Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer

Published on Jan 1, 2018in European Radiology4.101
· DOI :10.1007/S00330-017-4973-Y
Kieran Foley9
Estimated H-index: 9
(Cardiff University),
Robert Kerrin Hills76
Estimated H-index: 76
(Cardiff University)
+ 6 AuthorsStuart Ashley Roberts4
Estimated H-index: 4
(University Hospital of Wales)
Objectives This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. Methods Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). Results Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24–0.47), p < 0.001], log(TLG) [5.74 (1.44–22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10–0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04–1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X2 143.14, df 3, p < 0.001) and validation cohorts (X2 20.621, df 3, p < 0.001). Conclusions This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging.
Figures & Tables
📖 Papers frequently viewed together
2,108 Citations
477 Citations
1,627 Citations
#1Beatrice Berthon (Cardiff University)H-Index: 8
#2Christopher Marshall (Cardiff University)H-Index: 14
Last. Emiliano Spezi (Cardiff University)H-Index: 28
view all 4 authors...
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to th...
27 CitationsSource
#1Fanny Orlhac (Université Paris-Saclay)H-Index: 16
#2Benoit Thézé (Université Paris-Saclay)H-Index: 16
Last. Irène Buvat (Université Paris-Saclay)H-Index: 46
view all 5 authors...
: Characterizing tumor heterogeneity using texture indices derived from PET images has shown promise in predicting treatment response and patient survival in some types of cancer. Yet, the relationship between PET-derived texture indices, precise tracer distribution, and biologic heterogeneity needs to be clarified. We investigated this relationship using PET images, autoradiographic images, and histologic images. METHODS: Three mice bearing orthotopically implanted mammary tumors derived from t...
37 CitationsSource
#1Peter S.N. van Rossum (UU: Utrecht University)H-Index: 5
#2David V. Fried (University of Texas at Austin)H-Index: 15
Last. Steven H. Lin (University of Texas MD Anderson Cancer Center)H-Index: 42
view all 8 authors...
UNLABELLED: A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (1...
49 CitationsSource
#1Weimiao Wu (Harvard University)H-Index: 6
#2Chintan Parmar (UM: Maastricht University)H-Index: 24
Last. Hugo J.W.L. Aerts (Harvard University)H-Index: 65
view all 8 authors...
Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance. Methods: Two independent radiomic c...
171 CitationsSource
#1Robert J. GilliesH-Index: 106
#2Paul E. Kinahan (UW: University of Washington)H-Index: 69
Last. Hedvig Hricak (MSK: Memorial Sloan Kettering Cancer Center)H-Index: 137
view all 3 authors...
This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
2,108 CitationsSource
#1Philippe Lambin (Maastricht University Medical Centre)H-Index: 113
#2Jaap D. Zindler (Maastricht University Medical Centre)H-Index: 17
Last. Sean Walsh (Maastricht University Medical Centre)H-Index: 11
view all 30 authors...
ABSTRACTBackground. Trials are vital in informing routine clinical care; however, current designs have major deficiencies. An overview of the various challenges that face modern clinical research and the methods that can be exploited to solve these challenges, in the context of personalised cancer treatment in the 21st century is provided.Aim. The purpose of this manuscript, without intending to be comprehensive, is to spark thought whilst presenting and discussing two important and complementar...
49 CitationsSource
#1Ralph T.H. Leijenaar (Maastricht University Medical Centre)H-Index: 29
#2Georgi Nalbantov (Maastricht University Medical Centre)H-Index: 12
Last. Philippe Lambin (Maastricht University Medical Centre)H-Index: 113
view all 9 authors...
FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) RD, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per...
206 CitationsSource
#1Georgia Doumou ('KCL': King's College London)H-Index: 2
#1Georgia Doumou ('KCL': King's College London)H-Index: 6
Last. Gary CookH-Index: 62
view all 5 authors...
Objectives Measuring tumour heterogeneity by textural analysis in 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) provides predictive and prognostic information but technical aspects of image processing can influence parameter measurements. We therefore tested effects of image smoothing, segmentation and quantisation on the precision of heterogeneity measurements.
56 CitationsSource
#1Connie Yip ('KCL': King's College London)H-Index: 11
#2Fergus Davnall ('KCL': King's College London)H-Index: 3
Last. Vicky Goh ('KCL': King's College London)H-Index: 59
view all 8 authors...
Summary To assess the changes in computed tomography (CT) tumor heterogeneity following neoadjuvant chemotherapy in esophageal cancer. Thirty-one consecutive patients who received neoadjuvant chemotherapy for esophageal cancer were identified. Analysis of primary tumor heterogeneity (texture) was performed on staging and post-chemotherapy CT scans. Image texture parameters (mean grey-level intensity, entropy, uniformity, kurtosis, skewness, standard deviation of histogram) were derived for diffe...
49 CitationsSource
#1Mathieu Hatt (French Institute of Health and Medical Research)H-Index: 39
#2Mohamed Majdoub (French Institute of Health and Medical Research)H-Index: 7
Last. Dimitris Visvikis (French Institute of Health and Medical Research)H-Index: 55
view all 14 authors...
Intra-tumor uptake heterogeneity in 18F-FDG PET has been associated with patient treatment outcomes in several cancer types. Textural features (TF) analysis is a promising method for its quantification. An open issue associated with the use of TF for the quantification of intratumoral heterogeneity concerns its added contribution and dependence on the metabolically active tumor volume (MATV), which has already been shown as a significant predictive and prognostic parameter. Our objective was to ...
261 CitationsSource
Cited By27
#1Nazlı Pınar Karahan Şen (Dokuz Eylül University)H-Index: 1
#2Ayşegül AksuH-Index: 1
Last. Gamze Çapa Kaya (Dokuz Eylül University)H-Index: 8
view all 0 authors...
OBJECTIVE This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. METHODS The initial staging 18F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was c...
#1Chenyi XieH-Index: 3
#2Chun-Lap PangH-Index: 2
Last. Varut VardhanabhutiH-Index: 12
view all 6 authors...
Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification sy...
#1Manuel Piñeiro-Fiel (University of Santiago de Compostela)H-Index: 2
#2Alexis Moscoso (University of Santiago de Compostela)H-Index: 9
Last. Pablo Aguiar (University of Santiago de Compostela)H-Index: 16
view all 6 authors...
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and thos...
#1K.G. Foley (Royal Glamorgan Hospital)
view all 4 authors...
The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on ...
#1Chong Zhang (Maastricht University Medical Centre)H-Index: 2
#2Zhenwei Shi (Maastricht University Medical Centre)H-Index: 6
Last. Kieran Foley (Cardiff University)H-Index: 9
view all 14 authors...
Objectives:To improve clinical lymph node staging (cN-stage) in oesophageal adenocarcinoma by developing and externally validating three prediction models; one with clinical variables only, one wit...
1 CitationsSource
#1Jim Zhong (Leeds Teaching Hospitals NHS Trust)H-Index: 5
#2R. Frood (Leeds Teaching Hospitals NHS Trust)H-Index: 6
Last. Andrew Scarsbrook (Leeds Teaching Hospitals NHS Trust)H-Index: 29
view all 9 authors...
AIM To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[18F]-fluoro-2-deoxy- d- glucose (FDG) positron-emission tomography (PET) computed tomography (CT) predicts disease progression in patients with locally advanced larynx and hypopharynx squamous cell carcinoma (SCC) receiving (chemo)radiotherapy. MATERIALS AND METHODS Patients with larynx and hypopharynx SCC treated with definitive (chemo)radiotherapy at a specialist cancer centre undergoing pre-tre...
#1Yoshinobu Ishiwata (YCU: Yokohama City University)H-Index: 3
#2Tomohiro Kaneta (University of Tsukuba)H-Index: 1
Last. Daisuke Utsunomiya (YCU: Yokohama City University)H-Index: 4
view all 5 authors...
OBJECTIVE Cancers of unknown primary origin cannot be staged using images, making the prognosis difficult. We attempted to predict prognosis of patients with unknown primary origin using tumour heterogeneity recently introduced in F-fluorodeoxyglucose (F-FDG) PET/computed tomography (CT). METHODS Overall, 30 patients with unknown primary origin who underwent whole-body F-FDG PET/CT scans were retrospectively enrolled for texture analysis. The volume of interest was placed in the largest metastat...
#1Hui Xu (Southern Medical University)H-Index: 1
#1Hui Xu (Southern Medical University)H-Index: 1
Last. Lijun Lu (Southern Medical University)H-Index: 12
view all 11 authors...
Purpose This work aims to identify intratumoral habitats with distinct heterogeneity based on 2-deoxy-2-[18F]fluro-d-glucose positron emission tomography (PET)/X-ray computed tomography (CT) imaging, and to develop a subregional radiomics approach to predict progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC).
9 CitationsSource
#1Sherif B. ElsherifH-Index: 4
#2Sonia AndreouH-Index: 1
Last. Chandana LallH-Index: 21
view all 7 authors...
Esophageal cancer is a major cause of morbidity and mortality worldwide. Recent advancements in the management of esophageal cancer have allowed for earlier detection, improved ability to monitor progression, and superior treatment options. These innovations allow treatment teams to formulate more customized management plans and have led to an increase in patient survival rates. For example, in order for the most effective management plan to be constructed, accurate staging must be performed to ...