Abstract IS-3: Breast Imaging in Resource Constrained Regions: Lessons from Uganda
Notice bibliographique
Résumé
Abstract Breast cancer is the leading cause of cancer death in women and the most common cancer among women world-wide. Five-year survival rates in patients with breast cancer in the United States, Australia and Canada approach 90%, while in parts of Africa survival rates are less than 15%. In October 2013 the international New York Times front page reported on “Uganda's Neglected Epidemic of Breast Cancer.” In December 2013 the WHO International Agency for Research on Cancer (IARC) issued an update on the world's cancer statistics with the headline including the alarm that “marked increase in breast cancers must be addressed.” In Uganda, resources for surgical intervention (mastectomy) and medical oncology (hormonal therapy and limited chemotherapy) are available. Strong recent efforts by the National Cancer Institute, American Cancer Society, and global partners will increase the availability of affordable chemotherapy. In parallel, there is a growing community of breast cancer survivors in Uganda who are sharing their stories and emphasizing the importance of early detection and prompt treatment. Women with palpable breast lumps, identified by themselves or their healthcare providers, are encouraged to seek treatment. But with so many women having palpable breast lumps, and no efficient way to sort the majority of lumps that are benign from the minority of lumps that are malignant, systems are overwhelmed. Clinics do not have the capacity to detect the cancers amongst all the women who present with palpable lumps, and breast cancers remain undiagnosed and untreated, and mortality rates continue to rise. The delays in diagnosis can be significantly reduced with ultrasound (US) technology placed in the hands of non-physician healthcare providers, to streamline the diagnostic process and separate women with lumps that have features warranting biopsy from lumps that can be safely followed clinically in the patient's local, healthcare clinic. Mammography has little added value in countries with limited resources. With so many women who have clear symptoms of breast cancer, and with a significantly higher pre-test probability of cancer in women with (rather than without) symptoms, interventions targeted to asymptomatic women are neither logical nor feasible. The need is not to screen for breast cancers in asymptomatic women but rather to detect the breast cancers in women with symptoms, most notably palpable lumps. Innovative, effective, affordable strategies that emphasize prompt and accurate diagnostic methods in women with palpable breast lumps are needed to improve breast cancer survival in LMICs. These programs can be based on inexpensive, portable ultrasound machines that support triage of patients with palpable lumps to those that need biopsy and those that don't. These technologies are already being used by local health care personnel in developing countries for other prenatal and urgent care purposes. Going forward, novel ultrasound techniques for automated sonogram acquisition and machine learning and deep learning methods can combine to extend the impact of ultrasound imaging in breast cancer diagnosis by reducing the need for highly specialized breast imagers in both acquisition and interpretation. Citation Format: Lehman CD. Breast Imaging in Resource Constrained Regions: Lessons from Uganda [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr IS-3.
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 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».