Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes

Abstract

Background Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. Methods We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the ‘Deep-RadScore,’ a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. Findings The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. Interpretation This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.

Application of Chest CT Imaging Feature Model in Distinguishing Squamous Cell Carcinoma and Adenocarcinoma of the Lung

“…For patients who are difficult to undergo surgery or biopsy, or whose pathology cannot be decided after biopsy, distinguishing between lung squamous cell carcinoma and adenocarcinoma is a widespread problem that troubles clinical There have been many literature reports on the application of imaging features for pathological prediction in the past. [5][6][7][8][9][10][11][12][13]17,[23][24][25][26][27][28][29][30][31][32][33] Han et al found that a model for distinguishing lung squamous cell carcinoma and lung adenocarcinoma was constructed using CT texture features, with an AUC of 0.803, 5 Zhang et al established a model based on the variation of iodine concentration in enhanced CT, with an AUC of 0.871, 7 Fukuma et al established a model based on the attenuation rate of enhanced CT, with an AUC of only 0.625, 8 Jiang et al established a model based on the perfusion parameters of brain metastases, with an AUC of 0.845, 9 Tomori et al established a model using a combination of PET-CT…”