Acidosis-mediated increase in IFN-γ-induced PD-L1 expression on cancer cells as an immune escape mechanism in solid tumors

Immune checkpoint inhibitors have revolutionized cancer therapy, yet the efficacy of these treatments is often limited by the heterogeneous and hypoxic tumor microenvironment (TME) of solid tumors. In the TME, programmed death-ligand 1 (PD-L1) expression on cancer cells is mainly regulated by Interferon-gamma (IFN-γ), which induces T cell exhaustion and enables tumor immune evasion. In this study, we demonstrate that acidosis, a common characteristic of solid tumors, significantly increases IFN-γ-induced PD-L1 expression on aggressive cancer cells, thus promoting immune escape. Using preclinical models, we found that acidosis enhances the genomic expression and phosphorylation of signal transducer and activator of transcription 1 (STAT1), and the translation of STAT1 mRNA by eukaryotic initiation factor 4F (elF4F), resulting in an increased PD-L1 expression. We observed this effect in murine and human anti-PD-L1-responsive tumor cell lines, but not in anti-PD-L1-nonresponsive tumor cell lines. In vivo studies fully validated our in vitro findings and revealed that neutralizing the acidic extracellular tumor pH by sodium bicarbonate treatment suppresses IFN-γ-induced PD-L1 expression and promotes immune cell infiltration in responsive tumors and thus reduces tumor growth. However, this effect was not observed in anti-PD-L1-nonresponsive tumors. In vivo experiments in tumor-bearing IFN-γ-/- mice validated the dependency on immune cell-derived IFN-γ for acidosis-mediated cancer cell PD-L1 induction and tumor immune escape. Thus, acidosis and IFN-γ-induced elevation of PD-L1 expression on cancer cells represent a previously unknown immune escape mechanism that may serve as a novel biomarker for anti-PD-L1/PD-1 treatment response. These findings have important implications for the development of new strategies to enhance the efficacy of immunotherapy in cancer patients.

A machine learning approach that measures pH using acidoCEST MRI of iopamidol

Tumor acidosis is an important biomarker for aggressive tumors, and extracellular pH (pHe) of the tumor microenvironment can be used to predict and evaluate tumor responses to chemotherapy and immunotherapy. AcidoCEST MRI measures tumor pHe by exploiting the pH-dependent chemical exchange saturation transfer (CEST) effect of iopamidol, an exogenous CT agent repurposed for CEST MRI. However, all pH fitting methodologies for acidoCEST MRI data have limitations. Herein we present results of the application of machine learning for extracting pH values from CEST Z-spectra of iopamidol. We acquired 36,000 experimental CEST spectra from 200 phantoms of iopamidol prepared at five concentrations, five T1 values, and eight pH values at five temperatures, acquired at six saturation powers and six saturation times. We also acquired T1 , T2 , B1 RF power, and B0 magnetic field strength supplementary MR information. These MR images were used to train and validate machine learning models for the tasks of pH classification and pH regression. Specifically, we tested the L1-penalized logistic regression classification (LRC) model and the random forest classification (RFC) model for classifying the CEST Z-spectra for thresholds at pH 6.5 and 7.0. Our results showed that both RFC and LRC were effective for pH classification, although the RFC model achieved higher predictive value, and improved the accuracy of classification accuracy with CEST Z-spectra with a more limited set of saturation frequencies. Furthermore, we used LASSO and random forest regression (RFR) models to explore pH regression, which showed that the RFR model achieved higher accuracy and precision for estimating pH across the entire pH range of 6.2-7.3, especially when using a more limited set of features. Based on these results, machine learning for analysis of acidoCEST MRI is promising for eventual in vivo determination of tumor pHe.

A review of a strategic roadmapping exercise to advance clinical translation of photoacoustic imaging: From current barriers to future adoption

Photoacoustic imaging (PAI), also referred to as optoacoustic imaging, has shown promise in early-stage clinical trials in a range of applications from inflammatory diseases to cancer. While the first PAI systems have recently received regulatory approvals, successful adoption of PAI technology into healthcare systems for clinical decision making must still overcome a range of barriers, from education and training to data acquisition and interpretation. The International Photoacoustic Standardisation Consortium (IPASC) undertook an community exercise in 2022 to identify and understand these barriers, then develop a roadmap of strategic plans to address them. Here, we outline the nature and scope of the barriers that were identified, along with short-, medium- and long-term community efforts required to overcome them, both within and beyond the IPASC group.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

BACKGROUND

Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC.

PURPOSE

To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC.

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

A total of 108 patients with biopsy-proven TNBC who underwent NAST and definitive surgery during 2015-2020.

FIELD STRENGTH/SEQUENCE

A 3.0 T/rFOV single-shot diffusion-weighted echo-planar imaging sequence (DWI).

ASSESSMENT

Three scans were acquired longitudinally (pretreatment, after two cycles of NAST, and after four cycles of NAST). For each scan, 11 ADC histogram features (minimum, maximum, mean, median, standard deviation, kurtosis, skewness and 10th, 25th, 75th, and 90th percentiles) were extracted from tumors and from PTRs of 5 mm, 10 mm, 15 mm, and 20 mm in thickness with inclusion and exclusion of fat-dominant pixels.

STATISTICAL TESTS

ADC features were tested for prediction of pCR, both individually using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC), and in combination in multivariable models with k-fold cross-validation. A P value < 0.05 was considered statistically significant.

RESULTS

Fifty-one patients (47%) had pCR. Maximum ADC from PTR, measured after two and four cycles of NAST, was significantly higher in pCR patients (2.8 ± 0.69 vs 3.5 ± 0.94 mm2 /sec). The top-performing feature for prediction of pCR was the maximum ADC from the 5-mm fat-inclusive PTR after cycle 4 of NAST (AUC: 0.74; 95% confidence interval: 0.64, 0.84). Multivariable models of ADC features performed similarly for fat-inclusive and fat-exclusive PTRs, with AUCs ranging from 0.68 to 0.72 for the cycle 2 and cycle 4 scans.

DATA CONCLUSION

Quantitative ADC features from PTRs may serve as early predictors of the response to NAST in TNBC.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

Evaluations of the performances of PET and MRI in a simultaneous PET/MRI instrument for pre-clinical imaging

Background

PET/MRI is an attractive imaging modality due to the complementary nature of MRI and PET. Obtaining high quality small animal PET/MRI results is key for the translation of novel PET/MRI agents and techniques to the radiology clinic. To obtain high quality imaging results, a hybrid PET/MRI system requires additional considerations beyond the standard issues with separate PET and MRI systems. In particular, researchers must understand how their PET system affects the MR acquisitions and vice versa. Depending on the application, some of these effects may substantially influence image quality. Therefore, the goal of this report is to provide guidance, recommendations, and practical experiments for implementing and using a small animal PET/MRI instrument.

Results

Various PET and MR image quality parameters were tested with their respective modality alone and in the presence of both systems to determine how the combination of PET/MRI affects image quality. Corrections and calibrations were developed for many of these effects. While not all image characteristics were affected, some characteristics such as PET quantification, PET SNR, PET spatial resolution, PET partial volume effects, and MRI SNR were altered by the presence of both systems.

Conclusions

A full exploration of a new PET/MRI system before performing small animal PET/MRI studies is beneficial and necessary to ensure that the new instrument can produce highly accurate and precise PET/MR images.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00483-x.

Abstract 1302: Priming tumors using pH sensitizers potentiates the anti-tumor T cell response induced by immune checkpoint blockade therapy

While immune checkpoint blockade (ICB) treatments such as nivolumab and ipilimumab have spelled hope for many cancer patients, not all patients benefit from this form of therapy. One mechanism by which tumors suppress the anti-tumor T cell activity that results from ICB is through decreasing extracellular tumor pH (pHe), a function inherent to tumor cells due to the Warburg effect. We thus hypothesize that increasing pHe prior to ICB therapy potentiates the T cell-mediated anti-tumor effect of ICB treatment. To undergo this study, we first screened a panel of six inhibitors termed pH sensitizers, and found that esomeprazole significantly reduced the proton efflux rate (PER) of 4T1, but not B16-F10 cells. Furthermore, esomeprazole did not significantly reduce cell viability of either 4T1 or B16-F10 cells, and did not significantly inhibit T cell activity. Moving forward, our next objectives are to investigate the effects of esomeprazole and the combination of esomeprazole and ICB on tumor-bearing mice. Assessment of the tumor growth in response to esomeprazole and ICB and ex vivo interrogation of the immune cell infiltrate into the tumors will elucidate the efficacy of the treatment combination in terms of tumor control and anti-tumor immunity. Modification of pHe by esomeprazole will be examined by acidoCEST MRI, which will also provide insights as to the ideal pHe parameters for optimal ICB therapy in patients.

Citation Format: Renee Chin, Jorge de la Cerda, William Schuler, Mark D. Pagel. Priming tumors using pH sensitizers potentiates the anti-tumor T cell response induced by immune checkpoint blockade therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1302.

Review and consensus recommendations on clinical APT-weighted imaging approaches at 3T: Application to brain tumors

Amide proton transfer-weighted (APTw) MR imaging shows promise as a biomarker of brain tumor status. Currently used APTw MRI pulse sequences and protocols vary substantially among different institutes, and there are no agreed-on standards in the imaging community. Therefore, the results acquired from different research centers are difficult to compare, which hampers uniform clinical application and interpretation. This paper reviews current clinical APTw imaging approaches and provides a rationale for optimized APTw brain tumor imaging at 3 T, including specific recommendations for pulse sequences, acquisition protocols, and data processing methods. We expect that these consensus recommendations will become the first broadly accepted guidelines for APTw imaging of brain tumors on 3 T MRI systems from different vendors. This will allow more medical centers to use the same or comparable APTw MRI techniques for the detection, characterization, and monitoring of brain tumors, enabling multi-center trials in larger patient cohorts and, ultimately, routine clinical use.

Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved.

Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06

Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-03.

Development of a Nanoscale Chemical Exchange Saturation Transfer Magnetic Resonance Imaging Contrast Agent That Measures pH

AcidoCEST MRI can measure the extracellular pH (pHe) of the tumor microenvironment in mouse models of human cancers and in patients who have cancer. However, chemical exchange saturation transfer (CEST) is an insensitive magnetic resonance imaging (MRI) contrast mechanism, requiring a high concentration of small-molecule agent to be delivered to the tumor. Herein, we developed a nanoscale CEST agent that can measure pH using acidoCEST MRI, which may decrease the requirement for high delivery concentrations of agent. We also developed a monomer agent for comparison to the polymer. After optimizing CEST experimental conditions, we determined that the polymer agent could be used during acidoCEST MRI studies at 125-fold and 488-fold lower concentration than the monomer agent and iopamidol, respectively. We also determined that both agents can measure pH with negligible dependence on temperature. However, pH measurements with both agents were dependent on concentration, which may be due to concentration-dependent changes in hydrogen bonding and/or steric hindrance. We performed in vivo acidoCEST MRI studies using the three agents to study a xenograft MDA-MB-231 model of mammary carcinoma. The tumor pHe measurements were 6.33 ± 0.12, 6.70 ± 0.15, and 6.85 ± 0.15 units with iopamidol, the monomer agent, and polymer agent, respectively. The higher pHe measurements with the monomer and polymer agents were attributed to the concentration dependence of these agents. This study demonstrated that nanoscale agents have merit for CEST MRI studies, but consideration should be given to the dependence of CEST contrast on the concentration of these agents.

Simultaneous Evaluations of pH and Enzyme Activity with a CEST MRI Contrast Agent

The extracellular tumor microenvironment of many solid tumors has high acidosis and high protease activity. Simultaneously assessing both characteristics may improve diagnostic evaluations of aggressive tumors and the effects of anticancer treatments. Noninvasive imaging methods have previously been developed that measure extracellular pH or can detect enzyme activity using chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI). Herein, we developed a single-hybrid CEST agent that can simultaneously measure pH and evaluate protease activity using a combination of dual-power acidoCEST MRI and catalyCEST MRI. Our agent showed CEST signals at 9.2 ppm from a salicylic acid moiety and at 5.0 ppm from an aryl amide. The CEST signal at 9.2 ppm could be measured after selective saturation was applied at 1 and 4 μT, and these measurements could be used with a ratiometric analysis to determine pH. The CEST signal at 5.0 ppm from the aryl amide disappeared after the agent was treated with cathepsin B, while the CEST signal at 9.2 ppm remained, indicating that the agent could detect protease activity through the amide bond cleavage. Michaelis-Menten kinetics studies with catalyCEST MRI demonstrated that the binding affinity (as shown with the Michaelis constant K_M), the catalytic turnover rate (k_cat), and catalytic efficiency (k_cat/K_M) were each higher for cathepsin B at lower pH. The k_cat rates measured with catalyCEST MRI were lower than the comparable rates measured with liquid chromatography-mass spectrometry (LC-MS), which reflected a limitation of inherently noisy and relatively insensitive CEST MRI analyses. Although this level of precision limited catalyCEST MRI to semiquantitative evaluations, these semiquantitative assessments of high and low protease activity still had value by demonstrating that high acidosis and high protease activity can be used as synergistic, multiparametric biomarkers.

Projection-based stereolithography for direct 3D printing of heterogeneous ultrasound phantoms

Modern ultrasound (US) imaging is increasing its clinical impact, particularly with the introduction of US-based quantitative imaging biomarkers. Continued development and validation of such novel imaging approaches requires imaging phantoms that recapitulate the underlying anatomy and pathology of interest. However, current US phantom designs are generally too simplistic to emulate the structure and variability of the human body. Therefore, there is a need to create a platform that is capable of generating well-characterized phantoms that can mimic the basic anatomical, functional, and mechanical properties of native tissues and pathologies. Using a 3D-printing technique based on stereolithography, we fabricated US phantoms using soft materials in a single fabrication session, without the need for material casting or back-filling. With this technique, we induced variable levels of stable US backscatter in our printed materials in anatomically relevant 3D patterns. Additionally, we controlled phantom stiffness from 7 to >120 kPa at the voxel level to generate isotropic and anisotropic phantoms for elasticity imaging. Lastly, we demonstrated the fabrication of channels with diameters as small as 60 micrometers and with complex geometry (e.g., tortuosity) capable of supporting blood-mimicking fluid flow. Collectively, these results show that projection-based stereolithography allows for customizable fabrication of complex US phantoms.

AcidoCEST MRI Evaluates the Bone Microenvironment in Multiple Myeloma

PURPOSE

Multiple myeloma (MM) is an incurable disease of malignant plasma cells in the bone marrow (BM). Adaptive responses to hypoxia may be an essential element in MM progression and drug resistance. This metabolic adaptation involves a decrease in extracellular pH (pHe), and it depends on the upregulation of glucose transporters (GLUTs) that is common in hypoxia and in cancer cells. CEST MRI is an imaging technique that assesses pHe indirectly by the exchange rate of magnetic saturation transfer between labile protons on a solute and water. Thus, this study aimed to determine the feasibility of acidoCEST MRI for pHe measurement using an orthotopic mouse model of MM compared with GLUT1 immunofluorescence staining as a reference.

PROCEDURES

Orthotopic BM engrafted MM xenografts were established in NSG/NOD mice using the human RPMI8226 myeloma cell line. AcidoCEST MRI was performed approximately 6 weeks after intravenous challenge, before and after intravenous administration of iopamidol. BM pHe values were generated via fitting the CEST spectrum with the Bloch-McConnell equations. Samples were decalcified, sectioned, and immunostained for GLUT1 expression. Pearson's correlation was used to assess the relationship between pHe and [H3O+] versus GLUT1 expression.

RESULTS

Ten mice underwent acidoCEST MRI followed by immunofluorescent histologic analysis. A strong negative correlation was seen between pHe versus GLUT1 expression (r = - 0.75, p < 0.001). After transformation of pH to [H3O+], a strong positive correlation between [H3O+] and GLUT1 expression was observed (r = 0.8, p < 0.001).

CONCLUSIONS

AcidoCEST MRI can measure the extracellular pH of bone marrow affected by multiple myeloma. In this MM orthotopic mouse model, pHe measured by acidoCEST MRI showed strong correlations with the metabolic phenotype of BM tumor assessed by immunofluorescent histological assessment of GLUT1 overexpression.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study

Purpose To determine if amide proton transfer-weighted chemical exchange saturation transfer (APT_W CEST) MRI is useful in the early assessment of treatment response in persons with triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, a total of 51 participants (mean age, 51 years [range, 26-79 years]) with TNBC were included who underwent APT_W CEST MRI with 0.9- and 2.0-µT saturation power performed at baseline, after two cycles (C2), and after four cycles (C4) of neoadjuvant systemic therapy (NAST). Imaging was performed between January 31, 2019, and November 11, 2019, and was a part of a clinical trial (registry number NCT02744053). CEST MR images were analyzed using two methods-magnetic transfer ratio asymmetry (MTR_asym) and Lorentzian line shape fitting. The APT_W CEST signals at baseline, C2, and C4 were compared for 51 participants to evaluate the saturation power levels and analysis methods. The APT_W CEST signals and their changes during NAST were then compared for the 26 participants with pathology reports for treatment response assessment. Results A significant APT_W CEST signal decrease was observed during NAST when acquisition at 0.9-µT saturation power was paired with Lorentzian line shape fitting analysis and when the acquisition at 2.0 µT was paired with MTR_asym analysis. Using 0.9-µT saturation power and Lorentzian line shape fitting, the APT_W CEST signal at C2 was significantly different from baseline in participants with pathologic complete response (pCR) (3.19% vs 2.43%; P = .03) but not with non-pCR (2.76% vs 2.50%; P > .05). The APT_W CEST signal change was not significant between pCR and non-pCR at all time points. Conclusion Quantitative APT_W CEST MRI depended on optimizing acquisition saturation powers and analysis methods. APT_W CEST MRI monitored treatment effects but did not differentiate participants with TNBC who had pCR from those with non-pCR. © RSNA, 2021 Clinical trial registration no. NCT02744053 Supplemental material is available for this article.Keywords Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, MR-Imaging, Breast, Technical Aspects, Tumor Response, Technology Assessment.

Computer-aided detection of mantle cell lymphoma on 18F-FDG PET/CT using a deep learning convolutional neural network

¹⁸F-FDG PET/CT can provide quantitative characterization with prognostic value for mantle cell lymphoma (MCL). However, detection of MCL is performed manually, which is labor intensive and not a part of the routine clinical practice. This study investigates a deep learning convolutional neural network (DLCNN) for computer-aided detection of MCL on ¹⁸F-FDG PET/CT. We retrospectively analyzed 142 baseline ¹⁸F-FDG PET/CT scans of biopsy-confirmed MCL acquired between May 2007 and October 2018. Of the 142 scans, 110 were from our institution and 32 were from outside institutions. An Xception-based U-Net was constructed to classify each pixel of the PET/CT images as MCL or not. The network was first trained and tested on the within-institution scans by applying five-fold cross-validation. Sensitivity and false positives (FPs) per patient were calculated for network evaluation. The network was then tested on the outside-institution scans, which were excluded from network training. For the 110 within-institution patients (85 male; median age, 58 [range: 39-84] years), the network achieved an overall median sensitivity of 88% (interquartile range [IQR]: 25%) with 15 (IQR: 12) FPs/patient. Sensitivity was dependent on lesion size and SUV_max but not on lesion location. For the 32 outside-institution patients (24 male; median age, 59 [range: 40-67] years), the network achieved a median sensitivity of 84% (IQR: 24%) with 14 (IQR: 10) FPs/patient. No significant performance difference was found between the within and outside institution scans. Therefore, DLCNN can potentially help with MCL detection on ¹⁸F-FDG PET/CT with high sensitivity and limited FPs.

Measuring Kidney Perfusion, pH, and Renal Clearance Consecutively Using MRI and Multispectral Optoacoustic Tomography

Purpose: To establish multi-modal imaging for the assessment of kidney pH, perfusion, and clearance rate using magnetic resonance imaging (MRI) and multispectral optoacoustic tomography (MSOT) in healthy mice. Kidney pH and perfusion values were measured on a pixel-by-pixel basis using the MRI acidoCEST and FAIR-EPI methods. Kidney filtration rate was measured by analyzing the renal clearance rate of IRdye 800 using MSOT. To test the effect of one imaging method on the other, a set of 3 animals were imaged with MSOT followed by MRI, and a second set of 3 animals were imaged with MRI followed by MSOT. In a subsequent study, the reproducibility of pH, perfusion, and renal clearance measurements were tested by imaging 4 animals twice, separated by 4 days. The contrast agents used for acidoCEST based pH measurements influenced the results of MSOT. Specifically, the exponential decay time from the kidney cortex, as measured by MSOT, was significantly altered when MRI was performed prior to MSOT. However, no significant difference in the cortex to pelvis area under the curve (AUC) was noted. When the order of experiments was reversed, no significant differences were noted in the pH or perfusion values. Reproducibility measurements demonstrated similar pH and cortex to pelvis AUC; however, perfusion values were significantly different with the cortex values being higher and the pelvic values being lower in the second imaging time. We demonstrate that using a combination of MRI and MSOT, physiological measurements of pH, blood flow, and clearance rates can be measured in the mouse kidney in the same imaging session.

Effect of sarcopenia on systemic targeted therapy response in patients with advanced hepatocellular carcinoma

PURPOSE

Sarcopenia is an independent prognostic indicator for hepatocellular carcinoma (HCC). Our objective was to determine the effect of sarcopenia on response to systemic targeted therapy in patients with advanced HCC.

MATERIALS AND METHODS

This was a retrospective, Institutional Review Board approved study of 36 patients on systemic targeted therapy with immune checkpoint blockade (n = 25) or tyrosine kinase inhibitor (n = 11) for biopsy-proven advanced HCC. Skeletal muscle index (SMI) was calculated from erector spinae muscle area (SMA) at the level of T12 on pretreatment CT: [SMI = SMA (cm2)/height (m2)]. SMI was compared to treatment response defined as overall survival ≥ 1 year (nonsurgical patients) or > 50% HCC necrosis (surgical patients). Receiver operating characteristic curve and area under the curve was used for analysis with p < 0.05 for statistical significance.

RESULTS

Median age of men and women was 66.5 years (range 32-83) and 70 years (range 54-78), respectively. Liver disease etiology was nonalcoholic steatohepatitis (n = 9), hepatitis C (n = 10), hepatitis B (n = 5), alcohol (n = 3) and unknown (n = 9). Mean (± SD) height and SMI for men were 1.7 m (± 0.1) and 11.4 (± 3.6); values for women were 1.7 m (± 0.1) and 8.2 (± 1.9). Treatment was withdrawn in five patients due to treatment intolerance. Response occurred in 10/31 (32.3%) patients (23 men, 8 women). T12SMI correlated with treatment response using a threshold of 7.21-8.23 for women (AUC = 1; p = 0.037), and 11.47 for men (AUC = 0.83; p = 0.015); correlation was increased for men ≥ 60 years, (AUC = 0.87; p = 0.023).

CONCLUSION

Sarcopenia was associated with reduced survival and HCC necrosis in patients treated with systemic targeted therapy.

CLINICAL RELEVANCE

Sarcopenia may help in predicting outcomes to targeted therapy in advanced HCC.

Abstract PD6-06: Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

Background and Purpose:Early and accurate assessment ofbreast cancer response to NAST is important for patient management. In this study, we investigated the value of radiomic phenotypes derived from semi-quantitative and quantitative DCE-MRI parametric maps for early prediction of NASTresponse in TNBC patients. MATERIALS AND METHODS:This IRB approved study included 74 patients with stage I-III TNBC who were enrolled in the prospective ARTEMIS trial (NCT02276443). Pathologic complete response (pCR) and non-pCR were assessed by surgical histopathology after NAST (pCR=34; non-pCR=40).MRI scans were obtained at 3 time points during the NAST treatment with every 2-week anthracycline-based chemotherapy (AC): at baseline (BSL=74), post-2 cycles of AC (C2= 27) and post-4 cycles of AC (C4= 27). Patients went on to receive taxane-based chemotherapy prior to surgery. Tumor regions of interest (ROIs) were segmented by a breast radiologist at the early-phase subtractions of DCE-MRI scans using in-house developed software, followed by co-registration of the ROIs with quantitative (Ktrans, Veand Kep), and semi-quantitative DCE parametric maps (Maximum Slope Increase (MSI), Positive Enhancement Integral (PEI) and Peak Signal Enhancement Ratio (SER)).A total of 93 first order radiomic features were extracted from the tumor ROIs of each time point semi-quantitative DCE parametric map, while a total of 390 extracted radiomic features (first order-histogram features and second order grey-level-co-occurrence matrix) were extracted from each quantitative DCE parametric map using an in-house developed Matlab software.Radiomic features at each time point and changes between the 3 time points were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. Area under the receiver operating characteristics curve (AUC) was used to determine which features predicted pCR.Logistic regression was performed for feature selection, and used to build the radiomic phenotype model. The model performance was assessed by leave-one-out cross validation and 3-fold cross validation. RESULTS:Thirty-three radiomic features from PEI map were significantly different between pCR and non-pCR. The PEI most significant features were changesbetween BSL and C4 in skewness, mean and median (AUC=0.87, 0.85 and 0.87, p=&lt;0.001, 0.001 and 0.002 respectively). Additionally, 31 MSI features were significantly different between pCR and non-pCR. The top 2 features were the interscan-change in skewness between BSL and C2 (AUC=0.80, P=0.007) and C4 standard deviation (AUC=0.80, P=0.006). Four BSL Veradiomic features were statistically significant between pCR and non-pCR with the best being range of difference variance (AUC=0.64, P=0.03). One BSL Kepfeature (Angular-Variance of Information measure of correlation-2) was able to differentiate pCR from non-pCR (AUC=0.64, P=0.04). Five C4-Ktrans features were able to differentiate pCR and non-pCR, with the most significant being mean value (AUC=0.86, P=0.001). BSL-Kepradiomic model built from 24 features (AUC=0.80, p=0.003) and combined (Ktrans, Veand Kep)C2-radiomic model consisting of 20 features (AUC=0.97, p=0.01) showed the best performance for prediction of pCR. CONCLUSIONS:Radiomic phenotypes form DCE-MRI parametric maps were useful for differentiation between pCR and non-pCR and showed promise as noninvasive imaging biomarkers for early prediction of NAST response in TNBC. Potentially, DCE-MRI radiomic features may be used for development of diagnostic predictive model for early noninvasive assessment of NAST treatment response in TNBC patients.

Citation Format: Nabil Elshafeey, Beatriz E Adrada, Rosalind P Candelaria, Abeer H Abdelhafez, Benjamin C Musall, Jia Sun, Medine Boge, Rania M.M Mohamed, Hagar S Mahmoud, Jong Bum Son, Aikaterini Kotrosou, Shu Zhang, Jessica Leung, Deanna Lane, Marion Scoggins, David Spak, Elsa Arribas, Lumarie Santiago, Gary J. Whitman, Huong T Le-Petross, Tanya W Moseley, Jason B White, Elizabeth Ravenberg, Ken-Pin Hwang, Peng Wei, Jennifer K Litton, Lei Huo, Debu Tripathy, Vicente Valero, Alastair M Thompson, Stacy Moulder, Wei T Yang, Mark D Pagel, Jingfei Ma, Gaiane M Rauch. Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-06.

Abstract PS3-08: Assessment of early response to neoadjuvant systemic therapy (NAST) of triple-negative breast cancer (TNBC) using chemical exchange saturation transfer (CEST) MRI: A pilot study

Introduction CEST MRI permits quantitation of macromolecules such as amide proteins that are of interest in cancer metabolism. However, optimal CEST acquisition and analysis methods remain undetermined. In this study, we investigated CEST MRI as an imaging biomarker for early treatment response in 51 TNBC patients receiving NAST and compared the performance with two different CEST saturation power levels and two analysis methods.

Methods A total of 51 stage I-III TNBC patients enrolled in the prospective ARTEMIS trial (NCT02276443) had CEST imaging performed on a 3T MRI scanner at baseline before NAST (BL, N = 51), after 2 cycles (C2, N = 37), and 4 cycles (C4, N = 44) of NAST. 33 of the 51 patients had imaging at all 3 time points. 29 of the 33 patients had pathological findings, with N = 16 with pathological complete response (pCR) and N = 13 with non-pCR. Two sets of CEST images using 0.9 and 2.0 µT saturation power levels were acquired and analyzed using the magnetization transfer ratio asymmetry (MTRasym) and the Lorentzian line fitting (Mag3.5) methods, for a total of 4 acquisition/analysis combinations. The group averaged CEST signals, MTRasym at 0.9 and 2.0 µT and Mag3.5 at 0.9 and 2.0 µT, at BL, C2 and C4 were determined and evaluated using unpaired (51 patients) and paired (33 patients) Kruskal-Wallis tests. The Mag3.5 at 0.9 µT and the MTRasym at 2.0 µT were further compared between pCR and non-pCR. The group averaged CEST signals at BL, C2, and C4 were evaluated using the Friedman test for the pCR and the non-PCR groups. Separately, the change in the CEST signal from BL to C2 and C4 was determined for each patient and evaluated using the Mann-Whitney test for both groups. P &lt; 0.05 was considered statistically significant.

Results The MTRasym at BL was higher at 2.0 µT than at 0.9 µT. In contrast, the Mag3.5 at BL was higher at 0.9 µT than at 2.0 µT. The MTRasym at 2.0 µT and the Mag3.5 at 0.9 µT decreased during treatment while the MTRasym at 0.9 µT and the Mag3.5 at 2.0 µT were similar. Both the unpaired and the paired Mag3.5 at 0.9 µT showed a significant decrease at C2 and C4 vs. BL (p &lt; 0.01). The unpaired and paired MTRasym at 2.0 µT showed a decrease, although the change was not significant except for the unpaired data at C4. The decrease in the group averaged Mag3.5 at 0.9 µT was significant at C2 vs. BL for the pCR group (p = 0.04), while it was not significant for the pCR group at C4 vs. BL and for the non-pCR group at either C2 or C4 vs. BL. The group averaged MTRasym at 2.0 µT changes were not significant for either the pCR or the non-pCR groups. None of the CEST signal changes on a per patient basis at C2-BL, C4-BL and C4-C2 were significantly different between the pCR and the non-pCR groups. Further, none of the group averaged CEST signals at BL, C2 and C4 were significantly different between the pCR and the non-pCR groups.

Conclusion Our study demonstrates that the CEST quantitation in TNBC patients undergoing NAST depends on acquisition and analysis. For a maximum change in the CEST effect, Lorentzian line fitting is better paired with acquisition at a low saturation power (0.9 µT) and MTRasym is better paired with acquisition at a high saturation power (2.0 µT). Further, a significant CEST signal decrease was observed in TNBC patients with pCR after NAST when a 0.9 µT saturation power and the Lorentzian line fitting were used. In comparison, the decrease was not significant in non-pCR patients using the same saturation power and analysis method. The results suggest that the CEST signal acquired at 0.9 µT saturation power and analyzed using Lorentzian line fitting may be able to differentiate between pCR and non-pCR among TNBC patients undergoing NAST. Additional studies with a larger patient population are ongoing to further validate our findings and their potential for determining pCR.

Citation Format: Shu Zhang, Gaiane M Rauch, Beatriz E Adrada, Medine Boge, Rania MM Mohamed, Abeer H Abdelhafez, Jong Bum Son, Jia Sun, Nabil A Elshafeey, Jason B White, Deanna L Lane, Jessica WT Leung, Marion E Scoggins, David A Spak, Elsa Arribas, Elizabeth Ravenberg, Lumarie Santiago, Tanya W Moseley, Gary J Whitman, Huong Le-Petross, Benjamin C Musall, Mitsuharu Miyoshi, Xinzeng Wang, Brandy Willis, Stacy Hash, Aikaterini Kotrotsou, Peng Wei, Ken-Pin Hwang, Alastair Thompson, Stacy L Moulder, Rosalind P Candelaria, Wei Yang, Jingfei Ma, Mark D Pagel. Assessment of early response to neoadjuvant systemic therapy (NAST) of triple-negative breast cancer (TNBC) using chemical exchange saturation transfer (CEST) MRI: A pilot study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-08.

Abstract PD6-07: Volumetric changes on longitudinal dynamic contrast enhanced MR imaging (DCE-MRI) as an early treatment response predictor to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

Background and Purpose:There is currently a lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients with recent reports showing that breast MRI is the most accurate modality for evaluation of NAST response. DCE-MRI evaluates tumor perfusion that influences tumor enhancement at the post-contrast subtraction images and allows for more accurate measurement of changes in tumor volume during NAST. In this study, we evaluated the ability of tumor volumetric changes after 2 and 4 cycles of NAST by longitudinal ultrafast DCE-MRI to predict pathologic complete response (pCR) in TNBC undergoing NAST. Materials and Methods: Stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (ARTEMIS, NCT02276433) who had ultrafast DCE-MRI at baseline (BL, N=103), post 2 cycles (C2, N=59), and post 4 cycles (C4, N=103) of anthracycline-based NAST,and had surgery, were included in this analysis. Tumor volume was calculated using 3D measurements of the index lesion at BL, C2, and C4. Percent change of tumor volume (%TV) between BL, C2, and C4 was calculated at early (9-12 sec) and delayed (360-480 sec) phases of DCE-MRI. The largest lesion was used for analysis in patients with multicentric or multifocal disease. Demographic, clinical, and pathologic data and treatment response at surgery (pCR versus non-pCR) were documented. Receiver operating characteristics curve (ROC) analysis was performed for prediction of pCR status. Positive predictive value (PPV), negative predictive value (NPV) and Youden Index were used to select %TV cut-off thresholds for pCR prediction.Results: 103 patients (median age, 53 years; range, 24-79 years) were included, 48 (47%) had pCR, and 55 (53%) had non-pCR at surgical pathology. The %TV reduction at C2 DCE-MRI was predictive of pCR on both early phase DCE MRI (AUC, 0.873; CI:0.779-0.968, p &lt; .0001) and delayed phase DCE MRI (AUC, 0.844; CI:0.742-0.947, p &lt; .0001). Optimal thresholds were as follows: 70% TV reduction on early phase DCE MRI with Youden’s index of 1.58 was able to predict pCR correctly for 79% of patients with PPV of 81%; 75% TV reduction on delayed phase with Youden’s Index of 1.44 was able to predict pCR correctly for 71% of patients with PPV of 85%.%TV reduction was also predictive of pCR at the C4 time point on both early phase DCE MRI (AUC, 0.761; CI:0.665-0.856, p &lt; .0001) and delayed phase DCE MRI (AUC, 0.737; CI:0.641-0.833, p &lt; .0001). Optimal thresholds were as follows: 90% TV reduction on early phase DCE MRI with Youden’s index of 1.43 was able to correctly predict pCR in 72% of patients with PPV of 70%; and 90% TV reduction on delayed phase with Youden’s Index of 1.34 was able to predict pCR correctly in 68% of patients with PPV of 71%.Conclusion: Our data shows that percent tumor volume reduction by DCE-MRI after 2 and 4 cycles of NAST was able to predict pCR in TNBC with high accuracy and can be used as an early imaging biomarker of NAST response prediction. Volumetric changes by longitudinal DCE-MRI can be used to differentiate chemoresistant and chemosensitive TNBC patients as early as after 2 cycles of NAST, and can help to triage patients for treatment de-escalation or targeted therapy.

Citation Format: Gaiane Margishvili Rauch, Adrada E Beatriz, Rosalind P Candelaria, Nabil Elshafeey, Abeer H Abdelhafez, Benjamin C Musall, Jia Sun, Medina Boge, Rania M.M Mohamed, Jong Bum Son, Shu Zhang, Jessica Leung, Deanna Lane, Marion Scoggins, David Spak, Elsa Arribas, Lumarie Santiago, Gary J Whitman, Huong T. Le-Petross, Tanya W Moseley, Jason B. White, Elizabeth Ravenberg, Ken-Pin Hwang, Peng Wei, Lei Huo, Jennifer K Litton, Vicente Valero, Debu Tripathy, Alastair M Thompson, Mark D Pagel, Jingfei Ma, Wei T Yang, Stacy Moulder. Volumetric changes on longitudinal dynamic contrast enhanced MR imaging (DCE-MRI) as an early treatment response predictor to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-07.

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.

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.

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.

Clinical Translation of Tumor Acidosis Measurements with AcidoCEST MRI

PURPOSE

We optimized acido-chemical exchange saturation transfer (acidoCEST) magnetic resonance imaging (MRI), a method that measures extracellular pH (pHe), and translated this method to the radiology clinic to evaluate tumor acidosis.

PROCEDURES

A CEST-FISP MRI protocol was used to image a flank SKOV3 tumor model. Bloch fitting modified to include the direct estimation of pH was developed to generate parametric maps of tumor pHe in the SKOV3 tumor model, a patient with high-grade invasive ductal carcinoma, and a patient with metastatic ovarian cancer. The acidoCEST MRI results of the patient with metastatic ovarian cancer were compared with DCE MRI and histopathology.

RESULTS

The pHe maps of a flank model showed pHe measurements between 6.4 and 7.4, which matched with the expected tumor pHe range from past acidoCEST MRI studies in flank tumors. In the patient with metastatic ovarian cancer, the average pHe value of three adjacent tumors was 6.58, and the most reliable pHe measurements were obtained from the right posterior tumor, which favorably compared with DCE MRI and histopathological results. The average pHe of the kidney showed an average pHe of 6.73 units. The patient with high-grade invasive ductal carcinoma failed to accumulate sufficient agent to generate pHe measurements.

CONCLUSIONS

Optimized acidoCEST MRI generated pHe measurements in a flank tumor model and could be translated to the clinic to assess a patient with metastatic ovarian cancer.

Imaging Lung Cancer by Using Chemical Exchange Saturation Transfer MRI With Retrospective Respiration Gating

Performing chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) in lung tissue is difficult because of motion artifacts. We, therefore, developed a CEST MRI acquisition and analysis method that performs retrospective respiration gating. Our method used an acquisition scheme with a short 200-millisecond saturation pulse that can accommodate the timing of the breathing cycle, and with saturation applied at frequencies in 0.03-ppm intervals. The Fourier transform of each image was used to calculate the difference in phase angle between adjacent pixels in the longitudinal direction of the respiratory motion. Additional digital filtering techniques were used to evaluate the breathing cycle, which was used to construct CEST spectra from images during quiescent periods. Results from CEST MRI with and without respiration gating analysis were used to evaluate the asymmetry of the magnetization transfer ratio (MTR_asym), a measure of CEST, for an egg white phantom that underwent cyclic motion, in the liver of healthy patients, as well as liver and tumor tissues of patients diagnosed with lung cancer. Retrospective respiration gating analysis produced more precise measurements in all cases with significant motion compared with nongated analysis methods. Finally, a preliminary clinical study with the same respiration-gated CEST MRI method showed a large increase in MTR_asym after radiation therapy, a small increase or decrease in MTR_asym after chemotherapy, and mixed results with combined chemoradiation therapy. Therefore, our retrospective respiration-gated method can improve CEST MRI evaluations of tumors and organs that are affected by respiratory motion.