Transforming Esogastric Cancer Surgery Integrating SpiderMass Mass Spectrometry with Clinical and Microbiome Data for Margin Delineation and Prognosis
Ledoux, L.; Zirem, Y.; Goossen, A.; Renaud, F.; Dufour, C.; Viezant, J.; Lion, C.; Piessen, G.; Salzet, M.; FOURNIER, I.
Show abstract
Esophageal-gastric cancers (EC) represent a significant global health concern, with esophageal cancer ranking seventh in terms of incidence and mortality worldwide. Gastric cancer is especially concerning, with an estimated one million new cases and 800,000 deaths annually. Late diagnoses often lead to poor outcomes, requiring critical interventions such as radical surgical resection with clear margins, in conjunction with chemotherapy, or radiotherapy to prevent recurrences and enhance survival. Thus, EC represents a significant clinical challenge, especially given the difficulty in achieving precise surgical margins in aggressive subtypes like poorly cohesive carcinoma (PCC). Moreover, pathological intraoperative margin assessment encounters significant issues, especially for PCCs, due to lacks of sensitivity for microscopic infiltration, potentially leading to recurrence and poorer patient outcomes. We address these critical limitations by integrating SpiderMass, an ambient mass spectrometry (MS) technology, with clinical metadata and microbiome profiling couple along with Machine learning. We demonstrate SpiderMass capability in real-time molecular margin delineation and identify distinct lipidomic and microbiome signatures correlating with tissue type and prognosis. Our integrative approach provides a more precise and biologically informative intraoperative diagnostic tool, significantly enhancing surgical decision-makin, to improve patient outcomes and extend survival.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Spatial omics imaging of fresh-frozen tissue and routine FFPE histopathology on a single cancer needle core biopsy: freezing device and multimodal workflow 95%
- Altered plasma, urine, and tissue profiles of sulfatides and sphingomyelins in patients with renal cell carcinoma 93%
- An AI-powered blood test to detect cancer using nanoDSF 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.