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167 Assessment of pathologic complete response following neoadjuvant therapy in patients with resectable lung cancer is equivalent across scoring systems

2025· article· W4415900112 on OpenAlexaff
Julie S. Deutsch, Tricia R. Cottrell, Tingchang Wang, Hao Wang, Tina Cascone, Patrick M. Forde, Janis M. Taube

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsComplete responseNeoadjuvant therapyLung cancerLungCancerScoring system

Abstract

fetched live from OpenAlex

Background Neoadjuvant/perioperative chemoimmunotherapy is a standard of care for treating patients with resectable non-small lung cancer (NSCLC) based on several phase III registrational clinical trials demonstrating improvement in rates of pathologic complete response (pCR, 0% residual viable tumor, RVT, in the resection specimen), event-free survival (EFS), and overall survival (OS) in patients treated with chemoimmunotherapy vs chemotherapy alone. However, pathologic response assessment criteria varied amongst these trials, with some studies reporting scoring by pan-tumor pathologic response criteria (irPRC) described in 2018 1 and others using criteria described by the International Association for the Study of Lung Cancer published in 2020.2 Despite the fact that these criteria are very similar in their approach to pathologic response assessment, some investigators have questioned whether cross-trial differences exist with regard to the value of pCR due to the different scoring systems used. The purpose of this study was to determine whether pCR assessed by these two scoring systems was associated with differential pCR rates across these trials and/or predictive value in the form of hazard ratios (HR) for the association of pCR with EFS.Methods A literature review was performed to identify phase III trials in patients with resectable NSCLC treated with chemoimmunotherapy in the neoadjuvant/perioperative setting. pCR rates and HR of pCR for predicting EFS were extracted from each trial manuscript. If the latter was not provided, data from the published Kaplan-Meier survival curves were converted to patient-level data using R package IPDfromKM with iterative Kaplan-Meier method3 and a HR was calculated.Results Five trials met criteria for inclusion 4–8 (table 1). All studies defined pCR as 0% RVT in both the primary tumor and lymph nodes. pCR rates across these studies were similar (range 17-25%). HRs for pCR predicting EFS were also comparable (range 0.13-0.16; table 1), indicating that pCR as assessed by either method had similar associations with EFS.Conclusions Regardless of criteria used for assessment of pCR, equivalent rates of pCR and HRs measuring the association between pCR and EFS were found across five phase III registrational trials in patients with resectable lung cancer treated with neoadjuvant/perioperative chemoimmunotherapy. This finding thus supports meta-analyses assessing the association between pCR and EFS.References Cottrell TR, Thompson ED, Forde PM, Stein JE, Duffield AS, Anagnostou V, et al. Pathologic features of response to neoadjuvant anti-PD-1 in resected non-small-cell lung carcinoma: a proposal for quantitative immune-related pathologic response criteria (irPRC). Ann Oncol. 2018 Aug 1;29(8):1853–1860.Travis WD, Dacic S, Wistuba I, Sholl L, Adusumilli P, Bubendorf L, et al. IASLC multidisciplinary recommendations for pathologic assessment of lung cancer resection specimens after neoadjuvant therapy. J Thorac Oncol. 2020 May;15(5):709–740.Liu N, Zhou Y, Lee JJ. IPDfromKM: reconstruct individual patient data from published Kaplan-Meier survival curves. BMC Med Res Methodol. 2021 Jun 1;21(1):111.Heymach JV, Harpole D, Mitsudomi T, Taube JM, Galffy G, Hochmair M, et al. Perioperative durvalumab for resectable non-small-cell lung cancer. N Engl J Med. 2023 Nov 2;389(18):1672–1684.Cascone T, Awad MM, Spicer JD, He J, Lu S, Sepesi B, et al. Perioperative nivolumab in resectable lung cancer. N Engl J Med. 2024 May 16;390(19):1756–1769.Forde PM, Spicer J, Lu S, Provencio M, Mitsudomi T, Awad MM, et al. Neoadjuvant nivolumab plus chemotherapy in resectable lung cancer. N Engl J Med. 2022 May 26;386(21):1973–1985.Wakelee H, Liberman M, Kato T, Tsuboi M, Lee SH, Gao S, et al. Perioperative pembrolizumab for early-stage non-small-cell lung cancer. N Engl J Med. 2023 Aug 10;389(6):491–503.Lu S, Zhang W, Wu L, Wang W, Zhang P; Neotorch Investigators. Perioperative toripalimab plus chemotherapy for patients with resectable non-small cell lung cancer: the neotorch randomized clinical trial. JAMA. 2024 Jan 16;331(3):201-211.Abstract 167 Table 1Phase III trials investigating neoadjuvant/perioperative chemoimmunotherapy in NSCLCpCR = pathologic complete response; HR = hazard ratio, EFS = event-free survival; CI = confidence interval; chemo = chemotherapy; IASLC = International Association for the Study of Lung Cancer; irPRC = pan-tumor pathologic response criteria*HR and/or survival curves by pathologic response not published

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.335
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
Admission routes1
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