Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Ravegnini, Gloria; Ferioli, Martina; Morganti, Alessio Giuseppe; Strigari, Lidia; Pantaleo, Maria Abbondanza; Nannini, Margherita; Leo, Antonio De; Crescenzo, Eugenia De; Coe, Manuela; Palma, Alessandra De; Iaco, Pierandrea De; Rizzo, Stefania; Perrone, Anna Myriam

doi:10.3390/jpm11111179

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…In a recent systematic review carried out by our group, we discussed the heterogeneity of the various studies using artificial intelligence models to differentially diagnose leiomyosarcoma from leiomyomas [29]. Some studies showed the superiority of artificial intelligence applied to MRI in diagnostic accuracy compared to expert radiologists.…”

Section: Discussionmentioning

confidence: 99%

“…A growing body of research has shown the role of radiomics in predicting patients' prognosis for various types of tumors, including gynecological cancers [25][26][27][28]. However, regarding uterine sarcomas, our recent systematic review revealed limited evidence supporting the benefit of radiomics in this pathology for pre-operative evaluation, highlighting the need for further studies [29,30].…”

Section: Introductionmentioning

confidence: 98%

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Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

Santoro,

Zybin,

Coada

et al. 2024

Cancers

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Background: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. Methods: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. Results: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62–0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65–0.85). Conclusions: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

Santoro,

Zybin,

Coada

et al. 2024

Cancers

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“…In 2019, Nakagawa et al 59 found that a multiparametric machine learning MRI‐based method had better results in terms of diagnosis of malignancy than positron emission tomography alone and was comparable to experienced radiologists. However, according to Ravegnini et al, 60 despite growing interest for the application of AI in the differential diagnosis of uterine masses combined with MRI features, AI systems appear currently too complicated to be readily applied in daily clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Raffone,

Raimondo,

Neola

et al. 2023

Intl J Gynecology & Obste

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BackgroundDifferential diagnosis between uterine leiomyomas and sarcomas is challenging. Magnetic resonance imaging (MRI) represents the second‐line diagnostic method after ultrasound for the assessment of uterine masses.ObjectivesTo assess the accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas.Search StrategyA systematic review and meta‐analysis was performed searching five electronic databases from their inception to June 2023.Selection CriteriaAll peer‐reviewed observational or randomized clinical trials that reported an unbiased postoperative histologic diagnosis of uterine leiomyoma or uterine sarcoma, which also comprehended a preoperative MRI evaluation of the uterine mass.Data Collection and AnalysisSensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, and area under the curve on summary receiver operating characteristic of MRI in differentiating uterine leiomyomas and sarcomas were calculated as individual and pooled estimates, with 95% confidence intervals (CI).ResultsEight studies with 2495 women (2253 with uterine leiomyomas and 179 with uterine sarcomas), were included. MRI showed pooled sensitivity of 0.90 (95% CI 0.84–0.94), specificity of 0.96 (95% CI 0.96–0.97), positive likelihood ratio of 13.55 (95% CI 6.20–29.61), negative likelihood ratio of 0.08 (95% CI 0.02–0.32), diagnostic odds ratio of 175.13 (95% CI 46.53–659.09), and area under the curve of 0.9759.ConclusionsMRI has a high diagnostic accuracy in the differential diagnosis between uterine leiomyomas and sarcomas.

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“…Consequently, the confidence and trust vested in decision-makers who employ machine learning models within specific domains are of utmost importance [20]. The enhancement of decision-making hinges on the ability to detect flaws and concealed biases within the operations of these models [21]. The utilization of artificial intelligence, particularly deep learning and machine learning models, in predicting hydrogen production brings to light the invaluable potential of these technologies.…”

Section: Introductionmentioning

confidence: 99%

Security Concerns for Using Deep Learning Models in Predicting Hydrogen Production: A Comparative Study on Adversarial Attack

Ukwuoma,

Cai,

Ukwuoma

et al. 2024

Preprint

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In order to handle the rising global energy demand and lower carbon emissions, hydrogen, a clean and sustainable energy source, is essential. The creation of hydrogen is significant because it has the potential to transform the energy industry by providing a sustainable alternative to conventional fossil fuels. Deep learning has been a potent tool in recent years, exhibiting outstanding performance and dependability in a variety of domains, including the prediction of hydrogen generation. The optimization of hydrogen production methods to increase their effectiveness and reduce costs has shown promise. The susceptibility of deep learning models to adversarial attacks, which can reduce the precision and dependability of their predictions, is a growing worry. Adversarial attacks entail the purposeful alteration of input data to trick machine learning algorithms and provide false results. Such attacks may have far-reaching effects on hydrogen production prediction, thereby compromising the efficiency, economic feasibility, and safety of processes. To address these concerns, we conducted an extensive investigation into the susceptibility of deep learning models used for hydrogen production prediction to adversarial attacks using the co-gasification of biomass and plastics datasets. In the co-gasification of biomass and plastics dataset, the dependent variable was the quantity of hydrogen generated, and the independent variables included the gasification temperature, high-density polyethylene (HDPE) and rubber seed shell (RSS) particle size, and the quantity of plastic in the final product. The implemented adversarial attacks include the limitedmemory broyden-fletcher-goldfarb-shanno (L-BFGS), fast gradient sign method (FGSM), basic iterative method, and projected gradient descent method (PGD). This study employed 4 machine learning regression models and a novel deep learning model based on Keras API to analyze the effect of the adversarial attack models under several perturbations including 0.1, 0.2, 0.4, 0.6 and 0.8. From the yielded result, it was evident that the FGSM and PGD adversarial attack has a significant influence on the employed model prediction results while the L-BFGS and the basic iterative method yielded results that will be addressed in our future works. Our research highlights the potential risks of relying on these models for decision-making in hydrogen production processes while also revealing the vulnerabilities of deep learning models in this crucial domain. We also highlight the significance of developing defense mechanisms and security protocols to protect the integrity of deep learning-based predictions in this crucial sector.

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Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review

Cited by 10 publications

References 23 publications

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study

Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: A systematic review and meta‐analysis

Security Concerns for Using Deep Learning Models in Predicting Hydrogen Production: A Comparative Study on Adversarial Attack

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