Challenges for real‐time intraoperative diagnosis of high risk histology in lung adenocarcinoma: A necessity for sublobar resection. Issue 8 (9th July 2019) - Takahashi, Yusuke; Kuroda, Hiroaki; Oya, Yuko; Matsutani, Noriyuki; Matsushita, Hirokazu; Kawamura, Masafumi
Abstract : Recently, the incidence of small, peripheral lung adenocarcinoma has been increasing as lung cancer screening with radiologic examination is more widely performed. Tumor size is one of the determinants of the prognostic outcome in clinically node‐negative lung adenocarcinoma. Sublobar resection has been proposed as one of the minimally invasive surgical options for small‐sized adenocarcinomas. Despite the lack of robust clinical trial evidence, sublobar resection has become more popular, especially in developed countries where less extensive surgery may be of benefit in a population where the age of the elderly is growing. However, high risk histologic features such as micropapillary subtype and tumor spread through air space (STAS) have been associated with a significantly higher risk of local recurrence after sublobar resection, but not after lobectomy. Surgical decision‐making based on frozen section diagnosis of high risk histologic features may be useful to prevent local control failure after sublobar resection. At the present time, there is little evidence to demonstrate the diagnostic accuracy of identifying high risk histologic features on frozen section. One study has so far demonstrated that diagnostic accuracy of identifying STAS is higher than that of identifying the micropapillary subtype. Additionally, the presence of STAS has been found to be more strongly associated with local recurrence in patients who had undergone sublobar resection. Although further investigation is required for validation of this finding, STAS diagnosis on frozen section may shed further light on intraoperative surgical decision‐making during sublobar resection. To this end, we review the recently published data on the intraoperative identification of high risk features.