OGC P20 Metabolic tumour and nodal response to neoadjuvant chemotherapy on FDG PET-CT as a predictor of pathological response and survival in patients undergoing surgical resection for locally advanced oesophageal adenocarcinoma. (7th December 2022)

• Record Type: Journal Article
• Authors: Moore, Jonathan; Subesinghe, Manil; Santaolalla, Aida; Green, Michael; Deere, Harriet; Van Hemelrijck, Mieke; Lagergren, Jesper; Chicklore, Sugama; Maisey, Nick; Gossage, James; Kelly, Mark; Baker, Cara; Davies, Andrew
• Is Part Of: British journal of surgery. Volume 109(2022)Supplement 9
• Publication Date: 2022-12-07
• Subjects: Surgery -- Periodicals; 617.005
• DOI: 10.1093/bjs/znac404.183
• Languages: English
• ISSNs: 0007-1323

Abstract

Abstract: Background: 18F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) is routinely used for staging of oesophageal cancer and has an emerging role in assessing response to neoadjuvant therapy. Several studies have demonstrated that a reduction in FDG avidity of the primary tumour and loco-regional lymph nodes following neoadjuvant chemotherapy predicts pathological response and survival in this patient group. However, no studies have evaluated the prognostic significance of metabolic parameters with respect to pathological response in lymph nodes. Change in primary tumour maximum standardised uptake value (SUVmax) is the most widely used FDG PET-CT parameter to define metabolic response, with various thresholds described. The most commonly utilised are a 35% reduction in SUVmax (MUNICON), and a 30% reduction in SUVmax (PERCIST). However, there is no consensus regarding the optimal classification; the MUNICON threshold was derived from only 40 patients and PERCIST is not tumour specific. The primary aim of this study was to evaluate the ability of FDG PET-CT to predict pathological response in the primary tumour (pTR) and lymph nodes (pNR) in patients with oesophageal adenocarcinoma undergoing neoadjuvant chemotherapy before surgery. Secondary aims were to assess the prognostic effect of metabolic and pathological response and evaluate the predictive ability of response classifications. Methods: Cohort study of 75 patients with locally advanced oesophageal or oesophago-gastric junctional adenocarcinoma who underwent FDG-PET-CT before and after neoadjuvant chemotherapy, prior to surgical resection at Guy's and St Thomas' NHS Foundation Trust, London, UK, between 2017–2020. SUV metrics related to the primary tumour and loco-regional lymph nodes were derived on pre- and post- treatment FDG PET-CT. pTR and pNR were evaluated using the Mandard classification. Patients with Mandard scores of 1–3 were classified as pathological responders and those with Mandard scores of 4–5 as non-responders. Clinicopathological characteristics were compared using the Chi-squared test. Receiver operator characteristic (ROC) analysis was performed and area under the curve (AUC) calculated to determine optimum SUVmax thresholds for metabolic response in the primary tumour (mTR) and lymph nodes (mNR). Survival curves were created using the Kaplan-Meier method, with subgroups compared using the log-rank test. Survival analysis was performed using Cox proportional hazards regression providing hazard ratios (HR) with 95% confidence intervals (CI) adjusted for age (continuous), sex (male or female), chemotherapy regimen (ECX or FLOT), cT stage (cT1–2 or cT3–4), cN stage (cN0 or cN+), tumour grade (well / moderately differentiated or poorly differentiated) and presence of signet ring cells (yes or no). Results: Mean age was 63 years with the majority male (86.7%). Almost two thirds received FLOT (48/75, 64.0%) with the remainder receiving ECX chemotherapy. There was discordance between pathological response in the primary tumour and lymph nodes in several patients, including 23.1% (6/26) who demonstrated a lymph node response in the absence of a response in the primary tumour. ROC analysis demonstrated an optimum tumour SUVmax decrease of 51.2% for predicting pTR. Using a pragmatic cut-off of 50% this provided better prediction of pTR (AUC 0.714, sensitivity 73.5%, specificity 69.2%, p<0.001) than PERCIST (AUC 0.631, sensitivity 87.8%, specificity 38.5%, p=0.008) and MUNICON (AUC 0.659, sensitivity 85.7%, specificity 46.2%, p=0.003) criteria. ROC analysis demonstrated an optimum nodal SUVmax decrease of 32.6% for predicting pNR or pathological node negativity. Using a pragmatic 30% SUVmax cut-off and excluding metabolically negative nodes, mNR demonstrated high sensitivity but low specificity (AUC 0.749, sensitivity 92.6%, specificity 57.1%, p=0.010) for predicting pNR. pTR, mTR, pNR and mNR were independent predictive factors for overall survival on adjusted analysis (pTR responder HR 0.10 95% CI 0.03–0.34; mTR responder HR 0.17 95% CI 0.06–0.48; pNR responder HR 0.17 95% CI 0.06–0.54; mNR responder HR 0.13 95% CI 0.02–0.66). Conclusions: In this study metabolic response on FDG PET-CT after neoadjuvant chemotherapy was predictive of pathological response in both the primary tumour and lymph node metastases of patients with oesophageal adenocarcinoma. Patients who had a favourable metabolic or pathological response in the primary tumour or lymph nodes had improved survival compared to non-responders. It has been suggested that commonly utilised thresholds of SUVmax are not optimal for response assessment in this patient group. This is supported by the results of the present study which suggest a reduction in SUVmax of 50% in the primary tumour was not only a better predictor of pathologic response, but also tumour recurrence and survival compared with PERCIST and MUNICON criteria. No previous studies have evaluated the ability of FDG PET-CT to predict pathologic nodal response despite recent findings suggesting it is an independent predictor of survival and evidence of a discrepancy, in some patients, who demostrate a nodal response in the absence of a response in the primary tumour. The use of FDG PET-CT in assessing response to neoadjuvant chemotherapy remains an under researched area and further evaluation is needed to establish whether it could be used to tailor individualised treatment strategies.