Pretherapy 18F-fluorodeoxyglucose positron emission tomography/computed tomography robust radiomic features predict overall survival in non-small cell lung cancer

Radiotranscriptomics of non-small cell lung carcinoma for assessing high-level clinical outcomes using a machine learning-derived multi-modal signature

BACKGROUND

Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients.

METHODS

2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient's response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques.

RESULTS

Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03).

CONCLUSIONS

Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.

Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer

Aim

To evaluate the role of fluorine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features (RFs) for predicting clinicopathological factors (CPFs) and prognosis in patients with resected lung squamous cell cancer (LSCC).

Material and Methods

Patients with early-stage (stage I-III) LSCC who underwent 18F-FDG PET/CT before surgical resection between August 2012 and February 2020 were analyzed. Patients who received neoadjuvant chemotherapy or radiotherapy were excluded from the study. The maximum standard uptake value (SUVmax) and RFs were extracted from PET images for primary tumors. The diagnostic performances of PET parameters in groups of tumor differentiation, stage, and mediastinal lymph node metastasis (MLNM) status were evaluated. The study endpoints were overall survival (OS) and progression-free survival (PFS). Univariate and multivariate analyses were performed with RFs, SUVmax, and CPFs to find independent predictors of PFS and OS.

Results

A total of 77 patients (5 female, 72 male) were included in the study. SUVmax and GLCM entropy were independently associated with tumor differentiation. The only parameter with significant diagnostic performance for MLNM was GLZLM-SLZGE. Tumor diameter and NGLDM busyness were independently associated with the stage. MLNM and tumor differentiation were found to be independent predictors of PFS. NGLDM contrast and MLNM were independently associated with OS.

Conclusion

Using radiomic features in addition to CPFs to predict disease recurrence and shorter overall survival can guide precision medicine in patients with LSCC.