ReviewPathologie (Heidelberg, Germany)2026
[Update on the regression grading of non-small cell lung cancer].
Review in Pathologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
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Abstract
Immune checkpoint inhibitors combined with conventional chemotherapy have led to significantly improved outcomes in the neoadjuvant and perioperative treatment of non-small cell lung cancer (NSCLC), establishing chemoimmunotherapy as the standard of care for patients in UICC stages IIA-IIIA. As a result, pathological assessment of therapy-induced tumor response has become essential. This article provides an overview of the current recommendations for macroscopic handling, histological evaluation, and staging of NSCLC resection specimens after neoadjuvant treatment. Key aspects include comprehensive embedding of the tumor bed, quantitative assessment of tumor bed components (viable tumor, stroma/inflammation, necrosis) in 10% increments, and the application of established regression grading systems (IASLC, Junker). Challenges in distinguishing therapy-induced changes, evaluating lymph nodes, and determining post-treatment staging are discussed. Current findings highlight the need for standardized diagnostic procedures and further research to identify predictive biomarkers and improve the prognostic value of pathological regression grading after neoadjuvant chemoimmunotherapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.