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Title: Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers

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Journal Article: Publisher's Accepted Manuscript
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Cancer Letters
Additional Journal Information:
Journal Volume: 387; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-12-20 20:13:03; Journal ID: ISSN 0304-3835
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Pantel, Austin R., and Mankoff, David A.. Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers. Netherlands: N. p., 2017. Web. doi:10.1016/j.canlet.2016.05.008.
Pantel, Austin R., & Mankoff, David A.. Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers. Netherlands. doi:10.1016/j.canlet.2016.05.008.
Pantel, Austin R., and Mankoff, David A.. Wed . "Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers". Netherlands. doi:10.1016/j.canlet.2016.05.008.
title = {Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers},
author = {Pantel, Austin R. and Mankoff, David A.},
abstractNote = {},
doi = {10.1016/j.canlet.2016.05.008},
journal = {Cancer Letters},
number = C,
volume = 387,
place = {Netherlands},
year = {Wed Feb 01 00:00:00 EST 2017},
month = {Wed Feb 01 00:00:00 EST 2017}

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Publisher's Version of Record at 10.1016/j.canlet.2016.05.008

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  • Purpose: This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high-throughput image analysis. Methods: 86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG-PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged frommore » 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan-Meier analysis was used to assess the ability to distinguish high versus low-risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave-one-out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set. Results: For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low-risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression-free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression-free survival and distant progression-free survival showed similar trends. Conclusion: Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra-tumor heterogeneity reflected on FDG-PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.« less
  • Purpose: Clinical use of {sup 18}F-Sodium Fluoride (NaF) PET/CT in metastatic settings often lacks technology to quantitatively measure full disease dynamics due to high tumor burden. This study assesses radiomics-based extraction of NaF PET/CT measures, including global metrics of overall burden and local metrics of disease heterogeneity, in metastatic prostate cancer for correlation to clinical outcomes. Methods: Fifty-six metastatic Castrate-Resistant Prostate Cancer (mCRPC) patients had NaF PET/CT scans performed at baseline and three cycles into chemotherapy (N=16) or androgen-receptor (AR) inhibitors (N=39). A novel technology, Quantitative Total Bone Imaging (QTBI), was used for analysis. Employing hybrid PET/CT segmentation and articulatedmore » skeletal-registration, QTBI allows for response assessment of individual lesions. Various SUV metrics were extracted from each lesion (iSUV). Global metrics were extracted from composite lesion-level statistics for each patient (pSUV). Proportion of detected lesions and those with significant response (%-increase or %-decrease) was calculated for each patient based on test-retest limits for iSUV metrics. Cox proportional hazard regression analyses were conducted between imaging metrics and progression-free survival (PFS). Results: Functional burden (pSUV{sub total}) assessed mid-treatment was the strongest univariate predictor of PFS (HR=2.03; p<0.0001). Various global metrics outperformed baseline clinical markers, including fraction of skeletal burden, mean uptake (pSUV{sub mean}), and heterogeneity of average lesion uptake (pSUV{sub hetero}). Of 43 patients with paired baseline/mid-treatment imaging, 40 showed heterogeneity in lesion-level response, containing populations of lesions with both increasing/decreasing metrics. Proportion of lesions with significantly increasing iSUV{sub mean} was highly predictive of clinical PFS (HR=2.0; p=0.0002). Patients exhibiting higher proportion of lesions with decreasing iSUV{sub total} saw prolonged radiographic PFS (HR=0.51; p=0.02). Conclusion: Technology presented here provides comprehensive disease quantification on NaF PET/CT imaging, showing strong correlation to clinical outcomes. Total functional burden as well as proportions of similarly responding lesions was predictive of PFS. This supports ongoing development of NaF PET/CT based imaging biomarkers in mCRPC. Prostate Cancer Foundation.« less
  • Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162more » robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.« less
  • Purpose: Imaging biomarkers of resistance to radiation therapy can inform and guide treatment management. Most studies have so far focused on assessing a single imaging biomarker. The goal of this study was to explore a number of different molecular imaging biomarkers as surrogates of resistance to radiation therapy. Methods and Materials: Twenty-two canine patients with spontaneous sinonasal tumors were treated with accelerated hypofractionated radiation therapy, receiving either 10 fractions of 4.2 Gy each or 10 fractions of 5.0 Gy each to the gross tumor volume. Patients underwent fluorodeoxyglucose (FDG)-, fluorothymidine (FLT)-, and Cu(II)-diacetyl-bis(N4-methylthiosemicarbazone) (Cu-ATSM)-labeled positron emission tomography/computed tomography (PET/CT) imaging before therapymore » and FLT and Cu-ATSM PET/CT imaging during therapy. In addition to conventional maximum and mean standardized uptake values (SUV{sub max}; SUV{sub mean}) measurements, imaging metrics providing response and spatiotemporal information were extracted for each patient. Progression-free survival was assessed according to response evaluation criteria in solid tumor. The prognostic value of each imaging biomarker was evaluated using univariable Cox proportional hazards regression. Multivariable analysis was also performed but was restricted to 2 predictor variables due to the limited number of patients. The best bivariable model was selected according to pseudo-R{sup 2}. Results: The following variables were significantly associated with poor clinical outcome following radiation therapy according to univariable analysis: tumor volume (P=.011), midtreatment FLT SUV{sub mean} (P=.018), and midtreatment FLT SUV{sub max} (P=.006). Large decreases in FLT SUV{sub mean} from pretreatment to midtreatment were associated with worse clinical outcome (P=.013). In the bivariable model, the best 2-variable combination for predicting poor outcome was high midtreatment FLT SUV{sub max} (P=.022) in combination with large FLT response from pretreatment to midtreatment (P=.041). Conclusions: In addition to tumor volume, pronounced tumor proliferative response quantified using FLT PET, especially when associated with high residual FLT PET at midtreatment, is a negative prognostic biomarker of outcome in canine tumors following radiation therapy. Neither FDG PET nor Cu-ATSM PET were predictive of outcome.« less
  • Purpose: PET-based texture features are used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing whole body (3D) and respiratory-gated (4D) PET imaging. Methods: Twenty-six patients (34 lesions) received 3D and 4D [F-18]FDG-PET scans before chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Four texture features (Coarseness, Contrast, Busyness, and Complexity) were computed within the the physician-defined tumor volume. The relative difference (δ) in each measure between the 3D- and 4D-PET imaging was calculated. Wilcoxonmore » signed-rank test (p<0.01) was used to determine if δ was significantly different from zero. Coefficient of variation (CV) was used to determine the variability in the texture features between all 4D-PET phases. Pearson correlation coefficient was used to investigate the impact of tumor size and motion amplitude on δ. Results: Significant differences (p<<0.01) between 3D and 4D imaging were found for Coarseness, Busyness, and Complexity. The difference for Contrast was not significant (p>0.24). 4D-PET increased Busyness (∼20%) and Complexity (∼20%), and decreased Coarseness (∼10%) and Contrast (∼5%) compared to 3D-PET. Nearly negligible variability (CV=3.9%) was found between the 4D phase bins for Coarseness and Complexity. Moderate variability was found for Contrast and Busyness (CV∼10%). Poor correlation was found between the tumor volume and δ for the texture features (R=−0.34−0.34). Motion amplitude had moderate impact on δ for Contrast and Busyness (R=−0.64− 0.54) and no impact for Coarseness and Complexity (R=−0.29−0.17). Conclusion: Substantial differences in textures were found between 3D and 4D-PET imaging. Moreover, the variability between phase bins for Coarseness and Complexity was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging with any phase.« less