Scientific publications

A pilot study was conducted to evaluate the feasibility and performance of a predictive model for treatment responses in mesothelioma patients, leveraging radiomics and machine learning. Radiomics and delta-radiomics (∆radiomics) features from CT scans were analyzed for reproducibility and informativeness, identifying key features for training a random forest classifier. The model achieved an accuracy of 0.75–0.9 in predicting pleural tumor responses, supporting the design of future studies involving 250–400 tumors. This study demonstrated the reproducibility and effectiveness of radiomics/∆radiomics in relation to tumor localization, emphasizing the need for multiple tumor models to create an integrated patient model.

Published in Cancers 2025, 17, 463.

This study introduces an innovative way to predict clinical outcomes in non-small cell lung cancer (NSCLC) patients receiving immunotherapy by modeling early tumor growth dynamics with the Gompertz model alongside RECIST 1.1 criteria.

Published in Frontiers in Immunology 15:1470555.

In oncology, the seriousness of the disease amplifies the visibility of radiological errors, leading to both significant individual consequences and broader public health concerns. By leveraging quantitative approaches, the authors reframe the diagnostic process in radiology as a classification problem, a perspective aligned with recent neurocognitive theories on decision-making errors.

This structured model offers a practical framework for conducting root cause analysis of diagnostic errors in radiology and developing effective risk-management strategies.

Published in Frontiers in Oncology 14:1402838.

The article reviews the variability in radiologic oncology assessments, particularly focusing on RECIST 1.1 criteria, which standardize evaluations to improve consistency and accuracy. It discusses how variability arises from factors like radiologist expertise, image quality, and lesion selection, and emphasizes the importance of standardized protocols and training to mitigate these issues. By addressing the root causes of variability, the article aims to enhance the precision of response assessments, ultimately leading to better patient care and clinical outcomes.

Hepatocellular carcinoma (HCC) constitutes a prominent global health challenge. According to the American Association for the Study of Liver Diseases (AASLD) guideline HCC can be diagnosed by imaging examination. However, it shows that contrast-enhanced CT has limited accuracy in the diagnosis of HCC, particularly, small-size HCC lesions (≤ 20 mm) are the most difficult to identify with CT demonstrating sensitivity = 64% [1]. An Artificial Intelligence (AI) algorithm that can analyze liver CT images and localize HCC lesions would be valuable for patients at risk of HCC. Recent studies have presented promising outcomes regarding the application of AI algorithms in HCC diagnosis; However, the efficacy in localizing small-sized HCC lesions in CT images remains uncertain, and there is a need for improvement in reducing the false-positive rate [2-4].