Dataset Information

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

ABSTRACT: BACKGROUND:Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard. METHODS:MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a prospective clinical trial. Stereotactic biopsies were harvested from each patient immediately prior to surgical resection. For each biopsy, an imaging description (23 parameters) was developed, and the Ki-67 index was recorded. Machine learning models were built to estimate Ki-67 from imaging inputs, and cross validation was undertaken to determine the error in estimates. The best model was used to generate graphical maps of Ki-67 estimates across the whole brain. RESULTS:Fifty-two image-guided biopsies were collected from 23 evaluable patients. The random forest algorithm best modeled Ki-67 with 4 imaging inputs (T2-weighted, fractional anisotropy, cerebral blood flow, Ktrans). It predicted the Ki-67 expression levels with a root mean square (RMS) error of 3.5% (R2 = 0.75). A less accurate predictive result (RMS error 5.4%, R2 = 0.50) was found using conventional imaging only. CONCLUSION:Ki-67 can be predicted to clinically useful accuracies using clinical imaging data. Advanced imaging (diffusion, perfusion, and permeability) improves predictive accuracy over conventional imaging alone. Ki-67 predictions, displayed as graphical maps, could be used to guide biopsy, resection, and/or radiation in the care of glioma patients.

SUBMITTER: Gates EDH

PROVIDER: S-EPMC6422438 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

Gates Evan D H EDH Lin Jonathan S JS Weinberg Jeffrey S JS Hamilton Jackson J Prabhu Sujit S SS Hazle John D JD Fuller Gregory N GN Baladandayuthapani Veera V Fuentes David D Schellingerhout Dawid D

Neuro-oncology 20190301 4

<h4>Background</h4>Undersampling of gliomas at first biopsy is a major clinical problem, as accurate grading determines all subsequent treatment. We submit a technological solution to reduce the problem of undersampling by estimating a marker of tumor proliferation (Ki-67) using MR imaging data as inputs, against a stereotactic histopathology gold standard.<h4>Methods</h4>MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, in untreated glioma patients in a p ...[more]

PMID: 30657997

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Project description:BackgroundNuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC.MethodsThis retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility.ResultsThe AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model.ConclusionsA combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

Project description:Standard clinical parameters fail to accurately differentiate indolent from aggressive prostate cancer. Our previous studies showed that immunohistochemical testing for Ki-67 improved prediction of prostate cancer death in a previous cohort of conservatively treated clinically localized prostate cancer. However there is a need for validation of usage with whole biopsy sections rather than tissue micro-arrays for use in routine diagnostics. Prostate cancer biopsy cases were identified in the UK, between 1990 and 2003, treated conservatively. Tumor extent and prostate-specific antigen (PSA) serum measurements were available. Biopsy cases were centrally reviewed by three uropathologists and Gleason conformed to contemporary ISUP 2014 criteria. Follow-up was through cancer registries up until 2012. Deaths were divided into those from prostate cancer and those from other causes. The percentage of Ki-67 in tumor cells was evaluated by immunohistochemistry on whole biopsy sections and was available for 756 patients. This percentage was used in analysis of cancer specific survival using a Cox proportional hazards model. In univariate analysis, the interquartile hazard ratio (HR) (95% confidence intervals) for continuous Ki-67 was 1.68 (1.49, 1.89), χ12 = 47.975, P < 0.001. In grade groups 1 and 2, continuous Ki-67 was a statistically significant predictor of time to death from prostate cancer, HR (95% CI) = 1.97 (1.34, 2.88), χ12 = 9.017, p = 0.003. In multivariate analysis, continuous Ki-67 added significant predictive information to that provided by grade groups, extent of disease and serum PSA, HR (95% CI) = 1.34 (1.16, 1.54), Δχ12 = 13.703, P < 0.001. We now advocate the introduction of Ki-67 as a viable and practicable prognostic biomarker in clinical practice. The association of Ki-67 with mortality was highest in grade groups 1 and 2, showing that Ki-67 can be used as a routine biomarker in patients being considered for active surveillance.

Dataset Information

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

Publications

Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.

Similar Datasets

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