Radiomics score: a potential prognostic imaging feature for postoperative survival of solitary HCC patients

Abstract

BackgroundRadiomics is an emerging field in oncological research. In this study, we aimed at developing a radiomics score (rad-score) to estimate postoperative recurrence and survival in patients with solitary hepatocellular carcinoma (HCC).MethodsA total of 319 solitary HCC patients (training cohort: n = 212; validation cohort: n = 107) were enrolled. Radiomics features were extracted from the artery phase of preoperatively acquired computed tomography (CT) in all patients. A rad-score was generated by using the least absolute shrinkage and selection operator (lasso) logistic model. Kaplan-Meier and Cox’s hazard regression analyses were used to evaluate the prognostic significance of the rad-score. Final nomograms predicting recurrence and survival of solitary HCC patients were established based on the rad-score and clinicopathological factors. C-index and calibration statistics were used to assess the performance of nomograms.ResultsSix potential radiomics features were selected out of 110 texture features to formulate the rad-score. Low rad-score positively correlated with aggressive tumor phenotypes, like larger tumor size and vascular invasion. Meanwhile, low rad-score was significantly associated with increased recurrence and reduced survival. In addition, multivariate analysis identified the rad-score as an independent prognostic factor (recurrence: Hazard ratio (HR): 2.472, 95% confident interval (CI): 1.339–4.564, p = 0.004;survival: HR: 1.558, 95%CI: 1.022–2.375, p = 0.039). Notably, the nomogram integrating rad-score had a better prognostic performance as compared with traditional staging systems. These results were further confirmed in the validation cohort.ConclusionsThe preoperative CT image based rad-score was an independent prognostic factor for the postoperative outcome of solitary HCC patients. This score may be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5024-z) contains supplementary material, which is available to authorized users.

Radiomics in hepatocellular carcinoma: a quantitative review

“…The studies included in the present review typically use first-order, second-order and higher-order mathematical descriptors such as grayscale matrix analysis (cooccurrence) which take into account the relationships between neighboring pixels. A filtering step -using for example Gauss Laplacian filters -is usually performed prior to any analysis to reduce noise and improve performance [42][43][44][45][46][47].…”

“…Four studies dealt with quantitative features from contrast-enhanced multiple-phase CT images [43][44][45]63] and all six studies dealt with only semi-quantitative (semantic) features from contrast-enhanced multiple-phase CT images [35,[37][38][39][40][41]. The other four studies focused on quantitative characteristics from contrast-enhanced single-phase CT images (arterial phase in 2 [42,47], portal phase in 2 [46,62]).…”

“…Bakr et al [63] demonstrated that quantitative features which capture the lesion texture, intensities, and shape extracted from triphasic CT images had a better accuracy in MVI prediction, compared to two previously reported signatures based on semantic features, radiogenomic venous invasion [39,40] and TTPVI [35]. [42] demonstrated that a radiomics score measured on baseline CT was a prognostic factor of the outcome in patients that underwent liver resection for HCC. They concluded that this score might be complementary to the current staging system and help to stratify individualized treatments for solitary HCC patients.…”

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

“…Whether these image signs could be used for TFTY BCa recurrence prediction remains inconclusive to date. In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, as well as predicting mental disorders schizophrenia …”

“…14,15 Whether these image signs could be used for TFTY BCa recurrence prediction remains inconclusive to date. In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, [16][17][18] as well as predicting mental disorders schizophrenia. 19 Based on these findings above, we hypothesized that: 1) the radiomics features extracted from preoperative mpMRI to characterize the subtle variations of tissue distribution within the lesion [20][21][22] might be potential in predicting BCa recurrence; 2) the combination of the radiomics strategy with important clinical factors, mainly including age, gender, histological grade, and MIS of the archived tumor with the maximal size in bladder lumen, tumor size, NoT, operation choice, together with the imaging signs like stalk and SLE, 13,[23][24][25] might add the incremental value for TFTY BCa prediction.…”