Radiomics in colorectal cancer patients

Inchingolo, Riccardo; Maino, Cesare; Cannella, Roberto; Vernuccio, Federica; Cortese, Francesco; Dezio, Michele; Pisani, Antonio; Giandola, Teresa; Gatti, Marco; Giannini, Valentina; Ippolito, Davide; Faletti, Riccardo

doi:10.3748/wjg.v29.i19.2888

Cited by 18 publications

(5 citation statements)

References 132 publications

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“…Radiomics analysis could be a promising tool to evaluate a lesion "virtually", with the possibility to analyze the whole tumor during the disease history to obtain those markers which can affect the treatment choice [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65]. In addition, this approach is safe and inexpensive since radiomics data are obtained from radiological studies which a patient should be subjected during staging and follow-up [66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84].…”

Section: Discussionmentioning

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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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%

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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“…Therefore, accurately evaluating and analyzing the patient’s condition before surgery and clarifying the treatment plan is necessary. Currently, SCT and MRI are often used for the preoperative diagnosis of patients with CRC[ 11 ]. MRI uses electromagnetic signals when scanning the human body, establishing the information parameters of tissues and organs based on the obtained electromagnetic signals, observing changes in the internal characteristics of the body in real-time, and offering a dependable foundation for medical diagnosis and treatment creation strategies[ 12 ].…”

Section: Discussionmentioning

confidence: 99%

“…In this study, the specificity of Ve in predicting the prognosis of patients was the highest, reaching 0.914. The value of V e is the ratio of K trans to K ep and represents the volume ratio of extravascular extracellular space[ 11 ]. Therefore, V e is related to the activity of the cell environment: The smaller the V e value, the higher the malignancy of cancer.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

Bai,

Zhang

2024

World J Gastrointest Surg

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BACKGROUND Colorectal cancer (CRC) is a prevalent cancer type in clinical settings; its early signs can be difficult to detect, which often results in late-stage diagnoses in many patients. The early detection and diagnosis of CRC are crucial for improving treatment success and patient survival rates. Recently, imaging techniques have been hypothesized to be essential in managing CRC, with magnetic resonance imaging (MRI) and spiral computed tomography (SCT) playing a significant role in enhancing diagnostic and treatment approaches. AIM To explore the effectiveness of MRI and SCT in the preoperative staging of CRC and the prognosis of laparoscopic treatment. METHODS Ninety-five individuals admitted to Zhongshan Hospital Xiamen University underwent MRI and SCT and were diagnosed with CRC. The precision of MRI and SCT for the presurgical classification of CRC was assessed, and pathological staging was used as a reference. Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of blood volume, blood flow, time to peak, permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction on the prognosis of patients with CRC. RESULTS Pathological biopsies confirmed the following CRC stages: 23, 23, 32, and 17 at T1, T2, T3, and T4, respectively. There were 39 cases at the N0 stage, 22 at N1, 34 at N2, 44 at M0 stage, and 51 at M1. Using pathological findings as the benchmark, the combined use of MRI and SCT for preoperative TNM staging in patients with CRC demonstrated superior sensitivity, specificity, and accuracy compared with either modality alone, with a statistically significant difference in accuracy (P < 0.05). Receiver operating characteristic curve analysis revealed the predictive values for laparoscopic treatment prognosis, as indicated by the areas under the curve for blood volume, blood flow, time to peak, and permeability surface, blood reflux constant, volume transfer constant, and extracellular extravascular space volume fraction were 0.750, 0.683, 0.772, 0.761, 0.709, 0.719, and 0.910, respectively. The corresponding sensitivity and specificity values were also obtained (P < 0.05). CONCLUSION MRI with SCT is effective in the clinical diagnosis of patients with CRC and is worthy of clinical promotion.

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“…The application of radiomics in CRC has shown great promise in advancing cancer diagnosis, treatment, and prognosis [26]. By extracting and analyzing a multitude of quantitative imaging features from medical images, radiomics provides valuable insights into tumor characteristics and behavior [40].…”

Section: Discussionmentioning

confidence: 99%

“…Successful integration of radiomics into CRC treatment requires validation studies and standardized protocols. Collaboration among radiologists, oncologists, and computational scientists is crucial for the effective use of radiomics in clinical practice [26]. Radiomics enhances treatment strategies and facilitates monitoring of the therapeutic response of CRC patients [27].…”

Section: Applications Of Radiomics In Colorectal Cancer Treatmentmentioning

confidence: 99%

Radiomics in colorectal cancer

Wu,

et al. 2023

iRADIOLOGY

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Colorectal cancer (CRC) is a global health challenge with high morbidity and mortality. Radiomics, an emerging field, utilizes quantitative imaging features extracted from medical images for CRC diagnosis, staging, treatment response assessment, and prognostication. This review highlights the potential of radiomics for personalized CRC management. Radiomics enables noninvasive tumor characterization, aiding in early detection and accurate diagnosis, and it can be used to predict tumor stage, lymph node involvement, and prognosis. Furthermore, radiomics guides personalized therapies by assessing the treatment response and identifying patients who could benefit. Challenges include standardizing imaging protocols and analysis techniques. Robust validation frameworks and user‐friendly software are needed for the integration of radiomics into clinical practice. Despite challenges, radiomics offers valuable insights into tumor biology, treatment response, and prognosis in CRC. Overcoming technical and clinical hurdles will unlock its full potential in CRC management.

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Radiomics in colorectal cancer patients

Cited by 18 publications

References 132 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

Effectiveness of magnetic resonance imaging and spiral computed tomography in the staging and treatment prognosis of colorectal cancer

Radiomics in colorectal cancer

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