Clinical utility of texture analysis of 18F-FDG PET/CT in patients with Stage I lung cancer treated with stereotactic body radiotherapy

Takeda, Kazuya; Takanami, Kentaro; Shirata, Yuko; Yamamoto, Takaya; Takahashi, Noriyoshi; Ito, Kengo; Takase, Kei; Jingu, Keiichi

doi:10.1093/jrr/rrx050

Cited by 42 publications

(26 citation statements)

References 30 publications

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“…High-order features, in particular, coarseness, from 18 F-FDG PET have also shown predictive and prognostic capability in NSCLC patients who underwent chemoradiation therapy (57) . A retrospective study of 26 patients with stage 1 NSCLC tested standard metrics and texture parameters in pretherapy (SBRT) 18 F-FDG PET/CT scans for the prediction of local control, progression-free, and overall survival (59) . Although most features showed good interobserver reproducibility, only the texture feature of high-intensity large area emphasis predicted for local control ( P = .03) and SUVmax for progression-free survival ( P = .03), with no PET parameters found for overall survival on univariate analysis.…”

Section: Radiomics and Texture Analysis In Radiation Therapymentioning

confidence: 99%

Challenges and Promises of PET Radiomics

Cook

Azad

Owczarczyk

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

142

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PurposeRadiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.Methods and MaterialsRadiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.ResultsIn relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.ConclusionsAlthough at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

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Section: Radiomics and Texture Analysis In Radiation Therapymentioning

confidence: 99%

Challenges and Promises of PET Radiomics

Cook

Azad

Owczarczyk

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

142

View full text Add to dashboard Cite

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“…While recent work has evaluated the potential for radiomics features to augment traditional metrics of response (10–12), the majority of studies to date have focused on only the metabolic tumor volume (MTV) on PET and, to the best of our knowledge, no study has investigated the peritumoral region. Tumor invasion from the main mass can be defined by infiltration of stroma, blood vessels, or visceral pleura (13).…”

Section: Introductionmentioning

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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“…Nonetheless, in our study the average predictive performance for GLM all in all clinical endpoints ranged from 0.50 to 0.66. For comparison, other studies predicting outcome with both PET radiomics and clinical variables in NSCLC found predictive performances of 0.63 for predicting OS [41], 0.72 for local recurrence [71], and 0.71 for distant metastases [72]. Even with those results, neglecting any limitations of those studies, there is still no strong evidence that PET texture features exhibit complementary information.…”

Section: Discussionmentioning

confidence: 89%

Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?

et al. 2020

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In locally advanced lung cancer, established baseline clinical variables show limited prognostic accuracy and 18 F-fluorodeoxyglucose positron emission tomography (FDG PET) radiomics features may increase accuracy for optimal treatment selection. Their robustness and added value relative to current clinical factors are unknown. Hence, we identify robust and independent PET radiomics features that may have complementary value in predicting survival endpoints. A 4D PET dataset (n = 70) was used for assessing the repeatability (Bland-Altman analysis) and independence of PET radiomics features (Spearman rank: |ρ|<0.5). Two 3D PET datasets combined (n = 252) were used for training and validation of an elastic net regularized generalized logistic regression model (GLM) based on a selection of clinical and robust independent PET radiomics features (GLM all). The fitted model performance was externally validated (n = 40). The performance of GLM all (measured with area under the receiver operating characteristic curve, AUC) was highest in predicting 2-year overall survival (0.66±0.07). No significant improvement was observed for GLM all compared to a model containing only PET radiomics features or only clinical variables for any clinical endpoint. External validation of GLM all led to AUC values no higher than 0.55 for any clinical endpoint. In this study, robust independent FDG PET radiomics features did not have complementary value in predicting survival endpoints in lung cancer patients. Improving risk stratification and clinical decision making based on clinical variables and PET radiomics features has still been proven difficult in locally advanced lung cancer patients.

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Clinical utility of texture analysis of 18F-FDG PET/CT in patients with Stage I lung cancer treated with stereotactic body radiotherapy

Cited by 42 publications

References 30 publications

Challenges and Promises of PET Radiomics

Challenges and Promises of PET Radiomics

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

Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?

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