CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets

Robert N. Spaans; Catherine Chia; Tongjie Wang; Adam Kowalewski; Parandzem Khachatryan; Domingos Oliveira; Khrystyna Faryna; Jean-Paul A. van Basten

eess.IVAug 21, 2026

CHIMERA Challenge: Biochemical Recurrence Prediction in Prostate Cancer Patients using multimodal datasets

Authors: Robert N. Spaans, Catherine Chia, Tongjie Wang, Adam Kowalewski, Parandzem Khachatryan, Domingos Oliveira, Khrystyna Faryna, Jean-Paul A. van Basten,

Organizations: Radboud University Medical Center, Department of Pathology, Research Institute for Medical Innovation, Nijmegen, The Netherlands · Canisius Wilhelmina Hospital, Department of Urology, Nijmegen, The Netherlands · Oncode Institute, Utrecht, The Netherlands · Erasmus University Medical Center, Department of Pathology, Rotterdam, The Netherlands ·

Abstract

Biochemical recurrence (BCR), defined as any detectable prostate-specific antigen level after prostatectomy with confirmatory elevation, is widely used as a surrogate endpoint and typically assessed using clinical and pathological variables. Currently, no standardized benchmark exists for multimodal prognostic modeling in urological cancers, partly because curating heterogeneous multimodal data remains challenging. We developed the CHIMERA Challenge, a multimodal benchmark integrating preoperative mpMRI, post-prostatectomy histopathology, patient characteristics, and clinician-derived variables from 267 patients across two institutions. The dataset comprises 801 MRI sequences, 13 clinical variables per case, and 942 WSIs. Training (n=95), validation (n=23), and test (n=149) splits were established and hosted on the Grand Challenge platform. Baseline clinical and pathological characteristics did not differ significantly across splits. Models were evaluated on predicting time to BCR using the C-index. Post-challenge analyses tested how each model type performed when clinician-derived variables were withheld or randomized. Unimodal clinical models achieved the highest test C-index of 0.7402 but proved sensitive to the integrity of these variables, with performance collapsing toward chance (C approximately 0.50) when they were randomized. Multimodal models retained near-baseline performance when these variables were withheld (delta C at most 0.04), indicating their ability to recover prognostic signal directly from imaging data. CHIMERA is the first public, standardized multimodal benchmark for prostate cancer prognosis. Although models using only patient characteristics and clinician-derived variables yielded the highest leaderboard performance, multimodal models demonstrated greater robustness in clinically realistic scenarios where complete expert annotation is not guaranteed.

Figures & tables

Figure 1: Data distribution of prostate cancer patients who have had a radical prostatectomy between 2012 and 2021 for Task 1 of the CHIMERA challenge.

Figure 2: Flowchart of available data types provided in CHIMERA challenge.

Figure 3: Overview of the CHIMERA challenge and study setup. The global competition attracted 336 participants from 43 countries. The challenge was organized into three phases. Participants could download the open-source training set for algorithm development and submit candidate algorithms for evaluation on the hidden validation and test sets through the Grand Challenge platform. Each participant was allowed up to five successful submissions to the validation set and one successful submission to the final test set. After the challenge ended, algorithms that were successfully submitted to the test phase were included in additional analyses, including feature importance and modality importance analyses.

	WL			SMILE			TIA-Pegasus
Randomized Variable	$C_{r}$	$\Delta$ C	$q$	$C_{r}$	$\Delta$ C	$q$	$C_{r}$	$\Delta$ C	$q$
Reference	$C_{0}=0.7294$			$C_{0}=0.7280$			$C_{0}=0.7402$
Capsular Penetration	0.7493	$-0.0198$ [-0.0454, 0.0031]	0.185	0.7424	$-0.0144$ [-0.0372, 0.0093]	0.228	0.7527	$-0.0126$ [-0.0297, 0.0030]	0.138
Tertiary Gleason	0.7276	$0.0018$ [-0.0049, 0.0092]	0.749	0.7161	$0.0119$ [-0.0016, 0.0294]	0.137	0.7402	$0.0000$ [0, 0]	–
Pre-operative PSA	0.7282	$0.0012$ [-0.0074, 0.0118]	0.834	0.7163	$0.0117$ [0.0006, 0.0249]	0.080	0.7274	$0.0128$ [-0.0004, 0.0299]	0.116
pT Stage	0.7337	$-0.0043$ [-0.0289, 0.0173]	0.778	0.7078	$0.0203$ [-0.0097, 0.0501]	0.201	0.7333	$0.0069$ [-0.0065, 0.0208]	0.309

Table 3: Overview of performance per unimodal team for randomized variables. Models were bootstrapped (10,000 samples). Baseline performance is denoted by

C_{0}

and post-randomization performance by

C_{r}

;

\Delta

C-index (

C_{0}-C_{r}

) is reported with 95% bootstrap confidence intervals. Benjamini–Hochberg (BH) FDR-adjusted two-sided

p

-values (

q

) are shown separately. Values in bold denote

q<0.05

Team	Modalities	$C_{0}$	$C_{r}$	$\Delta$ C [95% CI]	$p$
IU CompPath	Clin + MRI + WSI	0.7197	0.6869	$0.0328$ [-0.0006, 0.0693]	0.056
VCMI	Clin + MRI + WSI	0.7153	0.6762	$0.0391$ [-0.0105, 0.0949]	0.124
OHSU-Cedar	Clin + WSI	0.6885	0.6881	$0.0004$ [-0.1027, 0.1053]	0.993

Table 4: Overview of performance per multimodal model when pathology-derived variables are removed. Models were bootstrapped (10,000 samples). Baseline performance is denoted by

C_{0}

and post-variable removal performance by

C_{r}

\Delta

C-index (

C_{0}-C_{r}

) is reported with 95% bootstrap confidence intervals.

Configuration	$C_{r}$	$\Delta$ C-index	$q$
WSI + MRI + Clinical (full) - Reference	$C_{0}=0.7153$	-	-
WSI + Clinical (full)	0.7076	0.0077	0.5435
WSI + Clinical (filtered)	0.6981	0.0172	0.5435
WSI + MRI	0.6902	0.0251	0.4254
Clinical only (full)	0.6831	0.0322	0.4402
WSI + MRI + Clinical (filtered)	0.6762	0.0391	0.2488

Table 5: Ablation analysis for VCMI under different modality and clinical feature configurations. Baseline (full model) performance is denoted by

C_{0}

and the ablated performance by

C_{r}

\Delta

C-index (

C_{0}-C_{r}

) is reported as the difference between baseline and ablation performance. Benjamini–Hochberg (BH) FDR-adjusted two-sided

p

-values are reported as

q

Supplementary Figure S1: Forest plot of the performance per unimodal team for randomized variables per cohort.

Supplementary Figure S2: Forest plot of the performance per multimodal team when pathology-derived variables were removed, split per origin center of the data.

Supplementary Figure S3: Forest plot of the ablation analysis for the VCMI model under different modality and clinical feature configurations per cohort

Clinical Outcome Prediction

Clinical outcome prediction uses patient records, imaging, signals, and biomarkers to estimate risks, disease trajectories, treatment response, and survival.

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Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands · Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy · Surgical Pathology Unit, Verona University Hospital Trust, Verona, Italy +4

Jun 8, 2026J/K move · Enter open · S save

GD-MIL: Grade-Disentangled Multiple Instance Learning for Multimodal Biochemical Recurrence Prediction in Prostate Cancer

Dasari Naga Raju

Adversarial Representation Learning Cancer Survival Prediction Survival Analysis Clinical Outcome Prediction Oncology Multimodal Survival Analysis+2

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Biochemical recurrence (BCR) after radical prostatectomy is a critical endpoint in prostate cancer, yet risk stratification relies almost entirely on variables dominated by Gleason grade. Whether H&E whole slide images (WSIs) carry prognostic signal beyond grade, and whether multiple instance learning (MIL) can recover it, remains unsettled. A key obstacle is that many pipelines select model checkpoints on the evaluation fold, artificially inflating concordance. We construct a rigorous benchmark on TCGA-PRAD (487 patients, 101 BCR events) using strict out-of-fold scoring over five-fold cross-validation repeated across five seeds. The choice of MIL aggregator (ABMIL, CLAM, TransMIL, PatchGCN) has little effect (C-index 0.61-0.64 with UNI2-h), while the feature extractor is the dominant factor (ResNet50 0.566 versus pathology foundation models up to 0.639). A clinical Cox model on grade, stage, and age reaches 0.687; no imaging-only model significantly outperforms it (p > 0.10). We introduce Grade-Disentangled MIL (GD-MIL), a gated-attention MIL encoder trained with a gradient-reversal grade adversary that encourages the slide representation to be invariant to Gleason grade before late fusion with clinical variables. GD-MIL achieves C-index 0.704, significantly outperforming both the clinical baseline (delta-c = +0.029, p = 0.0005) and the best imaging-only model (delta-c = +0.062, p = 0.039), suggesting H&E morphology contains prognostic information complementary to grade. A median risk split yields log-rank p < 0.0001 separation in BCR-free survival (~20% vs ~70% at five years).

Abstract

Clinical Outcome Prediction

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