Assessment of the Breast Cosmesis Using Deep Neural Networks: an Exploratory Study (ABCD)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 720
- 试验地点
- 1
- 主要终点
- Proportion of patients with excellent/good cosmesis
研究概览
简要总结
Surgery and radiotherapy in breast cancer patients can cause treatment changes and may affect the final breast appearance. In this study, we are trying to evaluate the post treatment breast photographs of the patients and subject these to Artificial Intelligence based program so as to classify into appropriate categories based upon changes from baseline. This automated solution will help in decreasing the time required to achieve this task by physicians in the clinic.
详细描述
A new algorithm was introduced which is based on deep neural network (DNN) which receives an image as input and returns the coordinates of the breast key points as output. These key points are then given to a shortest-path algorithm that models images as graphs to refine breast key point localization. The algorithm learns, directly from the image, to compute features and to use those features in the analysis of the aesthetic result. This comprises of two main modules: regression and refinement of heatmaps, and regression of key points. To perform the heatmap regression, the U-Net model is used.
The goal of the first module is to generate an intermediate representation consisting on a fuzzy localization for the key points that are to be detected.
The second module receives and refines this fuzzy localization, and through complex calculations, outputting the x and y coordinates of the keypoints, and the data generated from which can be used for disease / image classification.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 19 Years 至 80 Years（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •Confirmed diagnosis of primary breast cancer (invasive or in situ)
- •Patient undergone breast conservation / Whole breast reconstruction
- •Patient received breast RT
- •Already provided written informed consent on earlier projects
- •Patient provided photographs of both breasts
- •Non-metastatic disease or oligometastatic
- •Age > 18 years
- •Reconsent given
排除标准
- •Mastectomy without whole breast reconstruction
- •Bilateral breast cancer
- •Partial breast irradiation
- •Male patient
- •Limited life expectancy due to co-morbidity
- •Patients undergoing brachy boost
结局指标
主要结局
Proportion of patients with excellent/good cosmesis
时间窗: 3 years
The patient photographs will be processed for artificial intelligence based analysis of prediction of breast cosmesis
次要结局
- Kappa statistic between different deep neural networks(3 years)
研究者
Dr. Tabassum Wadasadawala
Professor Tabassum Wadasadawala
Tata Memorial Centre