Richard G. Abramson

Simultaneous PET-MRI in Oncology: A Solution Looking for a Problem?

With the recent development of integrated positron emission tomography-magnetic resonance imaging (PET-MRI) scanners, new possibilities for quantitative molecular imaging of cancer are realized. However, the practical advantages and potential clinical benefits of the ability to record PET and MRI data simultaneously must be balanced against the substantial costs and other requirements of such devices. In this review, we highlight several of the key areas where integrated PET-MRI measurements, obtained simultaneously, are anticipated to have a significant impact on clinical and/or research studies. These areas include the use of MR-based motion corrections and/or a priori anatomical information for improved reconstruction of PET data, improved arterial input function characterization for PET kinetic modeling, the use of dual-modality contrast agents, and patient comfort and practical convenience. For widespread acceptance, a compelling case could be made if the combination of quantitative MRI and specific PET biomarkers significantly improves our ability to assess tumor status and response to therapy, and some likely candidates are now emerging. We consider the relative advantages and disadvantages afforded by PET-MRI and summarize current opinions and evidence as to the likely value of PET-MRI in the management of cancer.

DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: Pilot study findings

The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE‐MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

ObjectivesThe purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. Materials and MethodsPatients with stage II/III breast cancer were enrolled in an institutional review board–approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (Ktrans), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = Ktrans/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. ResultsThe kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018–0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (−0.11 to 0.24). ConclusionsThe multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: Preliminary results

PURPOSE To evaluate whether semi-quantitative analysis of high temporal resolution dynamic contrast-enhanced MRI (DCE-MRI) acquired early in treatment can predict the response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS As part of an IRB-approved prospective study, 21 patients with LABC provided informed consent and underwent high temporal resolution 3T DCE-MRI before and after 1cycle of NAC. Using measurements performed by two radiologists, the following parameters were extracted for lesions at both examinations: lesion size (short and long axes, in both early and late phases of enhancement), radiologists subjective assessment of lesion enhancement, and percentages of voxels within the lesion demonstrating progressive, plateau, or washout kinetics. The latter data were calculated using two filters, one selecting for voxels enhancing ≥50% over baseline and one for voxels enhancing ≥100% over baseline. Pretreatment imaging parameters and parameter changes following cycle 1 of NAC were evaluated for their ability to discriminate patients with an eventual pathological complete response (pCR). RESULTS All 21 patients completed NAC followed by surgery, with 9 patients achieving a pCR. No pretreatment imaging parameters were predictive of pCR. However, change after cycle 1 of NAC in percentage of voxels demonstrating washout kinetics with a 100% enhancement filter discriminated patients with an eventual pCR with an area under the receiver operating characteristic curve (AUC) of 0.77. Changes in other parameters, including lesion size, did not predict pCR. CONCLUSION Semi-quantitative analysis of high temporal resolution DCE-MRI in patients with LABC can discriminate patients with an eventual pCR after one cycle of NAC.

Quantitative multimodality imaging in cancer research and therapy

Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET–CT and single-photon emission CT (SPECT–CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET–MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

Methods and Challenges in Quantitative Imaging Biomarker Development

Academic radiology is poised to play an important role in the development and implementation of quantitative imaging (QI) tools. This article, drafted by the Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force, reviews current issues in QI biomarker research. We discuss motivations for advancing QI, define key terms, present a framework for QI biomarker research, and outline challenges in QI biomarker development. We conclude by describing where QI research and development is currently taking place and discussing the paramount role of academic radiology in this rapidly evolving field.

Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning

Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining.

Complications of targeted drug therapies for solid malignancies: manifestations and mechanisms.

OBJECTIVE This article reviews important complications of targeted drug therapies for solid malignancies that can be identified on diagnostic imaging. Wherever possible, known or proposed mechanistic explanations for drug complications are emphasized. CONCLUSION Familiarity with the toxicity profiles of different targeted cancer therapies is important for identifying drug-related complications and for differentiating drug effects from disease progression. A mechanistic understanding may be useful for associating individual drugs with their complications and for predicting the complications of emerging agents.

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

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T1, were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy

Reliable early assessment of breast cancer response to neoadjuvant therapy (NAT) would provide considerable benefit to patient care and ongoing research efforts, and demand for accurate and noninvasive early-response biomarkers is likely to increase. Response assessment techniques derived from quantitative magnetic resonance imaging (MRI) hold great potential for integration into treatment algorithms and clinical trials. Quantitative MRI techniques already available for assessing breast cancer response to neoadjuvant therapy include lesion size measurement, dynamic contrast-enhanced MRI, diffusion-weighted MRI, and proton magnetic resonance spectroscopy. Emerging yet promising techniques include magnetization transfer MRI, chemical exchange saturation transfer MRI, magnetic resonance elastography, and hyperpolarized MR. Translating and incorporating these techniques into the clinical setting will require close attention to statistical validation methods, standardization and reproducibility of technique, and scanning protocol design.