Abstract C072: Comparing and combining existing radiological criteria for hyperprogressive disease in patients receiving immune-oncology therapy: Building towards a sensitive and conservative method to assess the presence of hyperprogressive disease
Bibliographic record
Abstract
Abstract Hyperprogressive disease (HPD) is characterized by an acceleration of tumor growth in a subset of patients due to receiving immune-oncology (IO) therapy. Patients, policy makers and treating physicians should be made aware of the potential HPD related to a specific IO-therapy. Multiple methods based on radiological criteria comparing the pre-treatment and post-treatment tumor growth have been proposed in the literature to identify HPD cases. In the absence of a consensus regarding which methods to use, we compared the different methods and proposed two simple combinations of the existing methods to estimate the potential incidence of HPD related to the received IO therapy. Data from 305 patients across multiple centres (Gustave Roussy, Vall d'Hebron Institute of Oncology, START Madrid-CIOCC and Institute of Cancer Research Marsden) were pooled. 163 patients had pre-baseline study visit disease assessment available and progressive disease (PD) as per RECIST version 1.1 at the first post-baseline assessment on target lesions exclusively. The presence of HPD according to the ‘tumor growth rate’ (TGR), ‘tumor growth kinetics’ (TGK) and ‘tumor growth difference’ (TGD) method was analyzed in this subsample of 163 patients. In addition, a conservative method requiring all three methods to declare HPD (“intersection method”) and a sensitive method requiring any of the three methods to declare HPD (“union method”) was also used. The three most common primary malignancies in the pooled data were lung cancer (117 patients, 38.36%), colorectal cancer (32 patients, 10.49%) and melanoma (30 patients, 9.84%). Median age was 59 (IQR=18), 176 patients (57.70%) were male and 129 patients were female (42.30%). The TGR method identified 53 patients (17.38%) with HPD, the TGK method 62 patients (20.33%) and the TGD method 41 patients (13.44%). The pairwise agreement in HPD cases identified across methods was estimated using Cohen’s Kappa. The Kappa statistics were 0.88, 0.71 and 0.70 for the pairwise concordance between TGR-TGK, TGR-TGD, and TGK-TGD, respectively. HPD was declared by all three methods in 37 patients (12.13%), i.e. the intersection method. The union approach identified HPD in 62 patients (20.33%) and was identical to the TGK method, making it the most sensitive method of the three in this analysis. HPD assessment methods comparing tumor growth acceleration before and after receiving IO-therapy provide insight in the dynamics of the target lesions. Our analysis suggests that the TGR, TGK and TGD methods are concordant. Rather than favouring one method over the other, we propose to combine the existing methods into a sensitive and restrictive method. The sensitive method can serve as an upper bound of HPD incidence, and the restrictive as a lower bound. These boundaries can inform patients, policy makers and treating physicians of the potential HPD related to a specific IO-therapy. Citation Format: Luc Boone, Roberto Ferrara, Giuseppe Lo Russo, Penelope Bradbury, Lesley Seymour, Stephane Champiat, Christophe Le Tourneau, Anna Minchom, Ruth Plummer, Larry Schwartz, Bingshu Chen, Elena Garralda Cabanas, Scott Laurie, Saskia Litière, Jan Bogaerts, Emiliano Calvo. Comparing and combining existing radiological criteria for hyperprogressive disease in patients receiving immune-oncology therapy: Building towards a sensitive and conservative method to assess the presence of hyperprogressive disease [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C072.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".