How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images

Strolin, Silvia; Santoro, Miriam; Paolani, Giulia; Ammendolia, I.; Arcelli, A.; Benini, Anna; Bisello, S.; Cardano, Raffaele; Cavallini, L; Deraco, Elisa; Donati, Costanza Maria; Galietta, Erika; Galuppi, Andrea; Guido, A.; Ferioli, Martina; Laghi, Viola; Medici, Federica; Ntreta, M.; Razganiayeva, Natalya; Siepe, G.; Tolento, G.; Vallerossa, Daria; Zamagni, Alice; Morganti, Alessio Giuseppe; Strigari, Lidia

doi:10.3389/fonc.2023.1089807

Cited by 9 publications

(1 citation statement)

References 30 publications

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“…In particular, an MDA of 0 indicates that the two contours are identical. In this study, DSCs ≥ 0.8 and MDA values ≤ 3 mm were used to verify the agreement among the physicians, according to the study by Strolin et al [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

Coada,

Santoro,

Zybin

et al. 2023

Cancers

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Background: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. Methods: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). Results: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. Conclusions: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

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Section: Methodsmentioning

confidence: 99%

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

Coada,

Santoro,

Zybin

et al. 2023

Cancers

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Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives

Erdur,

Rusche,

Scholz

et al. 2024

Strahlenther Onkol

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The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

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Artificial intelligence in radiation oncology

Peeken

Combs

2023

Onkologie

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How smart is artificial intelligence in organs delineation? Testing a CE and FDA-approved Deep-Learning tool using multiple expert contours delineated on planning CT images

Cited by 9 publications

References 30 publications

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives

Artificial intelligence in radiation oncology

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