Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer

Jensen, G.L.; Yost, Christine M.; Mackin, D.; Fried, David; Zhou, Shouhao; Court, Laurence Edward; Gomez, Daniel R.

doi:10.1016/j.radonc.2017.11.006

Cited by 25 publications

(15 citation statements)

References 58 publications

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“…Previous work in the field of radiomics has evaluated FDG-PET features for outcome prediction in lung cancer. Jansen et al found the GLCM energy texture feature was a significant predictor of overall survival in oligometastatic NSCLC (26). Others have shown that texture features may be beneficial for predicting local control, distant metastasis, and disease-free survival in lung cancer (10–12).…”

Section: Discussionmentioning

confidence: 99%

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

et al. 2019

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We identified computational imaging features on 18F-fluorodeoxyglucose positron emission tomography (PET) that predict recurrence/progression in non–small cell lung cancer (NSCLC). We retrospectively identified 291 patients with NSCLC from 2 prospectively acquired cohorts (training, n = 145; validation, n = 146). We contoured the metabolic tumor volume (MTV) on all pretreatment PET images and added a 3-dimensional penumbra region that extended outward 1 cm from the tumor surface. We generated 512 radiomics features, selected 435 features based on robustness to contour variations, and then applied randomized sparse regression (LASSO) to identify features that predicted time to recurrence in the training cohort. We built Cox proportional hazards models in the training cohort and independently evaluated the models in the validation cohort. Two features including stage and a MTV plus penumbra texture feature were selected by LASSO. Both features were significant univariate predictors, with stage being the best predictor (hazard ratio [HR] = 2.15 [95% confidence interval (CI): 1.56–2.95], P < .001). However, adding the MTV plus penumbra texture feature to stage significantly improved prediction ( P = .006). This multivariate model was a significant predictor of time to recurrence in the training cohort (concordance = 0.74 [95% CI: 0.66–0.81], P < .001) that was validated in a separate validation cohort (concordance = 0.74 [95% CI: 0.67–0.81], P < .001). A combined radiomics and clinical model improved NSCLC recurrence prediction. FDG PET radiomic features may be useful biomarkers for lung cancer prognosis and add clinical utility for risk stratification.

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Section: Discussionmentioning

confidence: 99%

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

et al. 2019

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“…Quantitative analysis of medical images using various softwares reportedly offers more and better information than a physician alone does [1]. Moreover, the prognostic power of radiomics has been recently established for various cancer types [2][3][4][5][6][7][8][9]. Nevertheless, radiomic features are susceptible to various factors; for example, CT protocols include scanning methods and parameter settings [10][11][12][13], multiple segmentation with several persons [14,15], and respiratory motion [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Investigation of thoracic four-dimensional CT-based dimension reduction technique for extracting the robust radiomic features

et al. 2019

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Robust feature selection in radiomic analysis is often implemented using the RIDER test-retest datasets. However, the CT Protocol between the facility and test-retest datasets are different. Therefore, we investigated possibility to select robust features using thoracic four-dimensional CT (4D-CT) scans that are available from patients receiving radiation therapy. In 4D-CT datasets of 14 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) and 14 test-retest datasets of non-small cell lung cancer (NSCLC), 1170 radiomic features (shape: n = 16, statistics: n = 32, texture: n = 1122) were extracted. A concordance correlation coefficient (CCC) > 0.85 was used to select robust features. We compared the robust features in various 4D-CT group with those in test-retest. The total number of robust features was a range between 846/1170 (72%) and 970/1170 (83%) in all 4D-CT groups with three breathing phases (40%-60%); however, that was a range between 44/1170 (4%) and 476/ 1170 (41%) in all 4D-CT groups with 10 breathing phases. In test-retest, the total number of robust features was 967/1170 (83%); thus, the number of robust features in 4D-CT was almost equal to that in test-retest by using 40-60% breathing phases. In 4D-CT, respiratory motion is a factor that greatly affects the robustness of features, thus by using only 40-60% breathing phases, excessive dimension reduction will be able to be prevented in any 4D-CT datasets, and select robust features suitable for CT protocol of your own facility.

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“…Benefiting from the ability to noninvasively visualize a cancer's appearance on a macroscopic level, medical imaging demonstrated strong prognostic value [10][11][12]. Computed tomography (CT), as a routinely used medical imaging modality, contains many mineable features associated with prognosis of cancer [13][14][15] including HGSOC [16].…”

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confidence: 99%

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

Wang

Liu

et al. 2019

Radiotherapy and Oncology

131

114

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Background and purpose: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing a non-invasive recurrence prediction model in HGSOC. Materials and methods: We enrolled 245 patients with HGSOC from two hospitals, which included a feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the featurelearning cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict the individual recurrence risk and 3-year recurrence probability of patients. Results: In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694. Kaplan-Meier's analysis clearly identified two patient groups with high and low recurrence risk (p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825), which was validated by the good calibration and decision curve analysis. Moreover, the DL feature demonstrated stronger prognostic value than clinical characteristics. Conclusions: The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for prognostic biomarker extraction.

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Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer

Cited by 25 publications

References 58 publications

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non–Small Cell Lung Cancer

Investigation of thoracic four-dimensional CT-based dimension reduction technique for extracting the robust radiomic features

Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer

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