Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

Avanzo, Michele; Pirrone, G.; Vinante, Lorenzo; Caroli, Angela; Stancanello, Joseph; Drigo, A.; Massarut, Samuele; Mileto, Mario; Urbani, Martina; Trovò, Marco; Naqa, Issam El; Paoli, Antonino De; Sartor, G.

doi:10.3389/fonc.2020.00490

Cited by 23 publications

(20 citation statements)

References 38 publications

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“…This kind of process has been performed widely in radiomics [5][6][7][8][9][10][11][12], and it is even more meaningful when performed in a multicentric setting [13,14]. Dosiomics is born directly as an extension of radiomics; it entails extracting features from the patients' three-dimensional (3D) radiotherapy dose distribution rather than from conventional medical images [15,16] to obtain specific spatial and statistical information. Furthermore, it can parameterise the dose distribution in particular regions of interest (ROIs) by intensity, textural and shape-based features allowing the description of the dose distribution at a high complexity level, distinct from those obtained from dose-volume histograms (DVHs) [17].…”

Section: Introductionmentioning

confidence: 99%

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Placidi

Gioscio

Garibaldi

et al. 2021

Cancers

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Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

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Section: Introductionmentioning

confidence: 99%

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Placidi

Gioscio

Garibaldi

et al. 2021

Cancers

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“…It can be seen that after correction, one feature in significant fibrosis classification and four features in advanced fibrosis and cirrhosis classifications are statistically significant. In addition, the values of features in two subsets of patients with extreme classifier prediction scores were investigated, one was chosen as the 20% with the lowest prediction scores in the control group, and the other was 20% with the highest prediction scores in the positive group [ 54 ]. The results are shown in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI

et al. 2021

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Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.

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“…Recently, dosomics, the application of radiomics or DL to the analysis of the dose distribution, eventually corrected into biologically effective dose to account for diverse fractionation, was investigated for the ability to predict side effects of radiation therapy [86,87]. Radiomics can also be applied to cone-beam CT (CBCTs) acquired for image-guidance of the radiotherapy treatment, making these images useful for data mining [88].…”

Section: Therapymentioning

confidence: 99%

“…The activation maps extracted by the CNN, overlaid with the image analyzed, can show on which image regions the CNN focuses strongly for prediction [112]. For ML classifiers, interpretation can be facilitated by identification of the most important variables or features for prediction and comparing their values in illustrative cases, e.g., patients with a poor and good prognosis, as done in many radiomics studies, e.g., [86,113,114]. In unsupervised learning, some methods, like t-distributed stochastic embedding (t-SNE), allow visualization of high-dimensional data by giving each data point a location in a two or three-dimensional map [20].…”

Section: Interpretabilitymentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

Cited by 23 publications

References 38 publications

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity

Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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