Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients

Arshad, Mubarik; Thornton, Andrew; Lu, Haonan; Tam, Henry; Wallitt, Kathryn; Rodgers, Nicola; Scarsbrook, Andrew; McDermott, Garry; Cook, Gary; Landau, David; Chua, Sue; O’Connor, Richard A.; Dickson, Jeanette; Power, Danielle; Barwick, Tara; Rockall, Andrea; Aboagye, Eric O.

doi:10.1007/s00259-018-4139-4

Cited by 62 publications

(50 citation statements)

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“…Early treatment response assessment and response and survival prediction is of interest to clinicians.as it may aid treatment selection and patient stratification, and justify a therapy switch. In 358 stage I-III NSCLC patients, Arshad et al used pre-therapeutic18F-FDG data from seven institutions to evaluate 18F-FDG-PET radiomics for overall survival (OS) prediction after radiotherapy or chemoradiotherapy (54).…”

Section: Clinical Outcome Predictionmentioning

confidence: 99%

Introduction to Radiomics

et al. 2020

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by on July 31, 2020. For personal use only. jnm.snmjournals.org Downloaded from ABSTRACT Radiomics is a rapidly evolving field of research concerned with the extraction and quantification of patterns -the so-called radiomic features -within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape, and may, alone or in combination with demographic, histological, genomic or proteomic data, be used for clinical problem-solving. The goal of this CE article is to provide an introduction to the field, covering the basic radiomics workflow:feature calculation and selection, dimensionality reduction, and data processing . Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

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Section: Clinical Outcome Predictionmentioning

confidence: 99%

Introduction to Radiomics

et al. 2020

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“…In one study, using 201 datasets and 43 textural features, the LASSO method was used and identified a single textural feature (SumMean) as an independent predictor of overall survival in large tumours treated with chemoradiotherapy [58]. In a further study of 358 datasets and 665 radiomic features, a similar LASSO methodology was used to derive predictive feature vectors that were tested on an independent validation set and predicted a 14-month survival difference [59].…”

Section: Nsclc: Defining Molecular Mechanisms From Imagingmentioning

confidence: 99%

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

Cook

2019

Eur J Nucl Med Mol Imaging

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While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of 'intelligent' functions performed by computers, including language processing, knowledge representation, problem solving and planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.

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“…Consequently, numerous additional prognostic or predictive factors [ 5 – 7 ], among image-derived parameters [ 8 – 12 ], have been investigated aiming at more differentiated outcome prediction and more differentiated management decisions. Among parameters from positron emission tomography/computed tomography with [ 18 F]fluorodeoxyglucose (FDG-PET/CT), asphericity (ASP) is a parameter that reflects shape irregularity of the primary tumor’s metabolic tumor volume (MTV), combining metric and metabolic features of the primary tumor.…”

Section: Introductionmentioning

confidence: 99%

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

et al. 2020

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Background Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies. Methods This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases. Results Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF4/8, PSF + TOF2/17, Q.Clear and the reference algorithm TOF4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF4/8, 2%; PSF + TOF2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF4/8, 38% reduced to 2%; PSF + TOF2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportions of discordant cases at < 90 s. Conclusions ASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Data with better resolution (i.e., lower FWHM) could be retrospectively smoothed to the desired FWHM, resulting in a comparable number of discordant cases.

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Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients

Abstract: PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy.

Cited by 62 publications

References 34 publications

Introduction to Radiomics

Introduction to Radiomics

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

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