Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT

Haotian Liao; Hanyu Jiang; Yuntian Chen; Ting Duan; Ting Yang; Miaofei Han; Zhong Xue; Feng Shi; Kefei Yuan; Mustafa R Bashir; Dinggang Shen; Bin Song; Yong Zeng

doi:10.1245/s10434-022-11505-4

Predicting Genomic Alterations of Phosphatidylinositol-3 Kinase Signaling in Hepatocellular Carcinoma: A Radiogenomics Study Based on Next-Generation Sequencing and Contrast-Enhanced CT

Ann Surg Oncol. 2022 Mar 14. doi: 10.1245/s10434-022-11505-4. Online ahead of print.

Authors

Haotian Liao^#¹, Hanyu Jiang^#², Yuntian Chen², Ting Duan², Ting Yang², Miaofei Han³, Zhong Xue³, Feng Shi³, Kefei Yuan¹, Mustafa R Bashir⁴, Dinggang Shen^{5

6}, Bin Song⁷, Yong Zeng⁸

Affiliations

¹ Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China.
² Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
³ Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
⁴ Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, USA.
⁵ Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China. [email protected].
⁶ School of Biomedical Engineering, ShanghaiTech University, Shanghai, China. [email protected].
⁷ Department of Radiology, West China Hospital, Sichuan University, Chengdu, China. [email protected].
⁸ Department of Liver Surgery & Liver Transplantation, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center of Biotherapy, Chengdu, China. [email protected].

^# Contributed equally.

PMID: 35286532
DOI: 10.1245/s10434-022-11505-4

Abstract

Background: Exploring the genomic landscape of hepatocellular carcinoma (HCC) provides clues for therapeutic decision-making. Phosphatidylinositol-3 kinase (PI3K) signaling is one of the key pathways regulating HCC aggressiveness, and its genomic alterations have been correlated with sorafenib response. In this study, we aimed to predict somatic mutations of the PI3K signaling pathway in HCC samples through machine-learning-based radiomic analysis.

Methods: HCC patients who underwent next-generation sequencing and preoperative contrast-enhanced CT were recruited from West China Hospital and The Cancer Genome Atlas for model training and validation, respectively. Radiomic features were extracted from volumes of interest (VOIs) covering the tumor (VOI_tumor) and peritumoral areas (5 mm [VOI_5mm], 10 mm [VOI_10mm], and 20 mm [VOI_20mm] from tumor margin). Factor analysis, logistic regression analysis, least absolute shrinkage and selection operator, and random forest analysis were applied for feature selection and model construction. Model performance was characterized based on the area under the receiver operating characteristic curve (AUC).

Results: A total of 132 HCC patients (mean age: 61.1 ± 14.7 years; 108 men) were enrolled. In the training set, the AUCs of radiomic signatures based on single CT phases were moderate (AUC 0.694-0.771). In the external validation set, the radiomic signature based on VOI_10mm in arterial phase demonstrated the highest AUC (0.733) among all models. No improvement in model performance was achieved after adding the tumor radiomic features or manually assessed qualitative features.

Conclusions: Machine-learning-based radiomic analysis had potential for characterizing alterations of PI3K signaling in HCC and could help identify potential candidates for sorafenib treatment.