MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

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Abstract

Background

. At present, the prognostic prediction in advanced oral cavity squamous cell carcinoma (OCSCC) is based on the tumor-node-metastasis (TNM) staging system, and the most used imaging modality in these patients is magnetic resonance image (MRI). With the aim to improve the prediction, we developed an MRI-based radiomic signature as a prognostic marker for overall survival (OS) in OCSCC patients and compared it with published gene expression signatures for prognosis of OS in head and neck cancer patients, replicated herein on our OCSCC dataset.

Methods

For each patient, 1072 radiomic features were extracted from T1 and T2-weighted MRI (T1w and T2w). Features selection was performed, and an optimal set of five of them was used to fit a Cox proportional hazard regression model for OS. The radiomic signature was developed on a multi-centric locally advanced OCSCC retrospective dataset (n = 123) and validated on a prospective cohort (n = 108).

Results

The performance of the signature was evaluated in terms of C-index (0.68 (IQR 0.66–0.70)), hazard ratio (HR 2.64 (95% CI 1.62–4.31)), and high/low risk group stratification (log-rank p < 0.001, Kaplan-Meier curves). When tested on a multi-centric prospective cohort (n = 108), the signature had a C-index of 0.62 (IQR 0.58–0.64) and outperformed the clinical and pathologic TNM stage and six out of seven gene expression prognostic signatures. In addition, the significant difference of the radiomic signature between stages III and IVa/b in patients receiving surgery suggests a potential association of MRI features with the pathologic stage.

Conclusions

Overall, the present study suggests that MRI signatures, containing non-invasive and cost-effective remarkable information, could be exploited as prognostic tools.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40364-023-00494-5.

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Most cited references 60

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Chintan Parmar, Ahmed Raafat Hosny, Hugo J.W.L. Aerts … (2017)

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.

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A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.

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Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

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Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.

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Author and article information

Contributors

Anna Corti: anna.corti@polimi.it

Journal

Journal ID (nlm-ta): Biomark Res

Journal ID (iso-abbrev): Biomark Res

Title: Biomarker Research

Publisher: BioMed Central (London )

ISSN (Electronic): 2050-7771

Publication date (Electronic): 16 July 2023

Publication date PMC-release: 16 July 2023

Publication date Collection: 2023

Volume: 11

Electronic Location Identifier: 69

Affiliations

[1 ]GRID grid.4643.5, ISNI 0000 0004 1937 0327, Department of Electronics, Information and Bioengineering, , Politecnico di Milano, ; Milan, Italy

[2 ]GRID grid.417893.0, ISNI 0000 0001 0807 2568, Integrated Biology of Rare Tumors, Department of Research, Fondazione IRCCS, , Istituto Nazionale dei Tumori, ; Milan, Italy

[3 ]GRID grid.417893.0, ISNI 0000 0001 0807 2568, Head and Neck Medical Oncology Department, Fondazione IRCCS, , Istituto Nazionale dei Tumori, ; Milan, Italy

[4 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Department of Oncology and Hemato-Oncology, , Università degli studi di Milano, ; Milan, Italy

[5 ]GRID grid.417893.0, ISNI 0000 0001 0807 2568, Radiology Department, Fondazione IRCCS, , Istituto Nazionale dei Tumori, ; Milan, Italy

[6 ]GRID grid.7637.5, ISNI 0000000417571846, Unit of Otorhinolaryngology-Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, , University of Brescia, ; Brescia, Italy

[7 ]GRID grid.509540.d, ISNI 0000 0004 6880 3010, Amsterdam UMC location Vrije Universiteit, Radiology and Nuclear Medicine, ; de Boelelaan 1117, Amsterdam, The Netherlands

[8 ]GRID grid.16872.3a, ISNI 0000 0004 0435 165X, Cancer Center Amsterdam, Imaging and Biomarkers, ; Amsterdam, The Netherlands

[9 ]GRID grid.509540.d, ISNI 0000 0004 6880 3010, Amsterdam UMC location Vrije Universiteit, Otolaryngology-Head and Neck Surgery, ; de Boelelaan 1117, Amsterdam, The Netherlands

[10 ]GRID grid.7637.5, ISNI 0000000417571846, Unit of Radiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, ASST Spedali Civili of Brescia, , University of Brescia, ; Brescia, Italy

[11 ]GRID grid.411482.a, Maxillo-Facial Surgery Division, Head and Neck Department, , University Hospital of Parma, ; Parma, Italy

[12 ]GRID grid.418230.c, ISNI 0000 0004 1760 1750, Cardiotech Lab, Centro Cardiologico Monzino IRCCS, ; Milan, Italy

Article

Publisher ID: 494

DOI: 10.1186/s40364-023-00494-5

PMC ID: 10350277

PubMed ID: 37455307

SO-VID: 93d4f8a3-13f1-43fe-866a-e4d2d206f412

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 3 March 2023

Date accepted : 3 May 2023

Custom metadata

Keywords: radiomics,radiogenomics,magnetic resonance imaging,head and neck cancer,survival models,overall survival,oral cavity squamous cell carcinoma

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Keywords: radiomics, radiogenomics, magnetic resonance imaging, head and neck cancer, survival models, overall survival, oral cavity squamous cell carcinoma

MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: development, testing and comparison with genomic prognostic signatures

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Abstract

Background

Methods

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Conclusions

Supplementary Information

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Most cited references 60

Computational Radiomics System to Decode the Radiographic Phenotype

N4ITK: improved N3 bias correction.

Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

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