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Avoiding underestimation of IDC when diagnosing DCIS has become an urgent challenge in an era discouraging overtreatment of DCIS. In this study, the metabolic profiles of 284 fresh frozen breast samples, including tumor tissues and adjacent benign tissues (ABTs) and distant surrounding tissues (DSTs), were analyzed using desorption electrospray ionization\u2010mass spectrometry (DESI\u2010MS) imaging. Metabolomics analysis using DESI\u2010MS data revealed significant differences in metabolite levels, including small\u2010molecule antioxidants, long\u2010chain polyunsaturated fatty acids (PUFAs) and phospholipids between pure DCIS and IDC. However, the metabolic profile in DCIS with invasive carcinoma components clearly shifts to be closer to adjacent IDC components. For instance, DCIS with invasive carcinoma components showed lower levels of antioxidants and higher levels of free fatty acids compared to pure DCIS. Furthermore, the accumulation of long\u2010chain PUFAs and the phosphatidylinositols (PIs) containing PUFA residues may also be associated with the progression of DCIS. These distinctive metabolic characteristics may offer valuable indications for investigating the malignant potential of DCIS. By combining DESI\u2010MS data with machine learning (ML) methods, various breast lesions were discriminated. Importantly, the pure DCIS components were successfully distinguished from the DCIS components in samples with invasion in postoperative specimens by a Lasso prediction model, achieving an AUC value of 0.851. In addition, pixel\u2010level prediction based on DESI\u2010MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI\u2010MS imaging on histopathological sections can provide abundant metabolic information about breast lesions. By analyzing the spatial metabolic characteristics in tissue sections, this technology has the potential to facilitate accurate diagnosis and individualized treatment of DCIS by inferring the presence of IDC components surrounding DCIS lesions. \u00a9 2023 The Pathological Society of Great Britain and Ireland.<\/jats:p>","DOI":"10.1002\/path.6154","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T04:44:25Z","timestamp":1691556265000},"page":"125-138","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of coexisting invasive carcinoma on ductal carcinoma <i>in situ<\/i> (<scp>DCIS<\/scp>) lesions by mass spectrometry imaging"],"prefix":"10.1002","volume":"261","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-9942","authenticated-orcid":false,"given":"Hong","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Pathology and Institute of Clinical 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