Youcai Li1, Yin Zhang2, Qi Fang1, Xiaoyao Zhang1, Peng Hou1, Hubing Wu3, Xinlu Wang4. 1. Department of Nuclear Medicine, The First Affiliated Hospital of Guangzhou Medical University, No. 151, Yanjiang Road, Yuexiu District, Guangzhou, 510000, Guangdong, China. 2. Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, Guangdong Province, China. 3. Nanfang PET Center, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, Guangdong Province, China. wuhbym@163.com. 4. Department of Nuclear Medicine, The First Affiliated Hospital of Guangzhou Medical University, No. 151, Yanjiang Road, Yuexiu District, Guangzhou, 510000, Guangdong, China. 71lu@163.com.
Abstract
As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [18F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC). METHODS: Patients (N = 80) with BCLC0-A HCC who underwent [18F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated. RESULTS: To predict MVI, the [18F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799-0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [18F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761-0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses. CONCLUSION: The newly developed [18F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [18F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise medicine.
As a reliable preoperative predictor for microvascular invasion (MVI) and disease-free survival (DFS) is lacking, we developed a radiomics nomogram of [18F]FDG PET/CT to predict MVI status and DFS in patients with very-early- and early-stage (BCLC 0, BCLC A) hepatocellular carcinoma (HCC). METHODS: Patients (N = 80) with BCLC0-A HCC who underwent [18F]FDG PET/CT before surgery were enrolled in this retrospective study and were randomized to a training cohort and a validation cohort. Texture features from patients obtained using Lifex software in the training cohort were subjected to LASSO regression to select the most useful predictive features of MVI and DFS. Then, the radiomics nomogram was constructed using the radiomics signature and clinical features and further validated. RESULTS: To predict MVI, the [18F]FDG PET/CT radiomics signature consisted of five texture features from the PET and six texture features from CT. The signature was significantly associated with MVI status in the training cohort (P = 0.001). None of the clinical features was independent predictors for MVI status (P > 0.05). The area under the curve value of the M-PET/CT model was 0.891 (95% CI: 0.799-0.984) in the training cohort and showed good discrimination and calibration. To predict DFS, the [18F]FDG PET/CT radiomics nomogram (D-PET/CT model) and a clinicopathologic nomogram were built in the training cohort. The D-PET/CT model, which integrated the D-PET/CT radiomics signature with INR and TB, provided better predictive performance (C-index: 0.831, 95% CI: 0.761-0.900) and larger net benefits than the simple clinical model, as determined by decision curve analyses. CONCLUSION: The newly developed [18F]FDG PET/CT radiomics signature was an independent biomarker for the estimation of MVI and DFS in patients with very-early- and early-stage HCC. Moreover, PET/CT nomogram, which incorporated the radiomics signature of [18F]FDG PET/CT and clinical risk factors in patients with very-early- and early-stage HCC, performed better for individualized DFS estimation, which might enable a step forward in precise medicine.
Entities:
Keywords:
DFS; Hepatocellular carcinoma; MVI; Radiomics; [18F]FDG PET/CT model
Authors: S Jonas; W O Bechstein; T Steinmüller; M Herrmann; C Radke; T Berg; U Settmacher; P Neuhaus Journal: Hepatology Date: 2001-05 Impact factor: 17.425
Authors: Sudeep Banerjee; David S Wang; Hyun J Kim; Claude B Sirlin; Michael G Chan; Ronald L Korn; Aaron M Rutman; Surachate Siripongsakun; David Lu; Galym Imanbayev; Michael D Kuo Journal: Hepatology Date: 2015-07-01 Impact factor: 17.425
Authors: Giorgio Maria Masci; Fabio Ciccarelli; Fabrizio Ivo Mattei; Damiano Grasso; Fabio Accarpio; Carlo Catalano; Andrea Laghi; Paolo Sammartino; Franco Iafrate Journal: Radiol Med Date: 2022-01-23 Impact factor: 3.469