Dataset Information

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver.

ABSTRACT:

Background

Deriving individual tumor genomic characteristics from patient imaging analysis is desirable. We explore the predictive value of 2-[18F]FDG uptake with regard to the KRAS mutational status of colorectal adenocarcinoma liver metastases (CLM).

Methods

2-[18F]FDG PET/CT images, surgical pathology and molecular diagnostic reports of 37 patients who underwent PET/CT-guided biopsy of CLM were reviewed under an IRB-approved retrospective research protocol. Sixty CLM in 39 interventional PET scans of the 37 patients were segmented using two different auto-segmentation tools implemented in different commercially available software packages. PET standard uptake values (SUV) were corrected for: (1) partial volume effect (PVE) using cold wall-corrected contrast recovery coefficients derived from phantom spheres with variable diameter and (2) variability of arterial tracer supply and variability of uptake time after injection until start of PET scan derived from the tumor-to-blood standard uptake ratio (SUR) approach. The correlations between the KRAS mutational status and the mean, peak and maximum SUV were investigated using Student's t test, Wilcoxon rank sum test with continuity correction, logistic regression and receiver operation characteristic (ROC) analysis. These correlation analyses were also performed for the ratios of the mean, peak and maximum tumor uptake to the mean blood activity concentration at the time of scan: SUR_MEAN, SUR_PEAK and SUR_MAX, respectively.

Results

Fifteen patients harbored KRAS missense mutations (KRAS+), while another 3 harbored KRAS gene amplification. For 31 lesions, the mutational status was derived from the PET/CT-guided biopsy. The Student's t test p values for separating KRAS mutant cases decreased after applying PVE correction to all uptake metrics of each lesion and when applying correction for uptake time variability to the SUR metrics. The observed correlations were strongest when both corrections were applied to SUR_MAX and when the patients harboring gene amplification were grouped with the wild type: p???0.001; ROC area under the curve?=?0.77 and 0.75 for the two different segmentations, respectively, with a mean specificity of 0.69 and sensitivity of 0.85.

Conclusion

The correlations observed after applying the described corrections show potential for assigning probabilities for the KRAS missense mutation status in CLM using 2-[18F]FDG PET images.

SUBMITTER: Popovic M

PROVIDER: S-EPMC7683631 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver.

Popovic M M Talarico O O van den Hoff J J Kunin H H Zhang Z Z Lafontaine D D Dogan S S Leung J J Kaye E E Czmielewski C C Mayerhoefer M E ME Zanzonico P P Yaeger R R Schöder H H Humm J L JL Solomon S B SB Sofocleous C T CT Kirov A S AS

EJNMMI research 20201123 1

<h4>Background</h4>Deriving individual tumor genomic characteristics from patient imaging analysis is desirable. We explore the predictive value of 2-[18F]FDG uptake with regard to the KRAS mutational status of colorectal adenocarcinoma liver metastases (CLM).<h4>Methods</h4>2-[18F]FDG PET/CT images, surgical pathology and molecular diagnostic reports of 37 patients who underwent PET/CT-guided biopsy of CLM were reviewed under an IRB-approved retrospective research protocol. Sixty CLM in 39 inte ...[more]

PMID: 33226505

Similar Datasets

Project description:OBJECTIVE:We aimed to improve prediction of outcome for patients with colorectal liver metastases, via prognostic models incorporating PET-derived measures, including radiomic features that move beyond conventional standard uptake value (SUV) measures. PATIENTS AND METHODS:A range of parameters including volumetric and heterogeneity measures were derived from FDG PET images of 52 patients with colorectal intrahepatic-only metastases (29 males and 23 females; mean age 62.9 years [SD 9.8; range 32-82]). The patients underwent PET/CT imaging as part of the clinical workup prior to final decision on treatment. Univariate and multivariate models were implemented, which included statistical considerations (to discourage false discovery and overfitting), to predict overall survival (OS), progression-free survival (PFS) and event-free survival (EFS). Kaplan-Meier survival analyses were performed, where the subjects were divided into high-risk and low-risk groups, from which the hazard ratios (HR) were computed via Cox proportional hazards regression. RESULTS:Commonly-invoked SUV metrics performed relatively poorly for different prediction tasks (SUVmax HR = 1.48, 0.83 and 1.16; SUVpeak HR = 2.05, 1.93, and 1.64, for OS, PFS and EFS, respectively). By contrast, the number of liver metastases and metabolic tumor volume (MTV) each performed well (with respective HR values of 2.71, 2.61 and 2.42, and 2.62, 1.96 and 2.29, for OS, PFS and EFS). Total lesion glycolysis (TLG) also resulted in similar performance as MTV. Multivariate prognostic modeling incorporating different features (including those quantifying intra-tumor heterogeneity) resulted in further enhanced prediction. Specifically, HR values of 4.29, 4.02 and 3.20 (p-values = 0.00004, 0.0019 and 0.0002) were obtained for OS, PFS and EFS, respectively. CONCLUSIONS:PET-derived measures beyond commonly invoked SUV parameters hold significant potential towards improved prediction of clinical outcome in patients with liver metastases, especially when utilizing multivariate models.

Project description:Early detection of gastrointestinal stromal tumor (GIST) liver metastases is crucial for the management and prognosis. In our experience, GIST liver metastases can display hypermetabolism on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and marked enhancement on magnetic resonance imaging (MRI), which are uncommon in other tumors before treatment. Most literature focus on the imaging evaluation, prognosis after treatment and less is known about imaging features on both imaging methods before treatment. This study analyzes the imaging features of newly diagnosed GIST liver metastases on 18F-FDG PET/CT and MRI, with goal of improving diagnostic accuracy. This retrospective study included 55 patients with pathological or radiographical confirmed GIST liver metastases who underwent PET/CT (n=29), MRI (n=22), or both methods (n=4). PET/CT and MRI interpretation including lesion's morphologic features, number, density or signal intensity, hemorrhage, cystic changes or necrosis, maximum standardized uptake value (SUVmax) of liver metastases and liver background on PET imaging, degree and pattern of enhancement on MRI were obtained by two experienced nuclear medicine physicians and two radiologists respectively. Data are presented as numbers, percentages, means ± standard deviations or median (interquartile range). The correlation between diameter and SUVmax of metastases, and primary tumor SUVmax and synchronous liver metastases SUVmax were analyzed by Spearman's rank test. On PET/CT visual analysis, 38.9%, 23.9%, and 37.2% of lesions showed significant hypermetabolism, slightly higher metabolism, and equal or lower metabolism than liver, respectively. There was a weak correlation between the diameter and SUVmax of liver metastases (rs =0.370, P<0.001), and a moderate correlation between SUVmax of synchronous liver metastases and the primary tumors (rs =0.492, P<0.001). On contrast-enhanced MRI, 90.8% of lesions showed heterogeneous enhancement in the arterial phase with the variable presentation, and 74.3% had different enhancement patterns between margins and intratumoral parenchyma. Liver lesions in GIST displaying significant, slight hypermetabolism on 18F-FDG PET/CT, marked or heterogeneous gradual enhancement within the intratumoral parenchyma with ring-like enhancement on MRI may denote the diagnosis of liver metastasis. However, GIST liver metastases may also display equal or lower metabolism than liver parenchyma on PET, making small lesions more difficult to diagnose.

Project description:BackgroundAssessment of dual time point (DTP) positron emission tomography was carried out with the aim of a quantitative determination of Km, the metabolic uptake rate of [18F]fluorodeoxyglucose as a measure of glucose consumption.MethodsStarting from the Patlak equation, it is shown that Km?mt/ca0+V?r/?a, where mt is the secant slope of the tissue response function between the dual time point measurements centered at t?=?t0. ca0=ca(t0) denotes arterial tracer concentration, V?r is an estimate of the Patlak intercept, and ?a is the time constant of the ca(t) decrease. We compared the theoretical predictions with the observed relation between Ks=mt/ca0 and Km in a group of nine patients with liver metastases of colorectal cancer for which dynamic scans were available, and Km was derived from conventional Patlak analysis. Twenty-two lesion regions of interest (ROIs) were evaluated. ca(t) was determined from a three-dimensional ROI in the aorta. Furthermore, the correlation between Km and late standard uptake value (SUV) as well as retention index was investigated. Additionally, feasibility of the approach was demonstrated in a whole-body investigation.ResultsPatlak analysis yielded a mean Vr of V?r=0.53±0.08 ml/ml. The patient averaged ?a was 99?±?23 min. Linear regression between Patlak-derived Km and DTP-derived Ks according to Ks?=?b?·?Km?+?a yielded b?=?0.98?±?0.05 and a?=?-0.0054?±?0.0013 ml/min/ml (r?=?0.98) in full accordance with the theoretical predictions b?=?1 and a?-V?r/?a. Ks exhibits better correlation with Km than late SUV and retention index, respectively. Ks(c)=Ks+V?r/?a is proposed as a quantitative estimator of Km which is independent of patient weight, scan time, and scanner calibration.ConclusionQuantification of Km from dual time point measurements compatible with clinical routine is feasible. The proposed approach eliminates the issues of static SUV and conventional DTP imaging regarding influence of chosen scanning times and inter-study variability of the input function. Ks and Ks(c) exhibit improved stability and better correlation with the true Km. These properties might prove especially relevant in the context of radiation treatment planning and therapy response control.

Project description:We introduce a novel computational framework to enable automated identification of texture and shape features of lesions on (18)F-FDG-PET images through a graph-based image segmentation method. The proposed framework predicts future morphological changes of lesions with high accuracy. The presented methodology has several benefits over conventional qualitative and semi-quantitative methods, due to its fully quantitative nature and high accuracy in each step of (i) detection, (ii) segmentation, and (iii) feature extraction. To evaluate our proposed computational framework, thirty patients received 2 (18)F-FDG-PET scans (60 scans total), at two different time points. Metastatic papillary renal cell carcinoma, cerebellar hemongioblastoma, non-small cell lung cancer, neurofibroma, lymphomatoid granulomatosis, lung neoplasm, neuroendocrine tumor, soft tissue thoracic mass, nonnecrotizing granulomatous inflammation, renal cell carcinoma with papillary and cystic features, diffuse large B-cell lymphoma, metastatic alveolar soft part sarcoma, and small cell lung cancer were included in this analysis. The radiotracer accumulation in patients' scans was automatically detected and segmented by the proposed segmentation algorithm. Delineated regions were used to extract shape and textural features, with the proposed adaptive feature extraction framework, as well as standardized uptake values (SUV) of uptake regions, to conduct a broad quantitative analysis. Evaluation of segmentation results indicates that our proposed segmentation algorithm has a mean dice similarity coefficient of 85.75 ± 1.75%. We found that 28 of 68 extracted imaging features were correlated well with SUV(max) (p<0.05), and some of the textural features (such as entropy and maximum probability) were superior in predicting morphological changes of radiotracer uptake regions longitudinally, compared to single intensity feature such as SUV(max). We also found that integrating textural features with SUV measurements significantly improves the prediction accuracy of morphological changes (Spearman correlation coefficient = 0.8715, p<2e-16).

Dataset Information

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver.

Background

Methods

Results

Conclusion

Publications

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets