Abstract 552P
Background
Increasingly, neoadjuvant treatments are being considered for patients with LACC. Baseline computed tomography (CT) is used to select high-risk patients for neoadjuvant chemotherapy (NAC), but unlike histopathological biomarkers, the prognostic value of data derived from CT scans is unclear. Here, we analysed baseline CT data from the FOxTROT trial to identify imaging biomarkers that can aid selection of high-risk patients.
Methods
In FOxTROT, patients with LACC were randomised 2:1 to NAC or straight-to-surgery (STS), with both groups receiving postoperative chemotherapy. NAC was associated with a significant reduction in long-term recurrence. In this analysis, the following baseline CT features were assessed for associations with time to recurrence: tumour side (right or left), primary tumour (T) stage (T3 or T4), maximum extramural tumour extension (mm), maximum tumour thickness (mm), lymph node (N) stage (N0, N1 or N2), the presence of a lymph node ≥10mm in short axis (yes or no) and the presence of extramural venous invasion (EMVI; yes or no). Data from NAC and STS patients were co-analysed using univariable and multivariable Cox models to assess prognostic biomarkers. Outcomes by treatment arm were explored to identify potential predictive biomarkers.
Results
1,052 patients were included, 698 randomised to NAC and 354 to STS. Recurrence risk was significantly higher with T4 radiological stage (P=0.001), greater extramural tumour extension (P<0.001), greater tumour thickness (P=0.001), a lymph node ≥10mm in short axis (P=0.01) and EMVI (P=0.02). Distance beyond the muscularis propria was the strongest predictor on multivariable analysis (P=0.05). A lymph node ≥10mm in short axis and EMVI showed differential effects with greater impact in NAC than STS, indicating potential as predictive biomarkers.
Conclusions
Several features on baseline CT are associated with recurrence risk in LACC and represent important biomarkers to guide the selection of high-risk patients for NAC and surveillance strategies. The integration of imaging data within multi-modal assessment should be prioritised to optimise patient selection for NAC.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
University of Birmingham.
Funding
Yorkshire Cancer Research.
Disclosure
N. West: Financial Interests, Personal, Advisory Role: Amgen, Astellas, BMS, GSK, Pfizer. D. Tolan: Financial Interests, Personal, Research Funding: GSK. J. Seligmann: Financial Interests, Personal, Other, Travel: Bristol Myers Squibb; Financial Interests, Personal, Other, CME activities: GI Connect; Financial Interests, Personal, Invited Speaker: Merck Serono, Pierre Fabre, Servier, tacked, GSK; Financial Interests, Personal, Advisory Board: Pierre Fabre, Seagan, Ventana, Zentalis, AstraZeneca, Elevate Oncology, Merck Serono, Takeda, GSK, sanofi; Financial Interests, Institutional, Other, Research Funding: Pierre Fabre Medicament; Financial Interests, Personal, Other, REview of guidelines, consultancy: Roche Diagnostics; Financial Interests, Personal, Other, travel: Servier. All other authors have declared no conflicts of interest.
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