[A clinical-radiomics nomogram for differentiating focal organizing pneumonia and lung adenocarcinoma].

OBJECTIVE

To evaluate the performance of a clinical-radiomics model for differentiating focal organizing pneumonia (FOP) and lung adenocarcinoma (LUAD).

METHODS

We retrospectively analyzed the data of 60 patients with FOP confirmed by postoperative pathology at the First Medical Center of the Chinese PLA General Hospital from January, 2019 to December, 2022, who were matched with 120 LUAD patients using propensity score matching in a 1∶2 ratio. The independent risk factors for FOP were identified by logistic regression analysis of the patients' clinical data. The cohort was divided into a training set (144 patients) and a test set (36 patients) by random sampling. Python 3.7 was used for extracting 1835 features from CT image data of the patients. The radiographic features and clinical data were used to construct the model, whose performance was validated using ROC curves in both the training and test sets. The diagnostic efficacy of the model for FOP and LUAD was evaluated and a diagnostic nomogram was constructed.

RESULTS

Statistical analysis revealed that an history of was an independent risk factor for FOP (=0.016), which was correlated with none of the hematological findings ( > 0.05). Feature extraction and dimensionality reduction in radiomics yielded 30 significant labels for distinguishing the two diseases. The top 3 most discriminative radiomics labels were GraylevelNonUniformity, SizeZoneNonUniformity and shape-Sphericity. The clinical-radiomics model achieved an AUC of 0.909 (95% : 0.855-0.963) in the training set and 0.901 (95% : 0.803-0.999) in the test set. The model showed a sensitivity of 85.4%, a specificity of 83.5%, and an accuracy of 84.0% in the training set, as compared with 94.7%, 70.6%, and 83.3% in the test set, respectively.

CONCLUSION

The clinical-radiomics nomogram model shows a good performance for differential diagnosis of FOP and LUAD and may help to minimize misdiagnosis-related overtreatment and improve the patients' outcomes.