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. 2024 Oct 8;14(19):2245.
doi: 10.3390/diagnostics14192245.

A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma

Rosie Kwon  1 , Hannah Kim  2 , Keun Soo Ahn  3   4 , Bong-Il Song  1   4   5 , Jinny Lee  1 , Hae Won Kim  1   4   5 , Kyoung Sook Won  1 , Hye Won Lee  6 , Tae-Seok Kim  3 , Yonghoon Kim  3 , Koo Jeong Kang  3
Affiliations

A Machine Learning-Based Clustering Using Radiomics of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography for the Prediction of Prognosis in Patients with Intrahepatic Cholangiocarcinoma

Rosie Kwon et al. Diagnostics (Basel). .

Abstract

Background: Intrahepatic cholangiocarcinoma (IHCC) is highly aggressive primary hepatic malignancy with an increasing incidence.

Objective: This study aimed to develop machine learning-based radiomic clustering using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for predicting recurrence-free survival (RFS) and overall survival (OS) in IHCC.

Methods: We retrospectively reviewed pretreatment F-18 FDG PET/CT scans of 60 IHCC patients who underwent surgery without neoadjuvant treatment between January 2008 and July 2020. Radiomic features such as first order, shape, and gray level were extracted from the scans of 52 patients and analyzed using unsupervised hierarchical clustering.

Results: Of the 60 patients, 36 experienced recurrence and 31 died during follow-up. Eight patients with a negative FDG uptake were classified as Group 0. The unsupervised hierarchical clustering analysis divided the total cohort into three clusters (Group 1: n = 27; Group 2: n = 23; Group 3: n = 2). The Kaplan-Meier curves showed significant differences in RFS and OS among the clusters (p < 0.0001). Multivariate analyses showed that the PET radiomics grouping was an independent prognostic factor for RFS (hazard ratio (HR) = 3.03, p = 0.001) and OS (HR = 2.39, p = 0.030). Oxidative phosphorylation was significantly activated in Group 1, and the KRAS, P53, and WNT β-catenin pathways were enriched in Group 2.

Conclusions: This study demonstrated that machine learning-based PET radiomics clustering can preoperatively predict prognosis and provide valuable information complementing the genomic profiling of IHCC.

Keywords: F-18 FDG PET/CT; clustering; intrahepatic cholangiocarcinoma; prognosis; survival.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of patient selection. Of the 85 patients who received curative surgery for the IHCC treatment, 25 patients were excluded. Finally, 60 patients were enrolled in this study.
Figure 2
Figure 2
An exemplary case of tumor segmentation. Using the maximum entropy method, segmentation of the IHCC was performed with 3D Slicer software (version 5.2.1).
Figure 3
Figure 3
Heatmap depicting the correlations between the patients’ characteristics and radiomics. Patient IDs are represented by columns, and radiomic features are represented by rows in the matrix.
Figure 4
Figure 4
(A) Cumulative recurrence-free survival curves and (B) overall survival curves according to the PET radiomics group. The high radiomics group was associated with a significantly lower recurrence-free survival rate and overall survival rate compared with the low radiomics group.
Figure 5
Figure 5
(A) Cumulative recurrence-free survival curves and (B) overall survival curves according to the AJCC stage. The advanced AJCC state was associated with a significantly lower recurrence-free survival rate and overall survival rate.
Figure 6
Figure 6
Gene set enrichment analysis, showing significantly activated oxidative phosphorylation in Cluster 1 (A) and significantly activated MYC targets v1 (B), the p53 pathway (C), the inflammatory response (D), KRAS signaling (E), and TNF alpha signaling via NF-κB (F) in Cluster 2.
See this image and copyright information in PMC

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