Published January 3, 2023 | Version v1
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Dataset related to article "PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival "

  • 1. IRCCS Humanitas Research Hospital, via Manzoni 56, 20072 Rozzano (Mi) - Italy
  • 2. MOX Laboratory, Department of Mathematics, Politecnico Di Milano, Milan, Italy
  • 3. IRCCS Humanitas Research Hospital, via Manzoni 56,20089 Rozzano (Mi) - Italy AND Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele – Milan, Italy
  • 4. MOX Laboratory, Department of Mathematics, Politecnico Di Milano, Milan, Italy AND CHDS - Center for Health Data Science, Human Technopole, Milan, Italy

Description

This record contains raw data related to article "PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival"

Abstract

Purpose: Intrahepatic cholangiocarcinoma (IHC) is an aggressive disease with few reliable preoperative biomarkers. This study aims to elucidate if radiomics extracted from preoperative [18F]FDG PET/CT may grant a non-invasive biological characterization of IHC and predict outcome after complete resection of the tumor.

Methods: All patients preoperatively imaged by [18F]FDG PET/CT who underwent hepatectomy for mass-forming IHC in the period 2010-2019 were retrospectively evaluated. On PET images, manual slice-by-slice segmentation of IHC was performed (Tumor-VOI). A 5-mm margin region was semi-automatically generated around the tumor (Margin-VOI). Textural analysis was performed using the LifeX software. Analyzed outcomes included tumor grading (G3 vs. G1-2), microvascular invasion (MVI), overall survival (OS), and progression-free survival (PFS). The performances of the combined clinical-radiomic models were compared with those of standard clinical models.

Results: Overall, 74 patients (40 females, median age 68 years) were included. Considering tumor grading and MVI, the models combining the clinical data and radiomics of the Tumor-VOI had better performances than the clinical ones (AUC = 0.78 vs. 0.72 for grading; 0.87 vs. 0.78 for MVI). The inclusion into the models of radiomics of the Margin-VOI further improved the prediction of grading (AUC = 0.83), but not of MVI. Considering OS and PFS, the models including the preoperative clinical data and radiomics of the Tumor-VOI and Margin-VOI had better performances than the pure clinical ones (C-index = 0.81 vs. 0.76 for OS; 0.81 vs. 0.72 for PFS) and similar to the models including the pathology and postoperative data (C-index = 0.81 for OS; 0.79 for PFS). No model retained the standard SUV measures.

Conclusion: The PET-based radiomics of IHC can predict pathology data and allow a reliable preoperative evaluation of prognosis. The radiomics of both the tumoral and peritumoral areas had clinical relevance. The combined clinical-radiomic models outperformed the pure preoperative clinical ones and achieved performances non-inferior to the postoperative models.

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Related works

Is supplement to
Journal article: 35347437 (PMID)
Journal article: 10.1007/s00259-022-05765-1 (DOI)