Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy

Abstract

BackgroundThis study aims to establish and validate a predictive model based on radiomics features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT).MethodsWe retrospectively reviewed 131 patients. Patients were randomly divided into the training (n = 105) and validation (n = 26) cohorts. The clinical target volume was contoured on pre-RT computed tomography images and 48 textural features were extracted. The least absolute shrinkage and selection operator regression was used to determine the radiomics score (rad-score). A nomogram based on rad-score, clinical features, and dosimetric parameters was developed using the results of multivariate regression analysis. The predictive nomogram was evaluated using Harrell’s concordance index (C-index), area under the curve (AUC), and calibration curve.ResultsTwo radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67–0.79) and an AUC of 0.71 (95% CI, 0.62–0.79). The predictive accuracy was assessed by a calibration curve.ConclusionThe radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.

Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images

“…Our study also did not combine clinical features in models. The majority of studies showed that imaging features combined with clinical features have a high value in predicting and diagnosing ( Stubblefield et al, 2020 ; Zhou et al, 2021 ; Huang et al, 2022 ). In future research, we will continue to enroll more cases and use the data augmentations method to addresses issues of small sample sizes.…”

Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence

“…The findings suggest that the image-omics-based nomogram outperforms the clinical nomogram in predicting OS, with a C index of 0.73 (95% CI, 0.67–0.79) and an AUC of 0.71 (95% CI, 0.62–0.79) ( Table 1 ). 45 Cheng et al performed a retrospective analysis in which they extracted 396 image omics features from baseline CT scans, created and verified a CT-based image omics model, and developed a random survival forest model utilizing features with varying importance and minimal depth selection. The study aimed to predict the OS of patients with HCC and PVTT who underwent treatment with Drug-Eluting Beads Transarterial Chemoembolization (DEB-TACE).…”

“…Furthermore, certain studies exhibit a deficiency in external validation, thereby undermining the credibility of established models and hindering the integration of imaging omics into clinical practice. 45–48 …”