Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Repeatability of 18F-FDG uptake in metastatic bone lesions of breast cancer patients and implications for accrual to clinical trials

BACKGROUND

Standard measures of response such as Response Evaluation Criteria in Solid Tumors are ineffective for bone lesions, often making breast cancer patients that have bone-dominant metastases ineligible for clinical trials with potentially helpful therapies. In this study we prospectively evaluated the test-retest uptake variability of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) in a cohort of breast cancer patients with bone-dominant metastases to determine response criteria. The thresholds for 95% specificity of change versus no-change were then applied to a second cohort of breast cancer patients with bone-dominant metastases.

METHODS

For this study, nine patients with 38 bone lesions were imaged with 18F-FDG in the same calibrated scanner twice within 14 days. Tumor uptake was quantified by the most commonly used PET parameter, the maximum tumor voxel normalized by dose and body weight (SUVmax) and also by the mean of a 1-cc maximal uptake volume normalized by dose and lean-body-mass (SULpeak). The asymmetric repeatability coefficients with confidence intervals for SUVmax and SULpeak were used to determine the limits of 18F-FDG uptake variability. A second cohort of 28 breast cancer patients with bone-dominant metastases that had 146 metastatic bone lesions was imaged with 18F-FDG before and after standard-of-care therapy for response assessment.

RESULTS

The mean relative difference of SUVmax and SULpeak in 38 bone tumors of the first cohort were 4.3% and 6.7%. The upper and lower asymmetric limits of the repeatability coefficient were 19.4% and - 16.3% for SUVmax, and 21.2% and - 17.5% for SULpeak. 18F-FDG repeatability coefficient confidence intervals resulted in the following patient stratification using SULpeak for the second patient cohort: 11-progressive disease, 5-stable disease, 7-partial response, and 1-complete response with three inevaluable patients. The asymmetric repeatability coefficients response criteria for SULpeak changed the status of 3 patients compared to the standard Positron Emission Tomography Response Criteria in Solid Tumors of ± 30% SULpeak.

CONCLUSION

In evaluating bone tumor response for breast cancer patients with bone-dominant metastases using 18F-FDG SUVmax, the repeatability coefficients from test-retest studies show that reductions of more than 17% and increases of more than 20% are unlikely to be due to measurement variability. Serial 18F-FDG imaging in clinical trials investigating bone lesions in these patients, such as the ECOG-ACRIN EA1183 trial, benefit from confidence limits that allow interpretation of response.

Repeatability of 18F-FDG uptake in metastatic bone lesions of breast cancer patients and implications for accrual to clinical trials

BACKGROUND

Standard measures of response such as Response Evaluation Criteria in Solid Tumors are ineffective for bone lesions, often making breast cancer patients with bone-dominant metastases ineligible for clinical trials with potentially helpful therapies. In this study we prospectively evaluated the test-retest uptake variability of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) in a cohort of breast cancer patients with bone-dominant metastases to determine response criteria. The thresholds for 95% specificity of change versus no-change were then applied to a second cohort of breast cancer patients with bone-dominant metastases.In this study, nine patients with 38 bone lesions were imaged with 18F-FDG in the same calibrated scanner twice within 14 days. Tumor uptake was quantified as the maximum tumor voxel normalized by dose and body weight (SUVmax) and the mean of a 1-cc maximal uptake volume normalized by dose and lean-body-mass (SULpeak). The asymmetric repeatability coefficients with confidence intervals of SUVmax and SULpeak were used to determine limits of 18F-FDG uptake variability. A second cohort of 28 breast cancer patients with bone-dominant metastases that had 146 metastatic bone lesions was imaged with 18F-FDG before and after standard-of-care therapy for response assessment.

RESULTS

The mean relative difference of SUVmax in 38 bone tumors of the first cohort was 4.3%. The upper and lower asymmetric limits of the repeatability coefficient were 19.4% and -16.3%, respectively. The 18F-FDG repeatability coefficient confidence intervals resulted in the following patient stratification for the second patient cohort: 11-progressive disease, 5-stable disease, 7-partial response, and 1-complete response with three inevaluable patients. The asymmetric repeatability coefficients response criteria changed the status of 3 patients compared to standard the standard Positron Emission Tomography Response Criteria in Solid Tumors of ±30% SULpeak.

CONCLUSIONS

In evaluating bone tumor response for breast cancer patients with bone-dominant metastases using 18F-FDG uptake, the repeatability coefficients from test-retest studies show that reductions of more than 17% and increases of more than 20% are unlikely to be due to measurement variability. Serial 18F-FDG imaging in clinical trials investigating bone lesions from these patients, such as the ECOG-ACRIN EA1183 trial, benefit from confidence limits that allow interpretation of response.

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging

Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.

Phantom and methodology for comparison of small lesion detectability in PET

PURPOSE

The main goal of this work is to describe a phantom design, data acquisition and data analysis methodology enabling comparison of small lesion detectability between PET imaging systems and reconstruction algorithms. Several methods are currently available to characterize intrinsic and image quality performance, but none focus exclusively on small lesion detectability.

METHODS

We previously developed a small-lesion detection phantom and described initial results using a head-size phantom. Unlike most fillable nuclear medicine phantoms, this phantom offers a semi-realistic heterogenous background and wall-less contrast features. In this work, the methodology is extended to include (a) the use of both head- and body-sized phantoms and (b) a multi-scan data collection and analysis method. We present an example use case of the phantom and detection estimation methodology, comparing the small-lesion detection performance across four commercial PET/CT systems.

RESULTS

Repeat acquisitions of the phantom enabled estimation of model observer performance and surrogates of detectability. As anticipated, estimated detectability increased with the square root of system sensitivity and TOF offered marked improvement in detectability, especially for the body sized object. The proposed approach characterizing detectability at different times during the decay of the phantom enabled comparison of small lesion detectability at matched activity concentrations (and scan durations) across different scanners.

CONCLUSION

The proposed approach offers a reproducible tool for evaluating relative tradeoffs of system performance on small lesion detectability.

Synthetic PET via Domain Translation of 3-D MRI

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

An Online Repository for Pre-Clinical Imaging Protocols (PIPs)

Providing method descriptions that are more detailed than currently available in typical peer reviewed journals has been identified as an actionable area for improvement. In the biochemical and cell biology space, this need has been met through the creation of new journals focused on detailed protocols and materials sourcing. However, this format is not well suited for capturing instrument validation, detailed imaging protocols, and extensive statistical analysis. Furthermore, the need for additional information must be counterbalanced by the additional time burden placed upon researchers who may be already overtasked. To address these competing issues, this white paper describes protocol templates for positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance imaging (MRI) that can be leveraged by the broad community of quantitative imaging experts to write and self-publish protocols in protocols.io. Similar to the Structured Transparent Accessible Reproducible (STAR) or Journal of Visualized Experiments (JoVE) articles, authors are encouraged to publish peer reviewed papers and then to submit more detailed experimental protocols using this template to the online resource. Such protocols should be easy to use, readily accessible, readily searchable, considered open access, enable community feedback, editable, and citable by the author.

Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

The availability of high-fidelity animal models for oncology research has grown enormously in recent years, enabling preclinical studies relevant to prevention, diagnosis, and treatment of cancer to be undertaken. This has led to increased opportunities to conduct co-clinical trials, which are studies on patients that are carried out parallel to or sequentially with animal models of cancer that mirror the biology of the patients' tumors. Patient-derived xenografts (PDX) and genetically engineered mouse models (GEMM) are considered to be the models that best represent human disease and have high translational value. Notably, one element of co-clinical trials that still needs significant optimization is quantitative imaging. The National Cancer Institute has organized a Co-Clinical Imaging Resource Program (CIRP) network to establish best practices for co-clinical imaging and to optimize translational quantitative imaging methodologies. This overview describes the ten co-clinical trials of investigators from eleven institutions who are currently supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group. Each team describes their corresponding clinical trial, type of cancer targeted, rationale for choice of animal models, therapy, and imaging modalities. The strengths and weaknesses of the co-clinical trial design and the challenges encountered are considered. The rich research resources generated by the members of the AMCT Working Group will benefit the broad research community and improve the quality and translational impact of imaging in co-clinical trials.

Evaluation of Apparent Diffusion Coefficient Repeatability and Reproducibility for Preclinical MRIs Using Standardized Procedures and a Diffusion-Weighted Imaging Phantom

Relevant to co-clinical trials, the goal of this work was to assess repeatability, reproducibility, and bias of the apparent diffusion coefficient (ADC) for preclinical MRIs using standardized procedures for comparison to performance of clinical MRIs. A temperature-controlled phantom provided an absolute reference standard to measure spatial uniformity of these performance metrics. Seven institutions participated in the study, wherein diffusion-weighted imaging (DWI) data were acquired over multiple days on 10 preclinical scanners, from 3 vendors, at 6 field strengths. Centralized versus site-based analysis was compared to illustrate incremental variance due to processing workflow. At magnet isocenter, short-term (intra-exam) and long-term (multiday) repeatability were excellent at within-system coefficient of variance, wCV [±CI] = 0.73% [0.54%, 1.12%] and 1.26% [0.94%, 1.89%], respectively. The cross-system reproducibility coefficient, RDC [±CI] = 0.188 [0.129, 0.343] µm2/ms, corresponded to 17% [12%, 31%] relative to the reference standard. Absolute bias at isocenter was low (within 4%) for 8 of 10 systems, whereas two high-bias (>10%) scanners were primary contributors to the relatively high RDC. Significant additional variance (>2%) due to site-specific analysis was observed for 2 of 10 systems. Base-level technical bias, repeatability, reproducibility, and spatial uniformity patterns were consistent with human MRIs (scaled for bore size). Well-calibrated preclinical MRI systems are capable of highly repeatable and reproducible ADC measurements.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified. The output must also be reproducible and be shown to have reasonably strong ability to predict the risk of an event of interest. Attention must be paid to statistical issues not often encountered in the single QIB scenario, including overfitting and bias in the estimates of model performance. This is the fourth in a five-part series on statistical methodology for assessing the technical performance of multiparametric quantitative imaging. Considerations for data acquisition are discussed and recommendations from the literature on methodology to construct and evaluate QIB-based models for risk prediction are summarized. The findings in the literature upon which these recommendations are based are demonstrated through simulation studies. The concepts in this manuscript are applied to a real-life example involving prediction of major adverse cardiac events using automated plaque analysis.

The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification

A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of β-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.

Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council

PURPOSE

The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches.

METHODS

In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives.

CONCLUSION

Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

The emergence of PET/CT: Engineering, innovation, and usage

The following article was published on Wiley Online Library on 07 December 2022 before its intended inclusion in the 50th Anniversary of the Medical Physics Journal special issue. The article has been temporarily removed and will be republished as part of the special issue. Wiley would like to apologize to the author(s) and the academic community for this mistake.

Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics?

BACKGROUND

Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC.

METHODS

Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [^99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout.

RESULTS

Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69-12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59-0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65-0.88]) or shape feature classes (c-index 0.79 [0.66-0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59-0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R² = 0.84-1.00), representing surrogates rather than independent predictors of pneumonitis risk.

CONCLUSIONS

In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted.

A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA

Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro‐deoxy‐glucose positron emission tomography/computed tomography (FDG‐PET/CT)‐derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B‐cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression‐free survival (PFS) and overall survival (OS) predictions. Baseline FDG‐PET scans were available for 1263 patients, 832 patients of these were cell‐of‐origin (COO)‐evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low‐, intermediate‐ and high‐risk groups. The random forest model with COO subgroups identified a clearer high‐risk population (45% 2‐year PFS [95% confidence interval (CI) 40%–52%]; 65% 2‐year OS [95% CI 59%–71%]) than the IPI (58% 2‐year PFS [95% CI 50%–67%]; 69% 2‐year OS [95% CI 62%–77%]). This study confirms that standard clinical risk factors can be combined with PET‐derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.

Prognostic Value of Early Fluorodeoxyglucose-Positron Emission Tomography Response Imaging and Peripheral Immunologic Biomarkers: Substudy of a Phase II Trial of Risk-Adaptive Chemoradiation for Unresectable Non-Small Cell Lung Cancer

Purpose

We sought to examine the prognostic value of fluorodeoxyglucose-positron emission tomography (PET) imaging during chemoradiation for unresectable non-small cell lung cancer for survival and hypothesized that tumor PET response is correlated with peripheral T-cell function.

Methods and Materials

Forty-five patients with American Joint Committee on Cancer version 7 stage IIB-IIIB non-small cell lung cancer enrolled in a phase II trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). Fluorodeoxyglucose-PET was performed before treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactorial radiologic interpretation. PET responders received 60 Gy in 30 fractions, while nonresponders received concomitant boosts to 74 Gy in 30 fractions. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor sequencing, and plasma cytokine analysis were performed.

Results

Median follow-up was 18.8 months, 1-year overall survival (OS) 82%, 1-year progression-free survival 53%, and 1-year locoregional control 88%. Higher midtreatment PET total lesion glycolysis was detrimental to OS (1 year 87% vs 63%, P < .001), progression-free survival (1 year 60% vs 26%, P = .044), and locoregional control (1 year 94% vs 65%, P = .012), even after adjustment for clinical/treatment factors. Twenty-nine of 45 patients (64%) were classified as PET responders based on a priori definition. Higher tumor programmed death-ligand 1 expression was correlated with response on PET (P = .017). Higher T-cell receptor richness and clone distribution slope were associated with improved OS (P = .018-0.035); clone distribution slope was correlated with PET response (P = .031).

Conclusions

Midchemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral T-cell biomarkers.

Harmonization of PET image reconstruction parameters in simultaneous PET/MRI

Objective

Simultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.

Method

The NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3–7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUV_mean, SUV_max and SUV_peak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction.

Results

The range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRC_mean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRC_max, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRC_peak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively.

Conclusions

For two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00416-0.

Reliability of Quantitative 18F-FDG PET/CT Imaging Biomarkers for Classifying Early Response to Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer

PURPOSE OF THE REPORT

We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer.

PATIENTS AND METHODS

Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data.

RESULTS

Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort.

CONCLUSIONS

The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.

A Path to Qualification of PET/MR Scanners for Multicenter Brain Imaging Studies: Evaluation of MR-based Attenuation Correction Methods Using a Patient Phantom

PET/MRI scanners cannot be qualified in the manner adopted for hybrid PET/CT devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms because of the difficulty in converting the MR images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MRI-based algorithms has been developed to more accurately derive the attenuation properties of the human head, including the skull. Although promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head AC from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MRI- and CT-based AC methods, and whole-brain qualitative and quantitative (both voxelwise and regional) analyses should be performed. The MRI-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified here. We have implemented this approach successfully across 2 PET/MRI scanner manufacturers at 2 sites.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images

Quantitative imaging biomarkers (QIBs) provide medical image-derived intensity, texture, shape, and size features that may help characterize cancerous tumors and predict clinical outcomes. Successful clinical translation of QIBs depends on the robustness of their measurements. Biomarkers derived from positron emission tomography images are prone to measurement errors owing to differences in image processing factors such as the tumor segmentation method used to define volumes of interest over which to calculate QIBs. We illustrate a new Bayesian statistical approach to characterize the robustness of QIBs to different processing factors. Study data consist of 22 QIBs measured on 47 head and neck tumors in 10 positron emission tomography/computed tomography scans segmented manually and with semiautomated methods used by 7 institutional members of the NCI Quantitative Imaging Network. QIB performance is estimated and compared across institutions with respect to measurement errors and power to recover statistical associations with clinical outcomes. Analysis findings summarize the performance impact of different segmentation methods used by Quantitative Imaging Network members. Robustness of some advanced biomarkers was found to be similar to conventional markers, such as maximum standardized uptake value. Such similarities support current pursuits to better characterize disease and predict outcomes by developing QIBs that use more imaging information and are robust to different processing factors. Nevertheless, to ensure reproducibility of QIB measurements and measures of association with clinical outcomes, errors owing to segmentation methods need to be reduced.

PET/CT acceptance testing and quality assurance: Executive summary of AAPM Task Group 126 Report

PURPOSE

A Positron Emission Tomography/Computed Tomography quality assurance program is necessary to ensure that patients receive optimal imaging and care. We summarize the AAPM Task Group (TG) 126 report on acceptance and quality assurance (QA) testing of PET/CT systems.

METHODS

TG 126 was charged with developing PET/CT acceptance testing and QA procedures. The TG aimed to develop procedures that would allow for standardized evaluation of existing short-axis cylindrical-bore PET/CT systems in the spirit of NEMA NU 2 standards without requiring specialized phantoms or proprietary software tools.

RESULTS

We outline eight performance evaluations using common phantoms and freely available software whereby the clinical physicist monitors each PET/CT system by comparing periodic Follow-Up Measurements to Baseline Measurements acquired during acceptance testing. For each of the eight evaluations, we also summarize the expected testing time and materials necessary and the recommended pass/fail criteria.

CONCLUSION

Our report provides a guideline for periodic evaluations of most clinical PET/CT systems that simplifies procedures and requirements outlined by other agencies and will facilitate performance comparisons across vendors, models, and institutions.

18F-fluorodeoxyglucose (FDG) PET or 18F-fluorothymidine (FLT) PET to assess early response to aromatase inhibitors (AI) in women with ER+ operable breast cancer in a window-of-opportunity study

PURPOSE

This study evaluated the ability of ¹⁸F-Fluorodeoxyglucose (FDG) and ¹⁸F-Fluorothymidine (FLT) imaging with positron emission tomography (PET) to measure early response to endocrine therapy from baseline to just prior to surgical resection in estrogen receptor positive (ER+) breast tumors.

METHODS

In two separate studies, women with early stage ER+ breast cancer underwent either paired FDG-PET (n = 22) or FLT-PET (n = 27) scans prior to endocrine therapy and again in the pre-operative setting. Tissue samples for Ki-67 were taken for all patients both prior to treatment and at the time of surgery.

RESULTS

FDG maximum standardized uptake value (SUVmax) declined in 19 of 22 lesions (mean 17% (range -45 to 28%)). FLT SUVmax declined in 24 of 27 lesions (mean 26% (range -77 to 7%)). The Ki-67 index declined in both studies, from pre-therapy (mean 23% (range 1 to 73%)) to surgery [mean 8% (range < 1 to 41%)]. Pre- and post-therapy PET measures showed strong rank-order agreement with Ki-67 percentages for both tracers; however, the percent change in FDG or FLT SUVmax did not demonstrate a strong correlation with Ki-67 index change or Ki-67 at time of surgery.

CONCLUSIONS

A window-of-opportunity approach using PET imaging to assess early response of breast cancer therapy is feasible. FDG and FLT-PET imaging following a short course of neoadjuvant endocrine therapy demonstrated measurable changes in SUVmax in early stage ER+ positive breast cancers. The percentage change in FDG and FLT-PET uptake did not correlate with changes in Ki-67; post-therapy SUVmax for both tracers was significantly associated with post-therapy Ki-67, an established predictor of endocrine therapy response.

Sensitivity analysis of FDG PET tumor voxel cluster radiomics and dosimetry for predicting mid-chemoradiation regional response of locally advanced lung cancer

We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.

Abstract 6497: Early assessment of liquid biomarkers to predict pneumonitis after chemoradiation in patients with locally advanced non-small cell lung cancer (LA-NSCLC)

PURPOSE: Patients undergoing chemoradiation (CRT) for LA-NSCLC are at significant risk of developing radiation pneumonitis. As consolidation checkpoint inhibitor immunotherapy (IO) becomes standard of care after chemoradiation, pneumonitis (either radiation or immune-mediated) is also the most common rationale for cessation of IO and management with steroids. Our goal is to develop an early predictive biomarker for pneumonitis post-CRT, to risk stratify patients for treatment toxicity. We analyzed relationships between plasma cytokine levels, single cell functional assays, and pneumonitis prevalence within a prospective phase II clinical trial of chemoradiation for LA-NSCLC.

METHODS: 35 Patients with AJCC v7 stage IIB-IIIB NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238) from 2016-2019. All patients underwent chemoradiation; 18 patients also received consolidation IO (durvalumab) after it became standard of care in late 2017. 24 patients consented to peripheral blood collection at baseline and 21 patients during week 3 of CRT. Plasma concentrations of 43 common inflammatory cytokines were measured at both time points. Pneumonitis was defined as CTCAE v4 grade 2 or higher, irrespective of radiation-induced or immune-mediated causality. Bootstrapping over 100 iterations of LASSO feature selection was performed to reduce dimensionality and guard against false discoveries. Univariate Cox regression of selected cytokine concentrations were evaluated for associations to time-dependent pneumonitis incidence.

RESULTS: Grade 2+ pneumonitis occurred in 13/35 patients, 7 of whom received durvalumab. Four plasma cytokines were most consistently associated with pneumonitis: GMCSF, E selectin, sIL6R (soluble Interleukin-6 Receptor), and SCF (stem cell factor). Higher baseline GMCSF and sIL6R (HR 2.1-4.2, p=0.01-0.03) were correlated with pneumonitis. Higher mid-CRT GMCSF, sIL6R and E selectin (HR 1.6-3.2, p=0.02-0.05), along with decline in mid-CRT SCF (HR 0.2, p=0.02), were associated with pneumonitis. The 4-cytokine signature at mid-CRT predicted for subsequent pneumonitis incidence (cross-validated AUC=0.84). Since GMCSF and SCF are linked to monocyte development, and sIL6R and E selectin are both associated with monocyte function, we performed single cell functional assay (Isocode chips) of mid-CRT peripheral monocytes from two patients with different pneumonitis status. A patient who subsequently developed grade 3 pneumonitis showed higher peripheral monocyte function (5.7% producing MIP1b) relative to a patient who did not develop pneumonitis (0% producing MIP1b).

CONCLUSIONS: Our results show it may be possible to predict development of pneumonitis after chemoradiation in patients with LA-NSCLC, with mid-treatment assessment of 4 plasma cytokines: GMCSF, E selectin, sIL6R, and SCF. Changes in these cytokines may be related to monocyte function. These biomarkers need to be validated in other independent patient populations.

Citation Format: Jing Zeng, Ramesh Rengan, Rafael Santana-Davila, Daniel S. Hippe, Ashley M. Houghton, Paul E. Kinahan, Hubert J. Vesselle, Paul Lampe, Stephen R. Bowen. Early assessment of liquid biomarkers to predict pneumonitis after chemoradiation in patients with locally advanced non-small cell lung cancer (LA-NSCLC) [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6497.

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

The novel coronavirus disease (COVID-19) complicated by pulmonary embolism and acute respiratory distress syndrome

Acute respiratory distress syndrome and coagulopathy played an important role in morbidity and mortality of severe COVID-19 patients. A higher frequency of pulmonary embolism (PE) than expected in COVID-19 patients was recently reported. The presenting symptoms for PE were untypical including dyspnea, which is one of the major symptoms in severe COVID-19, especially in those patients with acute respiratory distress syndrome (ARDS). We reported two COVID-19 cases with coexisting complications of PE and ARDS, aiming to consolidate the emerging knowledge of this global health emergency and raise the awareness that the hypoxemia or severe dyspnea in COVID-19 may be related to PE and not necessarily always due to the parenchymal disease.

Impact of Using Uniform Attenuation Coefficients for Heterogeneously Dense Breasts in a Dedicated Breast PET/X-ray Scanner

We investigated PET image quantification when using a uniform attenuation coefficient (μ) for attenuation correction (AC) of anthropomorphic density phantoms derived from high-resolution breast CT scans. A breast PET system was modeled with perfect data corrections except for AC. Using uniform μ for AC resulted in quantitative errors roughly proportional to the difference between μ used in AC (μ _AC) and local μ, yielding approximately ± 5% bias, corresponding to the variation of μ for 511 keV photons in breast tissue. Global bias was lowest when uniform μ _AC was equal to the phantom mean μ (μ _mean). Local bias in 10-mm spheres increased as the sphere μ deviated from μ _mean, but remained only 2-3% when the μ _sphere was 6.5% higher than μ _mean. Bias varied linearly with and was roughly proportional to local μ mismatch. Minimizing local bias, e.g., in a small sphere, required the use of a uniform μ value between the local μ and the μ _mean. Thus, biases from using uniform-μ AC are low when local μ _sphere is close to μ _mean. As the μ _sphere increasingly differs from the phantom μ _mean, bias increases, and the optimal uniform μ is less predictable, having a value between μ _sphere and the phantom μ _mean.

The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy

The Quantitative Imaging Biomarkers Alliance (QIBA) Profile for fluorodeoxyglucose (FDG) PET/CT imaging was created by QIBA to both characterize and reduce the variability of standardized uptake values (SUVs). The Profile provides two complementary claims on the precision of SUV measurements. First, tumor glycolytic activity as reflected by the maximum SUV (SUVmax) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUVmax of 39% or more, or a decrease of 28% or more, indicates that a true change has occurred with 95% confidence. Two applicable use cases are clinical trials and following individual patients in clinical practice. Other components of the Profile address the protocols and conformance standards considered necessary to achieve the performance claim. The Profile is intended for use by a broad audience; applications can range from discovery science through clinical trials to clinical practice. The goal of this report is to provide a rationale and overview of the FDG PET/CT Profile claims as well as its context, and to outline future needs and potential developments.

Bone material analogues for PET/MRI phantoms

PURPOSE

To develop bone material analogues that can be used in construction of phantoms for simultaneous PET/MRI systems.

METHODS

Plaster was used as the basis for the bone material analogues tested in this study. It was mixed with varying concentrations of an iodinated CT contrast, a gadolinium-based MR contrast agent, and copper sulfate to modulate the attenuation properties and MRI properties (T1 and T2*). Attenuation was measured with CT and ⁶⁸ Ge transmission scans, and MRI properties were measured with quantitative ultrashort echo time pulse sequences. A proof-of-concept skull was created by plaster casting.

RESULTS

Undoped plaster has a 511 keV attenuation coefficient (~0.14 cm^-1 ) similar to cortical bone (0.10-0.15 cm^-1 ), but slightly longer T1 (~500 ms) and T2* (~1.2 ms) MR parameters compared to bone (T1 ~ 300 ms, T2* ~ 0.4 ms). Doping with the iodinated agent resulted in increased attenuation with minimal perturbation to the MR parameters. Doping with a gadolinium chelate greatly reduced T1 and T2*, resulting in extremely short T1 values when the target T2* values were reached, while the attenuation coefficient was unchanged. Doping with copper sulfate was more selective for T2* shortening and achieved comparable T1 and T2* values to bone (after 1 week of drying), while the attenuation coefficient was unchanged.

CONCLUSIONS

Plaster doped with copper sulfate is a promising bone material analogue for a PET/MRI phantom, mimicking the MR properties (T1 and T2*) and 511 keV attenuation coefficient of human cortical bone.

Technical Note: A digital reference object representing Hoffman's 3D brain phantom for PET scanner simulations

PURPOSE

Physical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three-dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom.

METHODS

The DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal-thickness slices and re-labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance.

RESULTS

We then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in-slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50-75% accurate, and the remaining 4% of voxels had 25% or less agreement.

CONCLUSIONS

This DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.

Bias in PET Images of Solid Phantoms Due to CT-Based Attenuation Correction

The use of computed tomography (CT) images to correct for photon attenuation in positron emission tomography (PET) produces unbiased patient images, but it is not optimal for synthetic materials. For test objects made from epoxy, image bias and artifacts have been observed in well-calibrated PET/CT scanners. An epoxy used in commercially available sources was infused with long-lived ⁶⁸Ge/⁶⁸Ga nuclide and measured on several PET/CT scanners as well as on older PET scanners that measured attenuation with 511-keV photons. Bias in attenuation maps and PET images of phantoms was measured as imaging parameters and methods varied. Changes were made to the PET reconstruction to show the influence of CT-based attenuation correction. Additional attenuation measurements were made with a new epoxy intended for use in radiology and radiation treatment whose photonic properties mimic water. PET images of solid phantoms were biased by between 3% and 24% across variations in CT X-ray energy and scanner manufacturer. Modification of the reconstruction software reduced bias, but object-dependent changes were required to generate accurate attenuation maps. The water-mimicking epoxy formulation showed behavior similar to water in limited testing. For some solid phantoms, transformation of CT data to attenuation maps is a major source of PET image bias. The transformation can be modified to accommodate synthetic materials, but our data suggest that the problem may also be addressed by using epoxy formulations that are more compatible with PET/CT imaging.

Comparison of regional lung perfusion response on longitudinal MAA SPECT/CT in lung cancer patients treated with and without functional tissue-avoidance radiation therapy

OBJECTIVE

The effect of functional lung avoidance planning on radiation dose-dependent changes in regional lung perfusion is unknown. We characterized dose-perfusion response on longitudinal perfusion single photon emission computed tomography (SPECT)/CT in two cohorts of lung cancer patients treated with and without functional lung avoidance techniques.

METHODS

The study included 28 primary lung cancer patients: 20 from interventional (NCT02773238) (FLARE-RT) and eight from observational (NCT01982123) (LUNG-RT) clinical trials. FLARE-RT treatment plans included perfused lung dose constraints while LUNG-RT plans adhered to clinical standards. Pre- and 3 month post-treatment macro-aggregated albumin (MAA) SPECT/CT scans were rigidly co-registered to planning four-dimensional CT scans. Tumour-subtracted lung dose was converted to EQD2 and sorted into 5 Gy bins. Mean dose and percent change between pre/post-RT MAA-SPECT uptake (%ΔPERF), normalized to total tumour-subtracted lung uptake, were calculated in each binned dose region. Perfusion frequency histograms of pre/post-RT MAA-SPECT were analyzed. Dose-response data were parameterized by sigmoid logistic functions to estimate maximum perfusion increase (%ΔPERFmaxincrease), maximum perfusion decrease (%ΔPERFmaxdecrease), dose midpoint (Dmid), and dose-response slope (k).

RESULTS

Differences in MAA perfusion frequency distribution shape between time points were observed in 11/20 (55%) FLARE-RT and 2/8 (25%) LUNG-RT patients (p < 0.05). FLARE-RT dose response was characterized by >10% perfusion increase in the 0-5 Gy dose bin for 8/20 patients (%ΔPERFmaxincrease = 10-40%), which was not observed in any LUNG-RT patients (p = 0.03). The dose midpoint Dmid at which relative perfusion declined by 50% trended higher in FLARE-RT compared to LUNG-RT cohorts (35 GyEQD2 vs 21 GyEQD2, p = 0.09), while the dose-response slope k was similar between FLARE-RT and LUNG-RT cohorts (3.1-3.2, p = 0.86).

CONCLUSION

Functional lung avoidance planning may promote increased post-treatment perfusion in low dose regions for select patients, though inter-patient variability remains high in unbalanced cohorts. These preliminary findings form testable hypotheses that warrant subsequent validation in larger cohorts within randomized or case-matched control investigations.

ADVANCES IN KNOWLEDGE

This novel preliminary study reports differences in dose-response relationships between patients receiving functional lung avoidance radiation therapy (FLARE-RT) and those receiving conventionally planned radiation therapy (LUNG-RT). Following further validation and testing of these effects in larger patient populations, individualized estimation of regional lung perfusion dose-response may help refine future risk-adaptive strategies to minimize lung function deficits and toxicity incidence.

Performance evaluation of the 5-Ring GE Discovery MI PET/CT system using the national electrical manufacturers association NU 2-2012 Standard

The GE Discovery MI PET/CT system has a modular digital detector design allowing three, four, or five detector block rings that extend the axial field-of-view (FOV) from 15 to 25 cm in 5 cm increments. This study investigated the performance of the 5-ring system and compared it to 3- and 4-ring systems; the GE Discovery IQ system that uses conventional photomultiplier tubes; and the GE Signa PET/MR system that has a reduced transaxial FOV.

METHODS

PET performance was evaluated at three different institutions. Spatial resolution, sensitivity, counting rate performance, accuracy, and image quality were measured in accordance with National Electrical Manufacturers Association NU 2-2012 standards. The mean energy resolution, mean timing resolution, and PET/CT subsystem alignment were also measured. Phantoms were used to determine the effects of varying acquisition time and reconstruction parameters on image quality. Retrospective patient scans were reconstructed with various scan durations to evaluate the impact on image quality.

RESULTS

Results from all three institutions were similar. Radial/tangential/axial full width at half maximum spatial resolution measurements using the filtered back projection algorithm were 4.3/4.3/5.0 mm, 5.5/4.6/6.5 mm, and 7.4/5.0/6.6 mm at 1, 10, and 20 cm from the center of the FOV, respectively. Measured sensitivity at the center of the FOV (20.84 cps/kBq) was significantly higher than systems with reduced axial FOV. The peak noise-equivalent counting rate was 266.3 kcps at 20.8 kBq/ml, with a corresponding scatter fraction of 40.2%. The correction accuracy for count losses up to the peak noise-equivalent counting rate was 3.6%. For the 10-, 13-, 17-, 22-, 28-, and 37-mm spheres, contrast recoveries in the image quality phantom were measured to be 46.2%, 54.3%, 66.1%, 71.1%, 85.3%, and 89.3%, respectively. The mean energy and timing resolution were 9.55% and 381.7 ps, respectively. Phantom and patient images demonstrated excellent image quality, even at short acquisition times or low injected activity.

CONCLUSION

Compared to other PET/CT models, the extended axial FOV improved the overall PET performance of the 5-ring GE Discovery MI scanner. This system offers the potential to reduce scan times or injected activities through increased sensitivity.

Voxel Forecast for Precision Oncology: Predicting Spatially Variant and Multiscale Cancer Therapy Response on Longitudinal Quantitative Molecular Imaging

PURPOSE

Prediction of spatially variant response to cancer therapies can inform risk-adaptive management within precision oncology. We developed the "Voxel Forecast" multiscale regression framework for predicting spatially variant tumor response to chemoradiotherapy on fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging.

EXPERIMENTAL DESIGN

Twenty-five patients with locally advanced non-small cell lung cancer, enrolled on the FLARE-RT phase II trial (NCT02773238), underwent FDG PET/CT imaging prior to (PETpre) and during week 3 (PETmid) of concurrent chemoradiotherapy. Voxel Forecast was designed to predict tumor voxel standardized uptake value (SUV) on PETmid from baseline patient-level and voxel-level covariates using a custom generalized least squares (GLS) algorithm. Matérn covariance matrices were fit to patient- specific empirical variograms of distance-dependent intervoxel correlation. Regression coefficients from variogram-based weights and corresponding standard errors were estimated using the jackknife technique. The framework was validated using statistical simulations of known spatially variant tumor response. Mean absolute prediction errors (MAEs) of Voxel Forecast models were calculated under leave-one-patient-out cross-validation.

RESULTS

Patient-level forecasts resulted in tumor voxel SUV MAE on PETmid of 1.5 g/mL while combined patient- and voxel-level forecasts achieved lower MAE of 1.0 g/mL (P < 0.0001). PETpre voxel SUV was the most important predictor of PETmid voxel SUV. Patients with a greater percentage of under-responding tumor voxels were classified as PETmid nonresponders (P = 0.030) with worse overall survival prognosis (P < 0.001).

CONCLUSIONS

Voxel Forecast multiscale regression provides a statistical framework to predict voxel-wise response patterns during therapy. Voxel Forecast can be extended to predict spatially variant response on multimodal quantitative imaging and may eventually guide optimized spatial-temporal dose distributions for precision cancer therapy.

Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study

PURPOSE

To compare the ability of radiological semantic and quantitative texture features in lung cancer diagnosis of pulmonary nodules.

MATERIALS AND METHODS

A total of N = 121 subjects with confirmed non-small-cell lung cancer were matched with 117 controls based on age and gender. Radiological semantic and quantitative texture features were extracted from CT images with or without contrast enhancement. Three different models were compared using LASSO logistic regression: "CS" using clinical and semantic variables, "T" using texture features, and "CST" using clinical, semantic, and texture variables. For each model, we performed 100 trials of fivefold cross-validation and the average receiver operating curve was accessed. The AUC of the cross-validation study (AUC_CV) was calculated together with its 95% confidence interval.

RESULTS

The AUC_CV (and 95% confidence interval) for models T, CS, and CST was 0.85 (0.71-0.96), 0.88 (0.77-0.96), and 0.88 (0.77-0.97), respectively. After separating the data into two groups with or without contrast enhancement, the AUC (without cross-validation) of the model T was 0.86 both for images with and without contrast enhancement, suggesting that contrast enhancement did not impact the utility of texture analysis.

CONCLUSIONS

The models with semantic and texture features provided cross-validated AUCs of 0.85-0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients.

KEY POINTS

• Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way. • Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images. • Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate K^trans (volume transfer rate constant), v_e (extravascular, extracellular volume fraction), k_ep (efflux rate constant), and τ_i (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T₁ values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for K^trans, v_e, k_ep, and τ_i, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in K^trans and v_e (wCV = 0.50 and 0.10, respectively), but had smaller effects on k_ep and τ_i (wCV = 0.39 and 0.22, respectively). k_ep is less sensitive to AIF variation than K^trans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τ_i parameter may have advantages over the conventional PK parameters in a longitudinal study.

Abstract PD4-10: 18F-fluoroestradiol (FES) and 18F-fluorodeoxyglucose (FDG) PET imaging in staging extent of disease in metastatic lobular breast cancer

Background: The histology and pattern of spread in lobular breast cancer has presented challenges in estimating extent of disease and identifying treatment options. 18F-FES is an estrogen analogue PET imaging tracer which measures tumor ER expression at multiple tumor sites simultaneously and predicts response to endocrine therapy. We analyzed FES-PET and FDG-PET SUV uptake in patients with metastatic lobular and ductal carcinoma to identify sites of tumor and responsiveness to therapy.

Methods: We retrospectively reviewed FES and FDG SUV uptake between ER+ lobular (n = 36) and ductal (n= 173, including 6 men) metastatic breast cancer patients enrolled in various institutional studies. Up to 3 lesions in each patient were evaluated by FES SUVmax and/or FDG SUVmax for a total of 475 lesions in FES images and 462 lesions in FDG images. Classification into three categories (low FDG, high FDG/high FES, and high FDG/low FES) was generated using recursive portioning with 5-fold internal cross validation. Using a Pearson Chi-squared test, we compared degree of uptake in FES and FDG between lobular and ductal carcinomas. We used linear mixed effects model to assess association of FES SULmean3 (Lean body mass adjusted SUV) and FDG SULmean3 with histology. Overall survival (OS), from time of FES-PET scan to death, and progression free survival (PFS) was evaluated between classification groups in both histologies using Kaplan-Meier curves and Cox model.

Results: In patients with metastatic breast cancer, 72 patients had low FDG, 96 had high FES/high FDG, and 41 with high FES/low FDG. Lobular lesions tended to have a higher proportion of patients in the risk group with lower FDG (42% vs 33%) and a lower proportion in the risk group with high FDG/low FES (11% vs 21%) but the difference was not statistically significant (p = 0.32). Mean (range) FES SULmean3 and FDG SULmax3 respectively for ductal was 1.38 (0.10, 6.7) and 3.17 (0.88, 12.26) and for lobular was 1.42 (0.34, 3.43) and 3.13 (1.04, 13.87). There was no significant difference between in FES SULmean3 and FDG SULmax3 between histologies. Following FES-PET imaging, patients with lobular carcinomas and low FDG demonstrated a higher median survival time (7.7 years) compared to high FDG/low FES (4.3 years) and high FDG/high FES (2.6 years). Similarly, patients with ductal carcinomas and low FDG had an improved median survival time (5.6 years) compared to both high FDG/high FES (2.9 years) and high FDG/low FES (2.5 years). However, the interaction between histology and the FDG/FES classifications was not significant (p = 0.86). Across a variety of tumor sites, lobular histology can be detected by both FES and FDG with no difference between the imaging modalities.

Conclusions: In the metastatic setting, quantitative FES and FDG can be used to discriminate indolent and aggressive phenotypes in both lobular and ductal breast cancer. A greater proportion of lobular carcinoma lesions had higher FES/lower FDG and would be anticipated to be more sensitive to endocrine therapy. Further prospective trials are needed to confirm the utility of FES to stage extent of disease in metastatic breast cancer.

Citation Format: Manohar PM, Peterson LM, Wu V, Jenkins IC, Novakova-Jiresova A, Specht JM, Link JM, Krohn KA, Kinahan PE, Mankoff DA, Linden HM. 18F-fluoroestradiol (FES) and 18F-fluorodeoxyglucose (FDG) PET imaging in staging extent of disease in metastatic lobular breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr PD4-10.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Test-Retest Reproducibility of 18F-FDG PET/CT Uptake in Cancer Patients Within a Qualified and Calibrated Local Network

Calibration and reproducibility of quantitative ¹⁸F-FDG PET measures are essential for adopting integral ¹⁸F-FDG PET/CT biomarkers and response measures in multicenter clinical trials. We implemented a multicenter qualification process using National Institute of Standards and Technology-traceable reference sources for scanners and dose calibrators, and similar patient and imaging protocols. We then assessed SUV in patient test-retest studies. Methods: Five ¹⁸F-FDG PET/CT scanners from 4 institutions (2 in a National Cancer Institute-designated Comprehensive Cancer Center, 3 in a community-based network) were qualified for study use. Patients were scanned twice within 15 d, on the same scanner (n = 10); different but same model scanners within an institution (n = 2); or different model scanners at different institutions (n = 11). SUV_max was recorded for lesions, and SUV_mean for normal liver uptake. Linear mixed models with random intercept were fitted to evaluate test-retest differences in multiple lesions per patient and to estimate the concordance correlation coefficient. Bland-Altman plots and repeatability coefficients were also produced. Results: In total, 162 lesions (82 bone, 80 soft tissue) were assessed in patients with breast cancer (n = 17) or other cancers (n = 6). Repeat scans within the same institution, using the same scanner or 2 scanners of the same model, had an average difference in SUV_max of 8% (95% confidence interval, 6%-10%). For test-retest on different scanners at different sites, the average difference in lesion SUV_max was 18% (95% confidence interval, 13%-24%). Normal liver uptake (SUV_mean) showed an average difference of 5% (95% confidence interval, 3%-10%) for the same scanner model or institution and 6% (95% confidence interval, 3%-11%) for different scanners from different institutions. Protocol adherence was good; the median difference in injection-to-acquisition time was 2 min (range, 0-11 min). Test-retest SUV_max variability was not explained by available information on protocol deviations or patient or lesion characteristics. Conclusion: ¹⁸F-FDG PET/CT scanner qualification and calibration can yield highly reproducible test-retest tumor SUV measurements. Our data support use of different qualified scanners of the same model for serial studies. Test-retest differences from different scanner models were greater; more resolution-dependent harmonization of scanner protocols and reconstruction algorithms may be capable of reducing these differences to values closer to same-scanner results.

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms

Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography “pocket” phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the “pocket” phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET “pocket” phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

Abstract A12: Non-small cell lung tumor-derived autoantibodies can distinguish benign from malignant pulmonary nodules

High-risk smokers are currently being screened with low-dose CT imaging after the National Lung Screening Trial demonstrated a 20% reduction in lung cancer mortality. Unfortunately, small pulmonary nodules are a common finding in smokers (present in 25-50%), with very few (&lt;2.5%) nodules ultimately determined to be cancer. We observed a 4-fold increase in lung tumor-associated B cells compared to normal lung and hypothesized that lung-tumor derived autoantibodies could be useful biomarkers for malignant nodule diagnosis. We first discovered 13 IgG or IgM autoantibodies found in both non-small cell lung tumor and peripheral plasma (n=10) on a large (~17,000) protein array. These autoantibodies were predominantly absent in plasma from patients with benign nodules (n=10). In the plasma from an expanded nodule positive cohort (n=250) we validated 3/13 autoantibodies on a targeted protein array and 5/13 autoantibodies on a high-density antibody array. By using both antibody arrays (to measure antigen-autoantibody complexes) and protein arrays (to measure free autoantibody), we obtained a comprehensive picture of malignant nodule-associated autoantibodies. When we combine our top two autoantibodies (one free and one complexed with antigen) with two features from CT images, they yield an AUC = 0.92 (72% sensitivity at 90% specificity). These observations indicate that our noninvasive autoantibody biomarkers can increase the diagnostic accuracy of pulmonary nodules identified by CT imaging.

Citation Format: Kristin J. Lastwika, Julia Kargl, Yuzheng Zhang, Xiaodong Zhu, David Shelley, Edward Lo, Wei Wu, Sudhakar N. Pipavath, Paul E. Kinahan, A. McGarry Houghton, Paul D. Lampe. Non-small cell lung tumor-derived autoantibodies can distinguish benign from malignant pulmonary nodules [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr A12.

Simultaneous Estimation of Bias and Resolution in PET Images With a Long-Lived "Pocket" Phantom System

A challenge in multicenter trials that use quantitative positron emission tomography (PET) imaging is the often unknown variability in PET image values, typically measured as standardized uptake values, introduced by intersite differences in global and resolution-dependent biases. We present a method for the simultaneous monitoring of scanner calibration and reconstructed image resolution on a per-scan basis using a PET/computed tomography (CT) "pocket" phantom. We use simulation and phantom studies to optimize the design and construction of the PET/CT pocket phantom (120 × 30 × 30 mm). We then evaluate the performance of the PET/CT pocket phantom and accompanying software used alongside an anthropomorphic phantom when known variations in global bias (±20%, ±40%) and resolution (3-, 6-, and 12-mm postreconstruction filters) are introduced. The resulting prototype PET/CT pocket phantom design uses 3 long-lived sources (15-mm diameter) containing germanium-68 and a CT contrast agent in an epoxy matrix. Activity concentrations varied from 30 to 190 kBq/mL. The pocket phantom software can accurately estimate global bias and can detect changes in resolution in measured phantom images. The pocket phantom is small enough to be scanned with patients and can potentially be used on a per-scan basis for quality assurance for clinical trials and quantitative PET imaging in general. Further studies are being performed to evaluate its performance under variations in clinical conditions that occur in practice.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project

BACKGROUND AND PURPOSE

Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors.

MATERIALS AND METHODS

DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity.

RESULTS

For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%).

CONCLUSIONS

By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

Diffusion weighted MRI has become ubiquitous in many areas of medicine, including cancer diagnosis and treatment response monitoring. Reproducibility of diffusion metrics is essential for their acceptance as quantitative biomarkers in these areas. We examined the variability in the apparent diffusion coefficient (ADC) obtained from both postprocessing software implementations utilized by the NCI Quantitative Imaging Network and online scan time-generated ADC maps. Phantom and in vivo breast studies were evaluated for two ([Formula: see text]) and four ([Formula: see text]) [Formula: see text]-value diffusion metrics. Concordance of the majority of implementations was excellent for both phantom ADC measures and in vivo [Formula: see text], with relative biases [Formula: see text] ([Formula: see text]) and [Formula: see text] (phantom [Formula: see text]) but with higher deviations in ADC at the lowest phantom ADC values. In vivo [Formula: see text] concordance was good, with typical biases of [Formula: see text] to 3% but higher for online maps. Multiple b-value ADC implementations were separated into two groups determined by the fitting algorithm. Intergroup mean ADC differences ranged from negligible for phantom data to 2.8% for [Formula: see text] in vivo data. Some higher deviations were found for individual implementations and online parametric maps. Despite generally good concordance, implementation biases in ADC measures are sometimes significant and may be large enough to be of concern in multisite studies.

Impact of tumour motion compensation and delineation methods on FDG PET-based dose painting plan quality for NSCLC radiation therapy

INTRODUCTION

To quantitatively estimate the impact of different methods for both boost volume delineation and respiratory motion compensation of [18F] FDG PET/CT images on the fidelity of planned non-uniform 'dose painting' plans to the prescribed boost dose distribution.

METHODS

Six locally advanced non-small cell lung cancer (NSCLC) patients were retrospectively reviewed. To assess the impact of respiratory motion, time-averaged (3D AVG), respiratory phase-gated (4D GATED) and motion-encompassing (4D MIP) PET images were used. The boost volumes were defined using manual contour (MANUAL), fixed threshold (FIXED) and gradient search algorithm (GRADIENT). The dose painting prescription of 60 Gy base dose to the planning target volume and an integral dose of 14 Gy (total 74 Gy) was discretized into seven treatment planning substructures and linearly redistributed according to the relative SUV at every voxel in the boost volume. Fifty-four dose painting plan combinations were generated and conformity was evaluated using quality index VQ0.95-1.05, which represents the sum of planned dose voxels within 5% deviation from the prescribed dose. Trends in plan quality and magnitude of achievable dose escalation were recorded.

RESULTS

Different segmentation techniques produced statistically significant variations in maximum planned dose (P < 0.02), as well as plan quality between segmentation methods for 4D GATED and 4D MIP PET images (P < 0.05). No statistically significant differences in plan quality and maximum dose were observed between motion-compensated PET-based plans (P > 0.75). Low variability in plan quality was observed for FIXED threshold plans, while MANUAL and GRADIENT plans achieved higher dose with lower plan quality indices.

CONCLUSIONS

The dose painting plans were more sensitive to segmentation of boost volumes than PET motion compensation in this study sample. Careful consideration of boost target delineation and motion compensation strategies should guide the design of NSCLC dose painting trials.

Tumor radiomic heterogeneity: Multiparametric functional imaging to characterize variability and predict response following cervical cancer radiation therapy

BACKGROUND

Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy.

PURPOSE

To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer.

STUDY TYPE

Prospective observational study with longitudinal MRI and PET/CT pre-RT, early-RT (2 weeks), and mid-RT (5 weeks).

POPULATION

Twenty-one FIGO IB₂ -IVA cervical cancer patients receiving definitive external beam RT and brachytherapy.

FIELD STRENGTH/SEQUENCE

1.5T, precontrast axial T₁ -weighted, axial and sagittal T₂ -weighted, sagittal DWI (multi-b values), sagittal DCE MRI (<10 sec temporal resolution), postcontrast axial T₁ -weighted.

ASSESSMENT

Response assessment 1 month after completion of treatment by a board-certified radiation oncologist from manually delineated tumor volume changes.

STATISTICAL TESTS

Intensity histogram (IH) quantiles (DCE SI_10% and DWI ADC_10% , FDG-PET SUV_max ) and distribution moments (mean, variance, skewness, kurtosis) were extracted. Differences in IH features between timepoints and modalities were evaluated by Skillings-Mack tests with Holm's correction. Area under receiver-operating characteristic curve (AUC) and Mann-Whitney testing was performed to discriminate treatment response using IH features.

RESULTS

Tumor IH means and quantiles varied significantly during RT (SUV_mean : ↓28-47%, SUV_max : ↓30-59%, SI_mean : ↑8-30%, SI_10% : ↑8-19%, ADC_mean : ↑16%, P < 0.02 for each). Among IH heterogeneity features, FDG-PET SUV_CoV (↓16-30%, P = 0.011) and DW-MRI ADC_skewness decreased (P = 0.001). FDG-PET SUV_CoV was higher than DCE-MRI SI_CoV and DW-MRI ADC_CoV at baseline (P < 0.001) and 2 weeks (P = 0.010). FDG-PET SUV_kurtosis was lower than DCE-MRI SI_kurtosis and DW-MRI ADC_kurtosis at baseline (P = 0.001). Some IH features appeared to associate with favorable tumor response, including large early RT changes in DW-MRI ADC_skewness (AUC = 0.86).

DATA CONCLUSION

Preliminary findings show tumor heterogeneity was variable between patients, modalities, and timepoints. Radiomic assessment of changing tumor heterogeneity has the potential to personalize treatment and power outcome prediction.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1388-1396.

Simulation study of quantitative precision of the PET/X dedicated breast PET scanner

The goal for positron emission tomography (PET)/X is measuring changes in radiotracer uptake for early assessment of response to breast cancer therapy. Upper bounds for detecting such changes were investigated using simulation and two image reconstruction algorithms customized to the PET/X rectangular geometry. Analytical reconstruction was used to study spatial resolution, comparing results with the distance of the closest approach (DCA) resolution surrogate that is independent of the reconstruction method. An iterative reconstruction algorithm was used to characterize contrast recovery in small targets. Resolution averaged [Formula: see text] full width at half maximum when using depth-of-interaction (DOI) information. Without DOI, resolution ranged from [Formula: see text] to [Formula: see text] for scanner crystal thickness between 5 and 15 mm. The DCA resolution surrogate was highly correlated to image-based FWHM. Receiver-operating characteristic analysis showed specificity and sensitivity over 95% for detecting contrast change from 5:1 to 4:1 (area under curve [Formula: see text]). For PET/X parameters modeled here, the ability to measure contrast changes benefited from higher photon absorption efficiency of thicker crystals while being largely unaffected by degraded resolution obtained with thicker crystals; DOI provided marginal improvements. These results assumed perfect data corrections and other idealizations, and thus represent an upper bound for detecting changes in small lesion radiotracer uptake of clinical interest using the PET/X system.