Dataset Information

Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.

ABSTRACT: 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.

SUBMITTER: Rahmim A

PROVIDER: S-EPMC7507885 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.

Rahmim Arman A Bak-Fredslund Kirstine P KP Ashrafinia Saeed S Lu Lijun L Schmidtlein C Ross CR Subramaniam Rathan M RM Morsing Anni A Keiding Susanne S Horsager Jacob J Munk Ole L OL

European journal of radiology 20190208

<h4>Objective</h4>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.<h4>Patients and methods</h4>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 [S ...[more]

PMID: 30927933

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Project description:BackgroundDeriving 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).Methods2-[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: SURMEAN, SURPEAK and SURMAX, respectively.ResultsFifteen 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 SURMAX 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.ConclusionThe 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.

Project description:Purpose. To investigate the value of SUV metrics and radiomic features based on the ability of 18F-FDG PET/CT in differentiating between breast lymphoma and breast carcinoma. Methods. A total of 67 breast nodules from 44 patients who underwent 18F-FDG PET/CT pretreatment were retrospectively analyzed. Radiomic parameters and SUV metrics were extracted using the LIFEx package on PET and CT images. All texture parameters were divided into six groups: histogram (HISTO), SHAPE, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone-length matrix (GLZLM). Receiver operating characteristics (ROC) curves were generated to evaluate the discriminative ability of each parameter, and the optimal parameter in each group was selected to generate a new predictive variable by using binary logistic regression. PET predictive variable, CT predictive variable, the combination of PET and CT predictive variables, and SUVmax were compared in terms of areas under the curve (AUCs), sensitivity, specificity, and accuracy. Results. Except for SUVmin (p=0.971), the averages of FDG uptake metrics of lymphoma were significantly higher than those of carcinoma (p ≤ 0.001), with the following median values: SUVmean, 4.75 versus 2.38 g/ml (P < 0.001); SUVstd, 2.04 versus 0.88 g/ml (P=0.001); SUVmax, 10.69 versus 4.76 g/ml (P=0.001); SUVpeak, 9.15 versus 2.78 g/ml (P < 0.001); TLG, 42.24 versus 9.90 (P < 0.001). In the ROC curves analysis based on radiomic features and SUVmax, the AUC for SUVmax was 0.747, for CT texture parameters was 0.729, for PET texture parameters was 0.751, and for the combination of CT and PET texture parameters was 0.771. Conclusion. The SUV metrics in 18FDG PET/CT images showed a potential ability in the differentiation between breast lymphoma and carcinoma. The combination of SUVmax and PET/CT texture analysis may be promising to provide an effectively discriminant modality for the differential diagnosis of breast lymphoma and carcinoma, even for the differentiation of subtypes of lymphoma.

Project description:PurposeRecurrent disease following thermal ablation therapy is a frequently reported problem. Preoperative identification of patients with high risk of recurrent disease might enable individualized treatment based on patients' risk profile. The aim of the present work was to investigate the role of metabolic parameters derived from the pre-ablation 18F-FDG PET/CT as imaging biomarkers for recurrent disease in patients with colorectal liver metastases (CLM).MethodsIncluded in this retrospective study were all consecutive patients with CLM treated with percutaneous or open thermal ablation therapy who had a pre-treatment baseline 18F-FDG PET/CT available. Multivariable cox regression for survival analysis was performed using different models for the metabolic parameters (SULpeak, SULmean, SULmax, partial volume corrected SULmean (cSULmean), and total lesion glycolysis (TLG)) corrected for tumour and procedure characteristics. The study endpoints were defined as local tumour progression free survival (LTP-FS), new intrahepatic recurrence free survival (NHR-FS) and extrahepatic recurrence free survival (EHR-FS). Clinical and imaging follow-up data was used as the reference standard.ResultsFifty-four patients with 90 lesions were selected. Univariable cox regression analysis resulted in eight models. Multivariable analysis revealed that after adjusting for lesion size and the approach of the procedure, none of the metabolic parameters were associated with LTP-FS or EHR-FS. Percutaneous approach was significantly associated with a shorter LTP-FS. It was demonstrated that lower values of SULpeak, SULmax, SULmean , and cSULmean are associated with a significant better NHR-FS, independent of the lesion size and number and prior chemotherapy.ConclusionWe found no association between the metabolic parameters on pre-ablation 18F-FDG PET/CT and the LTP-FS. However, low values of the metabolic parameters were significantly associated with improved NHR-FS. The clinical implication of these findings might be the identification of high-risk patients who might benefit most from adjuvant or combined treatment strategies.

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Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.

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Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.

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