Abstract P5-02-04: Upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast to reduce inter-observer variability: The DCISion study
Notice bibliographique
Résumé
Abstract Background. Ductal carcinoma in situ (DCIS) of the breast is considered to be a non-obligatory precursor of invasive breast cancer. Its histopathological assessment is characterized by considerable inter-observer discordance. In a previous study, we performed post hoc dichotomization of multi-categorical variables to determine the ‘ideal’ cut-offs for dichotomous histopathological assessment. In the present multicenter study, inter-observer variability is evaluated among 39 pathologists who performed upfront dichotomous evaluation of a consecutive series of 149 DCIS. Methods. All participants were board-certified pathologists with a special interest in breast disease. The participants assessed at least 50 primary oncologic breast cancer resection specimens per year, in accordance with the EUSOMA criteria for dedicated breast pathologists. No training set was used. Instead, a written guideline with associated DCISion poster was provided, which contained all definitions of the histopathological features of interest. Representative digital slides of 149 DCIS were accessible via an online platform. All pathologists independently assessed the following histopathological features: nuclear atypia, necrosis, solid DCIS architecture, calcifications, stromal architecture and lobular cancerization. Stromal inflammation was assessed semi-quantitatively. Stromal tumor-infiltrating lymphocytes (TILs) were quantified as percentages and were also dichotomously assessed with a cut-off at 50%. Krippendorff’s alpha (KA), Cohen’s kappa (K) and intraclass correlation coefficient (ICC) were calculated for the appropriate variables. Results. Intraductal calcifications (KA 0,676) and solid DCIS architecture (KA 0,602) were characterized by the highest inter-observer concordance. Stromal inflammation (KA 0,564), dichotomously assessed TILs (KA 0,520) and comedonecrosis (KA 0,539) showed slightly higher inter-observer agreement. Lobular cancerization (KA 0,396), nuclear atypia (KA 0,422) and stromal architecture (KA 0,450) showed the lowest inter-observer concordance. Assessment of TILs as a percentage showed good overall agreement with a mean ICC of 0,821 (range 0,566 - 0,933). Semi-quantitative assessment of stromal inflammation (KA 0,564) resulted in somewhat lower inter-observer variability than upfront dichotomous TILs assessment (KA 0,520). High stromal inflammation corresponded best with dichotomously assessed TILs when the TILs cut-off was set at 10% (K 0,881). Nevertheless, a post hoc TILs cut-off set at 20% resulted in the highest inter-observer agreement (KA 0,669). Experience and time dedicated to breast pathology did not influence the degree of concordance. Conclusion. The DCSion study shows that, despite upfront dichotomous evaluation, the inter-observer variability remains considerable and is at most acceptable. Nevertheless, the discordance rate varies among the different histopathological features. Future studies should investigate its impact on DCIS risk stratification. Differences in prognostic value among the different methods to quantify TILs are of particular interest, since inter-observer variability may partly explain different outcomes among different studies. Artificial intelligence might be able to tackle this diagnostic challenge. Development of deep learning algorithms could result in more objective histopathological assessment. Although machine learning might represent the next “pathologist’s best friend”, we should be careful not to introduce inter-observer variability into these deep learning algorithms. The DCISion study therefore provides an excellent setting to investigate the value of such algorithms in rendering the final diagnosis more robust. Citation Format: Mieke Rosalie Van Bockstal, Hélène Dano, Serdar Altinay, Laurent Arnould, Noella Bletard, Cecile Colpaert, Franceska Dedeurwaerdere, Benjamin Dessauvagie, Valérie Duwel, Giuseppe Floris, Stephen Fox, Clara Gerosa, Shabnam Jaffer, Eline Kurpershoek, Magali Lacroix-Triki, Andoni Laka, Kathleen Lambein, Gaëtan Marie MacGrogan, Caterina Marchió, Dolores Martin Martinez, Sharon Nofech-Mozes, Dieter Peeters, Alberto Ravarino, Emily Reisenbichler, Erika Resetkova, Souzan Sanati, Anne-Marie Schelfhout, Vera Schelfhout, Abeer M Shaaban, Renata Sinke, Claudia Maria Stanciu-Pop, Claudia Stobbe, Carolien HM van Deurzen, Koen Van de Vijver, Anne-Sophie Van Rompuy, Stephanie Verschuere, Anne Vincent-Salomon, Hannah Wen, Caroline Bouzin, Christine Galant. Upfront dichotomous histopathological assessment of ductal carcinoma in situ of the breast to reduce inter-observer variability: The DCISion study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-04.
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 | 0,031 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 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 | 0,001 |
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 ».