Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology

Pillai, Malvika; Adapa, Karthik; Shumway, J.W.; Dooley, John; Das, Shiva K.; Chera, Bhishamjit S.; Mazur, Łukasz

doi:10.3233/shti220118

Cited by 3 publications

(3 citation statements)

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“…Feature selection was conducted for algorithms that support feature importance in sklearn: decision tree, random forest, and adaboost. 23 All features were used to train SVM and the neural network. The most meaningful features selected by each algorithm are shown in Table 2 .…”

Section: Resultsmentioning

confidence: 99%

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Pillai¹,

Shumway²,

Adapa³

et al. 2023

Advances in Radiation Oncology

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Section: Resultsmentioning

confidence: 99%

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Pillai¹,

Shumway²,

Adapa³

et al. 2023

Advances in Radiation Oncology

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“…This is where the process called feature engineering plays a significant role. It consists of selecting the most useful features (among the available features) and feature discovery (combining the existing features to obtain more useful features) [38,39].…”

Section: Unrepresentative Training Setmentioning

confidence: 99%

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

Kryszan,

Wylęgała,

Kijonka

et al. 2024

Diagnostics

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Artificial intelligence (AI) has seen significant progress in medical diagnostics, particularly in image and video analysis. This review focuses on the application of AI in analyzing in vivo confocal microscopy (IVCM) images for corneal diseases. The cornea, as an exposed and delicate part of the body, necessitates the precise diagnoses of various conditions. Convolutional neural networks (CNNs), a key component of deep learning, are a powerful tool for image data analysis. This review highlights AI applications in diagnosing keratitis, dry eye disease, and diabetic corneal neuropathy. It discusses the potential of AI in detecting infectious agents, analyzing corneal nerve morphology, and identifying the subtle changes in nerve fiber characteristics in diabetic corneal neuropathy. However, challenges still remain, including limited datasets, overfitting, low-quality images, and unrepresentative training datasets. This review explores augmentation techniques and the importance of feature engineering to address these challenges. Despite the progress made, challenges are still present, such as the “black-box” nature of AI models and the need for explainable AI (XAI). Expanding datasets, fostering collaborative efforts, and developing user-friendly AI tools are crucial for enhancing the acceptance and integration of AI into clinical practice.

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“…21 Feature engineering or multivariable feature analysis refers to the process of recruiting comprehensive statistical methods and domain knowledge to reveal the most relevant subset of features from the raw feature sets to be used in supervised or unsupervised predictive analytics processes. [22][23][24][25][26][27][28] Such analyses can be either performed in the spatialdomain (image feature space), or the frequency-domain to highlight specific information and reveal the most relevant and explanatory features towards the application of interest. The focus of the frequency-based feature engineering techniques is to decompose the raw information into different frequency bandwidths or information components in the frequencydomain which can be compared among different samples/cases and used for model development.…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional wavelet decomposition-based radiomics analysis for tumor characterization in patients with oropharyngeal squamous cell carcinoma

et al. 2022

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Background: We investigated the potential predictive value along with interpretability of the three-dimensional wavelet decomposition (3D-WD)-based radiomics analysis for characterization of gross-tumor-volumes (GTVs) for patients with Human Papilloma Virus (HPV) oropharyngeal squamous cell carcinoma (OPSCC). The goal was to characterize and identify the spatial frequencies and regions of primary tumor that are responsible for classifying the HPV status. Methods: One-hundred twenty-eight OPSCC patients (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-Protein) were retrospectively studied. 3D-WD analysis was performed on the contrast-enhanced-CT images of patients’ primary tumor-GTVs to decompose information into three decomposition levels explained by a series of high-pass and low-pass wavelet coefficients (WCs). Log-Energy-Entropy of the WCs was calculated as radiomics features. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) was applied on the feature space to identify and rank the frequency sub-bands associated with the HPV status. The classifier was validated using a nested-cross-validation technique. Average of Area Under ROC (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and performance of the classifier. The significant features were used to weight tumor sub-band frequencies to reconstruct the tumor zones with highest information towards characterization of HPV. Results: Among 22 frequency-based features, two low-frequency and two high-frequency features were statistically discriminant between the two cohorts. Results (AUC/PPV/NPV=0.798/0.745/0.823) imply that tumor’s high-frequency and low-frequency components are associated with its HPV positivity and negativity, respectively. Conclusions: This study suggests that compared to the central zones of tumor, peritumoral regions contain more information for characterization of the HPV-status. Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of frequency-based radiomics analysis towards characterization of tumor microenvironment in patients with OPSCC. By associating this information with tumor pathology, one can potentially link radiomics to underlying biological mechanisms.

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Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology

Cited by 3 publications

References 0 publications

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Augmenting Quality Assurance Measures in Treatment Review with Machine Learning in Radiation Oncology

Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review

Three-dimensional wavelet decomposition-based radiomics analysis for tumor characterization in patients with oropharyngeal squamous cell carcinoma

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