Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

Granata, Vincenza; Fusco, Roberta; De Muzio, Federica; Brunese, Maria Chiara; Setola, Sergio Venanzio; Ottaiano, Alessandro; Cardone, Claudia; Avallone, Antonio; Patrone, Renato; Pradella, Silvia; Miele, Vittorio; Tatangelo, Fabiana; Cutolo, Carmen; Maggialetti, Nicola; Caruso, Damiano; Izzo, Francesco; Petrillo, Antonella

doi:10.1007/s11547-023-01710-w

Cited by 7 publications

(4 citation statements)

References 269 publications

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“…Qu et al [54], in a retrospective study on 266 patients, showed that radiomics analysis based on MR T2W sequences allowed us to predict tumor budding in patients with rectal cancer. To the best of our knowledge, only our group has assessed budding in liver metastases [4,8,[85][86][87]. However, in a previous evaluation [85][86][87], we assessed specific phases of a contrast study.…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays, tumor budding can only be assessed in surgical resection specimens, so that this prognostic marker has a limited value in patient risk evaluation in a pre-surgical setting. Radiomics analysis is an emerging field in research settings, since, thorough a mathematical approach, this allows us to obtain biological data from medical images [4][5][6][7][8]. Radiomics analysis allows multiple features to be obtained from medical imaging, including shape features and first-, second-or higher-order statistical features.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Granata,

Fusco,

Brunese

et al. 2024

Diagnostics

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Purpose: We aimed to assess the efficacy of machine learning and radiomics analysis using magnetic resonance imaging (MRI) with a hepatospecific contrast agent, in a pre-surgical setting, to predict tumor budding in liver metastases. Methods: Patients with MRI in a pre-surgical setting were retrospectively enrolled. Manual segmentation was made by means 3D Slicer image computing, and 851 radiomics features were extracted as median values using the PyRadiomics Python package. Balancing was performed and inter- and intraclass correlation coefficients were calculated to assess the between observer and within observer reproducibility of all radiomics extracted features. A Wilcoxon–Mann–Whitney nonparametric test and receiver operating characteristics (ROC) analysis were carried out. Balancing and feature selection procedures were performed. Linear and non-logistic regression models (LRM and NLRM) and different machine learning-based classifiers including decision tree (DT), k-nearest neighbor (KNN) and support vector machine (SVM) were considered. Results: The internal training set included 49 patients and 119 liver metastases. The validation cohort consisted of a total of 28 single lesion patients. The best single predictor to classify tumor budding was original_glcm_Idn obtained in the T1-W VIBE sequence arterial phase with an accuracy of 84%; wavelet_LLH_firstorder_10Percentile was obtained in the T1-W VIBE sequence portal phase with an accuracy of 92%; wavelet_HHL_glcm_MaximumProbability was obtained in the T1-W VIBE sequence hepatobiliary excretion phase with an accuracy of 88%; and wavelet_LLH_glcm_Imc1 was obtained in T2-W SPACE sequences with an accuracy of 88%. Considering the linear regression analysis, a statistically significant increase in accuracy to 96% was obtained using a linear weighted combination of 13 radiomic features extracted from the T1-W VIBE sequence arterial phase. Moreover, the best classifier was a KNN trained with the 13 radiomic features extracted from the arterial phase of the T1-W VIBE sequence, obtaining an accuracy of 95% and an AUC of 0.96. The validation set reached an accuracy of 94%, a sensitivity of 86% and a specificity of 95%. Conclusions: Machine learning and radiomics analysis are promising tools in predicting tumor budding. Considering the linear regression analysis, there was a statistically significant increase in accuracy to 96% using a weighted linear combination of 13 radiomics features extracted from the arterial phase compared to a single radiomics feature.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Granata,

Fusco,

Brunese

et al. 2024

Diagnostics

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“…Seven standard machine learning algorithms, such as Naïve Bayes (NB), AdaBoost Classification Tree (AdaBoost), CancerClass, Random Forest (RF), Boost Logistic Regression (LogiBoost), K-nearest neighbors (KNN), and Support Vector Machine (SVM), were implemented to create a model for the prediction of a binary variable [14][15][16][17][18][19]. As there were no parameters that cancerclass required, the entire training dataset was utilized to train the model.…”

Section: Constructing Models For Predicting the Binary Variable With ...mentioning

confidence: 99%

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Liu,

Zhang,

Zhang

et al. 2023

Preprint

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With the widespread use of high-throughput sequencing technologies, understanding biology and cancer heterogeneity has been revolutionized. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patient’s outcome and clinical response. However, an open-source R package covering state-of-the-art machine learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct machine learning-based integration model with elegant performance (Mime). Mime streamlined the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting outcomes of patients compared with other published models. In addition, PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from PIEZO1-associated signatures presented high-potential role in glioma with targeted prospect. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields.

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“…Lesion features influence the choice of management, such as nephrectomy, partial nephrectomy, ablation, surveillance, and also stereotactic body radiotherapy [15][16][17][18][19]. In addition, it should be considered that up to 20% of SRMs are benign [15] and that the risk of malignancy rises with increasing size [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Trovato,

Simonetti,

Morrone

et al. 2024

JCM

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Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50–61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

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Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

Cited by 7 publications

References 269 publications

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

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