Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

Desseroit, Marie-Charlotte; Visvikis, Dimitris; Tixier, Florent; Majdoub, Mohamed; Perdrisot, R.; Guillevin, Rémy; Rest, Catherine Cheze Le; Hatt, Mathieu

doi:10.1007/s00259-016-3325-5

Cited by 94 publications

(72 citation statements)

References 39 publications

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“…Given that conventional indices, such as standard uptake value (SUV), maximum or metabolic tumor volume (MTV) are insufficient to characterize malignancies [268], several groups have started to investigate the feasibility of in vivo tumor characterization in the context of textural evaluation [269]. As a result of this initiative, first reports have appeared that point at promising results of a quantitative assessment of tumor heterogeneity in light of therapy response prediction [270,271], disease-specific survival [272] as well as prognostic stratification [273]. Meanwhile, challenges using textural features remain, since such calculations are highly sensitive to acquisition, reconstruction and sample size variations [274][275][276].…”

Section: In Vivo Disease Characterizationmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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State-of-the-art patient management frequently requires the use of non-invasive imaging methods to assess the anatomy, function or molecular-biological conditions of patients or study subjects. Such imaging methods can be singular, providing either anatomical or molecular information, or they can be combined, thus, providing "anato-metabolic" information. Hybrid imaging denotes image acquisitions on systems that physically combine complementary imaging modalities for an improved diagnostic accuracy and confidence as well as for increased patient comfort. The physical combination of formerly independent imaging modalities was driven by leading innovators in the field of clinical research and benefited from technological advances that permitted the operation of PET and MR in close physical proximity, for example. This review covers milestones of the development of various hybrid imaging systems for use in clinical practice and small-animal research. Special attention is given to technological advances that helped the adoption of hybrid imaging, as well as to introducing methodological concepts that benefit from the availability of complementary anatomical and biological information, such as new types of image reconstruction and data correction schemes. The ultimate goal of hybrid imaging is to provide useful, complementary and quantitative information during patient work-up. Hybrid imaging also opens the door to multi-parametric assessment of diseases, which will help us better understand the causes of various diseases that currently contribute to a large fraction of healthcare costs.

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Section: In Vivo Disease Characterizationmentioning

confidence: 99%

Hybrid Imaging: Instrumentation and Data Processing

et al. 2018

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“…Although a number of studies have included only between 20 and 70 patients [50, 71, 86–92], some of the most recent studies have included between 80 and more than 200 patients: 88 patients with oropharyngeal squamous cell carcinoma [93], 103 with bone and soft tissue lesions [94], 101 with early-stage NSCLC [95], 112 with oesophageal cancer and 101 with NSCLC [60], 113 with glioma [36], 107 and 217 with oesophageal cancer [96, 97], 132 with lymph node involvement in lung cancer [98], 116, 195 and 201 with NSCLC [99–101], 137 with pancreatic lesions [102], and 188 lesions in lymphoma patients [103]. Some of the most recent studies have also used more robust statistical analysis, compared to these recently reviewed [28], several of them using a machine-learning method, e.g.…”

Section: Promising Clinical Resultsmentioning

confidence: 99%

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Hatt

Tixier

Pierce

et al. 2016

Eur J Nucl Med Mol Imaging

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414

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After seminal papers over the period 2009 – 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

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“…The main aim of this study was to investigate and validate if the unsupervised two-way clustering could improve feature representations compared with conventional unsupervised feature dimension reduction methods. Advanced non-rigid multimodal image registration may help accurately register tumors in CT and PET scans, or tumors could be segmented for PET and CT scans separately [9,10]. The potential benefit of multimodal imaging features merits further investigation.…”

Section: Discussionmentioning

confidence: 99%

“…Particularly, radiomic features extracted from CT images have demonstrated promising performance for the prediction of overall survival and disease free survival in patients with non-small cell lung cancer (NSCLC) [5–10]. Promising performance of radiomic features for predicting distant metastasis has been demonstrated in lung adenocarcinoma patients [11] and early stage NSCLC patients [12].…”

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confidence: 99%

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

Galperin-Aizenberg

Pryma

et al. 2018

Radiotherapy and Oncology

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Background and purpose: To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. Material and methods: This study was performed based on an 18F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient’s tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. Results: Differences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p = 0.003) and freedom from nodal failure (p = 0.038). Average concordance measures for predicting survival and nodal failure were 0.640 ± 0.029 and 0.664 ± 0.063 respectively, better than those obtained by prediction models built upon clinical variables (p < 0.04). Conclusions: The evaluation results demonstrate that our method allows us to stratify patients and predict survival and freedom from nodal failure with better performance than current alternative methods.

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Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III

Abstract: Intratumour heterogeneity quantified using textural features on both CT and PET images from routine staging (18)F-FDG PET/CT acquisitions can be used to create a nomogram with higher stratification power than staging alone.

Cited by 94 publications

References 39 publications

Hybrid Imaging: Instrumentation and Data Processing

Hybrid Imaging: Instrumentation and Data Processing

Characterization of PET/CT images using texture analysis: the past, the present… any future?

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

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