The Chen-Dai Sentinel Lymph Node (SLN) Metastasis Prediction Tool uses a multimodal fusion deep learning model integrating clinical, imaging, and pathology data to estimate the probability that a breast cancer patient's sentinel lymph node may contain metastatic disease. This tool is for general health information and academic research purposes only.

This tool has not been reviewed or approved by any regulatory agencies and is not intended for use as a medical device. It is not to be used as a substitute for medical advice, diagnosis, or treatment of any health condition or problem. Users should not rely on any resulting information for their own health problems. Outputs are estimates based on survival models built from clinical databases of breast cancer patients and may not apply to a given individual. A licensed clinician should always be consulted for any health problem(s) or medical condition(s).

Users agree to use this prediction tool for educational and/or research purposes only, and not for any commercial purposes, including the distribution, licensing, or sale of their content to any other person or entity, whether alone or in combination with other materials, or the incorporation of any of the prediction tools into any commercial product.

The developers make no warranties, nor express or implied representations whatsoever, regarding the accuracy, completeness, timeliness, comparative or controversial nature, or usefulness of any information contained or referenced in this tool. The developers do not assume any risk whatsoever for use of this tool or the information contained therein. As health-related information changes frequently, information contained in this tool may be outdated, incomplete, or incorrect.

Use of this tool does not create an express or implied physician-patient relationship. The developers do not endorse or claim validity for this tool. The developers and their agents do not record specific user information and do not contact users of this tool.

Users are advised to consult with a physician or other professional healthcare provider prior to making any decisions, or undertaking any actions or not undertaking any actions related to any healthcare problem or issue that users might have at any time, now or in the future. In using this tool, users agree that neither the developers nor any other party is or will be liable or otherwise responsible for any decision made or any action taken or any action not taken due to the use of any information resulting from this tool.

Chen-Dai SLN Metastasis Prediction Result

This nomogram can be used to help newly diagnosed breast cancer patients assess the likelihood that their breast cancer has spread to the sentinel lymph nodes (SLN). It integrates clinical features (age, tumor characteristics, staging), imaging measurements (ultrasound, mammography, MRI), and pathology findings (tumor type, grade, receptor status) to generate a predicted probability. All fields marked with * are required.

Predicted Probability of SLN Metastasis

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0 Low Risk 0.5 High Risk 1

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About This Prediction

This nomogram helps physicians and patients to accurately predict the likelihood that a patient's breast cancer has spread to the sentinel lymph nodes. The model was developed using a multimodal fusion deep learning architecture that integrates 35 clinical features and 20 pathological features extracted from a cohort of breast cancer patients. The calculated estimates include the risk of any tumor cells being found in the sentinel lymph nodes, including isolated tumor cells detected only on immunohistochemistry. Predictions are generated by processing clinical data (age, breast side, tumor count and location, cT stage, axillary ultrasound findings, and Chen-Dai distance), imaging measurements (ultrasound, mammography, and MRI tumor dimensions, calcification status, breast density), and pathological findings (tumor type, histological grade, ER, PR, HER2, LVI, and Ki67 status) through the trained neural network. This tool is intended for academic and research use only and should not be used as a substitute for clinical judgment.

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