European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology · 2025
PET-CT-based host metabolic (PETMet) features are associated with pathologic response in gastroesophageal adenocarcinoma.
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White Charlie, Jayaprakasam Vetri Sudar, Tenet Megan, Tang Laura H, Schattner Mark A, Janjigian Yelena Y, Maron Steven B, Schöder Heiko, Larson Steven M, Gönen Mithat, Datta Jashodeep, Coit Daniel G, Mauguen Audrey, Strong Vivian E, Vitiello Gerardo A
Аннотация
BACKGROUND: 18F-FDG PET-CT-based host metabolic (PETMet) profiling of non-tumor tissue is a novel approach to incorporate the patient-specific response to cancer into clinical algorithms.
MATERIALS AND METHODS: A prospectively maintained institutional database of gastroesophageal cancer patients was queried for pretreatment PET-CTs, demographics, and clinicopathologic variables. 18F-FDG PET avidity was measured in 9 non-tumor tissue types (liver, spleen, 4 muscles, 3 fat locations). Logistic and Cox regression were used to model pathologic response (PR) and overall survival (OS) respectively. Classification and regression tree (CART) and random forest modeling were employed to create decision trees and identify PETMet features associated with outcome.
RESULTS: Two-hundred and one patients with distal gastroesophageal (48 %) or gastric (52 %) adenocarcinoma were included. PET-CT-derived scores were independently associated with PR after adjusting for clinical variables. CART and Random Forest methods identified critical split points of non-tumor tissue 18F-FDG avidity that can classify patients and predict PR. PET-CT risk groups created from decision trees predicted PR significantly better than the clinical model (p < 0.001). Specifically, an elevated erector spinae-to-gluteal fat 18F-FDG avidity ratio (≥2.7) combined with low 18F-FDG avidity in the spleen (<2.9) and rectus femoris (<0.52) predict PR. No advantage of PET-CT risk groups was seen for predicting OS (p = 0.155).
CONCLUSIONS: Pretreatment host PETMet features may be useful for predicting PR after neoadjuvant therapy in gastroesophageal cancer. Unsupervised decision trees indicate that low 18F-FDG avidity in visceral fat, subcutaneous fat, and muscle result in the most favorable PR, suggesting that systemic hypermetabolism adversely impacts prognosis.
MATERIALS AND METHODS: A prospectively maintained institutional database of gastroesophageal cancer patients was queried for pretreatment PET-CTs, demographics, and clinicopathologic variables. 18F-FDG PET avidity was measured in 9 non-tumor tissue types (liver, spleen, 4 muscles, 3 fat locations). Logistic and Cox regression were used to model pathologic response (PR) and overall survival (OS) respectively. Classification and regression tree (CART) and random forest modeling were employed to create decision trees and identify PETMet features associated with outcome.
RESULTS: Two-hundred and one patients with distal gastroesophageal (48 %) or gastric (52 %) adenocarcinoma were included. PET-CT-derived scores were independently associated with PR after adjusting for clinical variables. CART and Random Forest methods identified critical split points of non-tumor tissue 18F-FDG avidity that can classify patients and predict PR. PET-CT risk groups created from decision trees predicted PR significantly better than the clinical model (p < 0.001). Specifically, an elevated erector spinae-to-gluteal fat 18F-FDG avidity ratio (≥2.7) combined with low 18F-FDG avidity in the spleen (<2.9) and rectus femoris (<0.52) predict PR. No advantage of PET-CT risk groups was seen for predicting OS (p = 0.155).
CONCLUSIONS: Pretreatment host PETMet features may be useful for predicting PR after neoadjuvant therapy in gastroesophageal cancer. Unsupervised decision trees indicate that low 18F-FDG avidity in visceral fat, subcutaneous fat, and muscle result in the most favorable PR, suggesting that systemic hypermetabolism adversely impacts prognosis.