The role for frozen section analysis during partial nephrectomy: Outcomes after ten years of FU

Maruccia, Serena; Seveso, M.; Casellato, Stefano; Provenzano, Marco; Buffi, N.; Taverna, G.; Guazzoni, G.; Bozzini, Giorgio

doi:10.1016/s1569-9056(17)30843-6

Cited by 1 publication

(2 citation statements)

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“…PSM rates are one of the key metrics used for evaluating quality in PN surgeries for RCC in both the United States and Europe. 30 Given that frozen sections are poorly sensitive, finding only 15-30% of PSMs disclosed on final pathology 10,11 and potentially increase ischemia time, many surgeons advocate against obtaining frozen sections. 4,10 However, performance of frozen sections is associated with lower rates of PSM on final pathology, and contemporary large studies have suggested that PSM influence cancer outcomes.…”

Section: Discussionmentioning

confidence: 99%

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Identification of diagnostic metabolic signatures in clear cell renal cell carcinoma using mass spectrometry imaging

Vijayalakshmi

Shankar

Bain

et al. 2020

Intl Journal of Cancer

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Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy. However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In our study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI-MSI was conducted on fresh-frozen 23 normal tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal tumor pairs was analyzed. DESI-MSI provides two-dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log-ratio Lasso analysis. Lasso analysis with leave-one-patient-out crossvalidation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI-MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94 and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI-MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status.K.V.L. and V.S. contributed equally to this work Ryan M. Bain's current address is: Dow Chemical Co., Midland, MI 48674 Additional Supporting Information may be found in the online version of this article.

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Section: Discussionmentioning

confidence: 99%

“…In cases with true positive margins detected on final pathology, frozen sections are positive in only 15-30% of cases. 10,11 The intention of this work is to provide a proof of concept that IFS analysis using desorption electrospray ionization mass spectrometry imaging (DESI-MSI) can increase substantially the usefulness of this approach.…”

Section: Introductionmentioning

confidence: 99%