Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma

Liang, Zhong‐Guo; Tan, Hong Qi; Zhang, Fan; Tan, Lloyd Kuan Rui; Lin, Li; Lenkowicz, Jacopo; Wang, Haitao; Ong, Enya Hui Wen; Kusumawidjaja, Grace; Phua, Jun Hao; Gan, Soon Ann; Sin, S.Y.; Ng, Yan Yee; Tan, Terence; Soong, Yoke Lim; Fong, Kam Weng; Park, Sung Yong; Soo, Khee‐Chee; Wee, Joseph; Zhu, Xiaodong; Valentini, Vincenzo; Boldrini, Luca; Sun, Ying; Chua, Melvin L.K.

doi:10.1259/bjr.20190271

Cited by 42 publications

(39 citation statements)

References 23 publications

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“…Firstly, some features had significant association with H&N cancer overall survival in the IBSI-compliant software but not in IBEX. These findings concur with Liang et al who investigated two platforms and found differences in downstream clustering of known prognostic factors in patients with nasopharyngeal carcinoma [ 20 ]. Similar conclusions were drawn by Bogowicz et al who investigated this in PET scans of patients with H&N cancer [ 19 ].…”

Section: Discussionsupporting

confidence: 91%

“…Several studies have previously demonstrated that features can vary when calculated in different software platforms [18][19][20]. The Image Biomarker Standardisation Initiative (IBSI) is an international collaboration developed to help standardise radiomic feature calculation and has provided a framework to deliver practical solutions to this problem [21].…”

Section: Introductionmentioning

confidence: 99%

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Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

et al. 2020

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Objective To investigate the effects of Image Biomarker Standardisation Initiative (IBSI) compliance, harmonisation of calculation settings and platform version on the statistical reliability of radiomic features and their corresponding ability to predict clinical outcome. Methods The statistical reliability of radiomic features was assessed retrospectively in three clinical datasets (patient numbers: 108 head and neck cancer, 37 small-cell lung cancer, 47 non-small-cell lung cancer). Features were calculated using four platforms (PyRadiomics, LIFEx, CERR and IBEX). PyRadiomics, LIFEx and CERR are IBSI-compliant, whereas IBEX is not. The effects of IBSI compliance, user-defined calculation settings and platform version were assessed by calculating intraclass correlation coefficients and confidence intervals. The influence of platform choice on the relationship between radiomic biomarkers and survival was evaluated using univariable cox regression in the largest dataset. Results The reliability of radiomic features calculated by the different software platforms was only excellent (ICC > 0.9) for 4/17 radiomic features when comparing all four platforms. Reliability improved to ICC > 0.9 for 15/17 radiomic features when analysis was restricted to the three IBSI-compliant platforms. Failure to harmonise calculation settings resulted in poor reliability, even across the IBSI-compliant platforms. Software platform version also had a marked effect on feature reliability in CERR and LIFEx. Features identified as having significant relationship to survival varied between platforms, as did the direction of hazard ratios. Conclusion IBSI compliance, user-defined calculation settings and choice of platform version all influence the statistical reliability and corresponding performance of prognostic models in radiomics. Key Points • Reliability of radiomic features varies between feature calculation platforms and with choice of software version. • Image Biomarker Standardisation Initiative (IBSI) compliance improves reliability of radiomic features across platforms, but only when calculation settings are harmonised. • IBSI compliance, user-defined calculation settings and choice of platform version collectively affect the prognostic value of features.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

et al. 2020

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“…Yoon et al indicated that tumor features extracted through CT radiomics helped discriminate HER2-positive GC patients who had better survival rates and received trastuzumab-based treatment [22]. Radiomics is a recently proposed accurate prognosis prediction tool, but the complexity of feature extraction limits its unity in different centers [23]. The present study indicates that chemotherapy provided better survival benefits to patients with low splenic densities and diffusion tumor location.…”

Section: Discussionmentioning

confidence: 56%

Preoperative Splenic Density for the Prediction of Survival and Adjuvant Chemotherapy Benefits in Gastric Cancer

et al. 2020

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Background: We aimed to determine whether splenic features change during tumor progression by evaluating the clinicopathological characteristics relevant to splenic density in patients with gastric cancer (GC) and identify a new predictive indicator of prognosis and chemotherapy benefits. Methods: In the present analysis, 408 patients who underwent gastrectomy were included. Density was expressed in mean spleen Hounsfield units on computed tomography. Other clinical characteristics and detailed follow-up data were collected. The cutoff splenic density was 47.8 by the Xtile software. The R software was used for characteristic differential analysis in patients with different splenic densities. The Cox proportional hazards model and forest plot were used for prognosis and chemotherapy benefit analyses. Results: Patients with low splenic density had significantly worse 3-year disease-free survival (DFS) and overall survival (OS) rates (high vs low splenic density: DFS, 63.4% vs 44.6%, p<0.001; OS, 69.8% vs 52.4%, p<0.001). Splenic density showed strong negative correlations with age, number of metastasized lymph nodes, tumor size, and depth of tumor invasion. The benefits of adjuvant chemotherapy were better in the low splenic density group (hazard ratio of OS, 0.546; p=0.001) than in the high-density group (hazard ratio of OS, 0.701; p=0.106). Conclusions: Patients with low splenic density tended to have more advanced tumors and poor prognosis, but better chemotherapy benefits. Splenic density can be regarded as a new indicator of chemotherapy benefits and increase the accuracy of preoperative staging evaluation. Moreover, preoperative evaluation of splenic density may help establish individualized treatment strategies.

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“…The data source of radiomics is always obtained from retrospective medical imaging images. Different imaging techniques can lead to differences in image signals and image textures in medical imaging due to different acquisition parameters and reconstruction schemes [35][36]. If the parameters collected vary widely, this can introduce signal changes that are not caused by biological effects.…”

Section: Workflow Of Radiomics and Machine Learningmentioning

confidence: 99%

Application of radiomics and machine learning in head and neck cancers

Peng¹,

Wang²,

Wang³

et al. 2021

Int. J. Biol. Sci.

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With the continuous development of medical image informatics technology, more and more high-throughput quantitative data could be extracted from digital medical images, which has resulted in a new kind of omics-Radiomics. In recent years, in addition to genomics, proteomics and metabolomics, radiomic has attracted the interest of more and more researchers. Compared to other omics, radiomics can be perfectly integrated with clinical data, even with the pathology and molecular biomarker, so that the study can be closer to the clinical reality and more revealing of the tumor development. Mass data will also be generated in this process. Machine learning, due to its own characteristics, has a unique advantage in processing massive radiomic data. By analyzing mass amounts of data with strong clinical relevance, people can construct models that more accurately reflect tumor development and progression, thereby providing the possibility of personalized and sequential treatment of patients. As one of the cancer types whose treatment and diagnosis rely on imaging examination, radiomics has a very broad application prospect in head and neck cancers (HNC). Until now, there have been some notable results in HNC. In this review, we will introduce the concepts and workflow of radiomics and machine learning and their current applications in head and neck cancers, as well as the directions and applications of artificial intelligence in the treatment and diagnosis of HNC.

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Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma

Cited by 42 publications

References 23 publications

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform

Preoperative Splenic Density for the Prediction of Survival and Adjuvant Chemotherapy Benefits in Gastric Cancer

Application of radiomics and machine learning in head and neck cancers

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