Development and validation of a prognostic index for efficacy evaluation and prognosis of first-line chemotherapy in stage III-IV lung squamous cell carcinoma

Abstract

Objectives: To establish a pre-therapy prognostic index model (PIM) of the first-line chemotherapy aiming to achieve accurate prediction of time to progression (TTP) and overall survival among the patients diagnosed with locally advanced (stage III) or distant metastasis (stage IV) lung squamous cell carcinoma (LSCC).

Methods: Ninety-six LSCC patients treated with first-line chemotherapy were retrospectively enrolled to build the model. Fourteen epidermal growth factor receptor (EGFR)-mutant LSCC patients treated with first-line EGFR-tyrosine kinase inhibitor (TKI) therapy were enrolled for validation dataset. From CT images, 56,000 phenotype features were initially computed. PIM was constructed by integrating a CT phenotype signature selected by the least absolute shrinkage and selection operator and the significant blood-based biomarkers selected by multivariate Cox regression. PIM was then compared with other four prognostic models constructed by the CT phenotype signature, clinical factors, post-therapy tumor response, and Glasgow Prognostic Score.

Results: The signature includes eight optimal features extracted from co-occurrence, run length, and Gabor features. By using PIM, chemotherapy efficacy of patients categorized in the low-risk, intermediate-risk, and high-risk progression subgroups (median TTP = 7.2 months, 3.4 months, and 1.8 months, respectively) was significantly different (p < 0.0001, log-rank test). Chemotherapy efficacy of the low-risk progression subgroup was comparable with EGFR-TKI therapy (p = 0.835, log-rank test). Prognostic prediction of chemotherapy efficacy by PIM was significantly higher than other models (p < 0.05, z test).

Conclusion: The study demonstrated that the PIM yielded significantly higher performance to identify individual stage III-IV LSCC patients who can potentially benefit most from first-line chemotherapy, and predict the risk of failure from chemotherapy for individual patients.

Key points: • TTP and OS of first-line chemotherapy in individual stage III-IV LSCC patients could be predicted by pre-therapy blood-based biomarkers and image-based signatures. • Risk status of pre-therapy indicators affected the efficacy of first-line chemotherapy in stage III-IV LSCC patients. • Those stage III-IV LSCC patients who were able to achieve similar efficacy to EGFR-TKI therapy through chemotherapy were identified.

Keywords: Biomarkers; Carcinoma; Prognosis; Squamous cell; Tumor.

Conflict of interest statement

Guarantor

The scientific guarantor of this publication is Prof. Mingfang Zhao.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

Prof. Cui is an expert in the field of medical statistics. We consulted him about the statistics analysis in this study.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Figures

Fig. 1

Flowchart of this study. The first step was model construction, and based on the constructed model, model validation and comparison were performed. LSCC, lung squamous cell carcinoma; TTP, time to progression; OS, overall survival. NRI net reclassification improvement, IDI integrated discrimination improvement

Fig. 2

The diagram of manual segmentation by using ITK-SNAP. The subgraph in the upper left corner indicates that the manually segmented region of interest (ROI) by the radiologist from cross section. The subgraphs in the upper right and lower right corners represent the manual segmentation result of the tumor which is displayed from the sagittal and coronal planes, respectively. The tumor is then reconstructed in a view of three dimensions, which is represented in the subgraph in the lower left corner. Each of the subgraphs could be scaled to ensure accurate segmentation

Fig. 3

Results of progression risk prediction. a The Kaplan-Meier curves of groups classified by the signature, and all patients were stratified into good time to progression (TTP) group and poor TTP group according to the signature. b, c The progression risk prediction of the prognostic index model (PIM). b The result of low-risk (yellow line), intermediate-risk (blue line), and high-risk (pink line) progression subgroups by the PIM. c The comparison between the stage III–IV EGFR-mutant LSCC patients treated with first-line EGFR-TKI therapy (red line) and the different risk subgroups of chemotherapy patients stratified by the PIM. d The Kaplan-Meier curves of the patients with partial response (PR), stable disease (SD), and progressive disease (PD)

Fig. 4

The comparison of the models. a The plots depict the calibration of the model in terms of the agreement between predicted and observed TTP (time.inc = 6 months). Performances of the models are shown on the plots relative to the 45° line, which represents perfect prediction. b Decision curve analysis of the PIM (red line), clinical factor-based model (green line), post-treatment tumor response-based model (cyan line), and the intra-tumor heterogeneity signature-based model (blue line). The orange line represents the assumption that all patients were treated. The black line represents the assumption that no patient was treated. The x-axis represents the risk of progression (Pt). The y-axis measures the net benefit. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by (Pt/(1 − Pt)). The decision curve showed that if the threshold probability of a patient or doctor is > 26%, using the PIM to predict progression risk adds more benefit than the treat-all-patients scheme or the treat-none scheme, or other prognostic models. c The clinical impact curve of the PIM; the red line (number of high risk) represents the patients with a high risk of progression predicted by the PIM at each threshold (with 95% CI), and the green line (number of high risk with outcome) represents the patients with actual progression at each threshold (with 95% CI)

Fig. 5

Prognostication of overall survival, a further exploration of the proposed PIM. By applying the PIM on overall survival, the Kaplan-Meier survival curves are the stratified high-risk (pink), intermediate-risk (blue), and low-risk (yellow) chemotherapy patient subgroups