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Published on: August 11, 2017
Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic
Xiumei Li1, Chengxiu Zhang2, Tingting Li3
1Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 Cha-Zhong Road, Fuzhou, 350005, Fujian, China.
Abstract:
Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas before radiographic advance cannot be perceived by the naked eye. This study aimed to discover and validate a CT radiomic model to precisely identify the EAR. Training cohort (n = 67) and internal test cohort (n = 29) were from the First Affiliated Hospital of Fujian Medical University, and external test cohort (n = 29) was from the Second Affiliated Hospital of Xiamen Medical College. Follow-up CT images at three different times of each patient were collected: (1) baseline images before EGFR-TKIs therapy; (2) first follow-up images after EGFR-TKIs therapy (FFT); (3) EAR images, which were the last follow-up images before radiographic advance. The features extracted from FFT and EAR were used to construct the classic radiomic model. The delta features which were calculated by subtracting the baseline from either FFT or EAR were used to construct the delta radiomic model. The classic radiomic model achieved AUC 0.682 and 0.641 in training and internal test cohorts, respectively. The delta radiomic model achieved AUC 0.730 and 0.704 in training and internal test cohorts, respectively. Over the external test cohort, the delta radiomic model achieved AUC 0.661. The decision curve analysis showed that when threshold of the probability of the EAR to the EGFR-TKIs was between 0.3 and 0.82, the proposed model was more benefit than treating all patients. Based on two central studies, the delta radiomic model derived from the follow-up non-enhanced CT images can help clinicians to identify the EAR to EGFR-TKIs in lung adenocarcinomas before radiographic advance and optimize clinical outcomes.
Insights
A new CT radiomic model can identify early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinoma before it
Area of Science:
- Radiology and Imaging
- Oncology
- Medical Informatics
Background:
- Early acquired resistance (EAR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in lung adenocarcinomas is often undetectable before radiographic progression.
- Accurate identification of EAR is crucial for timely treatment adjustments and improved patient outcomes.
- Current methods lack the precision to detect EAR before overt radiographic signs.
Purpose of the Study:
- To develop and validate a computed tomography (CT) radiomic model for the precise identification of EAR to EGFR-TKIs in lung adenocarcinomas.
- To differentiate between classic radiomic features and delta radiomic features for improved EAR detection.
- To assess the clinical utility and benefit of the proposed radiomic model in optimizing treatment strategies.
Main Methods:
- Retrospective analysis of CT images from training (n=67), internal test (n=29), and external test (n=29) cohorts.
- Collection of baseline, first follow-up (FFT), and early acquired resistance (EAR) CT images.
- Construction of classic and delta radiomic models using extracted features from FFT/EAR and delta features (FFT/EAR minus baseline).
Main Results:
- The delta radiomic model demonstrated superior performance compared to the classic model, achieving an area under the curve (AUC) of 0.730 in the training cohort and 0.704 in the internal test cohort.
- The delta radiomic model achieved an AUC of 0.661 in the external test cohort.
- Decision curve analysis indicated that the delta radiomic model provides clinical benefit when the probability threshold for EAR is between 0.3 and 0.82.
Conclusions:
- The delta radiomic model derived from follow-up non-enhanced CT images effectively identifies EAR to EGFR-TKIs in lung adenocarcinomas prior to radiographic advance.
- This model offers a valuable tool for clinicians to optimize treatment strategies and potentially improve clinical outcomes for lung adenocarcinoma patients.
- The findings support the integration of radiomics into routine clinical practice for early detection of treatment resistance.
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