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Molecular imaging in oncology.

ABSTRACT: Cancer is a genetic disease that manifests in loss of normal cellular homeostatic mechanisms. The biology and therapeutic modulation of neoplasia occurs at the molecular level. An understanding of these molecular processes is therefore required to develop novel prognostic and early biomarkers of response. In addition to clinical applications, increased impetus for the development of such technologies has been catalysed by pharmaceutical companies investing in the development of molecular therapies. The discipline of molecular imaging therefore aims to image these important molecular processes in vivo. Molecular processes, however, operate at short length scales and concentrations typically beyond the resolution of clinical imaging. Solving these issues will be a challenge to imaging research. The successful implementations of molecular imaging in man will only be realised by the close co-operation amongst molecular biologists, chemists and the imaging scientists.

SUBMITTER: Dzik-Jurasz AS

PROVIDER: S-EPMC1434600 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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Molecular imaging in oncology.

Dzik-Jurasz A S K AS

Cancer imaging : the official publication of the International Cancer Imaging Society 20041021 2

Cancer is a genetic disease that manifests in loss of normal cellular homeostatic mechanisms. The biology and therapeutic modulation of neoplasia occurs at the molecular level. An understanding of these molecular processes is therefore required to develop novel prognostic and early biomarkers of response. In addition to clinical applications, increased impetus for the development of such technologies has been catalysed by pharmaceutical companies investing in the development of molecular therapi ...[more]

PMID: 18250026

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Project description:PurposePrediction 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 designTwenty-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.ResultsPatient-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).ConclusionsVoxel 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.

Dataset Information

Molecular imaging in oncology.

Publications

Molecular imaging in oncology.

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