AI Article Synopsis

  • The study explored how radiomics features from F-FDG PET/CT scans can help predict outcomes in patients with advanced esophageal cancers, specifically looking at tumor and nodal categories, response to chemotherapy, progression-free survival, and overall survival.
  • A total of 86 PET/CT scans were analyzed, with the data organized into training and test sets to develop models that select key features for outcome predictions.
  • The findings showed varying diagnostic accuracies, with combined PET/CT scans performing best in predicting outcomes such as clinical tumor category (81.5%), nodal category (86.2%), PET response (70%), progression-free survival (75%), and overall survival (62.1%), indicating the potential value of radiomics in clinical settings.

Article Abstract

This study aimed to assess the usefulness of radiomics features of F-FDG PET/CT in patients with locally advanced esophageal cancers (ESCC) in predicting outcomes such as clinical tumor (cT) and nodal (cN) categories, PET response to induction chemotherapy (PET response), progression-free survival (PFS), and overall survival (OS). Pretreatment PET/CT images from patients who underwent concurrent chemoradiotherapy from July 2002 to February 2017 were segmented, and data were split into training and test sets. Model development was performed on the training datasets and a maximum of five features were selected. Final diagnostic accuracies were determined using the test dataset. A total of 86 PET/CTs (58 men and 28 women, mean age 65 years) were segmented. Due to small lesion size, 12 patients were excluded. The diagnostic accuracies as derived from the CT, PET, and combined PET/CT test datasets were as follows: cT category-70.4%, 70.4%, and 81.5%, respectively; cN category-69.0%, 86.2%, and 86.2%, respectively; PET response-60.0%, 66.7%, and 70.0%, respectively; PFS-60.7%, 75.0%, and 75.0%, respectively; and OS-51.7%, 55.2%, and 62.1%, respectively. A radiomics assessment of locally advanced ESCC has the potential to predict various clinical outcomes. External validation of these models would be further helpful.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221147PMC
http://dx.doi.org/10.3390/cancers14123035DOI Listing

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