A combined diagnostic model to evaluate the outcome of neoadjuvant chemotherapy for pancreatic ductal adenocarcinoma
Author Block: E. V. Kondratyev, A. Zharikova, I. Gruzdev, A. Ustalov, S. A. Shmeleva, V. Egorov, E. P. Yasakova, P. V. Markov, D. V. Kalinin; Moscow/RU
Purpose: To develop and compare diagnostic models, including a combinеd model, so as to predict the patologic responce to neoadjuvant chemotherapy (NAC) for pancreatic ductal adenocarcinoma (PDAC).
Methods or Background: 59 patients with histologically confirmed PDAC and preoperative computed tomography (CT) were included in the study.
Patients were divided into two groups depending on the grade of histological response of the tumour based on Tumour Regression Score (TRS) criteria. The first group had a favourable response (TRS 0, 1 ,2), the second - unfavourable response (TRS 3).
A radiologist with 6 years of experience, segmented the region of interest (lesion) for radiomics structure analysis in the arterial and venous CT phases before and after NAC. The extracted texture features were divided into 3 groups (pre-NAC, post-NAC, combined model) and analysed using machine learning techniques.
Results or Findings: The AdaBoost ensemble model (pre-NAC) - ROC AUC (0,831), PR-AUC (0,874) и F1 Score (80%), accuracy (77%), precision (88%), specificity (85,7%) and the Optimized Random Forest (post-NAC) - ROC AUC (0,870), PR-AUC (0,941), F1 Score (85,7%), accuracy (83,3%), precision (90%), recall/sensitivity (81,8%) are the best models for recognising tumours with an unfavourable response, if the high accuracy is priority.
Gradient Boosting is the best fitting model both pre- and post-NAC, when focusing on ROC AUC(0.896) and PR-AUC (0.95).
Comparing the results of the pre- and post-NAC models, the latter were more efficient.
Conclusion: Machine learning models, specifically Optimized Random Forest and Gradient Boosting, trained on texture features from post-NAC CT scans demonstrated high accuracy in detecting non-responders with an unfavorable prognosis.
Limitations: A relatively small sample size and the absence of an external validation group, which complicates the wider application of our model.
Funding for this study: It was not required.
Has your study been approved by an ethics committee? Yes
Ethics committee - additional information: Extract from the minutes № 003-2025 of the meeting of the Scientific Research Ethics Committee of the Federal State Budgetary Institution “A.V. Vishnevsky National Medical Research Center of Surgery” of the Ministry of Health of the Russian Federation dated March 21, 2025.