About sMRD
sMRD (spatial Molecular Residual Disease) is an innovative, tissue-based, next-generation sequencing approach applied to tumor tissue, leveraging a novel tumor bed reconstruction method to define 3D spatial patterns associated with relapse. This online resource accompanies the sMRD manuscript by Ransohoff, et al. from Stanford University. We invite users to explore renders and relapse predictions of their own clinical cases or to explore those from our study.
Why It Matters
Defining metastatic potential in the primary tumor bed and improving relapse predictions
Relapse Prediction
Identifying the high risk spatial dispersion pattern linked to metastases
Clinical Integration
Leveraging routinely-collected clinical data
Precision Oncology
Toward risk-adapted precision adjuvant treatments
Research Foundation
Integrating MRD detection and spatial pathology to characterize spatial response patterns
Frequently Asked Questions
Find answers to common questions about sMRD technology, image requirements, data privacy, and getting started with the platform.
Image Requirements & Preparation
You may use any macroscopic image that captures the spatial registration of your tissue slabs. Images with a pure black background work best, as they simplify tissue detection and improve downstream rendering. If your images are brightfield with a non-uniform background, we have had good results using tools such as Adobe Photoshop to automatically detect the background and render it black prior to upload.
Images do not need to be pre-scaled, but scale information is strongly recommended. Best practice: - Know the pixel size of your images when possible - Use similar pixel resolution across all images within the same case To support this, the Annotate module includes a built-in tool that can derive pixel resolution from any known real-world distance visible in the image (for example, a ruler or labeled measurement).
- Rotated images are supported and can be corrected in the Stack module - Mirrored images are not currently supported If mirroring is required for your workflow, please contact us to discuss potential extensions.
The platform currently supports: - .jpeg - .jpg - .png Each image must be 50 MB or smaller. If you require support for additional formats, feel free to contact us.
Image Annotation & Naming Conventions
Using separate images is recommended, but not mandatory. The platform supports uploading two identical images (same dimensions and tissue alignment): - Raw image: {case_id}_{image_nr}_r.jpeg example: 34_1_r.jpeg - Annotated image: {case_id}_{image_nr}_a.jpeg example: 34_1_a.jpeg Note that case_id and image_nr may only contain integer numeric values. (SH-34 is not allowed, the integer numeric part 34 is allowed) If only a single image is uploaded and it contains annotations, those annotations may remain visible in the final render.
Following the naming convention is strongly recommended for smooth ingestion. The upload interface includes user input fields that allow you to correct or override naming inconsistencies if needed. Reminder: - Raw image: {case_id}_{image_nr}_r.jpeg example: 34_1_r.jpeg - Annotated image: {case_id}_{image_nr}_a.jpeg example: 34_1_a.jpeg Note that case_id and image_nr may only contain integer numeric values. (SH-34 is not allowed, the integer numeric part 34 is allowed)
Case Size & Upload Limits
There is no formal upper limit on the number of tissue slabs or images per case. Fair usage is expected, as extremely large uploads may affect performance or processing time.
Data Storage, Privacy & Compliance
Yes. Uploaded images are stored on secure Stanford servers. - Data is siloed per user - You can only access your own cases - You may delete your case data at any time via the website
Users should not upload content that may qualify as PHI under applicable regulations. Please ensure that: - Case IDs are fully de-identified - Case descriptions do not contain patient identifiers - Image metadata (EXIF tags) are removed prior to upload
Scope, Validation & Intended Use
Yes. - The 3D reconstruction pipeline works for any tissue type - Pathologist ledger metrics (e.g. dispersion score, RCB) are currently optimized for neoadjuvant-treated breast cancer
No. sMRD is currently for research use only and has not yet been clinically validated.
Support & Feedback
Please contact: cll@stanford.edu