Editorial for “Elaboration of Multiparametric MRI‐Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non‐Small‐Cell Lung Cancer”

e20610 Background: Oligometastatic non-small cell lung cancer (NSCLC) exists high heterogeneity with distinct outcome, and there is a lack of available biomarkers for patient stratification. In this study, we identified a positron emission tomography (PET)/computed tomography(CT)-based radiomics signature capable of predicting overall survival (OS) in patients with synchronous oligometastatic NSCLC. Methods: This study consisted of 46 patients with synchronous oligometastatic NSCLC (≤5 metastases) between 2012-2018. Clinicopathologic data was acquired from medical records and database. A total of 20648 radiomic features were extracted from pretreatment CT and PET images, which were generated from the same PET/CT scanner. A radiomics signature was built by using the least absolute shrinkage and selection operator (LASSO) regression model. Multivariate Cox regression analysis was performed to establish the predictive model. The performance was evaluated with Harrell' concordance index (C-index). Results: 7 radiomics features were selected to build the radiomics signature. Multivariate analysis indicated that the radiomics signature (P = 0.007) was an independent prognostic factor, with a C-index of 0.810. Smoking status (P = 0.01) was the only independent clinicopathologic risk factor for overall survival prediction. Incorporating the radiomics signature with clinicopathologic risk factors resulted in higher performance with a C-index of 0.899. Conclusions: This study developed a radiomics model for predicting OS in synchronous oligometastatic NSCLC, which may serve as a predictive tool to identify individualized treatment strategy. Further internal and external validation of the model are required. Support: 81572279, 2016J004, LC2016PY016, 2018CR033. [Table: see text]

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Pathology-Based Non-Small Cell Lung Cancer Radiomics Signature Describing the Local Tumor Immune Environment: Discovery and Validation

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2016.06.114 ◽

2016 ◽

Vol 96 (2) ◽

pp. S42-S43 ◽

Cited By ~ 2

Author(s):

C. Tang ◽

A. Amer ◽

B. Hobbs ◽

X. Li ◽

C. Behrens ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Local Tumor ◽

Small Cell Lung ◽

Radiomics Signature

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Radiomic and clinical predictors of cachexia in non-small cell lung cancer patients treated with immunotherapy

10.1101/2020.10.08.20207415 ◽

2020 ◽

Author(s):

Wei Mu ◽

Evangelia Katsoulakis ◽

Kenneth L. Gage ◽

Chris J. Whelan ◽

Matthew B. Schabath ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Prognostic Value ◽

Small Cell ◽

Operating Characteristics ◽

Small Cell Lung ◽

Prediction Ability ◽

Pet Ct ◽

Radiomics Signature

AbstractBackgroundCachexia is present in up to 50% of patients with cancer and may contribute to primary resistance to immunotherapy. Biomarkers to predict cachexia are urgently required for early intervention. Herein, we test the hypothesis that pre-treatment 18F-FDG-PET/CT-based radiomics can be used to predict cachexia and subsequently associated with clinical outcomes among patients with advanced non-small cell lung cancer (NSCLC) who are treated with immunotherapy.MethodsThis retrospective multi-institution study included 210 patients with histologically confirmed stage IIIB-IV NSCLC who were treated with immune checkpoint blockade between June 2011 and August 2019. Baseline (pre-immunotherapy) PET/CT images of 175 patients from Moffitt Cancer Center were used to train (N=123) and test (N=52) a radiomics signature to predict cachexia, which was also used to predict durable clinical benefit (DCB), progression-free survival (PFS) and overall survival (OS) subsequently. An external cohort that enrolled 35 patients from James A. Haley Veterans’ Hospital (VA) was used to further validate the predictive and prognostic value of this signature.ResultsA radiomics signature demonstrated cachexia prediction ability with areas under receiver operating characteristics curves (AUC) of 0.77 (95%CI:0.68-0.85), 0.75 (95%CI:0.60-0.86) and of 0.73 (95%CI:0.53-0.92) in the training, test and external VA cohorts, respectively. For the further investigation of prognostic value, this signature could identify the patients with DCB with AUC of 0.67 (95%CI:0.57-0.77), 0.66 (95%CI:0.51-0.81), and 0.72 (95%CI:0.54-0.89) in these three cohorts. Additionally, the PFS and OS were significantly shorter among patients with higher radiomics signature in all the three cohorts (p<0.05).ConclusionUsing PET/CT radiomics analysis, cachexia could be predicted before the start of the immunotherapy, making it possible to monitor the patients with a higher risk of cachexia and identify patients most likely to benefit from immunotherapy.

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A Radiomics Signature in Preoperative Predicting Degree of Tumor Differentiation in Patients with Non–small Cell Lung Cancer

Academic Radiology ◽

10.1016/j.acra.2018.02.019 ◽

2018 ◽

Vol 25 (12) ◽

pp. 1548-1555 ◽

Cited By ~ 9

Author(s):

Xin Chen ◽

Mengjie Fang ◽

Di Dong ◽

Xinhua Wei ◽

Lingling Liu ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Tumor Differentiation ◽

Small Cell Lung ◽

Radiomics Signature

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Histological Grade: Analysis of Prognosis of Non-small Cell Lung Cancer After Complete Resection

In Vivo ◽

10.21873/invivo.11407 ◽

2018 ◽

Vol 32 (6) ◽

pp. 1505-1512 ◽

Cited By ~ 2

Author(s):

MOTOAKI YASUKAWA ◽

NORIYOSHI SAWABATA ◽

TAKESHI KAWAGUCHI ◽

NORIKAZU KAWAI ◽

TOKIKO NAKAI ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Histological Grade ◽

Complete Resection ◽

Small Cell ◽

Small Cell Lung

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CMET-04. RADIOMICS FEATURES CAN DIFFERENTIATE THE EGFR MUTATION STATUS OF BRAIN METASTASES FROM NON-SMALL CELL LUNG CANCER

Neuro-Oncology ◽

10.1093/neuonc/noz175.205 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi51-vi51

Author(s):

Yae Won Park ◽

Sung Soo Ahn ◽

Dongmin Choi ◽

Hwiyoung Kim

Keyword(s):

Lung Cancer ◽

Brain Metastases ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Egfr Mutation ◽

Multiparametric Mri ◽

Small Cell ◽

Machine Learning Algorithms ◽

Small Cell Lung ◽

Mutation Status

Abstract BACKGROUND AND PURPOSE To assess whether radiomics features on DTI and conventional postcontrast T1-weighted (T1C) images can differentiate the epidermal growth factor receptor (EGFR) molecular status in brain metastases from non-small cell lung cancer (NSCLC). MATERIALS AND METHODS Radiomics features (n = 5046) were extracted from preoperative MRI including T1C and DTI from pathologically confirmed brain metastases of 59 patients with underlying NSCLC and known EGFR mutation status (31 EGFR wild type, 28 EGFR mutant). A subset of 4317 features (85.6%) with high stability (intraclass correlation coefficient > 0.9) were selected for further analysis. After feature selection by the least absolute shrinkage and selection operator, the radiomics classifiers were constructed by various machine learning algorithms. The prediction performance of the classifier was validated by using leave-one-out cross-validation. Diagnostic performance was compared between multiparametric MRI radiomics models and single imaging radiomics models using the area under the curve (AUC) from ROC analysis. RESULTS Thirty-seven significant radiomics features (6 from ADC, 6 from fractional anisotropy [FA], 25 from T1C) were selected. The best performing multiparametric radiomics model (AUC 0.97, 95% CI 0.94–1) showed better performance than any single radiomics model using ADC (AUC 0.79, p = 0.007), FA (AUC 0.75, p = 0.001), or T1C (AUC 0.96, p = 0.678); the accuracy, sensitivity, and specificity of this model were 94.4%, 96.6%, and 92.0%, respectively. CONCLUSION Radiomics classifiers integrating multiparametric MRI parameters may be useful to differentiate the EGFR mutation status in brain metastases from lung cancer.

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