Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

Qin, Yun; Zhu, Lihua; Zhao, Wei; Wang, Junjie; Wang, Hao

doi:10.3389/fonc.2022.913683

Cited by 11 publications

(6 citation statements)

References 83 publications

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“…However, the DVH features only describe the distribution of dose in general terms during radiotherapy and do not give a specific three-dimensional representation of the radiotherapy dose. The dosiomics method can describe dose distribution by intensity, texture, shape and other dose characteristics with high accuracy, granularity and spatial information, and is an effective method for parameterizing radiotherapy dose distribution [ 15 ]. It has been shown that dosiomics features based on dosimetry can be more effective in several directions such as local control after carbon-ion radiotherapy for skull base chordoma [ 16 ], prediction of weight loss in the acute phase in lung cancer patients receiving radiotherapy [ 17 ] and exploring the interaction between radiation and lymphocytopenia in lung cancer patients [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

et al. 2023

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Objectives By comparing the prognostic performance of 18F-FDG PET/CT-based radiomics combining dose features [Includes Dosiomics feature and the dose volume histogram (DVH) features] with that of conventional radiomics in head and neck cancer (HNC), multidimensional prognostic models were constructed to investigate the overall survival (OS) in HNC. Materials and methods A total of 220 cases from four centres based on the Cancer Imaging Archive public dataset were used in this study, 2260 radiomics features and 1116 dosiomics features and 8 DVH features were extracted for each case, and classified into seven different models of PET, CT, Dose, PET+CT, PET+Dose, CT+Dose and PET+CT+Dose. Features were selected by univariate Cox and Spearman correlation coefficients, and the selected features were brought into the least absolute shrinkage and selection operator (LASSO)-Cox model. A nomogram was constructed to visually analyse the prognostic impact of the incorporated dose features. C-index and Kaplan–Meier curves (log-rank analysis) were used to evaluate and compare these models. Results The cases from the four centres were divided into three different training and validation sets according to the hospitals. The PET+CT+Dose model had C-indexes of 0.873 (95% CI 0.812–0.934), 0.759 (95% CI 0.663–0.855) and 0.835 (95% CI 0.745–0.925) in the validation set respectively, outperforming the rest models overall. The PET+CT+Dose model did well in classifying patients into high- and low-risk groups under all three different sets of experiments (p < 0.05). Conclusion Multidimensional model of radiomics features combining dosiomics features and DVH features showed high prognostic performance for predicting OS in patients with HNC.

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Section: Introductionmentioning

confidence: 99%

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

et al. 2023

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“…The analysis of radiation toxicity in rectal cancer can be enhanced through the use of image-based features, which aid physicians in mitigating radiation risks and determining the feasibility of local tumor control [6,7]. Radiomics, a novel imaging analysis approach, involves the quantification of high-dimensional data extracted from medical images, providing valuable information about pathophysiological properties [8][9][10]. In the context of radiotherapy, radiomics feature analysis of the target volume and organs at risk (OARs) can have various applications, such as diagnostics, risk stratification, disease-free survival prediction, automatic segmentation, target volume definition, toxicity prognosis, treatment plan optimization, adaptive re-planning, decision support, treatment response assessment, and follow-up [8,[11][12][13].…”

Section: The Potential Of Radiomicsmentioning

confidence: 99%

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Abbaspour,

Barahman,

Abdollahi

et al. 2023

Biomed. Phys. Eng. Express

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Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

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“…Since CT is the most commonly used staging method for esophageal cancer (EC) and gastric cancer (GC), most radiomics studies on EC and GC are based on CT images[ 5 - 9 ]. In contrast, as MRI is widely used for colorectal cancer (CRC) staging, most radiomics studies on CRC are based on MRI features[ 10 - 13 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Qi¹,

Zhou²,

Zhao³

et al. 2022

World J Gastroenterol

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Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

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Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

Cited by 11 publications

References 83 publications

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

Prognostic value of 18F-FDG PET/CT-based radiomics combining dosiomics and dose volume histogram for head and neck cancer

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

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