Baseline MRI-Radiomics Can Predict Overall Survival in Non-Endemic EBV-Related Nasopharyngeal Carcinoma Patients.

Published on Oct 13, 2020in Cancers6.126
· DOI :10.3390/CANCERS12102958
Marco Bologna6
Estimated H-index: 6
(Polytechnic University of Milan),
Valentina D. A. Corino15
Estimated H-index: 15
(Polytechnic University of Milan)
+ 18 AuthorsEster Orlandi22
Estimated H-index: 22
Sources
Abstract
Advanced stage nasopharyngeal cancer (NPC) shows highly variable treatment outcomes, suggesting the need for independent prognostic factors. This study aims at developing a magnetic resonance imaging (MRI)-based radiomic signature as a prognostic marker for different clinical endpoints in NPC patients from non-endemic areas. A total 136 patients with advanced NPC and available MRI imaging (T1-weighted and T2-weighted) were selected. For each patient, 2144 radiomic features were extracted from the main tumor and largest lymph node. A multivariate Cox regression model was trained on a subset of features to obtain a radiomic signature for overall survival (OS), which was also applied for the prognosis of other clinical endpoints. Validation was performed using 10-fold cross-validation. The added prognostic value of the radiomic features to clinical features and volume was also evaluated. The radiomics-based signature had good prognostic power for OS and loco-regional recurrence-free survival (LRFS), with C-index of 0.68 and 0.72, respectively. In all the cases, the addition of radiomics to clinical features improved the prognostic performance. Radiomic features can provide independent prognostic information in NPC patients from non-endemic areas.
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2 CitationsSource
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2 CitationsSource
Jul 1, 2020 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Marco Bologna (Polytechnic University of Milan)H-Index: 6
#2Valentina D. A. Corino (Polytechnic University of Milan)H-Index: 15
Last. Luca T. Mainardi (Polytechnic University of Milan)H-Index: 36
view all 16 authors...
The purpose of this study was to establish a methodology and technology for the development of an MRI-based radiomic signature for prognosis of overall survival (OS) in nasopharyngeal cancer from non-endemic areas. The signature was trained using 1072 features extracted from the main tumor in T1-weighted and T2-weighted images of 142 patients. A model with 2 radiomic features was obtained (RAD model). Tumor volume and a signature obtained by training the model on permuted survival data (RADperm ...
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#1Shin-Hyung Park (KNU: Kyungpook National University)H-Index: 7
#2Myong Hun Hahm (KNU: Kyungpook National University)H-Index: 2
Last. Jae-Chul Kim (KNU: Kyungpook National University)H-Index: 8
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BACKGROUND: Current chemoradiation regimens for locally advanced cervical cancer are fairly uniform despite a profound diversity of treatment response and recurrence patterns. The wide range of treatment responses and prognoses to standardized concurrent chemoradiation highlights the need for a reliable tool to predict treatment outcomes. We investigated pretreatment magnetic resonance (MR) imaging features of primary tumor and involved lymph node for predicting clinical outcome in cervical canc...
1 CitationsSource
#1Yifei Liu (SYSU: Sun Yat-sen University)H-Index: 3
#2Shenghuan Chen (Guangzhou Medical University)H-Index: 1
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Objectives This study aimed to evaluate the value of nodal grouping (NG), defined as the presence of at least three contiguous lymph nodes (LNs) within one LN region, in staging and management of patients with non-metastatic nasopharyngeal carcinoma (NPC).
4 CitationsSource
#1Lina ZhaoH-Index: 10
#2Jie Gong (Xidian University)H-Index: 4
Last. Mei Shi (Fourth Military Medical University)H-Index: 4
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Objectives To establish and validate a radiomics nomogram for prediction of induction chemotherapy (IC) response and survival in nasopharyngeal carcinoma (NPC) patients.
15 CitationsSource
#1Marco Bologna (Polytechnic University of Milan)H-Index: 6
#2Valentina D. A. Corino (Polytechnic University of Milan)H-Index: 15
Last. Luca T. Mainardi (Polytechnic University of Milan)H-Index: 36
view all 3 authors...
PURPOSE: The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1-weighted and T2-weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability. METHODS: Stability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non-uniformity) was evaluated for both T1-weighted ...
11 CitationsSource
#1Marta Bogowicz (UZH: University of Zurich)H-Index: 13
#2Stephanie Tanadini-Lang (UZH: University of Zurich)H-Index: 18
Last. Oliver Riesterer (UZH: University of Zurich)H-Index: 24
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Loco-regional control (LRC) is a major clinical endpoint after definitive radiochemotherapy (RCT) of head and neck cancer (HNC). Radiomics has been shown a promising biomarker in cancer research, however closer related to primary tumor control than composite endpoints. Radiomics studies often focus on the analysis of primary tumor (PT). We hypothesize that the combination of PT and lymph nodes (LN) radiomics better predicts LRC in HNC treated with RCT. Radiomics analysis was performed in CT imag...
13 CitationsSource
#1Xue Ming (Fudan University)H-Index: 1
#2Ronald Wihal Oei (Fudan University)H-Index: 5
Last. Jiazhou Wang (Fudan University)H-Index: 14
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This study aimed to develop prognosis signatures through a radiomics analysis for patients with nasopharyngeal carcinoma (NPC) by their pretreatment diagnosis magnetic resonance imaging (MRI). A total of 208 radiomics features were extracted for each patient from a database of 303 patients. The patients were split into the training and validation cohorts according to their pretreatment diagnosis date. The radiomics feature analysis consisted of cluster analysis and prognosis model analysis for d...
13 CitationsSource
#1Lu-Lu Zhang (SYSU: Sun Yat-sen University)H-Index: 9
#2Meng-Yao Huang (SYSU: Sun Yat-sen University)H-Index: 1
Last. Ying Sun (SYSU: Sun Yat-sen University)H-Index: 54
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Abstract Background To identify a radiomics signature to predict local recurrence in patients with non-metastatic T4 nasopharyngeal carcinoma (NPC). Methods A total of 737 patients from Sun Yat-sen University Cancer Center (training cohort: n = 360; internal validation cohort: n = 120) and Wuzhou Red Cross Hospital (external validation cohort: n = 257) underwent feature extraction from the largest axial area of the tumor on pretreatment magnetic resonance imaging scans. Feature selection was bas...
15 CitationsSource
Cited By1
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#1Xiaofeng Zuo (CQMU: Chongqing Medical University)
#2Peixin Meng (CQMU: Chongqing Medical University)H-Index: 1
Last. Jiang Zhu (CQMU: Chongqing Medical University)H-Index: 4
view all 0 authors...
Abstract null null Nasopharyngeal carcinoma (NPC) is a common malignant carcinoma of the head and neck, and the biological mechanisms underlying the pathogenesis of NPC remain not fully understood. In the present study, we systematically analyzed four independent NPC transcriptomic datasets and focused on identifying the critical molecular networks and novel key hub genes implicated in NPC. We found totally 170 common overlapping differentially expressed genes (DEGs) in the four NPC datasets. GO...
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#1Gaia Spadarella (University of Naples Federico II)H-Index: 8
Last. Renato Cuocolo (University of Naples Federico II)H-Index: 14
view all 6 authors...
Abstract Background MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation. Methods A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using “MRI, magnetic resonance imaging, radiomic, texture analysis...
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#1Qian Li (ZJU: Zhejiang University)H-Index: 78
#2Fei Dong (ZJU: Zhejiang University)H-Index: 31
Last. Minming Zhang (ZJU: Zhejiang University)H-Index: 20
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Objectives: To explore the magnetic resonance imaging (MRI) characteristics of brain diffuse midline gliomas with the H3 K27M mutation (DMG-M) using radiomics. Materials and Methods: Thirty patients with diffuse midline gliomas, including 16 with the H3 K27M mutant and 14 with wild type tumors, were retrospectively included in this study. A total of 272 radiomic features were initially extracted from MR images of each tumor. Principal component analysis, univariate analysis, and three other feat...
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