Abstract
The aim of this study was to investigate the application of [18F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [18F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [18F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.
Highlights
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death worldwide
Radiomics is that part of artificial intelligence (AI) that aims to provide quantitative characteristics from biomedical images of different nature that cannot be assessed by the human eye, assuming that any smallest image’s constituent may encompass features of tumor’s phenotypes that may be potentially related to tumor’s outcome and patients’ response to therapy, reflecting the pathophysiological process and supporting medical decisions
The present study aimed to investigate the potential application of texture analysis on restaging [18F]FDG PET/CT images in metastatic colorectal patients, proposing a radiomics model able to select PET and CT imaging features for global disease status prediction, liver metastasis evaluation, and survival outcomes
Summary
Colorectal cancer (CRC) is the third most common cancer and the second leading cause of death worldwide. Alongside traditional imaging (e.g., ultrasonography, CT, MRI), [18F]FDG PET/CT is routinely used as a tool for accurate staging and restaging after therapy in patients with colorectal metastatic disease, and it represents a valuable ally for risk assessment, prognosis evaluation, and treatment strategy decisions making. Radiomics is that part of artificial intelligence (AI) that aims to provide quantitative characteristics (features) from biomedical images of different nature that cannot be assessed by the human eye, assuming that any smallest image’s constituent (i.e., voxel and/or pixel) may encompass features of tumor’s phenotypes that may be potentially related to tumor’s outcome and patients’ response to therapy, reflecting the pathophysiological process and supporting medical decisions. Several studies have demonstrated the correlation between the heterogeneity of the tissues and the radiomics features, which would allow obtaining relevant information through the analysis of the images alone [6]
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