Abstract P3-02-05: Does MRI influence surgical planning more than clinical outcome? A cohort study of breast cancer patients receiving neoadjuvant therapy
Notice bibliographique
Résumé
Abstract Background: While magnetic resonance imaging (MRI) is a powerful diagnostic tool, there is currently no consensus on its role for breast cancer patients prior to the initiation of neoadjuvant therapy (NAT). In the adjuvant setting, there is evidence that the use of MRI is correlated with an increase the rate of mastectomies performed. There is currently no data describing how MRI is influencing treatment decisions or surgical management in the neoadjuvant setting. This study aimed to determine the impact of MRI on patients' surgical plan, and to understand the demographic differences in patients who had an MRI compared to those that did not in the neoadjuvant setting. Methods: A secure database containing all potential NAT patients seen by medical oncologists at the BC Cancer Agency Vancouver Centre since 2012 was searched. Breast cancer patients who were treated with NAT and had undergone breast surgery before March 30, 2016 were identified. Tumour characteristics, surgical plan and surgical outcome were assessed retrospectively and compared between patients who had an MRI and patients who did not have an MRI. Results: 270 patients were identified who met the inclusion criteria. Of those, 107 patients had a breast MRI and 163 patients did not. The two groups showed no significant pre-treatment differences with regards to type of breast cancer, receptor status, or clinical stage. The median age was 10 years younger in the MRI group (47 years) compared to the non-MRI group (57 years), p < 0.0001. Patients who had an MRI had a non-significant higher rate of pathological complete response (pCR) than those who did not (30.8% and 21.5%, respectively, p=0.08). The surgical treatment did differ between these two groups; those who had MRI were more likely to have bilateral mastectomy (36.4% vs 23.3%, p=0.019) and less likely to have breast conserving surgery (BCS) (19.6% vs 31.9%, p=0.026). In the cohort that had an MRI, there was no significant difference in percentage of patients whose surgical plan was changed compared to the patients who did not have an MRI (33.6% and 28.8%, respectively). A change in surgical plan from a mastectomy to a BCS was more common in patients who did not have an MRI than those that did (31.9% and 13.9%, respectively). 45% of the surgeons who dictated a follow-up surgery consultation stated that the MRI was used to inform the surgical plan. Discussions/Conclusions: In this real-world cohort, patients who had an MRI were more likely to undergo a bilateral mastectomy and less likely to have a BCS than the patients who did not have an MRI, despite having a higher rate of pCR. Age was the only baseline demographic difference between the two groups. These findings suggest that the role of MRI in the neoadjuvant setting needs to be refined further in order to avoid over-treatment.Background: While magnetic resonance imaging (MRI) is a powerful diagnostic tool, there is currently no consensus on its role for breast cancer patients prior to the initiation of neoadjuvant therapy (NAT). In the adjuvant setting, there is evidence that the use of MRI is correlated with an increase the rate of mastectomies performed. There is currently no data describing how MRI is influencing treatment decisions or surgical management in the neoadjuvant setting. This study aimed to determine the impact of MRI on patients' surgical plan, and to understand the demographic differences in patients who had an MRI compared to those that did not in the neoadjuvant setting. Methods: A secure database containing all potential NAT patients seen by medical oncologists at the BC Cancer Agency Vancouver Centre since 2012 was searched. Breast cancer patients who were treated with NAT and had undergone breast surgery before March 30, 2016 were identified. Tumour characteristics, surgical plan and surgical outcome were assessed retrospectively and compared between patients who had an MRI and patients who did not have an MRI. Results: 270 patients were identified who met the inclusion criteria. Of those, 107 patients had a breast MRI and 163 patients did not. The two groups showed no significant pre-treatment differences with regards to type of breast cancer, receptor status, or clinical stage. The median age was 10 years younger in the MRI group (47 years) compared to the non-MRI group (57 years), p < 0.0001. Patients who had an MRI had a non-significant higher rate of pathological complete response (pCR) than those who did not (30.8% and 21.5%, respectively, p=0.08). The surgical treatment did differ between these two groups; those who had MRI were more likely to have bilateral mastectomy (36.4% vs 23.3%, p=0.019) and less likely to have breast conserving surgery (BCS) (19.6% vs 31.9%, p=0.026). In the cohort that had an MRI, there was no significant difference in percentage of patients whose surgical plan was changed compared to the patients who did not have an MRI (33.6% and 28.8%, respectively). A change in surgical plan from a mastectomy to a BCS was more common in patients who did not have an MRI than those that did (31.9% and 13.9%, respectively). 45% of the surgeons who dictated a follow-up surgery consultation stated that the MRI was used to inform the surgical plan. Discussions/Conclusions: In this real-world cohort, patients who had an MRI were more likely to undergo a bilateral mastectomy and less likely to have a BCS than the patients who did not have an MRI, despite having a higher rate of pCR. Age was the only baseline demographic difference between the two groups. These findings suggest that the role of MRI in the neoadjuvant setting needs to be refined further in order to avoid over-treatment. Citation Format: McDermott M, Wilson C, Xu J, Illmann C, Simmons C. Does MRI influence surgical planning more than clinical outcome? A cohort study of breast cancer patients receiving neoadjuvant therapy [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-02-05.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».