To evaluate noninvasive molecular imaging methods as correlative biomarkers of therapeutic efficacy of cetuximab in human colorectal cancer cell line xenografts grown in athymic nude mice. The correlation between molecular imaging and immunohistochemical analysis to quantify epidermal growth factor (EGF) binding, apoptosis, and proliferation was evaluated in treated and untreated tumor-bearing cohorts.Optical imaging probes targeting EGF receptor (EGFR) expression (NIR800-EGF) and apoptosis (NIR700-Annexin V) were synthesized and evaluated in vitro and in vivo. Proliferation was assessed by 3'-[18F]fluoro-3'-deoxythymidine ([18F]FLT) positron emission tomography. Assessment of inhibition of EGFR signaling by cetuximab was accomplished by concomitant imaging of NIR800-EGF, NIR700-Annexin V, and [18F]FLT in cetuximab-sensitive (DiFi) and insensitive (HCT-116) human colorectal cancer cell line xenografts. Imaging results were validated by measurement of tumor size and immunohistochemical analysis of total and phosphorylated EGFR, caspase-3, and Ki-67 immediately following in vivo imaging.NIR800-EGF accumulation in tumors reflected relative EGFR expression and EGFR occupancy by cetuximab. NIR700-Annexin V accumulation correlated with cetuximab-induced apoptosis as assessed by immunohistochemical staining of caspase-3. No significant difference in tumor proliferation was noted between treated and untreated animals by [18F]FLT positron emission tomography or Ki-67 immunohistochemistry.Molecular imaging can accurately assess EGF binding, proliferation, and apoptosis in human colorectal cancer xenografts. These imaging approaches may prove useful for serial, noninvasive monitoring of the biological effects of EGFR inhibition in preclinical studies. It is anticipated that these assays can be adapted for clinical use.
Abstract Introduction. Dynamic contrast-enhanced (DCE) MRI provides quantitative information on tissue properties that enhances the specificity of breast cancer diagnosis; however, mammography remains the standard screening protocol due to its lower cost. There is a push to develop more accessible abbreviated breast MRI scans for screening high-risk patients without compromising diagnostic power. Here we analyze the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the diagnostic performance of quantitative DCE-MRI. Methods and Results. We evaluate the ability of five quantitative measures to retrospectively differentiate malignant (N=21) and benign (N=24) lesions using DCE-MRI data acquired with 15 second temporal resolution in a cohort of 45 patients from the ACRIN 6883 multi-site breast trial. The first two measures are the volume transfer constant (Ktrans) estimated by, respectively, fitting the standard Kety-Tofts (STK) perfusion model and the Patlak approximation of the STK model (Patlak model) to patient data sets that have been truncated into a series of abbreviated-time courses (ATCs). An ATC is defined as containing the first n time points of a time course and is referred to as “ATC n.” For the first measure, n is the inclusive set of integers from 7 to 14, and, for the second measure, n is the set of 4 and 7 since the Patlak approximation only holds during the initial enhancement. For each patient, the fitting procedure provides Ktrans values for each voxel within the region of interest (ROI). The values are averaged and statistically evaluated for performance in discriminating malignant from benign tumors. For Ktrans, the maximum AUC of 0.61 was achieved using ATC 14 (i.e., 3.50 minutes), while for the Patlak model, the maximum AUC of 0.55 was achieved using ATC 7 (i.e., 1.75 minutes). The third measure is a modified median signal enhancement ratio (SER) computed for a series of four ATCs per patient, where n is now the inclusive set of integers 8 through 11. The SER is defined as (S1-S0)/(S2-S0), where S0 is the pre-contrast signal, S1 is the peak enhancement, and S2 is the signal at the last time point. Abbreviating the time course effectively shifts S2 to an earlier time point. For each ATC for each patient, the SERs are computed for all voxels within the ROI after which the median is computed. We calculate the AUC under the ROC curve to evaluate the diagnostic performance of each ATC-derived median SER. ATC 10 (i.e., 2.5 minutes) yields a maximum AUC of 0.79 among all ATCs. The fourth and fifth measures are, respectively, the area under the enhancement phase and the slope of the washout phase of the patient DCE time courses. The fourth measure is computed by numerically integrating the time course of each voxel within a patient’s ROI between the first and seventh time points and averaging the values. Similarly, for the fifth measure, the slope is calculated between the seventh and last time points within the ROI and averaged. We find that the fourth measure yields an AUC under the ROC curve of 0.76, and the fifth measure yields an AUC of 0.77. Discussion and Conclusion. The highest AUC from the two pharmacokinetic measures is 0.61, which suggests ineffective diagnostic ability in this data set. The median SER computed using ATC10 yields an AUC of 0.79, showing promise as a quantitative diagnostic tool in the abbreviated scan setting. Inter-site variability and the low temporal resolution of this data set may explain the relatively lower performance of measures one, two, four, and five as compared to measure three. It is necessary to repeat this analysis using a state-of-the-art acquisition of DCE-MRI data to determine the specificity of the remaining quantitative measures to imaging specifications. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Investigating the feasibility of performing quantitative DCE-MRI in an abbreviated breast examination [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 P6-02-04.
The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.
Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population (P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license.
Abstract Glucose plays a central role in tumor metabolism and development and is a target for novel therapeutics. To characterize the response of cancer cells to blockade of glucose uptake, we collected time-resolved microscopy data to track the growth of MDA-MB-231 breast cancer cells. We then developed a mechanism-based, mathematical model to predict how a glucose transporter (GLUT1) inhibitor (Cytochalasin B) influences the growth of the MDA-MB-231 cells by limiting access to glucose. The model includes a parameter describing dose dependent inhibition to quantify both the total glucose level in the system and the glucose level accessible to the tumor cells. Four common machine learning models were also used to predict tumor cell growth. Both the mechanism-based and machine learning models were trained and validated, and the prediction error was evaluated by the coefficient of determination ( R 2 ). The random forest model provided the highest accuracy predicting cell dynamics ( R 2 = 0.92), followed by the decision tree ( R 2 = 0.89), k -nearest-neighbor regression ( R 2 = 0.84), mechanism-based ( R 2 = 0.77), and linear regression model ( R 2 = 0.69). Thus, the mechanism-based model has a predictive capability comparable to machine learning models with the added benefit of elucidating biological mechanisms.
We present the development and validation of a mathematical model that predicts how glucose dynamics influence metabolism and therefore tumor cell growth. Glucose, the starting material for glycolysis, has a fundamental influence on tumor cell growth. We employed time-resolved microscopy to track the temporal change of the number of live and dead tumor cells under different initial glucose concentrations and seeding densities. We then constructed a family of mathematical models (where cell death was accounted for differently in each member of the family) to describe overall tumor cell growth in response to the initial glucose and confluence conditions. The Akaikie Information Criteria was then employed to identify the most parsimonious model. The selected model was then trained on 75% of the data to calibrate the system and identify trends in model parameters as a function of initial glucose concentration and confluence. The calibrated parameters were applied to the remaining 25% of the data to predict the temporal dynamics given the known initial glucose concentration and confluence, and tested against the corresponding experimental measurements. With the selected model, we achieved an accuracy (defined as the fraction of measured data that fell within the 95% confidence intervals of the predicted growth curves) of 77.2 ± 6.3% and 87.2 ± 5.1% for live BT-474 and MDA-MB-231 cells, respectively.
We generated a mouse model (MIP-Luc-VU-NOD) that enables non-invasive bioluminescence imaging (BLI) of beta cell loss during the progression of autoimmune diabetes and determined the relationship between BLI and disease progression. MIP-Luc-VU-NOD mice displayed insulitis and a decline in bioluminescence with age which correlated with beta cell mass, plasma insulin, and pancreatic insulin content. Bioluminescence declined gradually in female MIP-Luc-VU-NOD mice, reaching less than 50% of the initial BLI at 10 weeks of age, whereas hyperglycemia did not ensue until mice were at least 16 weeks old. Mice that did not become diabetic maintained insulin secretion and had less of a decline in bioluminescence than mice that became diabetic. Bioluminescence measurements predicted a decline in beta cell mass prior to the onset of hyperglycemia and tracked beta cell loss. This model should be useful for investigating the fundamental processes underlying autoimmune diabetes and developing new therapies targeting beta cell protection and regeneration.
Abstract Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [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 P3-02-09.
Multimodal fusion promises better pancreas segmentation. However, where to perform fusion in models is still an open question. It is unclear if there is a best location to fuse information when analyzing pairs of imperfectly aligned images. Two main alignment challenges in this pancreas segmentation study are 1) the pancreas is deformable and 2) breathing deforms the abdomen. Even after image registration, relevant deformations are often not corrected. We examine how early through late fusion impacts pancreas segmentation. We used 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas labels. We used image registration (deeds) to align the image pairs. We trained a collection of basic UNets with different fusion points, spanning from early to late, to assess how early through late fusion influenced segmentation performance on imperfectly aligned images. We assessed generalization of fusion points on nnUNet. The single-modality T2w baseline using a basic UNet model had a Dice score of 0.73, while the same baseline on the nnUNet model achieved 0.80. For the basic UNet, the best fusion approach occurred in the middle of the encoder (early/mid fusion), which led to a statistically significant improvement of 0.0125 on Dice score compared to the baseline. For the nnUNet, the best fusion approach was na\"ive image concatenation before the model (early fusion), which resulted in a statistically significant Dice score increase of 0.0021 compared to baseline. Fusion in specific blocks can improve performance, but the best blocks for fusion are model specific, and the gains are small. In imperfectly registered datasets, fusion is a nuanced problem, with the art of design remaining vital for uncovering potential insights. Future innovation is needed to better address fusion in cases of imperfect alignment of abdominal image pairs.
Background Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. Purpose/Hypothesis To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1-mapping of the breast in community radiology practices. Study Type Prospective. Subjects/Phantom Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test–retest analysis at one site in normal subjects (n = 12) was used to assess repeatability. Field Strength/Sequence 3T Siemens Skyra MRI quantitative DW-MRI and T1-mapping. Assessment Quantitative DW-MRI and T1-mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast. Statistical Tests Average values of breast tissue were quantified and Bland–Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility. Results ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively). Data Conclusion Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1-mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:695–707.
