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
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».