cs.CVApr 10, 2026

Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma

Authors: Francesca Fati, Felipe Coutinho, Marika Reinius, Marina Rosanu, Gabriel Funingana, Luigi De Vitis, Gabriella Schivardi, Hannah Clayton, 

Organizations: Department of Gynecologic Oncology, European Institute of Oncology, IEO, IRCCS, Italy. · Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. · INESC TEC, Faculty of Engineering, University of Porto, Portugal. · Department of Oncology, University of Cambridge, United Kingdom. · 

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Abstract

Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuvant chemotherapy (NACT) followed by delayed primary surgery is commonly employed in patients unsuitable for primary cytoreduction. The Chemotherapy Response Score (CRS) is a validated histopathological biomarker of response to NACT, but it is only available postoperatively. In this study, we investigate whether pre-treatment computed tomography (CT) imaging and clinical data can be used to predict CRS as an investigational decision-support adjunct to inform multidisciplinary team (MDT) discussions regarding expected treatment response. Methods. We proposed a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder and integrates the resulting visual representations with clinical variables through an intermediate fusion module to predict CRS. Results. Our multimodal model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort (IEO, n=41 patients). On the external test set (OV04, n=70 patients), it achieved a ROC-AUC of 0.68, alongside 67% accuracy and 75% precision. Conclusion. These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC using routine clinical data and CT imaging. As an investigational, pre-treatment decision-support tool, this approach may assist MDT discussions by providing early, non-invasive estimates of treatment response.

Figures & tables

Figure 1: Schematic of the proposed 2.5D multimodal classification pipeline. The three axial slices with the highest lesion density are extracted from the segmented omentum and stacked to form a multi-channel input for a DINOv3-small encoder. The resulting visual representation is combined with encoded clinical variables (age, CA125) via an intermediate fusion layer. The final MLP head outputs the probability of a non- or partial pathological chemotherapy response (CRS 1–2) versus a complete response (CRS 3).
Figure 2: Representative pre- and post-treatment CT scans of HGSOC patients from the IEO and OV04 cohorts, stratified by CRS grade: CRS 1 (minimal or no response), CRS 2 (partial response), and CRS 3 (complete or near-complete response). Axial, sagittal, and coronal views are shown for each case, with omental tumor regions used for CRS assessment highlighted in blue on pre-treatment scans and in violet on post-treatment scans.
CharacteristicIEO (Internal)OV04 (External)
 Train (n=191)Validation (n=39)Test (n=41)Test (n=70)
Age (years)64 [54–70]62 [55–66]65 [55–74]63 [54–70]
CA-125 (U/mL)1192 [404–2896]1006 [345–1722]914 [472–2875]898 [286–2070]
CRS, n (%)    
CRS 1 (no/minimal)80 (41.7%)17 (43.6%)17 (41.5%)19 (27.1%)
CRS 2 (partial)86 (44.8%)18 (46.1%)18 (43.9%)21 (30.0%)
Table 1: Baseline clinical characteristics and chemotherapy response score (CRS) distribution of the IEO and OV04 cohorts. The table reports patient age and serum cancer antigen 125 (CA-125) levels at diagnosis, together with CRS labels, for the IEO cohort (subdivided into training, validation, and internal test sets) and the independent external OV04 cohort. Continuous variables are presented as median [IQR].
 IEO (Internal)OV04 (External)
 CRS1 (n=114)CRS2 (n=122)CRS3 (n=38)CRS1 (n=19)CRS2 (n=21)CRS3 (n=30)
Age (years)65.563.560.564.265.561.0
CA-125 (U/mL)655.9946.7426.5867.0584.01423.5
Tumor Morphology      
Volume [ cm3cm^{3} ]102.480.9815.62119.261.0436.6
Surface Area [ cm2cm^{2} ]468.6437.8167.41276781457.9
Table 2: Comparison of patient demographics and omental tumor morphology across CRS grades in the IEO internal validation set and the OV04 external validation set. Data are presented as median values for age, pre-treatment CA-125 levels, and quantitative tumor morphology metrics, including tumor volume, surface area, and the largest connected component (CC) fraction. Patients are stratified by CRS classification: CRS 1 (minimal or no response), CRS 2 (partial response), and CRS 3 (complete or near-complete response).
Table 3: (a) Discriminative performance across external cohorts (ROC-AUC, F1-score, precision, recall, accuracy). (b) Corresponding confusion matrix components (percentages relative to positives/negatives: TP, TN, FP, FN). All values are medians with 95% confidence intervals, evaluated at ( τ0.5\tau_{0.5} and τopt=0.69\tau_{opt}=0.69 ).
    IEO TestOV04
CTVolumeAgeCA125AUCAUC
✗✓✓✓0.644 [0.459–0.792]0.537 [0.407–0.674]
✓✗✗✗0.894 [0.727–1.000]0.469 [0.312–0.632]
✓✗✓✗0.811 [0.333–1.000]0.572 [0.414–0.727]
✓✗✗✓0.933 [0.744–1.000]0.521 [0.364–0.677]
✓✗✓✓0.952 [0.862–1.000]0.677 [0.527–0.806]
Table 4: Median AUC (95% CI) for each feature configuration in the ablation study, evaluated on the IEO test split and the external OV04 cohort. Each row indicates which features are included: CT imaging, lesion volume, age, and CA-125 levels (✓ = included, ✗ = excluded). AUC values are reported at the threshold maximizing the F1 score for each cohort.
Figure 4: ROC curves for the ablation study on (a) IEO and (b) OV04 cohorts. The full multimodal model (CT + Age + CA-125) achieves the highest AUC, with the largest gains on OV04 observed when clinical features are added.
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