Selection of Radiomics Features based on their Reproducibility

Ligero, Marta; Torres, Guillermo; Sanchez, Carles; Diaz-Chito, Katerine; Perez, Raquel; Gil, Débora

doi:10.1109/embc.2019.8857879

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…The subset was selected according to reproducibility against different image acquisition conditions and interobserver variability in lesion identification. Reproducibility is based on the correlation of feature values obtained from data collected using different conditions and settings [32]. The selected set of (17) features are given in Table 2.…”

Section: Intelligent Radiomics For the Detection Of Pulmonary Embolismmentioning

confidence: 99%

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients

Baeza

Gil²,

García-Olivé

et al. 2022

EJNMMI Phys

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Background COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. Methods This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. Results The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. Conclusion This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. Highlights Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.

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Section: Intelligent Radiomics For the Detection Of Pulmonary Embolismmentioning

confidence: 99%

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients

Baeza

Gil²,

García-Olivé

et al. 2022

EJNMMI Phys

Self Cite

View full text Add to dashboard Cite

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“…A training and selection of models, which consists in a leaveone-out validation on a training set of patients to select the best model for the benign and malignant classification. In order to assess the benefits of our embedding (labelled t-test), models were also trained using all 24 GLCM features (labelled None) and the selection based on reproducibility (labelled Reproducibility) reported in [31] excluding the shape class (see Table 5).…”

Section: Resultsmentioning

confidence: 99%

An Intelligent Radiomic Approach for Lung Cancer Screening

et al. 2022

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The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.

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“…For clinical problems that are difficult to describe with simple image visual features, this high-dimensional abstract feature may play a different role in capturing clinical information that is not easily perceived by vision. 31,32 Compared with the naked eye, radiomics can also extract highthroughput image features from small lesions. The radiomics model based on ultrasound image features had a good ability to distinguish small breast masses.…”

Section: Discussionmentioning

confidence: 99%

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

Lyu,

Zhang,

Zhang

et al. 2023

J of Clinical Ultrasound

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BackgroundFemale breast cancer has surpassed lung cancer as the most common cancer, and is also the main cause of cancer death for women worldwide. Breast cancer <1 cm showed excellent survival rate. However, the diagnosis of minimal breast cancer (MBC) is challenging.ObjectiveThe purpose of our research is to develop and validate an radiomics model based on ultrasound images for early recognition of MBC.Methods302 breast masses with a diameter of <10 mm were retrospectively studied, including 159 benign and 143 malignant breast masses. The radiomics features were extracted from the gray‐scale ultrasound image of the largest face of each breast mass. The maximum relevance minimum reduncancy and recursive feature elimination methods were used to screen. Finally, 10 features with the most discriminating value were selected for modeling. The random forest was used to establish the prediction model, and the rad‐score of each mass was calculated. In order to evaluate the effectiveness of the model, we calculated and compared the area under the curve (AUC) value, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the model and three groups with different experience in predicting small breast masses, and drew calibration curves and decision curves to test the stability and consistency of the model.ResultsWhen we selected 10 radiomics features to calculate the rad‐score, the prediction efficiency was the best, the AUC values for the training set and testing set were 0.840 and 0.793, which was significantly better than the insufficient experience group (AUC = 0.673), slightly better than the moderate experience group (AUC = 0.768), and was inferior to the experienced group (AUC = 0.877). The calibration curve and decision curve also showed that the radiomics model had satisfied stability and clinical application value.ConclusionThe radiomics model based on ultrasound image features has a satisfied predictive ability for small breast masses, and is expected to become a potential tool for the diagnosis of MBC, and it is a zero cost (in terms of patient participation and imaging time).

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Selection of Radiomics Features based on their Reproducibility

Cited by 15 publications

References 21 publications

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients

An Intelligent Radiomic Approach for Lung Cancer Screening

The value of radiomics model based on ultrasound image features in the differentiation between minimal breast cancer and small benign breast masses

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