1. What data in particular will be shared?
   Response - Individual participant data that underlie the results reported in this article, after de-identification (text, tables, figures, and appendices).
   


2. What additional supporting information will be shared?
   Response -  Study Protocol
   Response -  Statistical Analysis Plan
   Response - Informed Consent Form
   Response - Clinical Study Report
   Response -  Analytic Code
   
   
3. Who will be able to view these files?
   Response (Others) -  Researchers whose proposed use of the data has been approved by an Institutional Ethics committee identified for this purpose, Editors of Journals and an appropriate Regulatory Authority


4. For what types of analyses will this data be available?
   Response - To achieve aims in the approved proposal.
   


5. By what mechanism will data be made available?
   Response (Others) -  To gain access, data requestors will need to sign a data access agreement.


6. For how long will this data be available start date provided 01-01-2027 and end date provided 01-01-2032?
   Response - Beginning 9 months and ending 36 months following article publication.
   


7. Any URL or additional information regarding plan/policy for sharing IPD? 
   Additional Information - Nil

Female breast cancer has become the most often diagnosed type of cancer in the world and has become a major health concern across rural and urban areas in India nowadays. Asian women are more likely to have dense breasts than other women in the world. Dense breast tissue can make it more difficult to detect breast cancer and is also linked to an increased risk of breast cancer. Digital Mammography is a very useful technique for screening and is acknowledged as the most reliable method for detecting breast cancer at an early stage, but its accuracy is limited by the radiologists’ clinical experience.

To solve this problem a novel fully automated deep learning-based breast cancer computer-aided diagnosis system (CAD) enabled with a graphical user interface is proposed. Quantitative research will be conducted focusing on Digital Mammogram pre-processing, image segmentation to detect regions of interest, and classification of these images based on the Breast Imaging-Reporting and Data System (BI-RADS) using deep learning models. We propose to design a novel CAD system based on the digital mammograms available at Kasturba Hospital, Manipal Patient Archival System. 

In the pre-processing phase, the raw mammography images collected from the above database will be anonymized, the BI-RADS reports and the histopathology reports will be analyzed and information related to various lesions will be extracted from the reports. The final curated data will be further processed through image resizing, and conversion to other image formats for the purpose of readability by the program. The need for various de-noising and enhancement techniques will be studied using various performance evaluation parameters.

In the segmentation phase, the model will be developed to detect the Region of Interest (ROI) after removing the pectoral muscles from the mediolateral oblique (MLO) view and any other artifacts which will be identified after data collection. Various traditional segmentation models and deep learning-based segmentation models will be studied and the best model based on the performance metrics will be identified. In the classification phase, the segmented image will be split into training, validation, and testing set which will be tested on various deep-learning models. 

The best model based on the performance will be used as the final model for the prospective testing. In the prospective testing phase we will validate the CAD in patients who consent to participate in the study and will be undergoing mammography for clinical indications