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Articles by Philippe Lambin
Imaging the hallmarks of cancer
Abstract The hallmarks of cancer were introduced by Hanahan and Weinberg as a conceptual organizing framework to distil the complexity of tumours. This concept of cancer hallmarks has become an enduring theme in cancer research. Moreover, an increasing number of therapeutic strategies are being aimed at targeting these hallmarks.
Robust multicentre detection and classification of colorectal liver metastases on CT: application of foundation models
Abstract Colorectal liver metastases (CRLM) remain a major cause of cancer-related mortality. Early, reliable detection on CT imaging is essential for curative treatment planning, yet the diagnostic performance of AI models often declines across scanners and institutions, limiting clinical generalizability. In this study, we developed and evaluated a multi-center foundation-model-based AI pipeline for patient-level classification and lesion-level detection of CRLM on contrast-enhanced CT.
Predicting homologous recombination deficiency and treatment responses using a computed tomography-based foundation model: a preclinical study - npj Precision Oncology
Abstract Homologous recombination deficiency (HRD) can lead to genomic instability, increased cancer susceptibility, and enhanced sensitivity to DNA-targeting therapies. Although radiomics has been used for various medical applications, its application in animal studies remains largely unexplored, primarily due to the typically limited availability of preclinical data.
Reply to ‘Radiomics Quality Score 2.0: what changed from version 1.0 and why it matters’ - Nature Reviews Clinical Oncology
We appreciate the Correspondence from Jeremy McGale and collaborators regarding our recent Review introducing the Radiomics Quality Score (RQS) 2.0 and Radiomics Readiness Levels (RRLs) (Lambin, P. et al. Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine. Nat. Rev. Clin. Oncol. 22, 831–846 (2025))1. Their constructive analysis (McGale, J. et al. Radiomics Quality Score 2.0: what changed from version 1.0 and why it matters. Nat. Rev. Clin. Oncol.
Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine - Nature Reviews Clinical Oncology
Abstract Radiomics is a tool for medical imaging analysis that could have a relevant role in precision oncology by offering precise quantitative support for clinical decision-making. The Radiomics Quality Score (RQS) is a tool developed to assess the rigour of radiomics studies that has now been widely adopted by researchers.
Feature of Review Papers in Nanotechnology and Applied Nanosciences
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Promising Diagnostic and Therapeutic Approaches Based on VHHs for Cancer Management
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Edge Magnetism in MoS2 Nanoribbons: Insights from a Simple One-Dimensional Model
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Criteria for the translation of radiomics into clinically useful tests - Nature Reviews Clinical Oncology
Abstract Computer-extracted tumour characteristics have been incorporated into medical imaging computer-aided diagnosis (CAD) algorithms for decades. With the advent of radiomics, an extension of CAD involving high-throughput computer-extracted quantitative characterization of healthy or pathological structures and processes as captured by medical imaging, interest in such computer-extracted measurements has increased substantially.
Automated detection and segmentation of non-small cell lung cancer computed tomography images - Nature Communications
Abstract Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions.
Reply to “COVID-19 prediction models should adhere to methodological and reporting standards”
Guangyao Wu, Henry C. Woodruff, Avishek Chatterjee, Philippe Lambin European Respiratory Journal 2020 56: 2002918; DOI: 10.1183/13993003.02918-2020 Abstract It is hard to follow a standardised methodology for prediction models, while researchers should adhere to generally accepted reporting standards according to research needs and journal submission requirements https://bit.ly/30zfMIw From the authors: We would like to thank G.S. Collins, M.
Reply to “COVID-19 prediction models should adhere to methodological and reporting standards”
Guangyao Wu, Henry C. Woodruff, Avishek Chatterjee, Philippe Lambin European Respiratory Journal 2020; DOI: 10.1183/13993003.02918-2020 Abstract Reply to “COVID-19 prediction models should adhere to methodological and reporting standards” From the authors We would like to thank G.S. Collins, M. van Smeden, and R.D. Richard for their commentary on the design, analysis, and reporting of our article [1].
Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study
Over the last years, the incidence of oropharyngeal squamous cell carcinoma (OPSCC) has shown a dramatic increase relative to other head and neck cancers, with a substantial proportion of OPSCC being linked to human papillomavirus (HPV) infections.1 HPV positive OPSCC is biologically and clinically different from HPV negative OPSCC, which is often related to alcohol and tobacco consumption. HPV positive OPSCC has been shown to have superior response to radiochemotherapy.
Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer
Publication HistoryPublished online: July 20, 2018Accepted: July 17, 2018; Received in revised form: May 19, 2018; Received: April 7, 2018;
Radiomics: the bridge between medical imaging and personalized medicine
1.Aerts, H. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).2.Hood, L. & Friend, S. H. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8, 184–187 (2011).3.Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).4.Kumar, V. et al. Radiomics: the process and the challenges. Magn. Reson.
Quantitative radiomics studies for tissue characterization: A review of technology and methodological procedures
Quantitative analysis of tumour characteristics based on medical imaging is an emerging field of research. In recent years quantitative imaging features derived from CT, PET and MR scans were shown to be of added value in the prediction of outcome parameters in oncology, in what is called the radiomics field. However, results might be difficult to compare due to a lack of standardized methodologies to conduct quantitative image analyses.
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