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. 2021 Feb 25:11:590836.
doi: 10.3389/fonc.2021.590836. eCollection 2021.

Positron Emission Tomography-Based Short-Term Efficacy Evaluation and Prediction in Patients With Non-Small Cell Lung Cancer Treated With Hypo-Fractionated Radiotherapy

Affiliations

Positron Emission Tomography-Based Short-Term Efficacy Evaluation and Prediction in Patients With Non-Small Cell Lung Cancer Treated With Hypo-Fractionated Radiotherapy

Yi-Qing Jiang et al. Front Oncol. .

Abstract

Background: Positron emission tomography is known to provide more accurate estimates than computed tomography when staging non-small cell lung cancer. The aims of this prospective study were to contrast the short-term efficacy of the two imaging methods while evaluating the effects of hypo-fractionated radiotherapy in non-small cell lung cancer, and to establish a short-term efficacy prediction model based on the radiomics features of positron emission tomography.

Methods: This nonrandomized-controlled trial was conducted from March 2015 to June 2019. Thirty-one lesions of 30 patients underwent the delineation of the regions of interest on positron emission tomography and computed tomography 1 month before, and 3 months after hypo-fractionated radiotherapy. Each patient was evaluated for the differences in local objective response rate between the two images. The Kaplan Meier method was used to analyze the local objective response and subsequent survival duration of the two imaging methods. The 3D Slicer was used to extract the radiomics features based on positron emission tomography. Least absolute shrinkage and selection operator regression was used to eliminate redundant features, and logistic regression analysis was used to develop the curative-effect-predicting model, which was displayed through a radiomics nomogram. Receiver operating characteristic curve and decision curve were used to evaluate the accuracy and clinical usefulness of the prediction model.

Results: Positron emission tomography-based local objective response rate was significantly higher than that based on computed tomography [70.97% (22/31) and 12.90% (4/31), respectively (p<0.001)]. The mean survival time of responders and non-responders assessed by positron emission tomography was 28.6 months vs. 11.4 months (p=0.29), whereas that assessed by computed tomography was 24.5 months vs. 26 months (p=0.66), respectively. Three radiomics features were screened to establish a personalized prediction nomogram with high area under curve (0.94, 95% CI 0.85-0.99, p<0.001). The decision curve showed a high clinical value of the radiomics nomogram.

Conclusions: We recommend positron emission tomography for evaluating the short-term efficacy of hypo-fractionated radiotherapy in non-small cell lung cancer, and that the radiomics nomogram could be an important technique for the prediction of short-term efficacy, which might enable an improved and precise treatment.

Registration number/url: ChiCTR1900027768/http://www.chictr.org.cn/showprojen.aspx?proj=46057.

Keywords: computed tomography; hypo-fractionated radiotherapy; non-small-cell lung cancer; positron emission tomography; radiomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Relative change rate of longest diameter and maximum standard uptake value (SUVmax) in each patient. Best overall response waterfall plots, in which computed tomography (CT) (A) is based on the rate of longest diameter changes, according to RECIST 1.1, and positron emission tomography (PET) (B) is based on the rate of SUVmax changes, according to European Organization for Research and Treatment of Cancer (EORTC). The top dotted line represents progressive disease, the bottom dotted line represents partial response, or complete response, while, stable disease is represented by the area between the two dotted lines.
Figure 2
Figure 2
Example of discordant positron emission tomography (PET/CT) and computed tomography (CT). (A, B) show pretreatment CT and PET/CT images respectively, and (C, D) show CT and PET/CT images of the large tumors 3 months after treatment. The red regions of interest (ROI) is manually segmented based on CT, and the green ROI is delineated according to SUV value higher than 2.5 based on PET. According to the images before and after treatment, CT shows stable disease; PET/CT shows partial response.
Figure 3
Figure 3
Effect of chemotherapy on treatment evaluation. The patients were divided into two groups: chemotherapy group after radiotherapy (Chemotherapy) and group without chemotherapy after radiotherapy (None). The difference of short-term efficacy between the two groups was compared.
Figure 4
Figure 4
Relationship between survival time and response evaluated by positron emission tomography (PET) and computed tomography (CT) scan. (A) Comparison of positron emission tomography scan response categories. (B) Comparison of computed tomography scan response categories.
Figure 5
Figure 5
Process of LASSO regression screening of radiomics features. Screening radiomics features using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (A) Tuning parameter (λ) in the LASSO model used 5-fold cross-validation. Dotted vertical lines are drawn by using the minimum criteria and the 1 standard error of the minimum criteria. (B) 851 normalized lasso coefficient plots of radiomics features. When log (λ) takes the minimum criteria, three non-zero coefficients are selected.
Figure 6
Figure 6
A radiomics nomogram that predicts the probability of effective treatment. Feature1: Wavelet.LHL_NGTDM_Busyness; Feature2: Wavelet.LHH_GLRLM_ShortRunHighGrayLevelEmphasis; Feature3: Wavelet.HHH_Firstorder_Median.
Figure 7
Figure 7
Receiver operating characteristic (ROC) and decision curve for the model. The ROC curve (A) shows the prediction accuracy of the model (AUC 0.94, 95% CI 0.85–0.99, p < 0.001). In the decision curve (B), the red line represents the radiomics nomogram. The gray line represents the assumption that all patients were responders. The thin black line represents the assumption that all patients were non-responders.

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