Radiomics in gastrointestinal stromal tumours: an up-to-date review

Galluzzo, Antonio; Boccioli, Sofia; Danti, Ginevra; De Muzio, Federica; Gabelloni, Michela; Fusco, Roberta; Borgheresi, Alessandra; Granata, Vincenza; Giovagnoni, Andrea; Gandolfo, Nicoletta; Miele, Vittorio

doi:10.1007/s11604-023-01441-y

Cited by 8 publications

(3 citation statements)

References 82 publications

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“…In gastrointestinal imaging, it aids in the identification of biomarkers associated with disease progression and treatment response. Some softwares have a promised impact on evaluation of prostate cancer multiparametric MRI values, which need a preliminary long, spending time, images elaboration before Radiologist report with p-rads criteria [32][33][34][35][36][37][38][39][40][41].…”

Section: Organs Software Applicationsmentioning

confidence: 99%

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

Ferrari,

Trinci,

Casinelli

et al. 2024

Preprint

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Radiomics represents the science of extraction and analysis of a multitude of quantitative features from medical imaging, unrevealing quantitative potentiality of Radiologic images. This scientific review aims to provide radiologists a comprehensive understanding of radiomics, emphasizing its principles, applications, challenges, limits, and future prospects. Limits of standardization of actual scientific production are analyzed with the possible solutions proposed by some papers reported and commented. As the continuous evolution of medical images is ongoing, radiologists must be aware of new perspective to perform a central role in patients management.

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Section: Organs Software Applicationsmentioning

confidence: 99%

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

Ferrari,

Trinci,

Casinelli

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Radiomics is an emerging tool for tumor diagnosis and prognosis prediction in recent years, playing an important role in the prognosis analysis of known lung nodules, liver, and kidney tumors. 3 - 6 Delta Radiomics is a comprehensive evaluation of the changes in imaging texture parameters of enhanced computed tomography (CT)/ magnetic resonance (MR) or post-neoadjuvant radiotherapy and chemotherapy, which plays an important role in the treatment of ovarian, brain, and lung tumors. Therefore, in the prediction of recurrence in MH-GIST, we have also introduced the relatively novel Delta Radiomics, which improves the efficiency of the prediction model by analyzing the differences in radiographic texture associated with enhanced CT over time-region, providing strong data support for the screening and decision-making of high-risk populations for MH-GIST recurrence.…”

Section: Introductionmentioning

confidence: 99%

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery

Ji,

Shang,

Tan

et al. 2024

Clin Med Insights Oncol

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Background: Medium- to high-risk classification-gastrointestinal stromal tumors (MH-GIST) have a high recurrence rate and are difficult to treat. This study aims to predict the recurrence of MH-GIST within 3 years after surgery based on clinical data and preoperative Delta-CT Radiomics modeling. Methods: A retrospective analysis was conducted on clinical imaging data of 242 cases confirmed to have MH-GIST after surgery, including 92 cases of recurrence and 150 cases of normal. The training set and test set were established using a 7:3 ratio and time cutoff point. In the training set, multiple prediction models were established based on clinical data of MH-GIST and the changes in radiomics texture of enhanced computed tomography (CT) at different time periods (Delta-CT radiomics). The area under curve (AUC) values of each model were compared using the Delong test, and the clinical net benefit of the model was tested using decision curve analysis (DCA). Then, the model was externally validated in the test set, and a novel nomogram predicting the recurrence of MH-GIST was finally created. Results: Univariate analysis confirmed that tumor volume, tumor location, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), diabetes, spicy hot pot, CT enhancement mode, and Radscore 1/2 were predictive factors for MH-GIST recurrence ( P < .05). The combined model based on these above factors had significantly higher predictive performance (AUC = 0.895, 95% confidence interval [CI] = [0.839-0.937]) than the clinical data model (AUC = 0.735, 95% CI = [0.6 62-0.800]) and radiomics model (AUC = 0.842, 95% CI = [0.779-0.894]). Decision curve analysis also confirmed the higher clinical net benefit of the combined model, and the same results were validated in the test set. The novel nomogram developed based on the combined model helps predict the recurrence of MH-GIST. Conclusions: The nomogram of clinical and Delta-CT radiomics has important clinical value in predicting the recurrence of MH-GIST, providing reliable data reference for its diagnosis, treatment, and clinical decision-making.

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“…Radiomics is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction. 18,19 Quantitative radiomic methods are also expected to be used to address the issue of the postoperative upgrade of DCIS. 20,21 However, only a few studies using a radiomics approach have examined the ability of MRI to predict the postoperative upgrade of DCIS to invasive carcinoma.…”

Section: Introductionmentioning

confidence: 99%

Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma <i>in Situ</i> to Invasive Carcinoma

Satake,

Kinoshita,

Ishigaki

et al. 2024

magn reson med sci

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To investigate the predictive performance of radiomic features extracted from breast MRI for upgrade of ductal carcinoma in situ (DCIS) to invasive carcinoma.Methods: This retrospective study included 71 women with DCIS lesions diagnosed preoperatively by biopsy. All women underwent breast dynamic contrast-enhanced (DCE) MRI of the breast, which included pre-contrast and five post-contrast phases continuously with a time resolution of 60s. Lesion segmentation was performed manually, and 144 radiomic features of the lesions were extracted from T2-weighted images (T2WI), pre-contrast T1-weighted images (T1WI), and post-contrast 1st, 2nd, and 5th phase subtraction images on DCE-MRI. Qualitative features of mammography, ultrasound, and MRI were also assessed. Clinicopathological features were evaluated using medical records. The least absolute shrinkage and selection operator (LASSO) algorithm was applied for features selection and model building. The predictive performance of postoperative upgrade to invasive carcinoma was assessed using the area under the receiver operating characteristic curve.Results: Surgical specimens revealed 13 lesions (18.3%) that were upgraded to invasive carcinoma. Among clinicopathological and qualitative features, age was the only significant predictive variable. No significant radiomic features were observed on T2WI and post-contrast 2nd phase subtraction images on DCE-MRI. The area under the curves (AUCs) of the LASSO radiomics model integrated with age were 0.915 for precontrast T1WI, 0.862 for post-contrast 1st phase subtraction images, and 0.833 for post-contrast 5th phase subtraction images. The AUCs of the 200-times bootstrap internal validations were 0.885, 0.832, and 0.775. Conclusion:A radiomics approach using breast MRI may be a promising method for predicting the postoperative upgrade of DCIS. The present study showed that the radiomic features extracted from pre-contrast T1WI and post-contrast subtraction images in the very early phase of DCE-MRI were more predictable.

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Radiomics in gastrointestinal stromal tumours: an up-to-date review

Cited by 8 publications

References 82 publications

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

Radiomics in Radiology: What the Radiologist Need to Know about Technical Aspects and Clinical Impact

Prediction of High-Risk Gastrointestinal Stromal Tumor Recurrence Based on Delta-CT Radiomics Modeling: A 3-Year Follow-up Study After Surgery

Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma <i>in Situ</i> to Invasive Carcinoma

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