A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules

The Journal of International Medical Research · 8 authors, 5 centres

AI SUMMARY

FIDELITY 80%

POPULATION848 participants with lung nodules detected by chest CT (478 lung cancer patients, 370 benign nodules) at the Second Affiliated Hospital of Dalian Medical University, China.

INTERVENTIONNomogram based on metabolic profiling (20 amino acids and 27 carnitines measured by LC-MS/MS) combined with clinical factors (age, sex).

COMPARISONBenign lung nodules vs. lung cancer (stage I/II).

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

This retrospective cohort study of 848 participants developed a nomogram combining age, sex, and 11 metabolic markers (amino acids and acylcarnitines) to discriminate lung cancer from benign lung nodules. The nomogram achieved an AUC of 0.836 in the training set and 0.781 in the validation set, outperforming any single biomarker. This tool offers a non-invasive, objective method to aid early lung cancer diagnosis and reduce unnecessary invasive procedures.

Full summary

3,430 CHARS

**Background:** Lung cancer has high morbidity and mortality worldwide. While chest CT screening improves detection, it has high false-positive rates, leading to unnecessary procedures and patient anxiety. Metabolic reprogramming is a hallmark of cancer, and amino acid and carnitine profiles may serve as objective biomarkers. This study aimed to develop a nomogram using metabolic profiling to discriminate early-stage lung cancer from benign lung nodules.

**Methods:** This retrospective cohort study recruited 848 participants (478 lung cancer, 370 benign nodules) screened by chest CT between April 2018 and December 2020 at the Second Affiliated Hospital of Dalian Medical University. Participants were randomly divided into training (n=594; 334 lung cancer, 260 benign) and validation (n=254; 144 lung cancer, 110 benign) sets at a 7:3 ratio. Blood samples were collected after overnight fasting. LC-MS/MS measured 20 amino acids and 27 carnitines. Variables with statistically significant differences between groups were selected for LASSO regression to avoid overfitting. Stepwise logistic regression identified 13 independent predictors for the nomogram. Model performance was evaluated using ROC curves, calibration curves with 1000 bootstrap resamples, and decision curve analysis.

**Key Results:** Thirty variables showed statistically significant differences between lung cancer and benign nodule groups. LASSO regression reduced these to 25 variables. Multivariate logistic regression identified 13 independent predictors: age (OR 1.065, 95%CI 1.043–1.087, P<0.001), sex (OR 2.274, 95%CI 1.405–3.680, P=0.001), ornithine (OR 0.952, 95%CI 0.926–0.980, P=0.001), tyrosine (OR 1.025, 95%CI 1.007–1.044, P=0.008), glutamine (OR 1.010, 95%CI 1.003–1.017, P=0.003), valine (OR 1.015, 95%CI 1.006–1.024, P=0.001), serine (OR 0.948, 95%CI 0.930–0.967, P<0.001), asparagine (OR 0.977, 95%CI 0.962–0.992, P=0.003), arginine (OR 1.098, 95%CI 1.037–1.162, P=0.001), methylmalonylcarnitine (C4DC) (OR 0.055, 95%CI 0.014–0.218, P<0.001), tetradecenoylcarnitine (C14:1) (OR 2.890, 95%CI 1.621–5.153, P<0.001), 3-hydroxyisovaleryl carnitine/2-methyl-3-hydroxybutyrylcarnitine (C5OH) (OR 1.386, 95%CI 1.023–1.877, P=0.035), and hydroxybutyrylcarnitine (C4OH) (OR 0.384, 95%CI 0.189–0.779, P=0.008). The nomogram achieved an AUC of 0.836 (95%CI 0.830–0.890) in the training set and 0.860 after 1000 bootstrap resamples. In the validation set, AUC was 0.781 (95%CI 0.722–0.841) and remained 0.781 after bootstrap resampling. Individual AUCs ranged from 0.552 (arginine) to 0.731 (ornithine). Calibration curves showed good agreement between predicted and actual values. Decision curve analysis demonstrated net benefit superior to baseline within the threshold probability range of 15% to 93%.

**Clinical Implications:** This nomogram provides a non-invasive, objective tool using peripheral blood metabolites to discriminate early-stage lung cancer from benign lung nodules. It avoids subjective imaging interpretation and may reduce unnecessary invasive procedures. The model includes four acylcarnitines (C4DC, C4OH, C5OH, C14:1) incorporated into a lung cancer prediction model for the first time. Limitations include lack of external validation, exclusion of nodule imaging features and smoking history, and suboptimal sensitivity/specificity. Further large-scale multicenter studies are needed before clinical implementation.

PICO

PPOPULATION

848 participants with lung nodules detected by chest CT (478 lung cancer patients, 370 benign nodules) at the Second Affiliated Hospital of Dalian Medical University, China.

IINTERVENTION

Nomogram based on metabolic profiling (20 amino acids and 27 carnitines measured by LC-MS/MS) combined with clinical factors (age, sex).

OOUTCOME

Discrimination of lung cancer from benign nodules, measured by area under the ROC curve (AUC), calibration, and decision curve analysis.

STUDY TYPE

cohort

SPECIALTY

oncology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

80% · B

ORIGINAL PAPER

PMC10052511

A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules

The Journal of International Medical Research

READ THE FULL PAPER

ABSTRACT

Abstract Objective To develop a nomogram that discriminates lung cancer from benign lung nodules through metabolic profiling. Methods This was a retrospective cohort study that recruited 848 participants who were randomized into training and validation sets at a 7:3 ratio. Clinical characteristics and metabolic profiles were retrieved. Variables in the training set with statistically significant differences were selected for further least absolute shrinkage and selection operator (LASSO) regression. The nomogram was built from 13 variables identified by stepwise regression analysis. Receiver operating characteristic, calibration curve, and decision curve analyses were conducted to evaluate the performance of the nomogram by internal validation. Results Thirteen variables were selected through LASSO regression to build the nomogram: age, sex, ornithine, tyrosine, glutamine, valine, serine, asparagine, arginine, methylmalonylcarnitine, tetradecenoylcarnitine, 3-hydroxyisovaleryl carnitine/2-methyl-3-hydroxybutyrylcarnitine, and hydroxybutyrylcarnitine. The nomogram had good discrimination for the training set, with an area under the curve of 0.836 (95% confidence interval: 0.830–0.890). Moreover, the calibration curve with 1000 bootstrap resamples showed that the predicted value coincided well with the actual value. Decision curve analysis described a net benefit superior to baseline within the threshold probability range of 15% to 93%. Conclusions The nomogram constructed from metabolic profiling accurately predicted risk of lung cancer. Keywords: Lung cancer, nomogram, amino acid, acylcarnitine, diagnosis, metabolic profiling

CITATION EXPORT

Chenwei Li, Zhuo Chen, Hui Zhao et al.. A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules. The Journal of International Medical Research. (2023). https://doi.org/10.1177/03000605231161204

AUTHORS

8 · 5 CENTRES

Chenwei Li, Zhuo Chen, Hui Zhao, Cuicui Wang, Shujun Yu, Hengde Ma, Qi Wang, Xiaohui Du

1Department of Respiratory Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
2Department of Critical Care Medicine, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
3Department of Health Examination Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China
4Technology Department, HPS Gene Technology Co., Ltd., Tianjin, China
5Department of Scientific Research Center, The Second Affiliated Hospital of Dalian Medical University, Dalian, China