Prognostic value of CT characteristics in GEP-NET: A systematic review.

AIM: A range of CT characteristics with potential prognostic value have previously been identified for gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs). Still, there is no widely accepted consensus on which characteristics should be reported as prognostic factors. This systematic review therefore aims to provide an overview of the available literature regarding CT characteristics and their prognostic significance for GEP-NET patients. MATERIALS AND METHODS: PubMed, Embase, and Scopus/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Eligible studies were conducted in patients with GEP-NET, and reported on the prognostic significance (in terms of tumor grade, spread of disease, and survival) of CT-based biomarkers. Study selection, quality assessment and data extraction were performed by two reviewers independently, resolving disagreement by consensus. RESULTS: In total, 5074 unique studies were identified, of which 37 were included. Given the paucity of data on GEP-NETs other than PNET, data extraction and analyses was restricted to PNETs. Fourteen CT characteristics were correlated to prognostic outcomes. Larger tumor size, hypo-enhancement, irregular shape and ill-defined margins, presence of locally invasive growth, lymphadenopathy and metastases were predictors of poorer prognosis according to 65-89% of the available studies. Most studies were regarded as having a low (65%) or moderate (24%) risk of bias. CONCLUSION: Evidence regarding prognostic value of CT-based biomarkers for PNETs is limited to heterogeneous, retrospective studies. Nonetheless, heterogeneity in data is more likely to obscure than to overestimate any correlation. Therefore, we feel that the before-mentioned characteristics should be regarded and reported as clinically relevant predictors of poorer prognosis.

Predicting local tumour progression after ablation for colorectal liver metastases: CT-based radiomics of the ablation zone.

PURPOSE: To assess whether CT-based radiomics of the ablation zone (AZ) can predict local tumour progression (LTP) after thermal ablation for colorectal liver metastases (CRLM). MATERIALS AND METHODS: Eighty-two patients with 127 CRLM were included. Radiomics features (with different filters) were extracted from the AZ and a 10 mm periablational rim (PAR)on portal-venous-phase CT up to 8 weeks after ablation. Multivariable stepwise Cox regression analyses were used to predict LTP based on clinical and radiomics features. Performance (concordance [c]-statistics) of the different models was compared and performance in an 'independent' dataset was approximated with bootstrapped leave-one-out-cross-validation (LOOCV). RESULTS: Thirty-three lesions (26 %) developed LTP. Median follow-up was 21 months (range 6-115). The combined model, a combination of clinical and radiomics features, included chemotherapy (HR 0.50, p = 0.024), cT-stage (HR 10.13, p = 0.016), lesion size (HR 1.11, p = <0.001), AZ_Skewness (HR 1.58, p = 0.016), AZ_Uniformity (HR 0.45, p = 0.002), PAR_Mean (HR 0.52, p = 0.008), PAR_Skewness (HR 1.67, p = 0.019) and PAR_Uniformity (HR 3.35, p < 0.001) as relevant predictors for LTP. The predictive performance of the combined model (after LOOCV) yielded a c-statistic of 0.78 (95 %CI 0.65-0.87), compared to the clinical or radiomics models only (c-statistic 0.74 (95 %CI 0.58-0.84) and 0.65 (95 %CI 0.52-0.83), respectively). CONCLUSION: Combining radiomics features with clinical features yielded a better performing prediction of LTP than radiomics only. CT-based radiomics of the AZ and PAR may have potential to aid in the prediction of LTP during follow-up in patients with CRLM.