A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study

Abstract

ObjectivesWe aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid–enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19–positive (+) hepatocellular carcinoma (HCC) and patients’ prognosis.MethodsA two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan–Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort.ResultsBy combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (−) group were 81% and 77%, and 80% and 74%, respectively. Kaplan–Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients.ConclusionThe combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment.

RETRACTED: Dual-phenotype hepatocellular carcinoma: correlation of MRI features with other primary hepatocellular carcinoma and differential diagnosis

“…This component could prolong the retention time of the contrast agent, leading to a persistent enhancement pattern resembling that of ICC. Our previous study ( 24 ) found that arterial rim enhancement was an independent predictor of CK19+ HCC. In the current study, arterial rim enhancement also emerged as an independent predictor for diagnosing DPHCC, aligning with the findings of Wang et al.…”

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies

“…Notably, these works studied expression of vascular endothelial growth factor (VEGF) ( 56 ), angiopoietin-2 ( 57 ), Forkhead Box M1 (FOXM1) ( 58 ), and Ki-67 ( 59 , 60 ). Additionally, the presence of β-catenin mutation ( 61 ), intra-tumoral tertiary lymphoid structures ( 62 ), cytokeratin 19 ( 63 , 64 ), glypican-3 (GPC3) ( 65 ), immunohistochemical cell type markers for T-cells (CD3), macrophages (CD68) and endothelial cells (CD31), PD1 and CTLA4 at mRNA expression level ( 66 ), as well as density of CD3+ and CD8+ T cells ( 67 ) were studied. All the aforementioned molecules have relevant task in carcinogenesis.…”

“…Each of these immune subtypes plays a critical role in unraveling the complex immune response within HCC, providing insights for prognostication and targeted therapeutic interventions. AUCs of these tasks fell somewhere between 0.76 to 0.95 (56)(57)(58)(59)(60)(61)(62)(63)(64)(65)(66)(67). Notably, when clinical factors were integrated with radiomics signatures, models' performance significantly improved.…”