Abstract
IntroductionWith the implementation of low-dose computed tomography screening, multiple pulmonary tumor nodules are diagnosed with increasing frequency and the selection of surgical treatments versus systemic therapies has become challenging on a daily basis in clinical practice. In the presence of multiple carcinomas, especially adenocarcinomas, pathologically determined to be of pulmonary origin, the distinction between separate primary lung carcinomas (SPLCs) and intrapulmonary metastases (IPMs) is important for staging, management, and prognostication. MethodsWe systemically reviewed various means that aid in the differentiation between SPLCs and IPMs explored by histopathologic evaluation and molecular profiling, the latter includes DNA microsatellite analysis, array comparative genomic hybridization, TP53 and oncogenic driver mutation testing and, more recently, with promising effectiveness, next-generation sequencing comprising small- or large-scale multi-gene panels. ResultsComprehensive histologic evaluation may suffice to differentiate between SPLCs and IPMs. Nevertheless, molecular profiling using larger-scale next-generation sequencing typically provides superior discriminatory power, allowing for more accurate classification. On the basis of the literature review and expert opinions, we proposed a combined four-step histologic and molecular classification algorithm for addressing multiple pulmonary tumor nodules of adenocarcinoma histology that encourages a multidisciplinary approach. It is also noteworthy that new technologies combining machine learning and digital pathology may develop into valuable diagnostic tools for distinguishing SPLCs from IPMs in the future. ConclusionsAlthough histopathologic evaluation is often adequate to differentiate SPLCs from IPMs, molecular profiling should be performed when possible, especially in cases with tumors exhibiting similar morphology. This manuscript summarized the previous efforts in resolving the current challenges and highlighted the recent progress in the differentiation methods and algorithms used in categorizing multiple lung adenocarcinomas into SPLCs or IPMs, which are becoming more and more critical in precision lung cancer management.
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