Deep Learning Model Predicts Pathological Complete Response of Esophageal Squamous Cell Carcinoma Following Neoadjuvant Immunochemotherapy
Ориентир для пациента и семьи
Простыми словами
Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.
- Что изучают
- В протоколе указаны: The high-throughput extraction of large amounts of quantitative image features from medical images.
- Кому может быть актуально
- Состояния в реестре: Esophageal Squamous Cell Carcinoma, Neoadjuvant Immunochemotherapy, Pathological Complete Response, Deep Learning. Базовые параметры: от 18 лет · Все.
- Что важно проверить
- Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
- Где проводится
- Китай
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Обзор
This study aims to develop and validate a deep learning model to predict pathological complete response (pCR) in patients with esophageal squamous cell carcinoma who have undergone neoadjuvant immunochemotherapy. Clinical, imaging, and pathological data from previously treated patients will be collected and analyzed. The model is expected to assist in predicting treatment outcomes and guide personalized therapeutic strategies.
Подробное описание
This multicenter retrospective study will collect chest CT images and clinical data from patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery following neoadjuvant immunochemotherapy between January 2019 and July 2025. Deep learning features will be extracted from the CT images to develop a predictive model of pathological complete response (pCR). The model's performance will be evaluated using metrics including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Additionally, SHapley Additive exPlanations (SHAP) analysis will be employed to quantify the contribution of CT imaging features to the model's predictions. This study aims to improve early identification of responders to neoadjuvant immunochemotherapy and support personalized treatment strategies for ESCC patients.
Вмешательства
- Диагностический тест The high-throughput extraction of large amounts of quantitative image features from medical images
The high-throughput extraction of large amounts of quantitative image features from medical images
Первичные конечные точки
- Pathological Complete Response (pCR) Rate [Срок оценки: Assessed at the time of surgery, within 1 month post-treatment.]
Вторичные конечные точки (1)
- Model Performance Metrics (AUC, Accuracy, Sensitivity, Specificity, PPV, NPV) [Срок оценки: At the time of model validation, approximately one year on average after the completion of the research.]
Критерии участия
Критерии включения
- Pathologically confirmed esophageal squamous cell carcinoma (ESCC).
- Received at least one cycle of neoadjuvant chemotherapy combined with immunotherapy.
- Underwent contrast-enhanced chest CT before initiation of neoadjuvant treatment.
- Underwent contrast-enhanced chest CT after completion of neoadjuvant treatment and prior to surgery.
Критерии исключения
- Diagnosis of other malignancies.
- Received other anti-tumor therapies before or during neoadjuvant chemo-immunotherapy.
- Incomplete clinical data.
- Poor-quality CT imaging.
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Здоровые добровольцы: Нет
Дизайн исследования
- Модель наблюдения
- Когортное
Центры проведения
Китай · 1 центр
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Ухань
Идентификаторы
NCT: NCT07088354 · ESRA-01