Radiomicsmetabolic signature profiles for advanced non-small cell lung cancer with chemoimmunotherapy by reflecting biological function and survival

Abstract

Background: Resistance to chemoimmunotherapy in patients with advanced non-small cell lung cancer (NSCLC) necessitates effective prognostic biomarkers. Although ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) has shown potential for efficacy assessment, it has been mainly evaluated in immuno-monotherapy setting, lacking elaborations in the scenarios of immunotherapy combined with chemotherapy. To tackle this dilemma, we aimed to build a non-invasive PET/CT-based model for stratifying tumor heterogeneity and predicting survival in advanced NSCLC patients undergoing chemoimmunotherapy. Meanwhile, we explored the interplay and combined effect between programmed death-ligand 1 (PD-L1) and metabolic parameters and probed into the prognostic differences between patients with similar total metabolic tumor volume (tMTV) but different tumor distribution (lesion locations and numbers).

Methods: We retrospectively recruited unresectable advanced NSCLC patients receiving immunotherapy in Jinling Hospital from 2018 to 2023 as the training cohort. The Cancer Imaging Archive (TCIA) cohort with early-stage NSCLC patients undergoing surgical resection was used for validation and the assessment of the biological function and tumor microenvironment (TME). PET/CT-based parameters were extracted, including radiomics score (Rad-score), bone marrow to liver ratio (BLR), tMTV, and total lesion glycolysis (TLG). The end-point events included overall survival (OS) and progression-free survival (PFS). Step-wise multivariate Cox regression and the least absolute shrinkage and selection operator (LASSO) were used to identify candidate variables and establish models.

Results: A total of 220 patients were identified for analysis, including 139 with unresectable advanced NSCLC receiving immunotherapy and 81 from TCIA. The Radiomicsmetabolicos model for OS encompassing Rad-score >0.705 [hazard ratio (HR) =2.455; 95% confidence interval (CI): 1.324-4.550], squamous cell subtype (HR =1.641; 95% CI: 0.900-2.992), liver metastases (HR =3.496; 95% CI: 1.435-8.517), BLR >0.94 (HR =1.885; 95% CI: 1.013-3.507), and tMTV >105 mL (HR =2.162; 95% CI: 1.134-4.119) exhibited reliable prognostic capacity with a notable 3-year area under the curve (AUC) of 0.837. Patients with Rad-score ≤0.705 demonstrated upregulation of immune-related pathways and favorable survival. Additionally, distant metastases metabolic tumor volume (MTV) and TLG, as well as intrathoracic lymph nodes MTV were associated with survival independently. For patients with similar tMTV (≤105 mL), the number of FDG-avid lesions was an independent protective factor for more-than-1-year OS, which indicated that patients with smaller lesions seemed to have better long-term prognoses than those with larger lesions, even of fewer in number.

Conclusions: Our findings proved that PET/CT could reveal survival and tumor heterogeneity in advanced NSCLC patients undergoing chemoimmunotherapy, which might guide the selection of immune-monotherapy for low-risk patients and facilitate the advancement of precision treatment.

Keywords: Fluorodeoxyglucose positron emission tomography (FDG PET); immunotherapy; non-small cell lung cancer (NSCLC); radiomics; tumor microenvironment (TME).

Figure 1

Study design and workflow. The figure was partly modified from Servier Medical Art (http://smart.servier.com/), licensed under a Creative Common Attribution 3.0 Generic License (https://creativecommons.org/licenses/by/3.0). SUV, standardized uptake value; NSCLC, non-small cell lung cancer; TCIA, The Cancer Imaging Archive; PET, positron emission tomography; SLR, spleen to liver ratio; BLR, bone marrow to liver ratio; tMTV, total metabolic tumor volume; wbTLG, whole-body total lesion glycolysis; PD-L1, programmed death-ligand 1; ICC, intraclass correlation coefficient; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; DCA, decision curve analysis; RNA-seq, RNA sequencing; TILs, tumor infiltrating lymphocytes.

Figure 2

Establishment of Radiomicsmetabolic model for PFS. (A,B) LASSO Cox regression analysis and (C) Kaplan-Meier survival curves between two Rad-score groups. (D) Consensus clustering analysis based on 26 prognosis-relevant radiomics features. (E,F) Survival curves and landmark analysis between two clusters. (G) Heatmap of Radiomicsmetabolic model. wbTLG, whole-body total lesion glycolysis; BLR, bone marrow to liver ratio; Rad-score, radiomics score; PFS, progression-free survival; LASSO, least absolute shrinkage and selection operator.

Figure 3

Establishment of Radiomicsmetabolicos model for OS. (A,B) LASSO Cox regression analysis and (C) Kaplan-Meier survival curves for OS in Cohort A. (D,E) Kaplan-Meier survival curves and landmark analysis between different Rad-score groups in Cohort B. (F) The heatmap of Radiomicsmetabolicos model. tMTV, total metabolic tumor volume; BLR, bone marrow to liver ratio; Rad-score, radiomics score; OS, overall survival; LASSO, least absolute shrinkage and selection operator.

Figure 4

Evaluation of Radiomicsmetabolic (for PFS) and Radiomicsmetabolicos (for OS) models. (A,G) Nomograms, (B,H) corresponding Kaplan-Meier survival curves, (C,I) bootstrap receiver operating characteristic curves, (D,J) time-dependent ROC curves, (E,K) calibration curves and (F,L) DCA of Radiomicsmetabolic and Radiomicsmetabolicos models. wbTLG, whole-body total lesion glycolysis; BLR, bone marrow to liver ratio; Rad-score, radiomics score; yr, year(s); mo, month(s); PFS, progression-free survival; AUC, area under the curve; CI, confidence interval; tMTV, total metabolic tumor volume; OS, overall survival; ROC, receiver operating characteristic; DCA, decision curve analysis.

Figure 5

Biological functions of Rad-score groups for OS. (A) DEGs, (B,C) GSEA of the top-ranked immune-related pathways, (D) ESTIMATE and (E) Cibersort analysis between two Rad-score groups. *, P<0.05. FC, fold change; ESTIMATE, Estimation of Stromal and Immune cells in malignant tumors using Expression data; Rad-score, radiomics score; ns, non-significant; OS, overall survival; DEG, differentially expressed gene; GSEA, gene set enrichment analysis; ns, non-significant.

Figure 6

Biological functions of Rad-score groups for PFS and clustering analysis according to metabolic parameters and PD-L1 expression. (A) DEGs, (B) GSEA, (C) Cibersort, and (D) ESTIMATE analysis by comparing low to high Rad-score groups. (E) Unsupervised K-means clustering analysis between PD-L1 expression and tMTV, wbTLG or BLR. (F-K) Kaplan-Meier survival curves of clusters for survival. *, P<0.05; ***, P<0.001. FC, fold change; ns, non-significant; ESTIMATE, Estimation of Stromal and Immune cells in malignant tumors using Expression data; Rad-score, radiomics score; PD-L1, programmed death-ligand 1; tMTV, total metabolic tumor volume; wbTLG, whole-body total lesion glycolysis; BLR, bone marrow to liver ratio; OS, overall survival; PFS, progression-free survival; DEG, differentially expressed gene; GSEA, gene set enrichment analysis.

Figure 7

The correlation and combined effect of total metabolic burden and the number of ¹⁸F-FDG-avid tumor lesions on survival. (A) In the low-tMTV group, the HR of the number of lesions tended to fall over time. (B) The schematic diagram demonstrated the rising trend of more-than-1-year OS with the increase of patients’ lesion number in the low-tMTV group. (C) Selected coronal section PET images from a short-term survivor (PA1) with 7 lesions who died at 12.7 months, and a long-term survivor (PA3) with 19 lesions who was still alive at 40.2 months. tMTV, total metabolic tumor volume; ln, loge; FDG, fluorodeoxyglucose; OS, overall survival; PA, patient; HR, hazard ratio; PET, positron emission tomography.