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Record W4400382932 · doi:10.14740/wjon1888

Proximal Femoral Metastasis From Epidermal Growth Factor Receptor-Mutated Lung Adenocarcinoma Mimicking Osteosarcoma on Magnetic Resonance Imaging

2024· article· en· W4400382932 on OpenAlexvenueno aff
Changjun Chen, Jun Yin, Hao Xuan Zhang, Qing Wei, Xin Zhao, Meng Chen, Da Yong Peng

Bibliographic record

VenueWorld Journal of Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging and Pathology Studies
Canadian institutionsnot available
FundersShandong Academy of Medical SciencesShandong First Medical UniversityNatural Science Foundation of Shandong ProvinceJinan Science and Technology Bureau
KeywordsMedicineMagnetic resonance imagingEpidermal growth factor receptorOsteosarcomaMetastasisLungAdenocarcinomaRadiologyPathologyCancer researchReceptorInternal medicineCancer

Abstract

fetched live from OpenAlex

The aggressive nature of lung cancer is frequently accompanied by a high incidence of bone metastasis; however, proximal femoral metastasis from lung cancer is comparatively uncommon when compared to other malignancies. In this report, we present the case of a 53-year-old Asian male who presented with pain in the left thigh and back. Magnetic resonance imaging revealed severe bone destruction with involvement of adjacent soft tissue mass at the left thigh, exhibiting imaging findings that mimic osteosarcoma. Subsequent bone biopsy confirmed the diagnosis of epidermal growth factor receptor ( EGFR )-mutated lung adenocarcinoma with bone metastasis. The patient achieved survival following administration of osimertinib and underwent surgery for femoral metastases without palliative surgery for lung cancer. Therefore, proximal femoral metastasis from EGFR -mutated lung adenocarcinoma should be considered as a differential diagnosis in patients suspected to have osteosarcoma. The imaging findings of proximal femoral metastasis from EGFR -mutated lung adenocarcinoma were presented, and their therapeutic management was discussed. World J Oncol. 2024;15(4):731-735 doi: https://doi.org/10.14740/wjon1888

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.379
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.326
Teacher spread0.293 · 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 teacher head, 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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