrast agent, with an average concentration of approximately 40 mM. pHe values in the tumor ROI ranged from 6.5 to 7.2 with an average value of 6.64. Granulomas demonstrated successful uptake of iopamidol with an average concentration of about 75 mM. pHe values in granulomas ranged from 7.2 to 7.4 with an average value of 7.29.

Conclusion: AcidoCEST MRI may be used to quantify the pHe of murine lung tumors and coccidioidomycosis granulomas in vivo. Our results show that pHe is a promising biomarker for the differential diagnosis of coccidioidomycosis and lung cancer.

Citation Format: Leila Renee Lindeman, Edward A. Randtke, Lisa F. Shubitz, Christine M. Howison, Kyle M. Jones, Mark D. Pagel. Imaging tissue extracellular pH for the differential diagnosis of coccidioidomycosis and lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3050.

Abstract 3046: Evaluation of pancreatic ductal adenocarcinoma with acidoCEST MRI

Introduction: Our goal is to evaluate the role of using extracelluar pH (pHe) as a predictor of pancreatic ductal adenocarcinoma (PDAC) development using a non-invasive, in vivo imaging technique called acidoCEST MRI, to improve early detection of pancreatic tumors.

Methods: Spontaneous PDAC development was initiated by administering 14 caerulein injections over a 62 hour period to a KrasLSL.G12D/+; PdxCre mouse model. Caerulein induces pancreatitis which drives tumor development in the context of a Kras mutation. Animals were imaged with a Bruker BioSpin 7T MRI scanner with an acidoCEST MRI protocol at pre-injection, 1 hour and 48 hours post final injection of caerulein, and weekly until tumors reached a size of 200mm3. During all MR imaging, mice were anesthetized with 2.0% isofluorane, maintained at a respiration rate of 35-40 bpm, and maintained at a body temperature of 37°C. For acidoCEST MR imaging, mice were injected with 370 mg/mL iopamidol (200 μL IV bolus and 400 μL/hr IV infusion) and scanned with a 6 sec saturation time at 3.5 μT, with a 300 ms acquisition time. Pixel-wise parametric maps of pancreatic pHe values were generated via fitting the CEST spectrum with the Bloch-McConnell equations in MATLAB R2016a. Average pancreatic pHe was recorded.

Results: AcidoCEST MRI was successful in acquiring in vivo pHe of normal and tumor pancreatic tissue, both with sufficient uptake of the iopamidol contrast agent. The pHe values for healthy pancreatic tissue ranged from 6.83 to 7.33 with an average of 6.96 (n=14). 1 hour after the last caerulein injection, during pancreatic inflammation, pHe ranged from 6.78 to 7.00, with an average of 6.92 (n=6). Tumors began to reach a size of 200mm3 at 5 weeks post caerulein injections. In tumor tissue, pHe was 6.59 (n=1), demonstrating tumor acidosis.

Conclusions: We have demonstrated that acidoCEST MRI can be used to quantify pHe in healthy pancreatic tissue, pancreatitis, and pancreatic ductal adenocarcinoma. These pHe values will be used to determine if acidity in pancreatitis correlates with future development of PDAC and/or a more aggressive phenotype. Further pre-clinical studies using acidoCEST MRI may be performed to monitor early therapeutic response to pancreatic tumor treatment.

Citation Format: Rachel A. High, Edward A. Randtke, Kyle M. Jones, Mark D. Pagel. Evaluation of pancreatic ductal adenocarcinoma with acidoCEST MRI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3046.

A light-fluence-independent method for the quantitative analysis of dynamic contrast-enhanced multispectral optoacoustic tomography (DCE MSOT)

MultiSpectral Optoacoustic Tomography (MSOT) is an emerging imaging technology that allows for data acquisition at high spatial and temporal resolution. These imaging characteristics are advantageous for Dynamic Contrast Enhanced (DCE) imaging that can assess the combination of vascular flow and permeability. However, the quantitative analysis of DCE MSOT data has not been possible due to complications caused by wavelength-dependent light attenuation and variability in light fluence at different anatomical locations. In this work we present a new method for the quantitative analysis of DCE MSOT data that is not biased by light fluence. We have named this method the two-compartment linear standard model (2C-LSM) for DCE MSOT.

A comparison of exogenous and endogenous CEST MRI methods for evaluating in vivo pH

PURPOSE

Extracellular pH (pHe) is an important biomarker for cancer cell metabolism. Acido-chemical exchange saturation transfer (CEST) MRI uses the contrast agent iopamidol to create spatial maps of pHe. Measurements of amide proton transfer exchange rates (kex ) from endogenous CEST MRI were compared to pHe measurements by exogenous acido-CEST MRI to determine whether endogenous kex could be used as a proxy for pHe measurements.

METHODS

Spatial maps of pHe and kex were obtained using exogenous acidoCEST MRI and an endogenous CEST MRI analyzed with the omega plot method, respectively, to evaluate mouse kidney, a flank tumor model, and a spontaneous lung tumor model. The pHe and kex results were evaluated using pixelwise comparisons.

RESULTS

The kex values obtained from endogenous CEST measurements did not correlate with the pHe results from exogenous CEST measurements. The kex measurements were limited to fewer pixels and had a limited dynamic range relative to pHe measurements.

CONCLUSION

Measurements of kex with endogenous CEST MRI cannot substitute for pHe measurements with acidoCEST MRI. Whereas endogenous CEST MRI may still have good utility for evaluating some specific pathologies, exogenous acido-CEST MRI is more appropriate when evaluating pathologies based on pHe values. Magn Reson Med 79:2766-2772, 2018. © 2017 International Society for Magnetic Resonance in Medicine.