Serum and Tissue Metabolite-based Prediction of Sentinel Lymph Node Metastasis in Breast Cancer
This is not medical advice. AI-assisted translation — inaccuracies may occur. Always verify the original and consult your oncologist before taking any steps.
About the trial
Breast cancer is a malignant tumor with the highest morbidity and mortality among women worldwide. Accurate staging of axillary lymph nodes is critical for metastatic assessment and decisions regarding treatment modalities in breast cancer patient. Among patients who underwent sentinel lymph node biopsy, about 70 % of the patients had negative pathological results and in other words, these 70 % of the patients received unnecessary surgery. At present, imaging and pathological diagnosis is the main measure of lymph node metastasis in breast cancer. However, limitations remained. Artificial intelligence, including deep learning and machine learning algorithms, has emerged as a possible technique, which can make a more accuracy prediction through machine-based collection, learning and processing of previous information, especially in radiology and pathology-based diagnosis. With the intensification of the concept of precision medicine and the development of non-invasive technology, the investigators intend to use the artificial intelligence technology to develop a serum and tissue-based predictive model for sentinel lymph node metastasis diagnosis combined with imaging and pathological information, providing specific, efficient and non-invasive biological indicators for the monitoring and early intervention of lymph node metastasis in patient with breast cancer. Therefore, the investigators retrospectively include serum samples from early breast cancer patients undergoing sentinel lymph node biopsy, including a discovery cohort and a modeling cohort. Metabolites were detected and screened in the discovery cohort and then as the target metabolites for targeted detection in the modeling cohort. Combined with preoperative imaging and pathological information, a prediction model of breast cancer sentinel lymph node metastasis based on serum metabolites would be established. Subsequently, multi-center breast cancer patients will prospectively be included to verify the accuracy and stability of the model.
Original English text from ClinicalTrials.gov
Who can (and can't) join
✓ Qualifies
- •Potwierdzony rozpoznaniem raka piersi
- •Brak wcześniejszego leczenia (chemioterapia lub hormony)
- •Brak przerzutów do odległych organów
- •Przeprowadzona mastektomia lub oszczędzająca operacja piersi z biopsją węzła wartowniczego
- •Zgoda na pobranie próbek krwi przed operacją
- •Dostęp do badań obrazowych, wyników patologicznych i danych obserwacji
✗ Disqualifies
- •Wcześniejsze leczenie przedoperacyjne
- •Przerzuty w momencie diagnozy
- •Inne nowotwory złośliwe niż rak piersi
- •Rak obu piersi lub wcześniejszy rak drugiej piersi
- •Brak biopsji węzła wartowniczego podczas operacji
- •Niekompletne dane patologiczne lub obserwacji
- •Ciąża lub inne stany uniemożliwiające uczestnictwo
Simplified criteria — AI translation
Trial details
- Minimum age
- 18 Years
- Last updated (source)
- September 28, 2023
- Sex
- Female only
Locations (1)
Shantou Central Hospital
Shantou, China
Trial contact
Xiaorong Lin, Dr.
Contact information from ClinicalTrials.gov. Contact in English.
Share this trial
Data from ClinicalTrials.gov. AI-assisted translation, last sync: 7/1/2026.
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