Identifying ductal carcinoma in situ cases not requiring surgery to exclude postoperative upgrade to invasive ductal carcinoma

Nakayama, Sadayoshi; Masuda, Hiroko; Miura, Sakiko; Kuwayama, Takashi; Hashimoto, Rikako; Taruno, Kanae; Sawada, Terumasa; Akashi‐Tanaka, Sadako; Nakamura, Seigo

doi:10.1007/s12282-022-01338-0

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…Accurate diagnosis of pure DCIS in biopsy is essential to provide patients with appropriate treatment options because traditional histological evaluation and immunohistochemistry (IHC) cannot accurately predict the probability of postoperative diagnostic upgrade [11]. If we can determine whether DCIS is accompanied by an invasive component by simply examining the DCIS tissue sampled, it will undoubtedly provide great value for pathological biopsy diagnosis and clinical treatment.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, an article published recently used clinicopathological indicators to predict which cases would be upgraded to IDC. Of the seven factors, only lesion size (≥2.5 cm) determined by mammography and ultrasound was a significant factor for upgrading [11]. These studies illustrate the requirement for a new method to aid in identifying whether biopsy‐sampled DCIS is associated with IDC, as examining the histopathology of the DCIS region alone is currently not sufficient to assess upgrade risk.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of coexisting invasive carcinoma on ductal carcinoma in situ (DCIS) lesions by mass spectrometry imaging

Chen

et al. 2023

The Journal of Pathology

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Due to limited biopsy samples, ~20% of DCIS lesions confirmed by biopsy are upgraded to invasive ductal carcinoma (IDC) upon surgical resection. 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‐mass spectrometry (DESI‐MS) imaging. Metabolomics analysis using DESI‐MS data revealed significant differences in metabolite levels, including small‐molecule antioxidants, long‐chain 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‐chain 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‐MS 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‐level prediction based on DESI‐MS data enabled automatic visualization of tissue properties across whole tissue sections. Summarily, DESI‐MS 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. © 2023 The Pathological Society of Great Britain and Ireland.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%