Can 18 F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?
Vetri Sudar JayaprakasamPeter GibbsNatalie GangaiRaazi BajwaRamon E SosaRandy YehMegan GreallyGeoffrey Y KuMarc J GollubViktoriya ParoderPublished in: Cancers (2022)
This study aimed to assess the usefulness of radiomics features of 18 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.
Keyphrases
- pet ct
- locally advanced
- positron emission tomography
- rectal cancer
- computed tomography
- neoadjuvant chemotherapy
- squamous cell carcinoma
- contrast enhanced
- lymph node metastasis
- phase ii study
- end stage renal disease
- radiation therapy
- free survival
- ejection fraction
- newly diagnosed
- magnetic resonance imaging
- prognostic factors
- dual energy
- peritoneal dialysis
- pet imaging
- clinical trial
- type diabetes
- electronic health record
- young adults
- metabolic syndrome
- rna seq
- study protocol
- weight loss
- insulin resistance
- risk assessment