The effect of preprocessing filters on predictive performance in radiomics

Demircioğlu, Aydın

doi:10.1186/s41747-022-00294-w

Cited by 40 publications

(24 citation statements)

References 29 publications

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“…The third and fourth aims (see Section 1.2 ) of the proposed study was to evaluate the stability of the feature selection procedure. It is evident that the feature stability decreased as the number of classes decreased, and that the more stable features (i.e., a frequency ≥ 80%) were those that belonged to the wavelet and LoG image types and texture class, as is shown in several studies [ 56 , 57 ]. Furthermore, the selected features changed by varying the number of classes and, consequently, the amount of data, even when considering them between the harmonized and non-harmonized datasets.…”

Section: Discussionmentioning

confidence: 62%

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

et al. 2023

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The aim of this study was to investigate the usefulness of radiomics in the absence of well-defined standard guidelines. Specifically, we extracted radiomics features from multicenter computed tomography (CT) images to differentiate between the four histopathological subtypes of non-small-cell lung carcinoma (NSCLC). In addition, the results that varied with the radiomics model were compared. We investigated the presence of the batch effects and the impact of feature harmonization on the models’ performance. Moreover, the question on how the training dataset composition influenced the selected feature subsets and, consequently, the model’s performance was also investigated. Therefore, through combining data from the two publicly available datasets, this study involves a total of 152 squamous cell carcinoma (SCC), 106 large cell carcinoma (LCC), 150 adenocarcinoma (ADC), and 58 no other specified (NOS). Through the matRadiomics tool, which is an example of Image Biomarker Standardization Initiative (IBSI) compliant software, 1781 radiomics features were extracted from each of the malignant lesions that were identified in CT images. After batch analysis and feature harmonization, which were based on the ComBat tool and were integrated in matRadiomics, the datasets (the harmonized and the non-harmonized) were given as an input to a machine learning modeling pipeline. The following steps were articulated: (i) training-set/test-set splitting (80/20); (ii) a Kruskal–Wallis analysis and LASSO linear regression for the feature selection; (iii) model training; (iv) a model validation and hyperparameter optimization; and (v) model testing. Model optimization consisted of a 5-fold cross-validated Bayesian optimization, repeated ten times (inner loop). The whole pipeline was repeated 10 times (outer loop) with six different machine learning classification algorithms. Moreover, the stability of the feature selection was evaluated. Results showed that the batch effects were present even if the voxels were resampled to an isotropic form and whether feature harmonization correctly removed them, even though the models’ performances decreased. Moreover, the results showed that a low accuracy (61.41%) was reached when differentiating between the four subtypes, even though a high average area under curve (AUC) was reached (0.831). Further, a NOS subtype was classified as almost completely correct (true positive rate ~90%). The accuracy increased (77.25%) when only the SCC and ADC subtypes were considered, as well as when a high AUC (0.821) was obtained—although harmonization decreased the accuracy to 58%. Moreover, the features that contributed the most to models’ performance were those extracted from wavelet decomposed and Laplacian of Gaussian (LoG) filtered images and they belonged to the texture feature class.. In conclusion, we showed that our multicenter data were affected by batch effects, that they could significantly alter the models’ performance, and that feature harmonization correctly removed them. Although wavelet features seemed to be the most informative features, an absolute subset could not be identified since it changed depending on the training/testing splitting. Moreover, performance was influenced by the chosen dataset and by the machine learning methods, which could reach a high accuracy in binary classification tasks, but could underperform in multiclass problems. It is, therefore, essential that the scientific community propose a more systematic radiomics approach, focusing on multicenter studies, with clear and solid guidelines to facilitate the translation of radiomics to clinical practice.

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Section: Discussionmentioning

confidence: 62%

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

et al. 2023

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“…Different follow-up images were compared with the initial MR image before treatment to evaluate tumor response to radiation and then, the target was delineated by an expert Radiologist. After this step, pre-processing filters (e.g., edge detection filters) which are commonly used for enhancing the predictive performance in radiomics studies were applied to MR images 13–15 . In the realm of image processing and computer vision applications, edge detection holds great significance.…”

Section: Methodsmentioning

confidence: 99%

“…After this step, pre-processing filters (e.g., edge detection filters) which are commonly used for enhancing the predictive performance in radiomics studies were applied to MR images. [13][14][15] In the realm of image processing and computer vision applications, edge detection holds great significance. It is used to detect objects, locate boundaries, and extract features.…”

Section: Pre-processingmentioning

confidence: 99%

Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

Salari

Elsamaloty

Ray

et al. 2023

American Journal of Clinical Oncology

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Objectives: Distinguishing between radiation necrosis (RN) and metastatic progression is extremely challenging due to their similarity in conventional imaging. This is crucial from a therapeutic point of view as this determines the outcome of the treatment. This study aims to establish an automated technique to differentiate RN from brain metastasis progression using radiomics with machine learning. Methods: Eighty-six patients with brain metastasis after they underwent stereotactic radiosurgery as primary treatment were selected. Discrete wavelets transform, Laplacian-of-Gaussian, Gradient, and Square were applied to magnetic resonance post-contrast T1-weighted images to extract radiomics features. After feature selection, dataset was randomly split into train/test (80%/20%) datasets. Random forest classification, logistic regression, and support vector classification were trained and subsequently validated using test set. The classification performance was measured by area under the curve (AUC) value of receiver operating characteristic curve, accuracy, sensitivity, and specificity. Results: The best performance was achieved using random forest classification with a Gradient filter (AUC=0.910±0.047, accuracy 0.8±0.071, sensitivity=0.796±0.055, specificity=0.922±0.059). For, support vector classification the best result obtains using wavelet_HHH with a high AUC of 0.890±0.89, accuracy of 0.777±0.062, sensitivity=0.701±0.084, and specificity=0.85±0.112. Logistic regression using wavelet_HHH provides a poor result with AUC=0.882±0.051, accuracy of 0.753±0.08, sensitivity=0.717±0.208, and specificity=0.816±0.123. Conclusion: This type of machine-learning approach can help accurately distinguish RN from recurrence in magnetic resonance imaging, without the need for biopsy. This has the potential to improve the therapeutic outcome.

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“…These factors have significant implications on the reproducibility of extracted radiomics features [29]. A remedy for this issue may be to initially subtract these unstable parameters, affected by the acquisition and reconstruction process by integrating pretested information into the acquisition and reconstruction algorithms [30].…”

Section: Challenges Of Image Acquisition and Reconstructionmentioning

confidence: 99%

Textural Analysis as a Predictive Biomarker in Rectal Cancer

Alrahawy¹,

Aker²,

Issa³

et al. 2022

Cureus

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Colorectal cancer (CRC) is a common deadly cancer. Early detection and accurate staging of CRC enhance good prognosis and better treatment outcomes. Rectal cancer staging is the cornerstone for selecting the best treatment approach. The standard gold method for rectal cancer staging is pelvic MRI. After staging, combining surgery and chemoradiation is the standard management aiming for a curative outcome.Textural analysis (TA) is a radiomic process that quantifies lesions' heterogenicity by measuring pixel distribution in digital imaging. MRI textural analysis (MRTA) of rectal cancer images is growing in current literature as a future predictor of outcomes of rectal cancer management, such as pathological response to neoadjuvant chemoradiotherapy (NCRT), survival, and tumour recurrence. MRTA techniques could validate alternative approaches in rectal cancer treatment, such as the wait-and-watch (W&W) approach in pathologically complete responders (pCR) following NCRT. We consider this a significant step towards implementing precision management in rectal cancer.In this narrative review, we summarize the current knowledge regarding the potential role of TA in rectal cancer management in predicting the prognosis and clinical outcomes, as well as aim to delineate the challenges which obstruct the implementing of this new modality in clinical practice.

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The effect of preprocessing filters on predictive performance in radiomics

Cited by 40 publications

References 29 publications

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

Phenotyping the Histopathological Subtypes of Non-Small-Cell Lung Carcinoma: How Beneficial Is Radiomics?

Differentiating Radiation Necrosis and Metastatic Progression in Brain Tumors Using Radiomics and Machine Learning

Textural Analysis as a Predictive Biomarker in Rectal Cancer

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