Abstract P1-08-41: Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near infrared imaging and tumor pathologic criteria
Notice bibliographique
Résumé
Abstract Purpose: In previous studies, the utilization of ultrasound guided near infrared diffused light imaging (US-NIR) has shown great potential in predicting and monitoring the pathologic tumor response to neoadjuvant chemotherapy (NAC). The purpose of the current study is to develop a prediction model utilizing pretreatment tumor hemoglobin content measured by US-NIR in conjunction with standard pathologic tumor characteristics to predict pathologic response even before NAC is given. Utilizing a multiple logistic regression model, the sensitivity, specificity, positive and negative predictive values (PPV and NPV), and the area under the receiver operating characteristic curve (AUC) are determined for the models. Materials and Methods: 34 patients’ data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into a training group (23 patients of 24 tumors) and testing group (11 patients of 12 tumors). Tumor vascularity was assessed pre-NAC using US-NIR and measurements of total hemoglobin (tHb), oxygenated (oxyHb), and deoxygenated hemoglobin concentrations (deoxyHb) as well as tumor reduced scatter coefficients acquired before treatment. Tumor pathologic variables including the estrogen (ER) and progesterone (PR) receptors, human epidermal growth factor receptor 2 (HER2) and Nottingham score (mitotic index and grade) were acquired before NAC in biopsy specimens and were also used in the prediction model. The patients’ pathologic response was graded based on the Miller-Payne system as non- and partial-responders (grades 1-3) and near-complete and complete responders (grades 4-5). Results: Utilizing initial tumor pathologic characteristics (grade and receptor status) a sensitivity of 100%, specificity of 73.3%, PPV and NPV of 69.5% and 100%, and AUC of 0.83(95% CI: 0.637-963) were obtained from training data. When pretreatment hemoglobin parameters and reduced scatter coefficients were included as additional predictors in training data, sensitivity, specificity, PPV and NPV improved to 100% and AUC of 1.0 (95% CI: 1.0-1.0). The performance of the predictive models were validated on testing data and corresponding values were 100%, 66.7%, 75.0% and 100%, and AUC of 0.83 (CI: 0.56-1.0) when tumor pathologic parameters alone were used as predictors. While the corresponding values were 100% and AUC of 1.0 (CI: 1.0-1.0) when hemoglobin and reduced scatter parameters were added as predictors. Discussion: These initial findings indicate that combining widely used tumor pathologic variables with hemoglobin and optical scatter functional parameters determined by NIR provides a powerful tool for predicting patient response to preoperative chemotherapy before the initiation of the treatment. With the current trend to treat in the neoadjuvant setting, such a tool will be invaluable for response assessment. Plans are underway to validate this model in larger patient settings and its applicability to non-chemotherapeutic regimens. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-41.
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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 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».