An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data

Schutte, Kathryn; Brulport, Fabien; Harguem-Zayani, Sana; Schiratti, Jean-Baptiste; Ghermi, Ridouane; Jehanno, Paul; Jaeger, Alexandre; Alamri, Talal; Naccache, Raphael; Haddag‐Miliani, Leila; Orsi, Teresa; Lamarque, Jean-Philippe; Hoferer, Isaline; Lawrance, Littisha; Benatsou, Baya; Bousaid, Imad; Azoulay, Mikael; Verdon, Antoine; Boyer, François; Balleyguier, Corinne; Aubert, Victor; Bendjebbar, Etienne; Maussion, Charles; Loiseau, Nicolas; Schmauch, Benoît; Sefta, Meriem; Wainrib, Gilles; Clozel, Thomas; Lassau, Nathalie

doi:10.1016/j.ejca.2022.06.055

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Moreover, if body composition corresponds to a description of the tumor host, its association with parameters describing the tumor could be of interest to better specify the prognosis. This could, for example, be associated with a radiomic analysis using artificial intelligence models, as performed by Schutte et al on the same database, combining ultrasound images, CT images and clinical data [31].…”

Section: Discussionmentioning

confidence: 99%

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

et al. 2023

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Background: Body composition could help to better define the prognosis of cancers treated with anti-angiogenics. The aim of this study is to evaluate the prognostic value of 3D and 2D anthropometric parameters in patients given anti-angiogenic treatments. Methods: 526 patients with different types of cancers were retrospectively included. The software Anthropometer3DNet was used to measure automatically fat body mass (FBM3D), muscle body mass (MBM3D), visceral fat mass (VFM3D) and subcutaneous fat mass (SFM3D) in 3D computed tomography. For comparison, equivalent two-dimensional measurements at the L3 level were also measured. The area under the curve (AUC) of the receiver operator characteristics (ROC) was used to determine the parameters’ predictive power and optimal cut-offs. A univariate analysis was performed using Kaplan–Meier on the overall survival (OS). Results: In ROC analysis, all 3D parameters appeared statistically significant: VFM3D (AUC = 0.554, p = 0.02, cutoff = 0.72 kg/m2), SFM3D (AUC = 0.544, p = 0.047, cutoff = 3.05 kg/m2), FBM3D (AUC = 0.550, p = 0.03, cutoff = 4.32 kg/m2) and MBM3D (AUC = 0.565, p = 0.007, cutoff = 5.47 kg/m2), but only one 2D parameter (visceral fat area VFA2D AUC = 0.548, p = 0.034). In log-rank tests, low VFM3D (p = 0.014), low SFM3D (p < 0.0001), low FBM3D (p = 0.00019) and low VFA2D (p = 0.0063) were found as a significant risk factor. Conclusion: automatic and 3D body composition on pre-therapeutic CT is feasible and can improve prognostication in patients treated with anti-angiogenic drugs. Moreover, the 3D measurements appear to be more effective than their 2D counterparts.

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Section: Discussionmentioning

confidence: 99%

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

et al. 2023

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“…The identification of prognostic biomarkers that provide information about the likelihood of a disease-related endpoint can allow establishing of the patient’s risk profile based on tumor characteristics and identifying patients with a poor prognosis who may be candidates for therapy escalation and/or enrollment in experimental trials [ 77 , 78 ].…”

Section: Predictionmentioning

confidence: 99%

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

et al. 2022

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Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.

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Predictive Patient Stratification Using Artificial Intelligence and Machine Learning

Nguyen,

T. Giang,

T. Pham

et al. 2024

Big Data Analysis and Artificial Intelligence for Medical Sciences

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An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data

Cited by 6 publications

References 21 publications

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

Body Composition to Define Prognosis of Cancers Treated by Anti-Angiogenic Drugs

Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Predictive Patient Stratification Using Artificial Intelligence and Machine Learning

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