ArticleTherapeutic advances in medical oncology2023
The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy.
Article in Therapeutic advances in medical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Biopsies Can Predict Lung Adenocarcinoma IASLC Grade: A New Proposal and Grading Pitfalls.JTO clinical and research reports · 2026Article
- Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients.Research square · 2026Article
- Article
- Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images.NPJ precision oncology · 2025Article
- Quantifying Early-Stage Lung Adenocarcinoma Progression with a Radiomic Trajectory.NPJ digital medicine · 2025Article
- Spread through air spaces may predict early progression after salvage surgery for EGFR-mutant advanced lung adenocarcinoma treated with targeted therapy.World journal of surgical oncology · 2025Article
- Multi-classification model incorporating radiomics and clinic-radiological features for predicting invasiveness and differentiation of pulmonary adenocarcinoma nodules.Biomedical engineering online · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Grading system for resected invasive pulmonary adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) was validated as a strong prognostic indicator. Nonetheless, the efficacy of utilizing such grading system in prognostic assessment of patients receiving neoadjuvant therapy still needs elucidating. Methods: A retrospective study was conducted including patients with resected adenocarcinoma following neoadjuvant chemotherapy or targeted therapy from August 2012 to December 2020 in Shanghai Pulmonary Hospital. All the surgical specimens were re-evaluated and graded. The prognostic value of the grading system was further validated. Results: Ultimately, a total of 198 patients were enrolled in this study, and subdivided into three cohorts according to the grading system. There were 13 (6.6%), 37 (18.7%), and 148 (74.7%) patients belonging to Grades 1, 2, and 3, respectively. IASLC grading system demonstrated significant power in prognosis differentiation of the entire cohort [recurrence-free survival (RFS), Conclusions: The prognostic efficacy of pathological evaluation of the residual proportion of pulmonary adenocarcinoma post-neoadjuvant therapy using IASLC grading system was preliminarily verified. Such grading system might assist prognostic evaluation of neoadjuvant cohort other than traditional pathological parameters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.