Abstract PO2-02-14: Development of a multi-institutional, photograph-rich clinical dataset to test and validate a novel inflammatory breast cancer (IBC) scoring system
Notice bibliographique
Résumé
Abstract Purpose: Susan G. Komen, The Inflammatory Breast Cancer (IBC) Research Foundation, and the Milburn Foundation convened a collaborative of patient advocates, clinicians, and researchers and proposed a novel quantitative scoring system for IBC diagnosis. The score includes timing of symptoms, extent of skin edema and erythema/other discoloration, breast and nipple asymmetry, lympho-vascular space invasion, nodal stage, and diffuse extent disease in the breast on breast imaging. Total scores were categorized (< 14 Not IBC, 14-24 Weak Possibility of IBC, 25-41 Strong Possibility of IBC, > 41 Definitely IBC). Herein, we developed a multi-institutional clinical dataset to retrospectively test and validate the proposed scoring system. Methods: IBC (N= 988) and non-IBC (N=322) cases were identified at two institutions with dedicated multi-disciplinary IBC programs, UT MD Anderson Cancer Center (MDA) and Dana-Farber Cancer Institute (DFCI). The DFCI cohort was diagnosed from 1986-2021 and the MDA cohort was diagnosed from 2010-2020. The non-IBC cohorts were identified as consecutive T4b and T4c cases in existing retrospective databases. Photographs were reviewed for available cases. All scoring variables were reviewed with data extractors and standard operating procedures (SOPs) developed for all ambiguous findings and language. A subset of scores were reviewed by IBC clinicians (FL, WW) and discrepancies discussed to inform the SOPs. Missing data imputation using three methods was examined. Area under the curve (AUC) - Receiver operator characteristic (ROC) curves and values, and sensitivity and specificity for the proposed score cut offs were examined. Results: Of 1320 patients currently in the analysis, 421 (31.9%) have missing data on > 1 characteristics and 47 (3.6%) are missing data on > 4. The rubric characteristic most commonly missing was “nipple abnormalities” accounting for 21.0% and 19.3% for IBC and non-IBC cohorts respectively. Unavailable bilateral breast photograph was the primary source of missing data. The highest scoring finding was present in the following percentage of IBC and non-IBC cases, respectively: timing of symptom onset < 3 months (74.1% vs 38.6%), any peau d’orange (61.2% vs 30.1%), clinically apparent enlargement or new asymmetry of breast size (87.8% vs 90.1%), complete or near complete involvement of the breast skin by erythema or discoloration (55.6% vs 25.6%), new nipple inversion (24.6% vs 9.0%), dermal lymphovascular emboli present (17.9% vs 1.8%), diffuse involvement of the breast parenchyma by imaging (72% vs 77.7%). True IBC cases were categorized as Definitely IBC, Strong Possibility, Weak Possibility, not IBC and unknown 19.7%, 49.1%, 0.4%, 0.1%, and 30.7%. True non-IBC were 0.6%, 51.8%, 9.9%, 2.1%, and 35.5%. Random imputation, lowest value imputation, and median value imputation were compared to impute missing data. AUC-ROC values for each method were similar, 0.80 - 0.84. Conclusion: We describe the development of the largest multi-institutional IBC clinical database to date. SOPs for the rubric characteristics were developed. The importance of bilateral photography to assess the score retrospectively is highlighted. The score is associated with a good AUC-ROC but still leaves much overlap among cases between 25 and 42. Further optimization of the current scoring system and association with outcomes are in progress. Citation Format: Filipa Lynce, Samuel Niman, Megumi Kai, Sean Ryan, Elizabeth Troll, Li Li, Kathy Miller, Reshma Jagsi, Ginny Mason, Beth Overmoyer, H. T. Carisa Le-Petross, Faina Nakhlis, Savitri Krishnamurthy, Beth Harrison, Susie X. Sun, Eren Yeh, Jennifer Bellon, Laura Warren, Michael Stauder, Meredith Regan, Wendy Woodward. Development of a multi-institutional, photograph-rich clinical dataset to test and validate a novel inflammatory breast cancer (IBC) scoring system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-14.
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 | 0,005 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 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) | 0,003 | 0,002 |
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 ».