Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors

Yousefi, Bardia; LaRiviere, Michael J.; Cohen, Éric A.; Buckingham, Thomas H.; Yee, Stephanie S.; Black, Taylor A.; Chien, Austin L.; Noël, Peter B.; Hwang, Wei–Ting; Katz, Sharyn I.; Aggarwal, Charu; Thompson, Jeffrey C.; Kontos, Despina

doi:10.1038/s41598-021-88239-y

Cited by 18 publications

(15 citation statements)

References 47 publications

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“…First and foremost, it is of high importance to derive the RS that will explain well a particular classification problem. Using a large number of features can lead to overfitting; thus, it is favorable to use feature selection methods to identify the most descriptive and reproducible traits [ 53 , 54 ]. In our study, we reduced the obtained features space with the LASSO model.…”

Section: Discussionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

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Automation of medical data analysis is an important topic in modern cancer diagnostics, aiming at robust and reproducible workflows. Therefore, we used a dataset of breast US images (252 malignant and 253 benign cases) to realize and compare different strategies for CAD support in lesion detection and classification. Eight different datasets (including pre-processed and spatially augmented images) were prepared, and machine learning algorithms (i.e., Viola–Jones; YOLOv3) were trained for lesion detection. The radiomics signature (RS) was derived from detection boxes and compared with RS derived from manually obtained segments. Finally, the classification model was established and evaluated concerning accuracy, sensitivity, specificity, and area under the Receiver Operating Characteristic curve. After training on a dataset including logarithmic derivatives of US images, we found that YOLOv3 obtains better results in breast lesion detection (IoU: 0.544 ± 0.081; LE: 0.171 ± 0.009) than the Viola–Jones framework (IoU: 0.399 ± 0.054; LE: 0.096 ± 0.016). Interestingly, our findings show that the classification model trained with RS derived from detection boxes and the model based on the RS derived from a gold standard manual segmentation are comparable (p-value = 0.071). Thus, deriving radiomics signatures from the detection box is a promising technique for building a breast lesion classification model, and may reduce the need for the lesion segmentation step in the future design of CAD systems.

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Section: Discussionmentioning

confidence: 99%

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Magnuska

Theek

Darguzyte

et al. 2022

Cancers

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“…The application of thermomics increased the dimensionality of the input thermal imaging and intensify the possibility of overfitting the random forest model, curse of dimensionality [28][29][30][31][32][33][34]. The Block-HSIC lasso reduced the dimensionality by removing the redundancy among the features by spanning thermomics to higher dimensional space using RBF Gaussian kernel and measuring HSIC lasso, which increases the robustness of feature selection versus outliers.…”

Section: Discussionmentioning

confidence: 99%

A Diagnostic Biomarker for Breast Cancer Screening via Hilbert Embedded Deep Low-Rank Matrix Approximation

Yousefi

Sharifipour

Maldague

2021

IEEE Trans. Instrum. Meas.

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Thermography has been used extensively as a complementary diagnostic tool in breast cancer detection. Among thermographic methods matrix factorization (MF) techniques show an unequivocal capability to detect thermal patterns corresponding to vasodilation in cancer cases. One of the biggest challenges in such techniques is selecting the best representation of the thermal basis. In this study, an embedding method is proposed to address this problem and Deep-semi-nonnegative matrix factorization (Deep-SemiNMF) for thermography is introduced, then tested for 208 breast cancer screening cases. First, we apply Deep-SemiNMF to infrared images to extract lowrank thermal representations for each case. Then, we embed lowrank bases to obtain one basis for each patient. After that, we extract 300 thermal imaging features, called thermomics, to decode imaging information for the automatic diagnostic model. We reduced the dimensionality of thermomics by spanning them onto Hilbert space using RBF kernel and select the three most efficient features using the block Hilbert Schmidt Independence Criterion Lasso (block HSIC Lasso). The preserved thermal heterogeneity successfully classified asymptomatic versus symptomatic patients applying a random forest model (crossvalidated accuracy of 71.36% (69.42%-73.3%)).

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“…Huang et al [ 4 ] concluded that EGFR mutation status can be determined using quantitative imaging from extracted tumor phenotypes in NSCLC. Similarly, Bardia et al [ 46 ] found that combining radiomic phenotypes, clinical variables, and circulating tumor DNA (ctDNA), enhanced prediction of EGFR-targeted therapy outcomes for NSCLC.…”

Section: Discussionmentioning

confidence: 99%

Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography

Hershman

Yousefi

Serletti

et al. 2021

Cancers

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This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen–Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients (p-value < 0.005). This supports that readers’ level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.

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Combining radiomic phenotypes of non-small cell lung cancer with liquid biopsy data may improve prediction of response to EGFR inhibitors

Cited by 18 publications

References 47 publications

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

Influence of the Computer-Aided Decision Support System Design on Ultrasound-Based Breast Cancer Classification

A Diagnostic Biomarker for Breast Cancer Screening via Hilbert Embedded Deep Low-Rank Matrix Approximation

Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography

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